USGS 15 minute stream flow data for Kings Creek on the Konza Prairie
USGS 15 minute stream flow data for Kings Creek on the Konza Prairie
ABSTRACT: USGS 15 minute stream flow data for Kings Creek on the Konza Prairie
The 2000 Pilot Environmental Sustainability Index (ESI) is an exploratory effort to construct an index that measures the ability of a nation's economy to achieve sustainable development, with the long term goal of finding a single indicator for environmental sustainability analagous to that of the Gross Domestic Product (GDP). The index covering 56 countries is a composite measure of the current status of a nation's environmental systems, pressures on those systems, human vulnerability to environmental change, national capacity to respond, and contributions to global environmental stewardship. The index was unveiled at the World Economic Forum's annual meeting, January 2000, Davos, Switzerland. The 2000 Pilot ESI is the result of collaboration among the World Economic Forum (WEF), Yale Center for Environmental Law and Policy (YCELP), and the Columbia University Center for International Earth Science Information Network (CIESIN).
The 2000 Pilot Environmental Sustainability Index (ESI) is an exploratory effort to construct an index that measures the ability of a nation's economy to achieve sustainable development, with the long term goal of finding a single indicator for environmental sustainability analagous to that of the Gross Domestic Product (GDP). The index covering 56 countries is a composite measure of the current status of a nation's environmental systems, pressures on those systems, human vulnerability to environmental change, national capacity to respond, and contributions to global environmental stewardship. The index was unveiled at the World Economic Forum's annual meeting, January 2000, Davos, Switzerland. The 2000 Pilot ESI is the result of collaboration among the World Economic Forum (WEF), Yale Center for Environmental Law and Policy (YCELP), and the Columbia University Center for International Earth Science Information Network (CIESIN).
The 2000 Pilot Environmental Sustainability Index (ESI) is an exploratory effort to construct an index that measures the ability of a nation's economy to achieve sustainable development, with the long term goal of finding a single indicator for environmental sustainability analagous to that of the Gross Domestic Product (GDP). The index covering 56 countries is a composite measure of the current status of a nation's environmental systems, pressures on those systems, human vulnerability to environmental change, national capacity to respond, and contributions to global environmental stewardship. The index was unveiled at the World Economic Forum's annual meeting, January 2000, Davos, Switzerland. The 2000 Pilot ESI is the result of collaboration among the World Economic Forum (WEF), Yale Center for Environmental Law and Policy (YCELP), and the Columbia University Center for International Earth Science Information Network (CIESIN). (Suggested Usage: To test the feasibility of creating a comparative index of national-level environmental sustainability.)
The 2001 Environmental Sustainability Index (ESI) utilizes a refined methodology based on the 2000 Pilot ESI effort, to construct an index covering 122 countries that measures the overall progress towards environmental sustainability. The index is a composite measure of the current status of a nation's environmental systems, pressures on those systems, human vulnerability to environmental change, national capacity to respond, and contributions to global environmental stewardship. The refinements included the addition and deletion of indicators, filling gaps in data coverage, new data sets, and the modification of the aggregation scheme. The index was unveiled at the World Economic Forum's annual meeting, January 2001, Davos, Switzerland. The 2001 ESI is the result of collaboration among the World Economic Forum (WEF), Yale Center for Environmental Law and Policy (YCELP), and the Columbia University Center for International Earth Science Information Network (CIESIN)
The 2001 Environmental Sustainability Index (ESI) utilizes a refined methodology based on the 2000 Pilot ESI effort, to construct an index covering 122 countries that measures the overall progress towards environmental sustainability. The index is a composite measure of the current status of a nation's environmental systems, pressures on those systems, human vulnerability to environmental change, national capacity to respond, and contributions to global environmental stewardship. The refinements included the addition and deletion of indicators, filling gaps in data coverage, new data sets, and the modification of the aggregation scheme. The index was unveiled at the World Economic Forum's annual meeting, January 2001, Davos, Switzerland. The 2001 ESI is the result of collaboration among the World Economic Forum (WEF), Yale Center for Environmental Law and Policy (YCELP), and the Columbia University Center for International Earth Science Information Network (CIESIN)
The 2001 Environmental Sustainability Index (ESI) utilizes a refined methodology based on the 2000 Pilot ESI effort, to construct an index covering 122 countries that measures the overall progress towards environmental sustainability. The index is a composite measure of the current status of a nation's environmental systems, pressures on those systems, human vulnerability to environmental change, national capacity to respond, and contributions to global environmental stewardship. The refinements included the addition and deletion of indicators, filling gaps in data coverage, new data sets, and the modification of the aggregation scheme. The index was unveiled at the World Economic Forum's annual meeting, January 2001, Davos, Switzerland. The 2001 ESI is the result of collaboration among the World Economic Forum (WEF), Yale Center for Environmental Law and Policy (YCELP), and the Columbia University Center for International Earth Science Information Network (CIESIN) (Suggested Usage: To provide a comparative index of national-level environmental sustainability based on lessons learned from the year 2000 pilot effort.)
The 2001 Environmental Sustainability Index (ESI) utilizes a refined methodology based on the 2000 Pilot ESI effort, to construct an index covering 122 countries that measures the overall progress towards environmental sustainability. The index is a composite measure of the current status of a nation's environmental systems, pressures on those systems, human vulnerability to environmental change, national capacity to respond, and contributions to global environmental stewardship. The refinements included the addition and deletion of indicators, filling gaps in data coverage, new data sets, and the modification of the aggregation scheme. The index was unveiled at the World Economic Forum's annual meeting, January 2001, Davos, Switzerland. The 2001 ESI is the result of collaboration among the World Economic Forum (WEF), Yale Center for Environmental Law and Policy (YCELP), and the Columbia University Center for International Earth Science Information Network (CIESIN) (Suggested Usage: To provide a comparative index of national-level environmental sustainability based on lessons learned from the year 2000 pilot effort.)
The 2002 Environmental Sustainability Index (ESI) measures overall progress toward environmental sustainability for 142 countries based on environmental systems, stresses, human vulnerability, social and institutional capacity and global stewardship. The addition of a climate change indicator, reduction in number of capacity indicators, and an improved imputation methodology contributed to an improvement from the 2001 ESI. The index was unveiled at the World Economic Forum's annual meeting, January 2002, New York. The 2002 ESI is the result of collaboration among the World Economic Forum (WEF), Yale Center for Environmental Law and Policy (YCELP), and the Columbia University Center for International Earth Science Information Network (CIESIN).
The 2002 Environmental Sustainability Index (ESI) measures overall progress toward environmental sustainability for 142 countries based on environmental systems, stresses, human vulnerability, social and institutional capacity and global stewardship. The addition of a climate change indicator, reduction in number of capacity indicators, and an improved imputation methodology contributed to an improvement from the 2001 ESI. The index was unveiled at the World Economic Forum's annual meeting, January 2002, New York. The 2002 ESI is the result of collaboration among the World Economic Forum (WEF), Yale Center for Environmental Law and Policy (YCELP), and the Columbia University Center for International Earth Science Information Network (CIESIN).
The 2002 Environmental Sustainability Index (ESI) measures overall progress toward environmental sustainability for 142 countries based on environmental systems, stresses, human vulnerability, social and institutional capacity and global stewardship. The addition of a climate change indicator, reduction in number of capacity indicators, and an improved imputation methodology contributed to an improvement from the 2001 ESI. The index was unveiled at the World Economic Forum's annual meeting, January 2002, New York. The 2002 ESI is the result of collaboration among the World Economic Forum (WEF), Yale Center for Environmental Law and Policy (YCELP), and the Columbia University Center for International Earth Science Information Network (CIESIN).
The 2005 Environmental Sustainability Index (ESI) is a measure of overall progress towards environmental sustainability, developed for 146 countries. The index provides a composite profile of national environmental stewardship based on a compilation of 21 indicators derived from 76 underlying datasets. The 2005 version of the ESI represents a significant update and improvement on earlier versions; the country ESI scores or rankings should not be compared to earlier versions because of changes to the methodology and underlying data. The index was unveiled at the World Economic Forum's annual meeting, January 2005, Davos, Switzerland. The 2005 ESI is a joint product of the Yale Center for Environmental Law and Policy (YCELP) and the Columbia University Center for International Earth Science Information Network (CIESIN), in collaboration with the World Economic Forum (WEF) and the Joint Research Centre (JRC), European Commission.
The 2005 Environmental Sustainability Index (ESI) is a measure of overall progress towards environmental sustainability, developed for 146 countries. The index provides a composite profile of national environmental stewardship based on a compilation of 21 indicators derived from 76 underlying datasets. The 2005 version of the ESI represents a significant update and improvement on earlier versions; the country ESI scores or rankings should not be compared to earlier versions because of changes to the methodology and underlying data. The index was unveiled at the World Economic Forum's annual meeting, January 2005, Davos, Switzerland. The 2005 ESI is a joint product of the Yale Center for Environmental Law and Policy (YCELP) and the Columbia University Center for International Earth Science Information Network (CIESIN), in collaboration with the World Economic Forum (WEF) and the Joint Research Centre (JRC), European Commission.
The 2005 Environmental Sustainability Index (ESI) is a measure of overall progress towards environmental sustainability, developed for 146 countries. The index provides a composite profile of national environmental stewardship based on a compilation of 21 indicators derived from 76 underlying datasets. The 2005 version of the ESI represents a significant update and improvement on earlier versions; the country ESI scores or rankings should not be compared to earlier versions because of changes to the methodology and underlying data. The index was unveiled at the World Economic Forum's annual meeting, January 2005, Davos, Switzerland. The 2005 ESI is a joint product of the Yale Center for Environmental Law and Policy (YCELP) and the Columbia University Center for International Earth Science Information Network (CIESIN), in collaboration with the World Economic Forum (WEF) and the Joint Research Centre (JRC), European Commission.
The 2008 Environmental Performance Index (EPI) centers on two broad environmental protection objectives: (1) reducing environmental stresses on human 

health, and (2) promoting ecosystem vitality and sound natural resource management. Derived from a careful review of the environmental literature, these twin goals mirror the priorities 

expressed by policymakers. Environmental health and ecosystem vitality are gauged using 25 indicators tracked in six well-established policy categories: Environmental Health 

(Environmental Burden of Disease, Water, and Air Pollution), Air Pollution (effects on ecosystems), Water (effects on ecosystems), Biodiversity and Habitat, Productive Natural Resources 

(Forestry, Fisheries, and Agriculture), and Climate Change. The 2008 EPI utilizes a proximity-to-target methodology in which performance on each indicator is rated on a 0 to 100 scale 

(100 represents at target). By identifying specific targets and measuring how close each country comes to them, the EPI provides a foundation for policy analysis and a context for 

evaluating performance. Issue-by-issue and aggregate rankings facilitate cross-country comparisons both globally and within relevant peer groups. The 2008 EPI is the result of 

collaboration among the Yale Center for Environmental Law and Policy (YCELP), Columbia University Center for International Earth Science Information Network (CIESIN), World Economic 

Forum (WEF), and the Joint Research Centre (JRC), European Commission.
The 2008 Environmental Performance Index (EPI) centers on two broad environmental protection objectives: (1) reducing environmental stresses on human 

health, and (2) promoting ecosystem vitality and sound natural resource management. Derived from a careful review of the environmental literature, these twin goals mirror the priorities 

expressed by policymakers. Environmental health and ecosystem vitality are gauged using 25 indicators tracked in six well-established policy categories: Environmental Health 

(Environmental Burden of Disease, Water, and Air Pollution), Air Pollution (effects on ecosystems), Water (effects on ecosystems), Biodiversity and Habitat, Productive Natural Resources 

(Forestry, Fisheries, and Agriculture), and Climate Change. The 2008 EPI utilizes a proximity-to-target methodology in which performance on each indicator is rated on a 0 to 100 scale 

(100 represents at target). By identifying specific targets and measuring how close each country comes to them, the EPI provides a foundation for policy analysis and a context for 

evaluating performance. Issue-by-issue and aggregate rankings facilitate cross-country comparisons both globally and within relevant peer groups. The 2008 EPI is the result of 

collaboration among the Yale Center for Environmental Law and Policy (YCELP), Columbia University Center for International Earth Science Information Network (CIESIN), World Economic 

Forum (WEF), and the Joint Research Centre (JRC), European Commission.
The 2008 Environmental Performance Index (EPI) centers on two broad environmental protection objectives: (1) reducing environmental stresses on human 

health, and (2) promoting ecosystem vitality and sound natural resource management. Derived from a careful review of the environmental literature, these twin goals mirror the priorities 

expressed by policymakers. Environmental health and ecosystem vitality are gauged using 25 indicators tracked in six well-established policy categories: Environmental Health 

(Environmental Burden of Disease, Water, and Air Pollution), Air Pollution (effects on ecosystems), Water (effects on ecosystems), Biodiversity and Habitat, Productive Natural Resources 

(Forestry, Fisheries, and Agriculture), and Climate Change. The 2008 EPI utilizes a proximity-to-target methodology in which performance on each indicator is rated on a 0 to 100 scale 

(100 represents at target). By identifying specific targets and measuring how close each country comes to them, the EPI provides a foundation for policy analysis and a context for 

evaluating performance. Issue-by-issue and aggregate rankings facilitate cross-country comparisons both globally and within relevant peer groups. The 2008 EPI is the result of 

collaboration among the Yale Center for Environmental Law and Policy (YCELP), Columbia University Center for International Earth Science Information Network (CIESIN), World Economic 

Forum (WEF), and the Joint Research Centre (JRC), European Commission.
The 2008 Natural Resources Management Index (NRMI) is a composite index for 226 countries derived from the average of four proximity-to-target scores from eco-region protection, access to improved sanitation, access to improved water and child mortality indicators. The 2008 NRMI includes data for the NRMI for 2006, 2007 and 2008. The dataset is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the Yale Center for Environmental Law and Policy (YCELP). (Suggested Usage:  To assist in the country selection process for the Millennium Challenge Account by providing indicators of natural resource management that complement the governance, social, and economic indicators.)
The 2008 Natural Resources Management Index (NRMI) is a composite index for 226 countries derived from the average of four proximity-to-target scores from eco-region protection, access to improved sanitation, access to improved water and child mortality indicators. The 2008 NRMI includes data for the NRMI for 2006, 2007 and 2008. The dataset is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the Yale Center for Environmental Law and Policy (YCELP). (Suggested Usage:  To assist in the country selection process for the Millennium Challenge Account by providing indicators of natural resource management that complement the governance, social, and economic indicators.)
The 2008 Natural Resources Management Index (NRMI) is a composite index for 226 countries derived from the average of four proximity-to-target scores from eco-region protection, access to improved sanitation, access to improved water and child mortality indicators. The 2008 NRMI includes data for the NRMI for 2006, 2007 and 2008. The dataset is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the Yale Center for Environmental Law and Policy (YCELP). (Suggested Usage:  To assist in the country selection process for the Millennium Challenge Account by providing indicators of natural resource management that complement the governance, social, and economic indicators.)
The 2010 Environmental Performance Index (EPI) ranks 163 countries on environmental performance based on twenty-five indicators grouped within ten core policy categories addressing environmental health, air quality, water resource management, biodiversity and habitat, forestry, fisheries, agriculture, and climate change in the context of two broad objectives: environmental health and ecosystem vitality. The EPI?s proximity-to-target methodology facilitates cross-country comparisons among economic and regional peer groups. It was formally released in Davos, Switzerland, at the annual meeting of the World Economic Forum on January 28, 2010. The 2010 EPI is the result of collaboration between the Yale Center for Environmental Law and Policy (YCELP) and the Columbia University Center for International Earth Science Information Network (CIESIN).
The 2010 Environmental Performance Index (EPI) ranks 163 countries on environmental performance based on twenty-five indicators grouped within ten core policy categories addressing environmental health, air quality, water resource management, biodiversity and habitat, forestry, fisheries, agriculture, and climate change in the context of two broad objectives: environmental health and ecosystem vitality. The EPI?s proximity-to-target methodology facilitates cross-country comparisons among economic and regional peer groups. It was formally released in Davos, Switzerland, at the annual meeting of the World Economic Forum on January 28, 2010. The 2010 EPI is the result of collaboration between the Yale Center for Environmental Law and Policy (YCELP) and the Columbia University Center for International Earth Science Information Network (CIESIN).
The 2010 Environmental Performance Index (EPI) ranks 163 countries on environmental performance based on twenty-five indicators grouped within ten core policy categories addressing environmental health, air quality, water resource management, biodiversity and habitat, forestry, fisheries, agriculture, and climate change in the context of two broad objectives: environmental health and ecosystem vitality. The EPI?s proximity-to-target methodology facilitates cross-country comparisons among economic and regional peer groups. It was formally released in Davos, Switzerland, at the annual meeting of the World Economic Forum on January 28, 2010. The 2010 EPI is the result of collaboration between the Yale Center for Environmental Law and Policy (YCELP) and the Columbia University Center for International Earth Science Information Network (CIESIN).
The 2012 Environmental Performance Index (EPI) ranks 132 countries on 22 performance indicators in the following 10 policy categories:  environmental burden of disease, water (effects on human health), air pollution (effects on human health), air pollution (ecosystem effects), water resources (ecosystem effects), biodiversity and habitat, forestry, fisheries, agriculture and climate change. These categories track performance and progress on two broad policy objectives, environmental health and ecosystem vitality. Each indicator has an associated environmental public health or ecosystem sustainability target. The EPI's proximity-to-target methodology facilitates cross-country comparisons among economic and regional peer groups.

The Pilot Trend Environmental Performance Index (Trend EPI) ranks countries on the change in their environmental performance over the last decade. As a complement to the EPI, the Trend EPI shows who is improving and who is declining over time. 

The 2012 EPI and Pilot Trend EPI were formally released in Davos, Switzerland, at the annual meeting of the World Economic Forum on January 27, 2012. These are the result of collaboration between the Yale Center for Environmental Law and Policy (YCELP) and the Columbia University Center for International Earth Science Information Network (CIESIN). The Interactive Website for the 2012 EPI is at http://epi.yale.edu/.
The 2012 Environmental Performance Index (EPI) ranks 132 countries on 22 performance indicators in the following 10 policy categories:  environmental burden of disease, water (effects on human health), air pollution (effects on human health), air pollution (ecosystem effects), water resources (ecosystem effects), biodiversity and habitat, forestry, fisheries, agriculture and climate change. These categories track performance and progress on two broad policy objectives, environmental health and ecosystem vitality. Each indicator has an associated environmental public health or ecosystem sustainability target. The EPI's proximity-to-target methodology facilitates cross-country comparisons among economic and regional peer groups.

The Pilot Trend Environmental Performance Index (Trend EPI) ranks countries on the change in their environmental performance over the last decade. As a complement to the EPI, the Trend EPI shows who is improving and who is declining over time. 

The 2012 EPI and Pilot Trend EPI were formally released in Davos, Switzerland, at the annual meeting of the World Economic Forum on January 27, 2012. These are the result of collaboration between the Yale Center for Environmental Law and Policy (YCELP) and the Columbia University Center for International Earth Science Information Network (CIESIN). The Interactive Website for the 2012 EPI is at http://epi.yale.edu/.
The 2012 Environmental Performance Index (EPI) ranks 132 countries on 22 performance indicators in the following 10 policy categories:  environmental burden of disease, water (effects on human health), air pollution (effects on human health), air pollution (ecosystem effects), water resources (ecosystem effects), biodiversity and habitat, forestry, fisheries, agriculture and climate change. These categories track performance and progress on two broad policy objectives, environmental health and ecosystem vitality. Each indicator has an associated environmental public health or ecosystem sustainability target. The EPI's proximity-to-target methodology facilitates cross-country comparisons among economic and regional peer groups.

The Pilot Trend Environmental Performance Index (Trend EPI) ranks countries on the change in their environmental performance over the last decade. As a complement to the EPI, the Trend EPI shows who is improving and who is declining over time. 

The 2012 EPI and Pilot Trend EPI were formally released in Davos, Switzerland, at the annual meeting of the World Economic Forum on January 27, 2012. These are the result of collaboration between the Yale Center for Environmental Law and Policy (YCELP) and the Columbia University Center for International Earth Science Information Network (CIESIN). The Interactive Website for the 2012 EPI is at http://epi.yale.edu/.
30 minute rainfall data for the Konza Prairie
30 minute rainfall data for the Konza Prairie
ABSTRACT: 30 minute rainfall data for the Konza Prairie
This data set provides carbon (C), nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mg) concentrations in green and senesced leaves. Vegetation characteristics reported include species growth habit, leaf area, mass, and mass loss with senescence. The data were compiled from 86 selected studies in 31 countries, and resulted in approximately 1,000 data points for both green and senesced leaves from woody and non-woody vegetation as described in Vergutz et al (2012). The studies were conducted from 1970-2009. There are two comma-delimited data files with this data set.
This data set provides carbon (C), nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mg) concentrations in green and senesced leaves. Vegetation characteristics reported include species growth habit, leaf area, mass, and mass loss with senescence. The data were compiled from 86 selected studies in 31 countries, and resulted in approximately 1,000 data points for both green and senesced leaves from woody and non-woody vegetation as described in Vergutz et al (2012). The studies were conducted from 1970-2009. There are two comma-delimited data files with this data set.
ABSTRACT: This data set provides carbon (C), nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mg) concentrations in green and senesced leaves. Vegetation characteristics reported include species growth habit, leaf area, mass, and mass loss with senescence. The data were compiled from 86 selected studies in 31 countries, and resulted in approximately 1,000 data points for both green and senesced leaves from woody and non-woody vegetation as described in Vergutz et al (2012). The studies were conducted from 1970-2009. There are two comma-delimited data files with this data set.
This database contains information compiled from published studies on gas flux from soil following rewetting or thawing. The resulting database includes 222 field and laboratory observations focused on rewetting of dry soils, and 116 field laboratory observations focused on thawing of frozen soils studies conducted from 1956 to 2010. Fluxes of carbon dioxide, methane, nitrous oxide, nitrogen oxide, and ammonia (CO2, CH4, N2O, NO and NH3) were compiled from the literature and the flux rates were normalized for ease of comparison. Field observations of gas flux following rewetting of dry soils include events caused by natural rainfall, simulated rainfall in natural ecosystems, and irrigation in agricultural lands. Similarly, thawing of frozen soils include field observations of natural thawing, simulated freezing-thawing events (i.e., thawing of simulated frozen soil by snow removal), and thawing of seasonal ice in temperate and high latitude regions (Kim et al., 2012). Reported parameters include experiment type, location, site type, vegetation, climate, soil properties, rainfall, soil moisture, soil gas flux after wetting and thawing, peak soil gas flux properties, and the corresponding study references. There is one comma-delimited data file.
This database contains information compiled from published studies on gas flux from soil following rewetting or thawing. The resulting database includes 222 field and laboratory observations focused on rewetting of dry soils, and 116 field laboratory observations focused on thawing of frozen soils studies conducted from 1956 to 2010. Fluxes of carbon dioxide, methane, nitrous oxide, nitrogen oxide, and ammonia (CO2, CH4, N2O, NO and NH3) were compiled from the literature and the flux rates were normalized for ease of comparison. Field observations of gas flux following rewetting of dry soils include events caused by natural rainfall, simulated rainfall in natural ecosystems, and irrigation in agricultural lands. Similarly, thawing of frozen soils include field observations of natural thawing, simulated freezing-thawing events (i.e., thawing of simulated frozen soil by snow removal), and thawing of seasonal ice in temperate and high latitude regions (Kim et al., 2012). Reported parameters include experiment type, location, site type, vegetation, climate, soil properties, rainfall, soil moisture, soil gas flux after wetting and thawing, peak soil gas flux properties, and the corresponding study references. There is one comma-delimited data file.
ABSTRACT: This database contains information compiled from published studies on gas flux from soil following rewetting or thawing. The resulting database includes 222 field and laboratory observations focused on rewetting of dry soils, and 116 field laboratory observations focused on thawing of frozen soils studies conducted from 1956 to 2010. Fluxes of carbon dioxide, methane, nitrous oxide, nitrogen oxide, and ammonia (CO2, CH4, N2O, NO and NH3) were compiled from the literature and the flux rates were normalized for ease of comparison. Field observations of gas flux following rewetting of dry soils include events caused by natural rainfall, simulated rainfall in natural ecosystems, and irrigation in agricultural lands. Similarly, thawing of frozen soils include field observations of natural thawing, simulated freezing-thawing events (i.e., thawing of simulated frozen soil by snow removal), and thawing of seasonal ice in temperate and high latitude regions (Kim et al., 2012). Reported parameters include experiment type, location, site type, vegetation, climate, soil properties, rainfall, soil moisture, soil gas flux after wetting and thawing, peak soil gas flux properties, and the corresponding study references. There is one comma-delimited data file.
ABSTRACT: This data set provides a soil respiration data database (SRDB), a near-universal compendium of published soil respiration (RS) data. Soil respiration, the flux of autotropically- and heterotrophically-generated CO2 from the soil to the atmosphere remains the least well-constrained component of the terrestrial C cycle. The database encompasses all published studies that report one of the following data measured in the field (not laboratory): annual RS, mean seasonal RS, a seasonal or annual partitioning of RS into its sources fluxes, RS temperature response (Q10), or RS at 10 degrees C. Its orientation is thus to seasonal and annual fluxes, not shorter-term or chamber-specific measurements. Data from 818 studies have been entered into the database, constituting 3379 records. The data span the measurement years 1961-2007 and are dominated by temperate, well-drained forests. The database includes a file of RS data and a linked file of study bibliographic data. Both files are in comma-separated format.
ABSTRACT: This data set provides a soil respiration data database (SRDB), a near-universal compendium of published soil respiration (RS) data. Soil respiration, the flux of autotropically- and heterotrophically-generated CO2 from the soil to the atmosphere remains the least well-constrained component of the terrestrial C cycle. The database encompasses all published studies that report one of the following data measured in the field (not laboratory): annual RS, mean seasonal RS, a seasonal or annual partitioning of RS into its sources fluxes, RS temperature response (Q10), or RS at 10 degrees C. Its orientation is thus to seasonal and annual fluxes, not shorter-term or chamber-specific measurements. Data from 818 studies have been entered into the database, constituting 3379 records. The data span the measurement years 1961-2007 and are dominated by temperate, well-drained forests. The database includes a file of RS data and a linked file of study bibliographic data. Both files are in comma-separated format.
ABSTRACT: This data set provides a soil respiration data database (SRDB), a near-universal compendium of published soil respiration (RS) data. Soil respiration, the flux of autotropically- and heterotrophically-generated CO2 from the soil to the atmosphere remains the least well-constrained component of the terrestrial C cycle. The database encompasses all published studies that report one of the following data measured in the field (not laboratory): annual RS, mean seasonal RS, a seasonal or annual partitioning of RS into its sources fluxes, RS temperature response (Q10), or RS at 10 degrees C. Its orientation is thus to seasonal and annual fluxes, not shorter-term or chamber-specific measurements. Data from 818 studies have been entered into the database, constituting 3379 records. The data span the measurement years 1961-2007 and are dominated by temperate, well-drained forests. The database includes a file of RS data and a linked file of study bibliographic data. Both files are in comma-separated format.
ABSTRACT: This data set provides an updated soil respiration database (SRDB), a near-universal compendium of published soil respiration (RS) data. Soil respiration, the flux of autotrophically- and heterotrophically-generated CO2 from the soil to the atmosphere remains the least well-constrained component of the terrestrial C cycle. The database encompasses all published studies that report at least one of the following data measured in the field (not laboratory): annual RS, mean seasonal RS, a seasonal or annual partitioning of RS into its sources fluxes, RS temperature response (Q10), or RS at 10 degrees C. SRDB's orientation is thus to seasonal and annual fluxes, not shorter-term or chamber-specific measurements, and the database is dominated by temperate, well-drained forest measurement locations. The database includes a file of RS data and a linked file of study bibliographic data. Both files are in comma-separated format.
ABSTRACT: This data set provides an updated soil respiration database (SRDB), a near-universal compendium of published soil respiration (RS) data. Soil respiration, the flux of autotrophically- and heterotrophically-generated CO2 from the soil to the atmosphere remains the least well-constrained component of the terrestrial C cycle. The database encompasses all published studies that report at least one of the following data measured in the field (not laboratory): annual RS, mean seasonal RS, a seasonal or annual partitioning of RS into its sources fluxes, RS temperature response (Q10), or RS at 10 degrees C. SRDB's orientation is thus to seasonal and annual fluxes, not shorter-term or chamber-specific measurements, and the database is dominated by temperate, well-drained forest measurement locations. The database includes a file of RS data and a linked file of study bibliographic data. Both files are in comma-separated format.
ABSTRACT: This data set provides an updated soil respiration database (SRDB), a near-universal compendium of published soil respiration (RS) data. Soil respiration, the flux of autotrophically- and heterotrophically-generated CO2 from the soil to the atmosphere remains the least well-constrained component of the terrestrial C cycle. The database encompasses all published studies that report at least one of the following data measured in the field (not laboratory): annual RS, mean seasonal RS, a seasonal or annual partitioning of RS into its sources fluxes, RS temperature response (Q10), or RS at 10 degrees C. SRDB's orientation is thus to seasonal and annual fluxes, not shorter-term or chamber-specific measurements, and the database is dominated by temperate, well-drained forest measurement locations. The database includes a file of RS data and a linked file of study bibliographic data. Both files are in comma-separated format.
The ALTUS Cloud Electrification Study (ACES) was Based at the Naval Air Facility Key West in Florida.  ACES researchers in August 2002 conducted overflights of thunderstorms over the southwestern corner of Florida.  For the first time in NASA research an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data.  Carrying field mills, optical sensors, electric field sensors and other instruments, it allowed scientists to collect cloud electrification data for the first time from above the storm from it's birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely, and testing new aircraft technology. This dataset consists of data collected from seven instruments: the Slow/Fast antenna, Electric Field Mill, Dual Optical Pulse Sensor, Searchcoil magnetometer, Accelerometers, Gerdien Conductivity Prove, and the Fluxgate Magnetometer. Data consists of sensor reads at 50HZ throughout the flight from all 64 channels.
The ALTUS Cloud Electrification Study (ACES) was Based at the Naval Air Facility Key West in Florida.  ACES researchers in August 2002 conducted overflights of thunderstorms over the southwestern corner of Florida.  For the first time in NASA research an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data.  Carrying field mills, optical sensors, electric field sensors and other instruments, it allowed scientists to collect cloud electrification data for the first time from above the storm from it's birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely, and testing new aircraft technology. This dataset consists of data collected from seven instruments: the Slow/Fast antenna, Electric Field Mill, Dual Optical Pulse Sensor, Searchcoil magnetometer, Accelerometers, Gerdien Conductivity Prove, and the Fluxgate Magnetometer. Data consists of sensor reads at 50HZ throughout the flight from all 64 channels.
The ALTUS Cloud Electrification Study (ACES) was Based at the Naval Air Facility Key West in Florida.  ACES researchers in August 2002 conducted overflights of thunderstorms over the southwestern corner of Florida.  For the first time in NASA research an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data.  Carrying field mills, optical sensors, electric field sensors and other instruments, it allowed scientists to collect cloud electrification data for the first time from above the storm from it's birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely, and testing new aircraft technology. This dataset consists of data collected from seven instruments: the Slow/Fast antenna, Electric Field Mill, Dual Optical Pulse Sensor, Searchcoil magnetometer, Accelerometers, Gerdien Conductivity Prove, and the Fluxgate Magnetometer. Data consists of sensor reads at 50HZ throughout the flight from all 64 channels.
The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida.  ACES researchers in August 2002 conducted overflights of thunderstorms over the southwestern corner of Florida.  For the first time in NASA research an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data.  Carrying field mills, optical sensors, electric field sensors and other instruments, it allowed scientists to collect cloud electrification data for the first time from above the storm from it's birth through dissipation.  This experiment allowed scientists to achieve the dual goals of gathering weather data safely, and testing new aircraft technology.  This dataset consists of data from the Electric Field Mills which yield information about the atmospheric electrical fields above the instruments.
The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida.  ACES researchers in August 2002 conducted overflights of thunderstorms over the southwestern corner of Florida.  For the first time in NASA research an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data.  Carrying field mills, optical sensors, electric field sensors and other instruments, it allowed scientists to collect cloud electrification data for the first time from above the storm from it's birth through dissipation.  This experiment allowed scientists to achieve the dual goals of gathering weather data safely, and testing new aircraft technology.  This dataset consists of data from the Electric Field Mills which yield information about the atmospheric electrical fields above the instruments.
The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida.  ACES researchers in August 2002 conducted overflights of thunderstorms over the southwestern corner of Florida.  For the first time in NASA research an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data.  Carrying field mills, optical sensors, electric field sensors and other instruments, it allowed scientists to collect cloud electrification data for the first time from above the storm from it's birth through dissipation.  This experiment allowed scientists to achieve the dual goals of gathering weather data safely, and testing new aircraft technology.  This dataset consists of data from the Electric Field Mills which yield information about the atmospheric electrical fields above the instruments.
The ALTUS Cloud Electrification Study (ACES) was Based at the Naval Air Facility Key West in Florida. ACES researchers in August 2002 conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, it allowed scientists to collect cloud electrification data for the first time from above the storm from it's birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely, and testing new aircraft technology.  This dataset consists of log data from each flight. It yields instrument and aircraft status throughout the flight.
The ALTUS Cloud Electrification Study (ACES) was Based at the Naval Air Facility Key West in Florida. ACES researchers in August 2002 conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, it allowed scientists to collect cloud electrification data for the first time from above the storm from it's birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely, and testing new aircraft technology.  This dataset consists of log data from each flight. It yields instrument and aircraft status throughout the flight.
The ALTUS Cloud Electrification Study (ACES) was Based at the Naval Air Facility Key West in Florida. ACES researchers in August 2002 conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, it allowed scientists to collect cloud electrification data for the first time from above the storm from it's birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely, and testing new aircraft technology.  This dataset consists of log data from each flight. It yields instrument and aircraft status throughout the flight.
The ALTUS Cloud Electrification Study (ACES) was Based at the Naval Air Facility Key West in Florida. ACES researchers in August 2002 conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, it allowed scientists to collect cloud electrification data for the first time from above the storm from it's birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely, and testing new aircraft technology.  This dataset consists of timing data used for the experiment. When used it provides: syncclock_time = time found at the syncclock (VSI-SYnCCLOCK-32) in seconds from first file name, syncclock_m_time = time found at the syncclock (VSI-SYnCCLOCK-32) in Matlab dateform format, system_time = system time  in seconds from first file name, system_m_time = system time in dateform format, gps_time = time found at the GPS unit in seconds from first file name, gps_m_time = time found at GPS unit in dateform, cmos_time = time found at the computer CMOS in seconds from first file name, cmos_m_time = time found at the computer CMOS in dateform
The ALTUS Cloud Electrification Study (ACES) was Based at the Naval Air Facility Key West in Florida. ACES researchers in August 2002 conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, it allowed scientists to collect cloud electrification data for the first time from above the storm from it's birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely, and testing new aircraft technology.  This dataset consists of timing data used for the experiment. When used it provides: syncclock_time = time found at the syncclock (VSI-SYnCCLOCK-32) in seconds from first file name, syncclock_m_time = time found at the syncclock (VSI-SYnCCLOCK-32) in Matlab dateform format, system_time = system time  in seconds from first file name, system_m_time = system time in dateform format, gps_time = time found at the GPS unit in seconds from first file name, gps_m_time = time found at GPS unit in dateform, cmos_time = time found at the computer CMOS in seconds from first file name, cmos_m_time = time found at the computer CMOS in dateform
The ALTUS Cloud Electrification Study (ACES) was Based at the Naval Air Facility Key West in Florida. ACES researchers in August 2002 conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, it allowed scientists to collect cloud electrification data for the first time from above the storm from it's birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely, and testing new aircraft technology.  This dataset consists of timing data used for the experiment. When used it provides: syncclock_time = time found at the syncclock (VSI-SYnCCLOCK-32) in seconds from first file name, syncclock_m_time = time found at the syncclock (VSI-SYnCCLOCK-32) in Matlab dateform format, system_time = system time  in seconds from first file name, system_m_time = system time in dateform format, gps_time = time found at the GPS unit in seconds from first file name, gps_m_time = time found at GPS unit in dateform, cmos_time = time found at the computer CMOS in seconds from first file name, cmos_m_time = time found at the computer CMOS in dateform
The ALTUS Cloud Electrification Study (ACES) was Based at the Naval Air Facility Key West in Florida.  ACES researchers in August 2002 conducted overflights of thunderstorms over the southwestern corner of Florida.  For the first time in NASA research an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data.  Carrying field mills, optical sensors, electric field sensors and other instruments, it allowed scientists to collect cloud electrification data for the first time from above the storm from it's birth through dissipation.  This experiment allowed scientists to achieve the dual goals of gathering weather data safely, and testing new aircraft technology.  This dataset consists of data collected from seven instruments: the Slow/Fast antenna, Electric Field Mill, two Optical Pulse Sensors, Searchcoil magnetometer and Accelerometers.  These data are collected at 200KHz from the first 16 telemetry items collected on the aircraft and are initiated by an operator selected trigger (e.g. DOPS), and continues collecting for as long as the trigger continues.
The ALTUS Cloud Electrification Study (ACES) was Based at the Naval Air Facility Key West in Florida.  ACES researchers in August 2002 conducted overflights of thunderstorms over the southwestern corner of Florida.  For the first time in NASA research an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data.  Carrying field mills, optical sensors, electric field sensors and other instruments, it allowed scientists to collect cloud electrification data for the first time from above the storm from it's birth through dissipation.  This experiment allowed scientists to achieve the dual goals of gathering weather data safely, and testing new aircraft technology.  This dataset consists of data collected from seven instruments: the Slow/Fast antenna, Electric Field Mill, two Optical Pulse Sensors, Searchcoil magnetometer and Accelerometers.  These data are collected at 200KHz from the first 16 telemetry items collected on the aircraft and are initiated by an operator selected trigger (e.g. DOPS), and continues collecting for as long as the trigger continues.
The ALTUS Cloud Electrification Study (ACES) was Based at the Naval Air Facility Key West in Florida.  ACES researchers in August 2002 conducted overflights of thunderstorms over the southwestern corner of Florida.  For the first time in NASA research an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data.  Carrying field mills, optical sensors, electric field sensors and other instruments, it allowed scientists to collect cloud electrification data for the first time from above the storm from it's birth through dissipation.  This experiment allowed scientists to achieve the dual goals of gathering weather data safely, and testing new aircraft technology.  This dataset consists of data collected from seven instruments: the Slow/Fast antenna, Electric Field Mill, two Optical Pulse Sensors, Searchcoil magnetometer and Accelerometers.  These data are collected at 200KHz from the first 16 telemetry items collected on the aircraft and are initiated by an operator selected trigger (e.g. DOPS), and continues collecting for as long as the trigger continues.
This file contains Level 2 total solar irradiance in the form of daily means gathered by the ACRIM instrument on the ACRIMSAT satellite. (Suggested Usage: This collection is comprised of processed daily mean data collected by the ACRIMSAT spacecraft. This collection is ACRIM III results of total solar irradiance.)
This file contains Level 2 total solar irradiance in the form of shutter cycles gathered by the ACRIM instrument on the ACRIMSAT satellite. (Suggested Usage: This collection is comprised of processed shutter cycle data collected by the ACRIMSAT spacecraft. This collection is ACRIM III results of total solar irradiance.)
AMSU-A,the Advanced Microwave Sounding Unit, is a 15-channel passive microwave radiometer used to profile atmospheric temperature and moisture from the earth's surface to ~45 km (3 millibars).  All orbits beginning in the day (00:00:00 - 23:59:59 UTC) are stored in one daily HDF-EOS file.  There are 15 (channel) arrays, as well as corresponding Latitude, Longitude, and Time.  Each daily file is compressed (gzip).
AMSU-A,the Advanced Microwave Sounding Unit, is a 15-channel passive microwave radiometer used to profile atmospheric temperature and moisture from the earth's surface to ~45 km (3 millibars).  All orbits beginning in the day (00:00:00 - 23:59:59 UTC) are stored in one daily HDF-EOS file.  There are 15 (channel) arrays, as well as corresponding Latitude, Longitude, and Time.  Each daily file is compressed (gzip).
AMSU-A,the Advanced Microwave Sounding Unit, is a 15-channel passive microwave radiometer used to profile atmospheric temperature and moisture from the earth's surface to ~45 km (3 millibars).  All orbits beginning in the day (00:00:00 - 23:59:59 UTC) are stored in one daily HDF-EOS file.  There are 15 (channel) arrays, as well as corresponding Latitude, Longitude, and Time.  Each daily file is compressed (gzip).
AMSU-A,the Advanced Microwave Sounding Unit, is a 15-channel passive microwave radiometer used to profile atmospheric temperature and moisture from the earth's surface to ~45 km (3 millibars).  All orbits beginning in the day (00:00:00 - 23:59:59 UTC) are stored in one daily HDF-EOS file.  There are 15 (channel) arrays, as well as corresponding Latitude, Longitude, and Time.  Each daily file is compressed (gzip).
AMSU-A,the Advanced Microwave Sounding Unit, is a 15-channel passive microwave radiometer used to profile atmospheric temperature and moisture from the earth's surface to ~45 km (3 millibars).  All orbits beginning in the day (00:00:00 - 23:59:59 UTC) are stored in one daily HDF-EOS file.  There are 15 (channel) arrays, as well as corresponding Latitude, Longitude, and Time.  Each daily file is compressed (gzip).
AMSU-A,the Advanced Microwave Sounding Unit, is a 15-channel passive microwave radiometer used to profile atmospheric temperature and moisture from the earth's surface to ~45 km (3 millibars).  All orbits beginning in the day (00:00:00 - 23:59:59 UTC) are stored in one daily HDF-EOS file.  There are 15 (channel) arrays, as well as corresponding Latitude, Longitude, and Time.  Each daily file is compressed (gzip).
AMSU-A,the Advanced Microwave Sounding Unit, is a 15-channel passive microwave radiometer used to profile atmospheric temperature and moisture from the earth's surfaceto ~45 km (3 millibars). There are 15 (channel) arrays, as well as corresponding Latitude, Longitude, and Time.
AMSU-A,the Advanced Microwave Sounding Unit, is a 15-channel passive microwave radiometer used to profile atmospheric temperature and moisture from the earth's surfaceto ~45 km (3 millibars). There are 15 (channel) arrays, as well as corresponding Latitude, Longitude, and Time.
AMSU-A,the Advanced Microwave Sounding Unit, is a 15-channel passive microwave radiometer used to profile atmospheric temperature and moisture from the earth's surfaceto ~45 km (3 millibars). There are 15 (channel) arrays, as well as corresponding Latitude, Longitude, and Time.
The Agency for Toxic Substances and Disease Registry (ATSDR) Hazardous Waste Site Polygon Data, 1996 consists of 2042 polygons for selected hazardous waste sites that were compiled in January 1996. The Hazardous Waste Site ATSDR layer was created by linking HAZ_SITES_ATSDR_BASE with additional data. Most polygons represent sites considered for cleanup under the Comprehensive Environmental Response, Compensation and Liability Act (CERCLA or Superfund). Typical sites are either on the EPA National Priorities List (NPL) or are being considered for inclusion on the NPL. This dataset is distributed by the Columbia University Center for International Earth Science Information Network (CIESIN). (Suggested Usage: To provide a polygon dataset of hazardous waste sites in the United States, which can be used to identify nearby populations and assess their potential risk)
The Agency for Toxic Substances and Disease Registry (ATSDR) Hazardous Waste Site Polygon Data, 1996 consists of 2042 polygons for selected hazardous waste sites that were compiled in January 1996. The Hazardous Waste Site ATSDR layer was created by linking HAZ_SITES_ATSDR_BASE with additional data. Most polygons represent sites considered for cleanup under the Comprehensive Environmental Response, Compensation and Liability Act (CERCLA or Superfund). Typical sites are either on the EPA National Priorities List (NPL) or are being considered for inclusion on the NPL. This dataset is distributed by the Columbia University Center for International Earth Science Information Network (CIESIN). (Suggested Usage: To provide a polygon dataset of hazardous waste sites in the United States, which can be used to identify nearby populations and assess their potential risk)
The Agency for Toxic Substances and Disease Registry (ATSDR) Hazardous Waste Site Polygon Data, 1996 consists of 2042 polygons for selected hazardous waste sites that were compiled in January 1996. The Hazardous Waste Site ATSDR layer was created by linking HAZ_SITES_ATSDR_BASE with additional data. Most polygons represent sites considered for cleanup under the Comprehensive Environmental Response, Compensation and Liability Act (CERCLA or Superfund). Typical sites are either on the EPA National Priorities List (NPL) or are being considered for inclusion on the NPL. This dataset is distributed by the Columbia University Center for International Earth Science Information Network (CIESIN). (Suggested Usage: To provide a polygon dataset of hazardous waste sites in the United States, which can be used to identify nearby populations and assess their potential risk)
The Agency for Toxic Substances Disease Registry (ATSDR) Hazardous Waste Site Polygon Data with CIESIN Modifications, 1996 is a database providing georeferenced data for 1562 National Priorities List (NPL) Superfund sites. These were selected from the larger set of the GRASP Hazardous Waste polygon boundary database, based on NPL site identification numbers provided by EPA for 2008, but excluding 7 sites in 4 US Territorial areas plus the now independent Palau, Federated States of Micronesia. There are 72 NPL sites for which there are no corresponding ATSDR polygons. The database includes all the attributes for these NPL sites provided in the ATSDR GRASP Polygon database and the EPA CERCLIS NPL database. The Hazardous Waste Site ATSDR layer was created by linking HAZ_SITES_ATSDR_BASE with additional data. Most polygons represent sites considered for cleanup under the Comprehensive Environmental Response, Compensation and Liability Act (CERCLA or Superfund). Typical sites are either on the EPA National Priorities List (NPL) or are being considered for inclusion on the NPL.This dataset is modified and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN). (Suggested Usage: To provide an easily accessible, corrected dataset of polygons for hazardous waste sites in the United States which can be used to identify nearby populations and assess their potential risk.)
The Agency for Toxic Substances Disease Registry (ATSDR) Hazardous Waste Site Polygon Data with CIESIN Modifications, 1996 is a database providing georeferenced data for 1562 National Priorities List (NPL) Superfund sites. These were selected from the larger set of the GRASP Hazardous Waste polygon boundary database, based on NPL site identification numbers provided by EPA for 2008, but excluding 7 sites in 4 US Territorial areas plus the now independent Palau, Federated States of Micronesia. There are 72 NPL sites for which there are no corresponding ATSDR polygons. The database includes all the attributes for these NPL sites provided in the ATSDR GRASP Polygon database and the EPA CERCLIS NPL database. The Hazardous Waste Site ATSDR layer was created by linking HAZ_SITES_ATSDR_BASE with additional data. Most polygons represent sites considered for cleanup under the Comprehensive Environmental Response, Compensation and Liability Act (CERCLA or Superfund). Typical sites are either on the EPA National Priorities List (NPL) or are being considered for inclusion on the NPL.This dataset is modified and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN). (Suggested Usage: To provide an easily accessible, corrected dataset of polygons for hazardous waste sites in the United States which can be used to identify nearby populations and assess their potential risk.)
The Agency for Toxic Substances Disease Registry (ATSDR) Hazardous Waste Site Polygon Data with CIESIN Modifications, 1996 is a database providing georeferenced data for 1562 National Priorities List (NPL) Superfund sites. These were selected from the larger set of the GRASP Hazardous Waste polygon boundary database, based on NPL site identification numbers provided by EPA for 2008, but excluding 7 sites in 4 US Territorial areas plus the now independent Palau, Federated States of Micronesia. There are 72 NPL sites for which there are no corresponding ATSDR polygons. The database includes all the attributes for these NPL sites provided in the ATSDR GRASP Polygon database and the EPA CERCLIS NPL database. The Hazardous Waste Site ATSDR layer was created by linking HAZ_SITES_ATSDR_BASE with additional data. Most polygons represent sites considered for cleanup under the Comprehensive Environmental Response, Compensation and Liability Act (CERCLA or Superfund). Typical sites are either on the EPA National Priorities List (NPL) or are being considered for inclusion on the NPL.This dataset is modified and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN). (Suggested Usage: To provide an easily accessible, corrected dataset of polygons for hazardous waste sites in the United States which can be used to identify nearby populations and assess their potential risk.)
Detrended boundary layer fluxes recorded on aircraft flights over the Konza
Detrended boundary layer fluxes recorded on aircraft flights over the Konza
ABSTRACT: Detrended boundary layer fluxes recorded on aircraft flights over the Konza
Detrended boundary layer fluxes recorded on aircraft flights over the Konza
Detrended boundary layer fluxes recorded on aircraft flights over the Konza
ABSTRACT: Detrended boundary layer fluxes recorded on aircraft flights over the Konza
Detrended boundary layer fluxes recorded on aircraft flights over the Konza
Detrended boundary layer fluxes recorded on aircraft flights over the Konza
ABSTRACT: Detrended boundary layer fluxes recorded on aircraft flights over the Konza
Filtered boundary layer fluxes recorded on aircraft flights over the Konza
Filtered boundary layer fluxes recorded on aircraft flights over the Konza
ABSTRACT: Filtered boundary layer fluxes recorded on aircraft flights over the Konza
Filtered boundary layer fluxes recorded on aircraft flights over the Konza
Filtered boundary layer fluxes recorded on aircraft flights over the Konza
ABSTRACT: Filtered boundary layer fluxes recorded on aircraft flights over the Konza
Filtered boundary layer fluxes recorded on aircraft flights over the Konza
Filtered boundary layer fluxes recorded on aircraft flights over the Konza
ABSTRACT: Filtered boundary layer fluxes recorded on aircraft flights over the Konza
Raw (unmodified) boundary layer fluxes recorded on aircraft flights over Konza
Raw (unmodified) boundary layer fluxes recorded on aircraft flights over Konza
ABSTRACT: Raw (unmodified) boundary layer fluxes recorded on aircraft flights over Konza
Raw (unmodified) boundary layer fluxes recorded on aircraft flights over Konza
Raw (unmodified) boundary layer fluxes recorded on aircraft flights over Konza
ABSTRACT: Raw (unmodified) boundary layer fluxes recorded on aircraft flights over Konza
Raw (unmodified) boundary layer fluxes recorded on aircraft flights over Konza
Raw (unmodified) boundary layer fluxes recorded on aircraft flights over Konza
ABSTRACT: Raw (unmodified) boundary layer fluxes recorded on aircraft flights over Konza
Airborne Microwave Observatory of Subcanopy and Subsurface
Airborne Microwave Observatory of Subcanopy and Subsurface
Airborne Microwave Observatory of Subcanopy and Subsurface
AirMSPI Ellipsoid-projected georegistered radiance product
AirMSPI terrain-projected georegistered radiance product
AirMSPI ellipsoid-projected georegistered radiance product
AirMSPI terrain-projected georegistered radiance product
This is AIRS-AMSU-CloudSat collocation indexes, in ASCII format. These data map CloudSat profile indexes to the collocated AMSU field of views, and AIRS IR footprints, per AIRS 6-min granule time. Hence it can be considered as Level 1. These data are created within the frames of the MEaSUREs project.

The basic task is to bring together retrievals of water vapor and cloud properties from multiple "A-train" instruments (AIRS, AMSR-E, MODIS, AMSU, MLS, & CloudSat), classify each "scene" (instrument look) using the cloud information, and develop a merged, multi-sensor climatology of atmospheric water vapor as a function of altitude, stratified by the cloud classes. This is a large science analysis project that will require the use of SciFlo technologies to discover and organize all of the datasets, move and cache datasets as required, find space/time "matchups" between pairs of instruments, and process years of satellite data to produce the climate data records.

The short name for this collections is AIRS_CPR_IND.
This is AIRS-CloudSat collocated subset, in NetCDF 4 format. These data contain collocated: AIRS Level 1b radiances spectra, CloudSat radar reflectivities, and MODIS cloud mask. These data are created within the frames of the MEaSUREs project. 

The basic task is to bring together retrievals of water vapor and cloud properties from multiple "A-train" instruments (AIRS, AMSR-E, MODIS, AMSU, MLS, CloudSat), classify each "scene" (instrument look) using the cloud information, and develop a merged, multi-sensor climatology of atmospheric water vapor as a function of altitude, stratified by the cloud classes. This is a large science analysis project that will require the use of SciFlo technologies to discover and organize all of the datasets, move and cache datasets as required, find space/time "matchups" between pairs of instruments, and process years of satellite data to produce the climate data records.

The short name for this collection is AIRS_CPR_MAT

Parameters contained in the data files include the following:
Variable Name|Description|Units 
  CldFrcStdErr|Cloud Fraction|(None)
 CloudLayers| Number of hydrometeor layers| (count)
 CPR_Cloud_mask| CPR Cloud Mask| (None)
 DEM_elevation| Digital Elevation Map| (m)
 dust_flag|Dust Flag|(None)
 latAIRS|AIRS IR latitude|(deg)
 Latitude|CloudSat Latitude |(degrees)
 LayerBase| Height of Layer Base| (m)
 LayerTop| Height of layer top| (m)
 lonAIRS|AIRS IR longitude|(deg)
 Longitude|CloudSat Longitude| (degrees)
 MODIS_cloud_flag| MOD35_bit_2and3_cloud_flag| (None)
 Radar_Reflectivity| Radar Reflectivity Factor| (dBZe)
 radiances|Radiances|(milliWatts/m**2/cm**-1/steradian)
 Sigma-Zero| Sigma-Zero| (dB*100)
 spectral_clear_indicator|Spectral Clear Indicator|(None)
 Vertical_binsize|CloudSat vertical binsize| (m)
End of parameter information
AIRS/Aqua Level-1B calibration subset including clear cases, special calibration sites, random nadir spots, and high clouds.
      
      The Atmospheric Infrared Sounder (AIRS) is a facility instrument aboard thesecond Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors.
      
      The AIRS Visible/Near Infrared (VIS/NIR) level 1B data set contains AIRS visible and near-infrared calibrated and geolocated radiances in W/m^2/micron/steradian. This data set is generated from AIRS level 1A digital numbers (DN), including 4 channels in the 0.4 to 1.0 um region of the spectrum.

                  (The Shortname for this product is AIRXBCAL).
The AIRS Infrared (IR) level 1B data set contains AIRS infrared calibrated and geolocated radiances in milliWatts/m^2/cm^-1/steradian. This data set is generated from AIRS level 1A digital numbers (DN), including 2378 infrared channels in the 3.74 to 15.4 micron region of the spectrum. A day's worth of AIRS data is divided into 240 scenes each of 6 minute duration. For the AIRS infrared measurements, an individual scene consists of 135 scanlines containing 90 cross-track footprints; thus there is a total of 135 x 90 = 12,150 footprints per AIRS IR scene.

                  (The Shortname for this product is AIRIBRAD).
The Atmospheric Infrared Sounder (AIRS) is a facility instrument
            aboard the second Earth Observing System (EOS) polar-orbiting
            platform, EOS Aqua. In combination with the Advanced Microwave
            Sounding Unit (AMSU) and the Humidity Sounder for Brazil Humidity
            Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric
            sounding group of visible, infrared, and microwave sensors.  AIRS data
            will be generated continuously. Global coverage will be obtained twice
            daily (day and night) on a 1:30pm sun synchronous orbit from a 705-km
            altitude.
                                                      
            The AIRS IR Level 1B QA Subset contains Quality Assurance (QA)
            parameters that a user of may use to filter AIRS IR Level 1B radiance
            data to create a subset of analysis. QA parameters indicate quality of
            granule-per-channel, scan-per-channel, field of view, and channel and
            should be accessed before any data of analysis. It also contains
            "glintlat", "glintlon", and "sun_glint_distant" that users can use to
            check for possibility of solar glint contamination.
                                                      
            AIRS IR Level 1B radiance data can be found in AIRIBRAD.  

            (The Shortname for this product is AIRIBQAP).
                                                      
            Coverage: Global, Twice Daily (Daytime and Nighttime)
            Resolution: 13.5 km at nadir; 41 km x 21.4 km at the scan extremes
            Spectral Range: 2378 channels from
            3.74 - 4.61 um
            6.20 - 8.22 um
            8.8 - 15.4 um
The Atmospheric Infrared Sounder (AIRS) is a facility instrument aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors.  AIRS data will be generated continuously. Global coverage will be obtained twice daily (day and night) on a 1:30pm sun synchronous orbit from a 705-km altitude.
                  
                  Cloud-Cleared Radiances contain calibrated, geolocated channel-by-channel AIRS infrared radiances (milliWatts/m2/cm-1/steradian) that would have been observed within each Advanced Microwave Sounding Unit (AMSU) footprint if there were no clouds in the FOV and produced along with the AIRS Standard Product, as they are the radiances used to retrieve the Standard Product. Nevertheless, they are an order of magnitude larger in data volume than the remainder of the Standard Products and, many Standard Product users are expected to have little interest in the Cloud Cleared Radiance. For these reasons they are a separate output file, but like the Standard Product, are generated at all locations.
                  
                  (The Shortname for this product is AIRI2CCF).
      With the availability of newly released V6 products, this product is no longer produced effective 2013-02-28.
This product is similar to AIRI2CCF. However, it contains science retrievals that use the Humidity Sounder for Brazil (HSB). Because the HSB instrument lived only from September 2002 through January 2003 when it terminally failed, the data set covers these five months only.
            
            Cloud-Cleared Radiances contain calibrated, geolocated channel-by-channel AIRS infrared radiances (milliWatts/m2/cm-1/steradian) that would have been observed within each Advanced Microwave Sounding Unit (AMSU) footprint if there were no clouds in the FOV and produced along with the AIRS Standard Product, as they are the radiances used to retrieve the Standard Product. Nevertheless, they are an order of magnitude larger in data volume than the remainder of the Standard Products and, many Standard Product users are expected to have little interest in the Cloud Cleared Radiance. For these reasons they are a separate output file, but like the Standard Product, are generated at all locations.
            
            (The Shortname for this product is AIRH2CCF).
This product is similar to AIRI2CCF. It is newest products produced using AIRS IR only because the radiometric noise in AMSU channel 4 started to increase significantly (since June 2007).
                  
                  The Atmospheric Infrared Sounder (AIRS) is a facility instrument aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors.  AIRS data will be generated continuously. Global coverage will be obtained twice daily (day and night) on a 1:30pm sun synchronous orbit from a 705-km altitude.
                  
                  Cloud-Cleared Radiances contain calibrated, geolocated channel-by-channel AIRS infrared radiances (milliWatts/m2/cm-1/steradian) that would have been observed within each Advanced Microwave Sounding Unit (AMSU) footprint if there were no clouds in the FOV and produced along with the AIRS Standard Product, as they are the radiances used to retrieve the Standard Product. Nevertheless, they are an order of magnitude larger in data volume than the remainder of the Standard Products and, many Standard Product users are expected to have little interest in the Cloud Cleared Radiance. For these reasons they are a separate output file, but like the Standard Product, are generated at all locations.
                  
                  (The Shortname for this product is AIRS2CCF).
The Atmospheric Infrared Sounder (AIRS) is a facility instrument aboard the second Earth Observing System (EOS) polar-orbiting  platform, EOS Aqua. In combination with the Advanced Microwave  Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible,  infrared, and microwave sensors. AIRS data will be generated continuously. Global coverage will be obtained twice daily (day and night) on a 1:30pm sun synchronous orbit from a 705-km altitude.
                                          
                                          The AIRS Carbon Dioxide (CO2) Standard Retrieval Product consists of retrieved estimates of CO2, plus estimates of the errors associated with the retrieval. In contrast to AIRX2RET, the horizontal resolution of this standard product is about 110 km (1x1 degree). An AIRS granule has been set as 6 minutes of data, 15 footprints cross track by 22 lines along track.
                                          
                                          (The Shortname for this product is AIRX2STC).
The Atmospheric Infrared Sounder (AIRS) is a facility instrument aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. AIRS data will be generated   continuously. Global coverage will be obtained twice daily (day and night) on a 1:30pm sun synchronous orbit from a 705-km altitude.
                        
                        In particular, this support product focuses on the tropospheric CO2 retrieval. In general, AIRS Support Products include higher vertical resolution profiles of the quantities found in the Standard Product plus intermediate output (e.g., microwave-only retrieval), research products such as the abundance of trace gases, and detailed quality assessment information. The Support Product profiles contain 100 pressure levels between 1100 and .016 mb; this higher resolution will simplify the generation of radiances using forward models, though the vertical information content is no greater than in the Standard Product profiles. The horizontal resolution is 50 km. The intended users of the Support Product are researchers interested in generating forward radiance, or in examining research products, and the AIRS algorithm development team. The Support Product will be generated at all locations as Standard Products. An AIRS granule has been set as 6 minutes of data. This will normally correspond to approximately 1/15 of an orbit but exactly 45 scanlines of AMSU-A data or 135 scanlines of AIRS and HSB data. 
                        
                        (The Shortname for this product is AIRX2SPC).
The Atmospheric Infrared Sounder (AIRS) is a facility instrument aboard the second Earth Observing System (EOS) polar-orbiting  platform, EOS Aqua. In combination with the Advanced Microwave  Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible,  infrared, and microwave sensors. AIRS data will be generated continuously. Global coverage will be obtained twice daily (day and night) on a 1:30pm sun synchronous orbit from a 705-km altitude.
                        
                        The AIRS Standard Retrieval Product consists of retrieved estimates of  cloud and surface properties, plus profiles of retrieved temperature,  water vapor, ozone, carbon monoxide and methane. Estimates of the errors associated with these quantities will also be part of the Standard Product. The temperature profile vertical resolution is 28 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at 14 atmospheric layers between 1100 mb and 50 mb. The horizontal resolution is 50 km. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track.
                        
                        (The Shortname for this product is AIRX2RET).
   With the availability of newly released V6 products, this product is no longer produced effective 2013/02/28.
This product is similar to AIRX2RET. However, it contains science retrievals that use the Humidity Sounder for Brazil (HSB). Because the HSB instrument lived only from September 2002 through January 2003 when it terminally failed, the data set covers these five months only.
            
            The AIRS Standard Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. Estimates of the errors associated with these quantities will also be part of the Standard Product. The temperature profile vertical resolution is 28 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at 14 atmospheric layers between 1100 mb and 50 mb.  The horizontal resolution is 50 km. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track.
            
            (The Shortname for this product is AIRH2RET).
This product is similar to AIRX2RET. It is newest products produced using AIRS IR only because the radiometric noise in AMSU channel 4 started to increase significantly (since June 2007).
                        
                        The AIRS Standard Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. Estimates of the errors associated with these quantities will also be part of the Standard Product. The temperature profile vertical resolution is 28 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at 14 atmospheric layers between 1100 mb and 50 mb.  The horizontal resolution is 50 km. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track.
                        
                        (The Shortname for this product is AIRS2RET).
The Atmospheric Infrared Sounder (AIRS) is a facility instrument  aboard the second Earth Observing System (EOS) polar-orbiting  platform, EOS Aqua. In combination with the Advanced Microwave  Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible,  infrared, and microwave sensors. AIRS data will be generated continuously. Global coverage will be obtained twice daily (day and night) on a 1:30pm sun synchronous orbit from a 705-km altitude.
                  
                  The Support Product includes higher vertical resolution profiles of the quantities found in the Standard Product plus intermediate output (e.g., microwave-only retrieval), research products such as the abundance of trace gases, and detailed quality assessment information. The Support Product profiles contain 100 pressure levels between 1100 and .016 mb; this higher resolution will simplify the generation of radiances using forward models, though the vertical information content is no greater than in the Standard Product profiles. The horizontal resolution is 50 km. The intended users of the Support Product are researchers interested in generating forward radiance, or in examining research products, and the AIRS algorithm development team. The Support Product will be generated at all locations as Standard Products. An AIRS granule has been set as 6 minutes of data. This will normally correspond to approximately 1/15 of an orbit but exactly 45 scanlines of AMSU-A data or 135 scanlines of AIRS and HSB data.
                  
                  (The Shortname for this product is AIRX2SUP).
    With the availability of newly released V6 products, this product is no longer produced effective 2013/02/28.
This product is similar to AIRX2SUP. However, it contains science retrievals that use the Humidity Sounder for Brazil (HSB). Because the HSB instrument lived only from September 2002 through January 2003 when it terminally failed, the data set covers these five months only.
            
            The Support Product includes higher vertical resolution profiles of the quantities found in the Standard Product, plus intermediate outputs (e.g., microwave-only retrieval), research products such as the abundance of trace gases, and detailed quality assessment information. The Support Product profiles contain 100 levels between 1100 and .016 mb; this higher resolution will simplify the generation of radiances using forward models, though the vertical information content is no greater than that in the Standard Product profiles. 
            
            The intended users of the Support Product are researchers interested in generating forward radiance or in examining research products, and the AIRS algorithm development team. The Support Product will be generated at all locations as Standard Products. An AIRS granule has been set as 6 minutes of data. This will normally correspond to approximately 1/15 of an orbit but exactly 45 scanlines of AMSU-A data or 135 scanlines of AIRS and HSB data.
            
            (The Shortname for this product is AIRH2SUP).
This product is similar to AIRX2SUP. It is newest product produced using AIRS IR only because the radiometric noise in AMSU channel 4 started to increase significantly (since June 2007).
                              
                              The Atmospheric Infrared Sounder (AIRS) is a facility instrument aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors.  AIRS data will be generated continuously. Global coverage will be obtained twice daily (day and night) on a 1:30pm sun synchronous orbit from a 705-km altitude.
                              
                              The Support Product includes higher vertical resolution profiles of the quantities found in the Standard Product, plus intermediate outputs (e.g., microwave-only retrieval), research products such as the abundance of trace gases, and detailed quality assessment information. The Support Product profiles contain 100 levels between 1100 and .016 mb; this higher resolution will simplify the generation of radiances using forward models, though the vertical information content is no greater than that in the Standard Product profiles. The intended users of the Support Product are researchers interested in generating forward radiance or in examining research products, and the AIRS algorithm development team. The Support Product will be generated at all locations as Standard Products. An AIRS granule has been set as 6 minutes of data. This will normally correspond to approximately 1/15 of an orbit but exactly 45 scanlines of AMSU-A data or 135 scanlines of AIRS and HSB data.
                              
                              (The Shortname for this product is AIRS2SUP).
The Atmospheric Infrared Sounder (AIRS) is a facility instrument aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In       combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors.  AIRS data are generated continuously. Global coverage will be obtained twice daily (day and night) on a 1:30pm sun synchronous orbit from a 705-km altitude.
 
This precipitation estimate from AIRS is using TOVS-like algorithm, and is intended for TRMM multi-satellite merged precipitation retrieval. The precipitation estimate from AIRS Level 2 Support product,  which are 6-min swath granules (240 per day) are combined here into one daily "Level 2G" global grid with dimensions (24x1440x720). Thus every hour is a  "layer", and the resulting grid cell size is 0.25 degree (~25 km).   Thus grid size is made to fit TRMM products. Since AIRS precipitation is retrieved at AMSU footprint resolution, which is about 45 km at nadir,  many grid cells in this 0.25-deg grid are "empty".  The data are stored such that the first line is the South Pole. The geolocation information  for every hour-layer is also provided in the file.
  
(The Shortname for this product is AIRG2SSD).
This is the AIRS mid-tropospheric Carbon Dioxide (CO2) Level 3 8-day Gridded Retrieval, from the AIRS and AMSU instruments on board of Aqua satellite. It is a 8-day gridded data, at 2.5x2 deg (lon)x(lat) grid cell size. The data is in mole fraction units (data x 10^6 =ppm in volume). This is a total tropospheric column property. The file format is HDF-EOS 2.12 corresponding to HDF4.
      
      This AIRS mid-tropospheric CO2 Level 3, 8-day, Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts as well as the latitude and longitude arrays giving the centers of the grid boxes.  Each file covers an 8-day period. The mean values are simply the arithmetic means of the individual CO2 retrievals which fall within that grid box over the 8-day period.
      
      The mid-tropospheric CO2 retrievals have been averaged and binned into 2.5 x 2 deg grid cells, from -180.0 to +180.0 deg longitude and from -60.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean."
      
      (The Shortname for this product is AIRX3C28).
The AIRS Level 3 8-Day Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts.  Each file covers an 8-day period, or one-half of the Aqua orbit repeat cycle. The mean values are simply the arithmetic means of the daily products, weighted by the number of input counts for each day in that grid box.
            
            The geophysical parameters have been averaged and binned into 1 x 1 deg grid cells, from -180.0 to +180.0 deg longitude and from -90.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean and can be used to generate custom multi-day maps from the daily gridded products.
            
            The geophysical parameters are: Skin Temperature, Surface Air Temperature, Air Temperature, Tropopause, Precipitable Water, Water Vapor, Cloud Amount/Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Reflectance, Emissivity, Sea Surface Temperature, Ozone, Surface Pressure, Cloud Vertical Distribution.

                  (The Shortname for this product is AIRX3ST8).
This product is similar to AIRX3ST8. However, it contains science retrievals that use the Humidity Sounder for Brazil (HSB). Because the HSB instrument lived only from September 2002 through January 2003 when it terminally failed, the data set covers these five months only.
            
            The AIRS Level 3 8-Day Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts.  Each file covers an 8-day period, or one-half of the Aqua orbit repeat cycle. The mean values are simply the arithmetic means of the daily products, weighted by the number of input counts for each day in that grid box.
            
            The geophysical parameters have been averaged and binned into 1 x 1 deg grid cells, from -180.0 to +180.0 deg longitude and from -90.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean and can be used to generate custom multi-day maps from the daily gridded products.
            
            The thermodynamic parameters are: Skin Temperature (land and sea surface), Air Temperature at the surface, Profiles of Air Temperature and Water Vapor, Tropopause Characteristics, Column Precipitable Water, Cloud Amount/Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Reflectance, Emissivity, Surface Pressure, Cloud Vertical Distribution. 

            The trace gases parameters are: Total Amounts and Vertical Profiles of Carbon Monoxide, Methane, and Ozone.

            The actual names of the variables in the data files should be inferred from the Processing File Description document.

                  (The Shortname for this product is AIRH3ST8).
The AIRS Only Level 3 8-Day Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts.  Each file covers an 8-day period, or one-half of the Aqua orbit repeat cycle. The mean values are simply the arithmetic means of the daily products, weighted by the number of input counts for each day in that grid box.
      
      The geophysical parameters have been averaged and binned into 1 x 1 deg grid cells, from -180.0 to +180.0 deg longitude and from -90.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean and can be used to generate custom multi-day maps from the daily gridded products.
      
            The thermodynamic parameters are: Skin Temperature (land and sea surface), Air Temperature at the surface, Profiles of Air Temperature and Water Vapor, Tropopause Characteristics, Column Precipitable Water, Cloud Amount/Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Reflectance, Emissivity, Surface Pressure, Cloud Vertical Distribution. 

            The trace gases parameters are: Total Amounts and Vertical Profiles of Carbon Monoxide, Methane, and Ozone.

            The actual names of the variables in the data files should be inferred from the Processing File Description document.

                  (The Shortname for this product is AIRS3ST8).
This is the AIRS mid-tropospheric Carbon Dioxide (CO2) Level 3 Daily Gridded Retrieval, from the AIRS and AMSU instruments on board of Aqua satellite. It is a daily gridded data, at 2.5x2 deg (lon)x(lat) grid cell size. The data is in mole fraction units (data x 10^6 =ppm in volume). This is a total tropospheric column property. The file format is HDF-EOS 2.12 corresponding to HDF4.
      
      This AIRS mid-tropospheric CO2 Level 3 daily Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts as well as the latitude and longitude arrays giving the centers of the grid boxes.  Each file covers a 24-hour period. The mean values are simply the arithmetic means of the individual CO2 retrievals which fall within that grid box over the period.
      
      The mid-tropospheric CO2 retrievals have been averaged and binned into 2.5 x 2 deg grid cells, from -180.0 to +180.0 deg longitude and from -60.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean."
      
      (The Shortname for this product is AIRX3C2D).
This is the AIRS mid-tropospheric Carbon Dioxide (CO2) Level 3 Monthly Gridded Retrieval, from the AIRS and AMSU instruments on board of Aqua satellite. It is a monthly gridded data, at 2.5x2 deg (lon)x(lat) grid cell size. The data is in mole fraction units (data x 10^6 =ppm in volume). This quantity is not a total column quantity because the sensitivity function of the AIRS mid-tropospheric CO2 retrieval system peaks over the altitude range 6-10 km. The quantity is what results when the true atmospheric CO2 profile is weighted, level-by-level, by the AIRS sensitivity function. The file format is HDF-EOS 2.12 corresponding to HDF4.
      
      This AIRS mid-tropospheric CO2 Level 3 Monthly Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts as well as the latitude and longitude arrays giving the centers of the grid boxes.  Each file covers a calendar month. The mean values are simply the arithmetic means of the individual CO2 retrievals which fall within that grid box over the month.
      
      The mid-tropospheric CO2 retrievals have been averaged and binned into 2.5 x 2 deg grid cells, from -180.0 to +180.0 deg longitude and from -60.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean.

                  (The Shortname for this product is AIRX3C2M).
AIRS/Aqua Level 3 monthly quantization product in physical units (Without HSB).
      
      The quantization products (QP) are distributional summaries derived from the Level-2 standard retrieval products (of swath type) to provide a more comprehensive set of statistical summaries than the traditional means and standard deviation. The QP products combine the Level 2 standard data parameters over grid cells of 5 x 5 deg spatial extent for temporal periods of a month. They preserve the multivariate distributional features of the original data and so provide a compressed data set that more accurately describes the disparate atmospheric states that is in the original Level-2 swath data set.
      
      The geophysical parameters are: Air Temperature and Water Vapor profiles (11 levels/layers), Cloud fraction (vertical distribution).
      
      The Shortname for this product is AIRX3QPM).
AIRS/Aqua Level 3 monthly quantization product in physical units (With HSB).
            
            The quantization products (QP) are distributional summaries derived from the Level-2 standard retrieval products (of swath type) to provide a more comprehensive set of statistical summaries than the traditional means and standard deviation. The QP products combine the Level 2 standard data parameters over grid cells of 5 x 5 deg spatial extent for temporal periods of a month. They preserve the multivariate distributional features of the original data and so provide a compressed data set that more accurately describes the disparate atmospheric states that is in the original Level-2 swath data set.
            
            The geophysical parameters are: Air Temperature and Water Vapor profiles (11 levels/layers), Cloud fraction (vertical distribution).
            
            (The Shortname for this product is AIRH3QPM).
AIRS/Aqua Level 3 monthly quantization product in physical units (AIRS Only).
      
      The quantization products (QP) are distributional summaries derived from the Level-2 standard retrieval products (of swath type) to provide a more comprehensive set of statistical summaries than the traditional means and standard deviation. The QP products combine the Level 2 standard data parameters over grid cells of 5 x 5 deg spatial extent for temporal periods of a month. They preserve the multivariate distributional features of the original data and so provide a compressed data set that more accurately describes the disparate atmospheric states that is in the original Level-2 swath data set.
      
      The geophysical parameters are: Air Temperature and Water Vapor profiles (11 levels/layers), Cloud fraction (vertical distribution).
      
      (The Shortname for this product is AIRS3QPM).
The AIRS Level 3 Monthly Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts.  Each file covers a calendar month. The mean values are simply the arithmetic means of the daily products, weighted by the number of input counts for each day in that grid box.
            
            The geophysical parameters have been averaged and binned into 1 x1 deg grid cells, from -180.0 to +180.0 deg longitude and from -90.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean and can be used to generate custom multi-day maps from the daily gridded products.
            
            The geophysical parameters are: Skin Temperature, Surface Air Temperature, Air Temperature, Tropopause, Precipitable Water, Water Vapor, Cloud Amount/Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Reflectance, Emissivity, Sea Surface Temperature, Ozone, Surface Pressure, Cloud Vertical Distribution.

                  (The Shortname for this product is AIRX3STM).
This product is similar to AIRX3STM. However, it contains science retrievals that use the Humidity Sounder for Brazil (HSB). Because the HSB instrument lived only from September 2002 through January 2003 when it terminally failed, the data set covers these five months only.
                  
                  The AIRS Level 3 Monthly Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts.  Each file covers a calendar month. The mean values are simply the arithmetic means of the daily products, weighted by the number of input counts for each day in that grid box.
                  
                  The geophysical parameters have been averaged and binned into 1 x 1 grid cells, from -180.0 to +180.0 deg longitude and from -90.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean and can be used to generate custom multi-day maps from the daily gridded products.
                  
            The thermodynamic parameters are: Skin Temperature (land and sea surface), Air Temperature at the surface, Profiles of Air Temperature and Water Vapor, Tropopause Characteristics, Column Precipitable Water, Cloud Amount/Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Reflectance, Emissivity, Surface Pressure, Cloud Vertical Distribution. 

            The trace gases parameters are: Total Amounts and Vertical Profiles of Carbon Monoxide, Methane, and Ozone.

            The actual names of the variables in the data files should be inferred from the Processing File Description document.

                  (The Shortname for this product is AIRH3STM).
The AIRS Only Level 3 Monthly Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts.  Each file covers a calendar month. The mean values are simply the arithmetic means of the daily products, weighted by the number of input counts for each day in that grid box.
      
      The geophysical parameters have been averaged and binned into 1 x 1 deg grid cells, from -180.0 to +180.0 deg longitude and from -90.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean and can be used to generate custom multi-day maps from the daily gridded products.
      
            The thermodynamic parameters are: Skin Temperature (land and sea surface), Air Temperature at the surface, Profiles of Air Temperature and Water Vapor, Tropopause Characteristics, Column Precipitable Water, Cloud Amount/Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Reflectance, Emissivity, Surface Pressure, Cloud Vertical Distribution. 

            The trace gases parameters are: Total Amounts and Vertical Profiles of Carbon Monoxide, Methane, and Ozone.

            The actual names of the variables in the data files should be inferred from the Processing File Description document.

                  (The Shortname for this product is AIRS3STM).
AIRS/Aqua Level 3 pentad quantization product in physical units (Without HSB).
      
      The geophysical parameters have been averaged and binned into 1 x 1 deg grid cells, from -180.0 to +180.0 deg longitude and from -90.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean and can be used to generate custom multi-day maps from the daily gridded products.
      
      The geophysical parameters are: Skin Temperature, Surface Air Temperature, Air Temperature, Tropopause, Precipitable Water, Water Vapor, Cloud Amount/Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Reflectance, Emissivity, Sea Surface Temperature, Ozone, Surface Pressure, Cloud Vertical Distribution.

                  (The Shortname for this product is AIRX3QP5).
AIRS/Aqua Level 3 pentad quantization product in physical units (With HSB).
            
            The quantization products (QP) are distributional summaries derived from the Level-2 standard retrieval products (of swath type) to provide a more comprehensive set of statistical summaries than the traditional means and standard deviation. The QP products combine the Level 2 standard data parameters over grid cells of 5 x 5 deg spatial extent for temporal periods of five days. Pentads always start on the 1st, 6th, 11th, 16th, 21st, and 26th days of the month and may contain as few as 3 days of data or as much as 6 days. They preserve the multivariate distributional features of the original data and so provide a compressed data set that more accurately describes the disparate atmospheric states that is in the original Level-2 swath data set.
            
            The geophysical parameters are: Air Temperature and Water Vapor profiles (11 levels/layers), Cloud fraction (vertical distribution).
            
            (The Shortname for this product is AIRH3QP5).
AIRS/Aqua Level 3 pentad quantization product in physical units (AIRS Only).
      
      The quantization products (QP) are distributional summaries derived from the Level-2 standard retrieval products (of swath type) to provide a more comprehensive set of statistical summaries than the traditional means and standard deviation. The QP products combine the Level 2 standard data parameters over grid cells of 5 x 5 deg spatial extent for temporal periods of five days. Pentads always start on the 1st, 6th, 11th, 16th, 21st, and 26th days of the month and may contain as few as 3 days of data or as much as 6 days. They preserve the multivariate distributional features of the original data and so provide a compressed data set that more accurately describes the disparate atmospheric states that is in the original Level-2 swath data set.
      
      The geophysical parameters are: Air Temperature and Water Vapor profiles (11 levels/layers), Cloud fraction (vertical distribution).
      
      (The Shortname for this product is AIRS3QP5)
The AIRS Infrared (IR) level 1B data set contains AIRS infrared calibrated and geolocated radiances in milliWatts/m^2/cm^-1/steradian. This data set is generated from AIRS level 1A digital numbers (DN), including 2378 infrared channels in the 3.74 to 15.4 micron region of the spectrum. A day&apos;s worth of AIRS data is divided into 240 scenes each of 6 minute duration. For the AIRS infrared measurements, an individual scene consists of 135 scanlines containing 90 cross-track footprints; thus there is a total of 135 x 90 = 12,150 footprints per AIRS IR scene.

(The Shortname for this product is AIRIBRAD_NRT).
The Atmospheric Infrared Sounder (AIRS) is a facility instrument
      aboard the second Earth Observing System (EOS) polar-orbiting
      platform, EOS Aqua. In combination with the Advanced Microwave
      Sounding Unit (AMSU) and the Humidity Sounder for Brazil Humidity
      Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric
      sounding group of visible, infrared, and microwave sensors.  AIRS data
      will be generated continuously. Global coverage will be obtained twice
      daily (day and night) on a 1:30pm sun synchronous orbit from a 705-km
      altitude.
                                                
      The AIRS IR Level 1B QA Subset contains Quality Assurance (QA)
      parameters that a user of may use to filter AIRS IR Level 1B radiance
      data to create a subset of analysis. QA parameters indicate quality of
      granule-per-channel, scan-per-channel, field of view, and channel and
      should be accessed before any data of analysis. It also contains
      &quot;glintlat&quot;, &quot;glintlon&quot;, and &quot;sun_glint_distant&quot; that users can use to
      check for possibility of solar glint contamination.
                                                
      AIRS IR Level 1B radiance data can be found in AIRIBRAD.  

      (The Shortname for this product is AIRIBQAP_NRT).
                                                
      Coverage: Global, Twice Daily (Daytime and Nighttime)
      Resolution: 13.5 km at nadir; 41 km x 21.4 km at the scan extremes
      Spectral Range: 2378 channels from
      3.74 - 4.61 um
      6.20 - 8.22 um
      8.8 - 15.4 um
The Atmospheric Infrared Sounder (AIRS) is a facility instrument aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors.  AIRS data will be generated continuously. Global coverage will be obtained twice daily (day and night) on a 1:30pm sun synchronous orbit from a 705-km altitude.
                                                
The AIRS Visible/Near Infrared (VIS/NIR) Level 1B QA Subset contains Quality Assurance (QA) parameters that a may use of filter AIRS VIS/NIR Level 1B radiance data to create a subset of analysis. It includes &quot;state&quot; that user should check before using any VIS/NIR Level 1B data radiance and &quot;glintlat&quot;, &quot;glintlon&quot;, and &quot;sun_glint_distant&quot; that users can use to check for possibility of solar glint contamination.
                                                
AIRS VIS/NIR Level 1B radiance data can be found in AIRVBRAD.
                                                
(The Shortname for this product is AIRVBQAP_NRT)
                                                
Coverage: Global, Twice Daily (Daytime and Nighttime)
Resolution: 2.3 km IFOV at nadir
Spectral Range: Four channels, from 0.4 - 1.0 um:
Wavelengths at 50% peak response:
 Channel 1 0.41 um - 0.44 um
 Channel 2 0.58 um - 0.68 um
 Channel 3 0.71 um - 0.92 um
 Channel 4 0.49 um - 0.94 um
AIRSAR along-track interferometric product
AIRSAR along-track interferometric product
AIRSAR along-track interferometric product
AIRSAR three-frequency polarimetric frame product
AIRSAR three-frequency polarimetric frame product
AIRSAR three-frequency polarimetric frame product
AIRSAR three-frequency polarimetric synoptic product
AIRSAR three-frequency polarimetric synoptic product
AIRSAR three-frequency polarimetric synoptic product
AIRSAR topographic SAR digital elevation model product
AIRSAR topographic SAR digital elevation model product
AIRSAR topographic SAR digital elevation model product
ALOS PALSAR Level 1.0
ALOS PALSAR scene Processed to ALOS PALSAR Level 1.0
ALOS PALSAR scene Processed to ALOS PALSAR Level 1.0
ALOS PALSAR Level 1.1
ALOS PALSAR scene Processed to ALOS PALSAR Level 1.1
ALOS PALSAR scene Processed to ALOS PALSAR Level 1.1
ALOS PALSAR Level 1.5
ALOS PALSAR scene Processed to ALOS PALSAR Level 1.5
ALOS PALSAR scene Processed to ALOS PALSAR Level 1.5
The precipitation data is 0.2 degree gridded monthly precipitation data based upon monthly rain data from Peru and Bolivia and daily rain data from Brazil. The extent of the data ranges from 5.2N and -20.0S to -49.4W to -79.6W
The precipitation data is 0.2 degree gridded monthly precipitation data based upon monthly rain data from Peru and Bolivia and daily rain data from Brazil. The extent of the data ranges from 5.2N and -20.0S to -49.4W to -79.6W
ABSTRACT: The precipitation data is 0.2 degree gridded monthly precipitation data based upon monthly rain data from Peru and Bolivia and daily rain data from Brazil. The extent of the data ranges from 5.2N and -20.0S to -49.4W to -79.6W
The Advanced Microwave Precipitation Radiometer (AMPR) was deployed during the First Kwajelein Experiment (KWAJEX).  AMPR data were collected at four microwave frequencies (10.7, 19.35, 37.1 and 85.5 GHz) for the period of 30 July - 14 Sep 1999.  The CAMEX-3 mission was to study hurricanes over land and ocean in the U.S., Gulf of Mexico, Carribean, and Western Atlantic Ocean in coordination with multiple aircraft and research-quality radar, lightning, radiosonde and raingage sites.
The Advanced Microwave Precipitation Radiometer (AMPR) was deployed during the First Kwajelein Experiment (KWAJEX).  AMPR data were collected at four microwave frequencies (10.7, 19.35, 37.1 and 85.5 GHz) for the period of 30 July - 14 Sep 1999.  The CAMEX-3 mission was to study hurricanes over land and ocean in the U.S., Gulf of Mexico, Carribean, and Western Atlantic Ocean in coordination with multiple aircraft and research-quality radar, lightning, radiosonde and raingage sites.
The Advanced Microwave Precipitation Radiometer (AMPR) was deployed during the First Kwajelein Experiment (KWAJEX).  AMPR data were collected at four microwave frequencies (10.7, 19.35, 37.1 and 85.5 GHz) for the period of 30 July - 14 Sep 1999.  The CAMEX-3 mission was to study hurricanes over land and ocean in the U.S., Gulf of Mexico, Carribean, and Western Atlantic Ocean in coordination with multiple aircraft and research-quality radar, lightning, radiosonde and raingage sites.
The Advanced Microwave Precipitation Radiometer (AMPR) data set was part of the
atmospheric measurements collected during the intensive observation period of
the Tropical Ocean Global Atmosphere-Coupled Ocean Atmosphere Response
Experiment (TOGA-COARE). AMPR data were collected at a combination of
frequencies (10.7, 19 .35, 37.1, and 85.5 GHz) and for the period Jan.
11, 1993 - Feb. 23, 1993. The TOGA COARE geographic domain pertinent to the
AMPR data set was from the equator to 21 S latitude and 145 E - 161 E longitude.
The Advanced Microwave Precipitation Radiometer (AMPR) data set was part of the
atmospheric measurements collected during the intensive observation period of
the Tropical Ocean Global Atmosphere-Coupled Ocean Atmosphere Response
Experiment (TOGA-COARE). AMPR data were collected at a combination of
frequencies (10.7, 19 .35, 37.1, and 85.5 GHz) and for the period Jan.
11, 1993 - Feb. 23, 1993. The TOGA COARE geographic domain pertinent to the
AMPR data set was from the equator to 21 S latitude and 145 E - 161 E longitude.
The Advanced Microwave Precipitation Radiometer (AMPR) data set was part of the
atmospheric measurements collected during the intensive observation period of
the Tropical Ocean Global Atmosphere-Coupled Ocean Atmosphere Response
Experiment (TOGA-COARE). AMPR data were collected at a combination of
frequencies (10.7, 19 .35, 37.1, and 85.5 GHz) and for the period Jan.
11, 1993 - Feb. 23, 1993. The TOGA COARE geographic domain pertinent to the
AMPR data set was from the equator to 21 S latitude and 145 E - 161 E longitude.
The Advanced Microwave Precipitation Radiometer (AMPR) was deployed during the Convection and Moisture Experiments (CAMEX-1)  conducted at Wallops Island, VA. AMPR data were collected at a combination of frequencies (10.7, 19.35, 37.1, and 85.5 GHz) and  for the period of September 26 - October 5, 1993. The geographic domain of the CAMEX region was between 25N - 45N latitude and 70W -  83W longitude
The Advanced Microwave Precipitation Radiometer (AMPR) was deployed during the Convection and Moisture Experiments (CAMEX-1)  conducted at Wallops Island, VA. AMPR data were collected at a combination of frequencies (10.7, 19.35, 37.1, and 85.5 GHz) and  for the period of September 26 - October 5, 1993. The geographic domain of the CAMEX region was between 25N - 45N latitude and 70W -  83W longitude
The Advanced Microwave Precipitation Radiometer (AMPR) was deployed during the Convection and Moisture Experiments (CAMEX-1)  conducted at Wallops Island, VA. AMPR data were collected at a combination of frequencies (10.7, 19.35, 37.1, and 85.5 GHz) and  for the period of September 26 - October 5, 1993. The geographic domain of the CAMEX region was between 25N - 45N latitude and 70W -  83W longitude
The Advanced Microwave Precipitation Radiometer (AMPR) was deployed during the Convection and Moisture Experiment 2 (CAMEX-2).  AMPR data were collected at a combination of frequencies (10.7, 19.35, 37.1, and 85.5 GHz) and for  the period of August 21 - September 2 1995. The geographic domain of the CAMEX-2 region was between 25 N - 45 N latitude and 83 W - 70 W longitude.
The Advanced Microwave Precipitation Radiometer (AMPR) was deployed during the Convection and Moisture Experiment 2 (CAMEX-2).  AMPR data were collected at a combination of frequencies (10.7, 19.35, 37.1, and 85.5 GHz) and for  the period of August 21 - September 2 1995. The geographic domain of the CAMEX-2 region was between 25 N - 45 N latitude and 83 W - 70 W longitude.
The Advanced Microwave Precipitation Radiometer (AMPR) was deployed during the Convection and Moisture Experiment 2 (CAMEX-2).  AMPR data were collected at a combination of frequencies (10.7, 19.35, 37.1, and 85.5 GHz) and for  the period of August 21 - September 2 1995. The geographic domain of the CAMEX-2 region was between 25 N - 45 N latitude and 83 W - 70 W longitude.
The Advanced Microwave Precipitation Radiometer (AMPR) was deployed during the Convection and Precipitation / Electrification Experiment (CaPE).  AMPR data were collected at a combination of frequencies (10.7, 19.35, 37.1, and 85.5 GHz)and for the period of July 18, 1991 - Aug. 17, 1991. CaPE took place in central Florida between 43 N -  25.5 N latitude and 86 W and 69 W longitude.
The Advanced Microwave Precipitation Radiometer (AMPR) was deployed during the Convection and Precipitation / Electrification Experiment (CaPE).  AMPR data were collected at a combination of frequencies (10.7, 19.35, 37.1, and 85.5 GHz)and for the period of July 18, 1991 - Aug. 17, 1991. CaPE took place in central Florida between 43 N -  25.5 N latitude and 86 W and 69 W longitude.
The Advanced Microwave Precipitation Radiometer (AMPR) was deployed during the Convection and Precipitation / Electrification Experiment (CaPE).  AMPR data were collected at a combination of frequencies (10.7, 19.35, 37.1, and 85.5 GHz)and for the period of July 18, 1991 - Aug. 17, 1991. CaPE took place in central Florida between 43 N -  25.5 N latitude and 86 W and 69 W longitude.
The Advanced Microwave Precipitation Radiometer (AMPR) was deployed during the First ISCCP Regional Experiment-III Arctic Cloud Experiment (FIRE-III/ACE).  AMPR data were collected at four microwave frequencies (10.7, 19.35, 37.1 and 85.5 GHz) for the period of 10 May 1998 through 08 June 1998.  The FIRE-III/ACE mission is to study sea-ice melting, sea-ice drift, and other sea-ice properties.  The experiment was focused on the Arctic Ocean in and near the Beaufort Sea off the northern coast of Alaska, in coordination with the ice-bound research ship Sheba.
The Advanced Microwave Precipitation Radiometer (AMPR) was deployed during the First ISCCP Regional Experiment-III Arctic Cloud Experiment (FIRE-III/ACE).  AMPR data were collected at four microwave frequencies (10.7, 19.35, 37.1 and 85.5 GHz) for the period of 10 May 1998 through 08 June 1998.  The FIRE-III/ACE mission is to study sea-ice melting, sea-ice drift, and other sea-ice properties.  The experiment was focused on the Arctic Ocean in and near the Beaufort Sea off the northern coast of Alaska, in coordination with the ice-bound research ship Sheba.
The Advanced Microwave Precipitation Radiometer (AMPR) was deployed during the First ISCCP Regional Experiment-III Arctic Cloud Experiment (FIRE-III/ACE).  AMPR data were collected at four microwave frequencies (10.7, 19.35, 37.1 and 85.5 GHz) for the period of 10 May 1998 through 08 June 1998.  The FIRE-III/ACE mission is to study sea-ice melting, sea-ice drift, and other sea-ice properties.  The experiment was focused on the Arctic Ocean in and near the Beaufort Sea off the northern coast of Alaska, in coordination with the ice-bound research ship Sheba.
The Advanced Microwave Precipitation Radiometer (AMPR) was deployed in Jacksonville, FL for the initial AMPR instrument validation.  AMPR data were collected at four microwave frequencies (10.7, 19.35, 37.1 and 85.5 GHz) for the period of 10 October 1990 through 22 October 1990.  The Jacksonville mission was to study convection over land and ocean for validation along with clear (dry) vertical columns of atmosphere over the ocean for calibration.
The Advanced Microwave Precipitation Radiometer (AMPR) was deployed in Jacksonville, FL for the initial AMPR instrument validation.  AMPR data were collected at four microwave frequencies (10.7, 19.35, 37.1 and 85.5 GHz) for the period of 10 October 1990 through 22 October 1990.  The Jacksonville mission was to study convection over land and ocean for validation along with clear (dry) vertical columns of atmosphere over the ocean for calibration.
The Advanced Microwave Precipitation Radiometer (AMPR) was deployed in Jacksonville, FL for the initial AMPR instrument validation.  AMPR data were collected at four microwave frequencies (10.7, 19.35, 37.1 and 85.5 GHz) for the period of 10 October 1990 through 22 October 1990.  The Jacksonville mission was to study convection over land and ocean for validation along with clear (dry) vertical columns of atmosphere over the ocean for calibration.
The Advanced Microwave Precipitation Radiometer (AMPR) was deployed during the Texas-Florida Underflights (TEFLUN-A); the first of three TRMM ground validation missions. AMPR data were collected at four microwave frequencies (10.7, 19.35, 37.1 and 85.5 GHz) for the period of 05 April 1999 through 06 May 1999. The TEFLUN-A mission was to study convection over sub-tropical land and ocean regions within the range of research-quality radar, lightning, radiosonde, and raingage sites in Florida and Texas.
The Advanced Microwave Precipitation Radiometer (AMPR) was deployed during the Texas-Florida Underflights (TEFLUN-A); the first of three TRMM ground validation missions. AMPR data were collected at four microwave frequencies (10.7, 19.35, 37.1 and 85.5 GHz) for the period of 05 April 1999 through 06 May 1999. The TEFLUN-A mission was to study convection over sub-tropical land and ocean regions within the range of research-quality radar, lightning, radiosonde, and raingage sites in Florida and Texas.
The Advanced Microwave Precipitation Radiometer (AMPR) was deployed during the Texas-Florida Underflights (TEFLUN-A); the first of three TRMM ground validation missions. AMPR data were collected at four microwave frequencies (10.7, 19.35, 37.1 and 85.5 GHz) for the period of 05 April 1999 through 06 May 1999. The TEFLUN-A mission was to study convection over sub-tropical land and ocean regions within the range of research-quality radar, lightning, radiosonde, and raingage sites in Florida and Texas.
30 minute average meteorological data from NCARs Portable Automated Mesonet Station
30 minute average meteorological data from NCARs Portable Automated Mesonet Station
ABSTRACT: 30 minute average meteorological data from NCARs Portable Automated Mesonet Station
The AMSR-E/Aqua Level-3 5-day snow water equivalent (SWE) product includes global 5-day maximum SWE on Northern and Southern Hemisphere 25 km EASE-Grids, generated by the GSFC algorithm using Level-2A TBs.
This document pertains to two data sets: AMSR-E/Aqua Daily EASE-Grid Brightness Temperatures (NSIDC-0301) and AMSR-E/Aqua Daily Global Quarter-Degree Gridded Brightness Temperatures (NISDC-0302). 

The Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) instrument on the NASA EOS Aqua satellite provides global passive microwave measurements of the Earth. NSIDC produces AMSR-E gridded brightness temperature data by interpolating AMSR-E data (6.9 GHz, 10.7 GHz, 18.7 GHz, 23.8 GHz, 36.5 GHz, and 89.0 GHz) to the output grids from swath space using an Inverse Distance Squared (ID2) method. AMSR-E/Aqua L2A Global Swath Spatially-Resampled Brightness Temperatures (AE_L2A) input source data are used.

These data are provided in three EASE-Grid projections (north and south Lambert azimuthal and global cylindrical) at 25 km resolution, and in one global cylindrical, equidistant latitude-longitude projection at 0.25 degree (quarter-degree) resolution. For both data sets, spatial coverage is global, data are daily, and coverage begins 19 June 2002 and ends 27 September 2011. 

Data are provided via FTP. Brightness temperature data (in tenths of kelvins) are two-byte unsigned integers and time files are two-byte signed integers.
This document pertains to two data sets: AMSR-E/Aqua Daily EASE-Grid Brightness Temperatures (NSIDC-0301) and AMSR-E/Aqua Daily Global Quarter-Degree Gridded Brightness Temperatures (NISDC-0302). 

The Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) instrument on the NASA EOS Aqua satellite provides global passive microwave measurements of the Earth. NSIDC produces AMSR-E gridded brightness temperature data by interpolating AMSR-E data (6.9 GHz, 10.7 GHz, 18.7 GHz, 23.8 GHz, 36.5 GHz, and 89.0 GHz) to the output grids from swath space using an Inverse Distance Squared (ID2) method. AMSR-E/Aqua L2A Global Swath Spatially-Resampled Brightness Temperatures (AE_L2A) input source data are used.

These data are provided in three EASE-Grid projections (north and south Lambert azimuthal and global cylindrical) at 25 km resolution, and in one global cylindrical, equidistant latitude-longitude projection at 0.25 degree (quarter-degree) resolution. For both data sets, spatial coverage is global, data are daily, and coverage begins 19 June 2002 and ends 27 September 2011. 

Data are provided via FTP. Brightness temperature data (in tenths of kelvins) are two-byte unsigned integers and time files are two-byte signed integers.
This document pertains to two data sets: AMSR-E/Aqua Daily EASE-Grid Brightness Temperatures (NSIDC-0301) and AMSR-E/Aqua Daily Global Quarter-Degree Gridded Brightness Temperatures (NISDC-0302). 

The Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) instrument on the NASA EOS Aqua satellite provides global passive microwave measurements of the Earth. NSIDC produces AMSR-E gridded brightness temperature data by interpolating AMSR-E data (6.9 GHz, 10.7 GHz, 18.7 GHz, 23.8 GHz, 36.5 GHz, and 89.0 GHz) to the output grids from swath space using an Inverse Distance Squared (ID2) method. AMSR-E/Aqua L2A Global Swath Spatially-Resampled Brightness Temperatures (AE_L2A) input source data are used.

These data are provided in three EASE-Grid projections (north and south Lambert azimuthal and global cylindrical) at 25 km resolution, and in one global cylindrical, equidistant latitude-longitude projection at 0.25 degree (quarter-degree) resolution. For both data sets, spatial coverage is global, data are daily, and coverage begins 19 June 2002 and ends 27 September 2011. 

Data are provided via FTP. Brightness temperature data (in tenths of kelvins) are two-byte unsigned integers and time files are two-byte signed integers.
The AMSR-E/Aqua Level 3 12.5 km daily sea ice product includes 18.7 - 89.0 GHz TBs, sea ice concentration averages (asc & desc), and 5-day snow depth over sea ice on a 12.5 km polar stereographic grid, generated by the NT2 algorithm using Level-2A TBs.
The AMSR-E/Aqua Level-3 25 km daily sea ice product includes 6.9 - 89.0 GHz TBs and sea ice concentration averages (daily, ascending, and descending) on a 25 km polar stereographic grid, generated by the NT2 algorithm using Level-2A TBs.
The AMSR-E/Aqua Level-3 6.25 km daily sea ice product includes 89.0 GHz brightness temperature averages (daily, ascending, and descending) on a 6.25 km polar stereographic grid, generated by the NT2 algorithm using Level-2A TBs.
The AMSR-E/Aqua Level-3 6.25 km daily sea ice drift product includes speed and direction on a 6.25 km polar stereographic grid, generated by the GSFC algorithm using Level-3 6.25 km 89H GHz TBs and Level-3 12.5 km 18V and 37V GHz TBs to mask sea ice.
The AMSR-E/Aqua Level-3 daily ocean product includes Sea Surface Temperature, near-surface wind speed, columnar water vapor, and columnar cloud liquid water over ocean on 0.25 x 0.25 degree ascending and descending grids, averaged from Level-2B products.
The AMSR-E/Aqua Level-3 daily Snow Water Equivalent (SWE) product includes global SWE on Northern and Southern Hemisphere 25 km EASE-Grids, generated by the GSFC algorithm using Level-2A TBs.
The AMSR-E/Aqua Level-3 daily land product includes surface soil moisture, vegetation/roughness water content interpretive information, and TBs on global 25 km ascending and descending EASE-Grids, averaged from Level-2B soil moisture.
The AMSR-E/Aqua Level-1A product includes raw observation counts and conversion factors required to compute the antenna temperatures for each of the AMSR-E microwave channels.
The AMSR-E/Aqua Level-2A swath product includes brightness temperatures at 6.9 GHz, 10.7 GHz, 18.7 GHz, 23.8 GHz, 36.5 GHz, and 89.0 GHz resampled at resolutions of 56 km, 38 km, 21 km, 12 km, and 5.4 km.
The AMSR-E/Aqua Level-2B ocean product includes Sea Surface Temperature at 56 and 38 km, near-surface wind speed at 38 and 21 km, columnar water vapor at 21 km, and columnar cloud liquid water at 12 km, generated by the Wentz algorithm using Level-2A TBs.
The AMSR-E/Aqua Level-2B rain product includes instantaneous rain rate and rain type over ice-free and snow-free land and ocean between 70 degrees north and south latitudes at 5.4 km, generated by the GPROF algorithm using Level-2A TBs.
The AMSR-E/Aqua Level-2B land surface product includes surface soil moisture and vegetation/roughness water content interpretive information on a global 25 km EASE-Grid, generated by the Njoku algorithm using Level-2A TBs.
The AMSR-E/Aqua Level-3 monthly rainfall product includes global averaged rainfall accumulation on two 5 x 5 degree grids; ocean rainfall is generated by the Wilheit algorithm using Level-2A TBs and land rainfall is the averaged Level-2B GPROF products.
The AMSR-E/Aqua Level-3 monthly ocean product includes Sea Surface Temperature, near-surface wind speed, columnar water vapor, and columnar cloud liquid water over ocean on 0.25 x 0.25 degree ascending & descending grids, averaged from Level-2B products.
The AMSR-E/Aqua Level-3 monthly snow water equivalent (SWE) product includes global maximum SWE on Northern and Southern Hemisphere 25 km EASE-Grids, generated by the GSFC algorithm using Level-2A TBs.
The AMSR-E/Aqua Level-3 weekly ocean product includes Sea Surface Temperature, near-surface wind speed, columnar water vapor, and columnar cloud liquid water over ocean on 0.25 x 0.25 degree ascending and descending grids, averaged from Level-2B products.
Data for the ADEOS-II Advanced Microwave Scanning Radiometer L1A product contain raw observation counts and conversion factors required to compute the antenna temperatures for each of the AMSR's fourteen microwave channels.
AMSR/ADEOS-II global swath brightness temperatures are resampled at resolutions of 56 km, 38 km, 21 km, 12 km, and 5.4 km.
Global lower stratospheric temperatures are derived from a series of microwave sounding instruments flown on a series of satellites. Daily Zonal and Monthly Temperature Anomalies for the lower stratosphere on a global 2.5 degree grid are derived from both the Advanced Microwave Sounding Unit (AMSU) channel 9 and from the Microwave Sounding Unit (MSU) channel 4 using the Limb 90 correction. The data set begins Jan 1 1979 and is ongoing.
Global lower stratospheric temperatures are derived from a series of microwave sounding instruments flown on a series of satellites. Daily Zonal and Monthly Temperature Anomalies for the lower stratosphere on a global 2.5 degree grid are derived from both the Advanced Microwave Sounding Unit (AMSU) channel 9 and from the Microwave Sounding Unit (MSU) channel 4 using the Limb 90 correction. The data set begins Jan 1 1979 and is ongoing.
Global lower stratospheric temperatures are derived from a series of microwave sounding instruments flown on a series of satellites. Daily Zonal and Monthly Temperature Anomalies for the lower stratosphere on a global 2.5 degree grid are derived from both the Advanced Microwave Sounding Unit (AMSU) channel 9 and from the Microwave Sounding Unit (MSU) channel 4 using the Limb 90 correction. The data set begins Jan 1 1979 and is ongoing.
Global lower tropospheric temperatures are derived from a series of microwave sounding instruments flown on a series of satellites. Daily Zonal and Monthly Temperature Anomalies for the lower troposphere on a global 2.5 degree grid are derived from both the Advanced Microwave Sounding Unit (AMSU) and from the Microwave Sounding Unit (MSU) using the Limb 90 correction. The data set begins Jan 1 1979 and is ongoing.
Global lower tropospheric temperatures are derived from a series of microwave sounding instruments flown on a series of satellites. Daily Zonal and Monthly Temperature Anomalies for the lower troposphere on a global 2.5 degree grid are derived from both the Advanced Microwave Sounding Unit (AMSU) and from the Microwave Sounding Unit (MSU) using the Limb 90 correction. The data set begins Jan 1 1979 and is ongoing.
Global lower tropospheric temperatures are derived from a series of microwave sounding instruments flown on a series of satellites. Daily Zonal and Monthly Temperature Anomalies for the lower troposphere on a global 2.5 degree grid are derived from both the Advanced Microwave Sounding Unit (AMSU) and from the Microwave Sounding Unit (MSU) using the Limb 90 correction. The data set begins Jan 1 1979 and is ongoing.
Global mid tropospheric temperatures are derived from a series of microwave sounding instruments flown on a series of satellites. Daily Zonal and Monthly Temperature Anomalies for the middle troposphere on a global 2.5 degree grid are derived from both the Advanced Microwave Sounding Unit (AMSU) channel 5 and from the Microwave Sounding Unit (MSU) channel 2 using the Limb 90 correction. The data set begins Jan 1 1979 and is ongoing.
Global mid tropospheric temperatures are derived from a series of microwave sounding instruments flown on a series of satellites. Daily Zonal and Monthly Temperature Anomalies for the middle troposphere on a global 2.5 degree grid are derived from both the Advanced Microwave Sounding Unit (AMSU) channel 5 and from the Microwave Sounding Unit (MSU) channel 2 using the Limb 90 correction. The data set begins Jan 1 1979 and is ongoing.
Global mid tropospheric temperatures are derived from a series of microwave sounding instruments flown on a series of satellites. Daily Zonal and Monthly Temperature Anomalies for the middle troposphere on a global 2.5 degree grid are derived from both the Advanced Microwave Sounding Unit (AMSU) channel 5 and from the Microwave Sounding Unit (MSU) channel 2 using the Limb 90 correction. The data set begins Jan 1 1979 and is ongoing.
This data set provides a 1 km resolution Digital Elevation Model (DEM) of Antarctica. The DEM combines measurements from the European Remote Sensing Satellite-1 (ERS-1) Satellite Radar Altimeter (SRA) and the Ice, Cloud, and land Elevation Satellite (ICESat) Geosciences Laser Altimeter System (GLAS). The ERS-1 data are from two long repeat cycles of 168 days initiated in March 1994, and the GLAS data are from 20 February 2003 through 21 March 2008. The data set is approximately 240 MB comprised of two gridded binary files and two Environment for Visualizing Images (ENVI) header files viewable using ENVI or other similar software packages. The data are available via FTP.
This data set provides a 1 km resolution Digital Elevation Model (DEM) of Antarctica. The DEM combines measurements from the European Remote Sensing Satellite-1 (ERS-1) Satellite Radar Altimeter (SRA) and the Ice, Cloud, and land Elevation Satellite (ICESat) Geosciences Laser Altimeter System (GLAS). The ERS-1 data are from two long repeat cycles of 168 days initiated in March 1994, and the GLAS data are from 20 February 2003 through 21 March 2008. The data set is approximately 240 MB comprised of two gridded binary files and two Environment for Visualizing Images (ENVI) header files viewable using ENVI or other similar software packages. The data are available via FTP.
This data set provides a 1 km resolution Digital Elevation Model (DEM) of Antarctica. The DEM combines measurements from the European Remote Sensing Satellite-1 (ERS-1) Satellite Radar Altimeter (SRA) and the Ice, Cloud, and land Elevation Satellite (ICESat) Geosciences Laser Altimeter System (GLAS). The ERS-1 data are from two long repeat cycles of 168 days initiated in March 1994, and the GLAS data are from 20 February 2003 through 21 March 2008. The data set is approximately 240 MB comprised of two gridded binary files and two Environment for Visualizing Images (ENVI) header files viewable using ENVI or other similar software packages. The data are available via FTP.
The Anthropogenic Biomes of the World Dataset, Version 1 describes globally-
significant ecological patterns within the terrestrial biosphere caused 

by sustained direct human interaction with ecosystems, including agriculture, urbanization, forestry and other land uses. 

Conventional biomes, such as tropical rainforests or grasslands, 

are based on global vegetation patterns related to climate. Now that humans have fundamentally altered global patterns of 

ecosystem form, process, and biodiversity, anthropogenic biomes 

provide a contemporary view of the terrestrial biosphere in its human-altered form. Anthropogenic biomes may also be termed 

"anthromes" to distinguish them from conventional biome 

systems, or "human biomes" (a simpler but less precise term). This dataset is distributed by the Columbia University Center 

for International Earth Science Information Network 

(CIESIN).
The Anthropogenic Biomes of the World Dataset, Version 1 describes globally-
significant ecological patterns within the terrestrial biosphere caused 

by sustained direct human interaction with ecosystems, including agriculture, urbanization, forestry and other land uses. 

Conventional biomes, such as tropical rainforests or grasslands, 

are based on global vegetation patterns related to climate. Now that humans have fundamentally altered global patterns of 

ecosystem form, process, and biodiversity, anthropogenic biomes 

provide a contemporary view of the terrestrial biosphere in its human-altered form. Anthropogenic biomes may also be termed 

"anthromes" to distinguish them from conventional biome 

systems, or "human biomes" (a simpler but less precise term). This dataset is distributed by the Columbia University Center 

for International Earth Science Information Network 

(CIESIN).
The Anthropogenic Biomes of the World Dataset, Version 1 describes globally-
significant ecological patterns within the terrestrial biosphere caused 

by sustained direct human interaction with ecosystems, including agriculture, urbanization, forestry and other land uses. 

Conventional biomes, such as tropical rainforests or grasslands, 

are based on global vegetation patterns related to climate. Now that humans have fundamentally altered global patterns of 

ecosystem form, process, and biodiversity, anthropogenic biomes 

provide a contemporary view of the terrestrial biosphere in its human-altered form. Anthropogenic biomes may also be termed 

"anthromes" to distinguish them from conventional biome 

systems, or "human biomes" (a simpler but less precise term). This dataset is distributed by the Columbia University Center 

for International Earth Science Information Network 

(CIESIN).
The Anthropogenic Biomes of the World, Version 2: 1700 data set describes anthropogenic transformations within the terrestrial biosphere caused by sustained direct human interaction with ecosystems, including agriculture and urbanization c. 1700. Potential natural vegetation, biomes, such as tropical rainforests or grasslands, are based on global vegetation patterns related to climate and geology. Anthropogenic transformation within each biome is approximated using population density, agricultural intensity (cropland and pasture) and urbanization. This data set is part of a time series for the years 1700, 1800, 1900, and 2000 that provides global patterns of historical transformation of the terrestrial biosphere during the Industrial Revolution.
The Anthropogenic Biomes of the World, Version 2: 1700 data set describes anthropogenic transformations within the terrestrial biosphere caused by sustained direct human interaction with ecosystems, including agriculture and urbanization c. 1700. Potential natural vegetation, biomes, such as tropical rainforests or grasslands, are based on global vegetation patterns related to climate and geology. Anthropogenic transformation within each biome is approximated using population density, agricultural intensity (cropland and pasture) and urbanization. This data set is part of a time series for the years 1700, 1800, 1900, and 2000 that provides global patterns of historical transformation of the terrestrial biosphere during the Industrial Revolution.
The Anthropogenic Biomes of the World, Version 2: 1700 data set describes anthropogenic transformations within the terrestrial biosphere caused by sustained direct human interaction with ecosystems, including agriculture and urbanization c. 1700. Potential natural vegetation, biomes, such as tropical rainforests or grasslands, are based on global vegetation patterns related to climate and geology. Anthropogenic transformation within each biome is approximated using population density, agricultural intensity (cropland and pasture) and urbanization. This data set is part of a time series for the years 1700, 1800, 1900, and 2000 that provides global patterns of historical transformation of the terrestrial biosphere during the Industrial Revolution.
The Anthropogenic Biomes of the World, Version 2: 1800 data set describes anthropogenic transformations within the terrestrial biosphere caused by sustained direct human interaction with ecosystems, including agriculture and urbanization c. 1800. Potential natural vegetation, biomes, such as tropical rainforests or grasslands, are based on global vegetation patterns related to climate and geology. Anthropogenic transformation within each biome is approximated using population density, agricultural intensity (cropland and pasture) and urbanization. This data set is part of a time series for the years 1800, 1800, 1900, and 2000 that provides global patterns of historical transformation of the terrestrial biosphere during the Industrial Revolution.
The Anthropogenic Biomes of the World, Version 2: 1800 data set describes anthropogenic transformations within the terrestrial biosphere caused by sustained direct human interaction with ecosystems, including agriculture and urbanization c. 1800. Potential natural vegetation, biomes, such as tropical rainforests or grasslands, are based on global vegetation patterns related to climate and geology. Anthropogenic transformation within each biome is approximated using population density, agricultural intensity (cropland and pasture) and urbanization. This data set is part of a time series for the years 1800, 1800, 1900, and 2000 that provides global patterns of historical transformation of the terrestrial biosphere during the Industrial Revolution.
The Anthropogenic Biomes of the World, Version 2: 1800 data set describes anthropogenic transformations within the terrestrial biosphere caused by sustained direct human interaction with ecosystems, including agriculture and urbanization c. 1800. Potential natural vegetation, biomes, such as tropical rainforests or grasslands, are based on global vegetation patterns related to climate and geology. Anthropogenic transformation within each biome is approximated using population density, agricultural intensity (cropland and pasture) and urbanization. This data set is part of a time series for the years 1800, 1800, 1900, and 2000 that provides global patterns of historical transformation of the terrestrial biosphere during the Industrial Revolution.
The Anthropogenic Biomes of the World, Version 2: 1900 data set describes anthropogenic transformations within the terrestrial biosphere caused by sustained direct human interaction with ecosystems, including agriculture and urbanization c. 1900. Potential natural vegetation, biomes, such as tropical rainforests or grasslands, are based on global vegetation patterns related to climate and geology. Anthropogenic transformation within each biome is approximated using population density, agricultural intensity (cropland and pasture) and urbanization. This data set is part of a time series for the years 1900, 1900, 1900, and 2000 that provides global patterns of historical transformation of the terrestrial biosphere during the Industrial Revolution.
The Anthropogenic Biomes of the World, Version 2: 1900 data set describes anthropogenic transformations within the terrestrial biosphere caused by sustained direct human interaction with ecosystems, including agriculture and urbanization c. 1900. Potential natural vegetation, biomes, such as tropical rainforests or grasslands, are based on global vegetation patterns related to climate and geology. Anthropogenic transformation within each biome is approximated using population density, agricultural intensity (cropland and pasture) and urbanization. This data set is part of a time series for the years 1900, 1900, 1900, and 2000 that provides global patterns of historical transformation of the terrestrial biosphere during the Industrial Revolution.
The Anthropogenic Biomes of the World, Version 2: 1900 data set describes anthropogenic transformations within the terrestrial biosphere caused by sustained direct human interaction with ecosystems, including agriculture and urbanization c. 1900. Potential natural vegetation, biomes, such as tropical rainforests or grasslands, are based on global vegetation patterns related to climate and geology. Anthropogenic transformation within each biome is approximated using population density, agricultural intensity (cropland and pasture) and urbanization. This data set is part of a time series for the years 1900, 1900, 1900, and 2000 that provides global patterns of historical transformation of the terrestrial biosphere during the Industrial Revolution.
The Anthropogenic Biomes of the World, Version 2: 2000 data set describes anthropogenic transformations within the terrestrial biosphere caused by sustained direct human interaction with ecosystems, including agriculture and urbanization c. 2000. Potential natural vegetation, biomes, such as tropical rainforests or grasslands, are based on global vegetation patterns related to climate and geology. Anthropogenic transformation within each biome is approximated using population density, agricultural intensity (cropland and pasture) and urbanization. This data set is part of a time series for the years 1700, 1800, 1900, and 2000 that provides global patterns of historical transformation of the terrestrial biosphere during the Industrial Revolution.
The Anthropogenic Biomes of the World, Version 2: 2000 data set describes anthropogenic transformations within the terrestrial biosphere caused by sustained direct human interaction with ecosystems, including agriculture and urbanization c. 2000. Potential natural vegetation, biomes, such as tropical rainforests or grasslands, are based on global vegetation patterns related to climate and geology. Anthropogenic transformation within each biome is approximated using population density, agricultural intensity (cropland and pasture) and urbanization. This data set is part of a time series for the years 1700, 1800, 1900, and 2000 that provides global patterns of historical transformation of the terrestrial biosphere during the Industrial Revolution.
The Anthropogenic Biomes of the World, Version 2: 2000 data set describes anthropogenic transformations within the terrestrial biosphere caused by sustained direct human interaction with ecosystems, including agriculture and urbanization c. 2000. Potential natural vegetation, biomes, such as tropical rainforests or grasslands, are based on global vegetation patterns related to climate and geology. Anthropogenic transformation within each biome is approximated using population density, agricultural intensity (cropland and pasture) and urbanization. This data set is part of a time series for the years 1700, 1800, 1900, and 2000 that provides global patterns of historical transformation of the terrestrial biosphere during the Industrial Revolution.
The Anthropogenic Sulfur Dioxide Emissions, 1850-2005: National and Regional Data Set by Source Category, Version 2.86 provides annual estimates of 

anthropogenic global and regional sulfur dioxide emissions spanning the period 1850- 2005 using a bottom-up mass balance method, calibrated to country-level inventory data. It includes 

emissions by country and by source category (coal, petroleum, and biomass combustion, smelting, fuel processing, and other processes). This dataset is developed at the Pacific Northwest 

National Laboratory (PNNL) and the maps are produced at the Center for International Earth Science Information Network (CIESIN). The data and maps created using the data set are distributed 

by CIESIN at Columbia University.
The Anthropogenic Sulfur Dioxide Emissions, 1850-2005: National and Regional Data Set by Source Category, Version 2.86 provides annual estimates of 

anthropogenic global and regional sulfur dioxide emissions spanning the period 1850- 2005 using a bottom-up mass balance method, calibrated to country-level inventory data. It includes 

emissions by country and by source category (coal, petroleum, and biomass combustion, smelting, fuel processing, and other processes). This dataset is developed at the Pacific Northwest 

National Laboratory (PNNL) and the maps are produced at the Center for International Earth Science Information Network (CIESIN). The data and maps created using the data set are distributed 

by CIESIN at Columbia University.
The Anthropogenic Sulfur Dioxide Emissions, 1850-2005: National and Regional Data Set by Source Category, Version 2.86 provides annual estimates of 

anthropogenic global and regional sulfur dioxide emissions spanning the period 1850- 2005 using a bottom-up mass balance method, calibrated to country-level inventory data. It includes 

emissions by country and by source category (coal, petroleum, and biomass combustion, smelting, fuel processing, and other processes). This dataset is developed at the Pacific Northwest 

National Laboratory (PNNL) and the maps are produced at the Center for International Earth Science Information Network (CIESIN). The data and maps created using the data set are distributed 

by CIESIN at Columbia University.
This product is similar to AIRI2CCF. However, it contains science retrievals that use the Humidity Sounder for Brazil (HSB). Because the HSB instrument lived only from September 2002 through January 2003 when it terminally failed, the data set covers these five months only.
            
            Cloud-Cleared Radiances contain calibrated, geolocated channel-by-channel AIRS infrared radiances (milliWatts/m2/cm-1/steradian) that would have been observed within each Advanced Microwave Sounding Unit (AMSU) footprint if there were no clouds in the FOV and produced along with the AIRS Standard Product, as they are the radiances used to retrieve the Standard Product. Nevertheless, they are an order of magnitude larger in data volume than the remainder of the Standard Products and, many Standard Product users are expected to have little interest in the Cloud Cleared Radiance. For these reasons they are a separate output file, but like the Standard Product, are generated at all locations.
            
            (The Shortname for this product is AIRH2CCF).
This product is similar to AIRX2RET. However, it contains science retrievals that use the Humidity Sounder for Brazil (HSB). Because the HSB instrument lived only from September 2002 through January 2003 when it terminally failed, the data set covers these five months only.
            
            The AIRS Standard Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. Estimates of the errors associated with these quantities will also be part of the Standard Product. The temperature profile vertical resolution is 28 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at 14 atmospheric layers between 1100 mb and 50 mb.  The horizontal resolution is 50 km. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track.
            
            (The Shortname for this product is AIRH2RET).
This product is similar to AIRX2SUP. However, it contains science retrievals that use the Humidity Sounder for Brazil (HSB). Because the HSB instrument lived only from September 2002 through January 2003 when it terminally failed, the data set covers these five months only.
            
            The Support Product includes higher vertical resolution profiles of the quantities found in the Standard Product, plus intermediate outputs (e.g., microwave-only retrieval), research products such as the abundance of trace gases, and detailed quality assessment information. The Support Product profiles contain 100 levels between 1100 and .016 mb; this higher resolution will simplify the generation of radiances using forward models, though the vertical information content is no greater than that in the Standard Product profiles. 
            
            The intended users of the Support Product are researchers interested in generating forward radiance or in examining research products, and the AIRS algorithm development team. The Support Product will be generated at all locations as Standard Products. An AIRS granule has been set as 6 minutes of data. This will normally correspond to approximately 1/15 of an orbit but exactly 45 scanlines of AMSU-A data or 135 scanlines of AIRS and HSB data.
            
            (The Shortname for this product is AIRH2SUP).
The Atmospheric Infrared Sounder (AIRS) is a facility instrument aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors.  AIRS data will be generated continuously. Global coverage will be obtained twice daily (day and night) on a 1:30pm sun synchronous orbit from a 705-km altitude.
                  
                  Cloud-Cleared Radiances contain calibrated, geolocated channel-by-channel AIRS infrared radiances (milliWatts/m2/cm-1/steradian) that would have been observed within each Advanced Microwave Sounding Unit (AMSU) footprint if there were no clouds in the FOV and produced along with the AIRS Standard Product, as they are the radiances used to retrieve the Standard Product. Nevertheless, they are an order of magnitude larger in data volume than the remainder of the Standard Products and, many Standard Product users are expected to have little interest in the Cloud Cleared Radiance. For these reasons they are a separate output file, but like the Standard Product, are generated at all locations.
                  
                  (The Shortname for this product is AIRI2CCF).
This product is similar to AIRI2CCF. It is newest products produced using AIRS IR only because the radiometric noise in AMSU channel 4 started to increase significantly (since June 2007).
                  
                  The Atmospheric Infrared Sounder (AIRS) is a facility instrument aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors.  AIRS data will be generated continuously. Global coverage will be obtained twice daily (day and night) on a 1:30pm sun synchronous orbit from a 705-km altitude.
                  
                  Cloud-Cleared Radiances contain calibrated, geolocated channel-by-channel AIRS infrared radiances (milliWatts/m2/cm-1/steradian) that would have been observed within each Advanced Microwave Sounding Unit (AMSU) footprint if there were no clouds in the FOV and produced along with the AIRS Standard Product, as they are the radiances used to retrieve the Standard Product. Nevertheless, they are an order of magnitude larger in data volume than the remainder of the Standard Products and, many Standard Product users are expected to have little interest in the Cloud Cleared Radiance. For these reasons they are a separate output file, but like the Standard Product, are generated at all locations.
                  
                  (The Shortname for this product is AIRS2CCF).
The Atmospheric Infrared Sounder (AIRS) is a facility instrument aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors.  AIRS data will be generated continuously. Global coverage will be obtained twice daily (day and night) on a 1:30pm sun synchronous orbit from a 705-km altitude.
                  
                  Cloud-Cleared Radiances contain calibrated, geolocated channel-by-channel AIRS infrared radiances (milliWatts/m2/cm-1/steradian) that would have been observed within each Advanced Microwave Sounding Unit (AMSU) footprint if there were no clouds in the FOV and produced along with the AIRS Standard Product, as they are the radiances used to retrieve the Standard Product. Nevertheless, they are an order of magnitude larger in data volume than the remainder of the Standard Products and, many Standard Product users are expected to have little interest in the Cloud Cleared Radiance. For these reasons they are a separate output file, but like the Standard Product, are generated at all locations.
                  
                  (The Shortname for this product is AIRI2CCF_NRT).
The Atmospheric Infrared Sounder (AIRS) is a facility instrument aboard the second Earth Observing System (EOS) polar-orbiting  platform, EOS Aqua. In combination with the Advanced Microwave  Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible,  infrared, and microwave sensors. AIRS data will be generated continuously. Global coverage will be obtained twice daily (day and night) on a 1:30pm sun synchronous orbit from a 705-km altitude.
                        
                        The AIRS Standard Retrieval Product consists of retrieved estimates of  cloud and surface properties, plus profiles of retrieved temperature,  water vapor, ozone, carbon monoxide and methane. Estimates of the errors associated with these quantities will also be part of the Standard Product. The temperature profile vertical resolution is 28 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at 14 atmospheric layers between 1100 mb and 50 mb. The horizontal resolution is 50 km. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track.
                        
                        (The Shortname for this product is AIRX2RET).
This product is similar to AIRX2RET. It is newest products produced using AIRS IR only because the radiometric noise in AMSU channel 4 started to increase significantly (since June 2007).
                        
                        The AIRS Standard Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. Estimates of the errors associated with these quantities will also be part of the Standard Product. The temperature profile vertical resolution is 28 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at 14 atmospheric layers between 1100 mb and 50 mb.  The horizontal resolution is 50 km. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track.
                        
                        (The Shortname for this product is AIRS2RET).
The Atmospheric Infrared Sounder (AIRS) is a facility instrument  aboard the second Earth Observing System (EOS) polar-orbiting  platform, EOS Aqua. In combination with the Advanced Microwave  Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible,  infrared, and microwave sensors. AIRS data will be generated continuously. Global coverage will be obtained twice daily (day and night) on a 1:30pm sun synchronous orbit from a 705-km altitude.
                  
                  The Support Product includes higher vertical resolution profiles of the quantities found in the Standard Product plus intermediate output (e.g., microwave-only retrieval), research products such as the abundance of trace gases, and detailed quality assessment information. The Support Product profiles contain 100 pressure levels between 1100 and .016 mb; this higher resolution will simplify the generation of radiances using forward models, though the vertical information content is no greater than in the Standard Product profiles. The horizontal resolution is 50 km. The intended users of the Support Product are researchers interested in generating forward radiance, or in examining research products, and the AIRS algorithm development team. The Support Product will be generated at all locations as Standard Products. An AIRS granule has been set as 6 minutes of data. This will normally correspond to approximately 1/15 of an orbit but exactly 45 scanlines of AMSU-A data or 135 scanlines of AIRS and HSB data.
                  
                  (The Shortname for this product is AIRX2SUP).
This product is similar to AIRX2SUP. It is newest product produced using AIRS IR only because the radiometric noise in AMSU channel 4 started to increase significantly (since June 2007).
                              
                              The Atmospheric Infrared Sounder (AIRS) is a facility instrument aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors.  AIRS data will be generated continuously. Global coverage will be obtained twice daily (day and night) on a 1:30pm sun synchronous orbit from a 705-km altitude.
                              
                              The Support Product includes higher vertical resolution profiles of the quantities found in the Standard Product, plus intermediate outputs (e.g., microwave-only retrieval), research products such as the abundance of trace gases, and detailed quality assessment information. The Support Product profiles contain 100 levels between 1100 and .016 mb; this higher resolution will simplify the generation of radiances using forward models, though the vertical information content is no greater than that in the Standard Product profiles. The intended users of the Support Product are researchers interested in generating forward radiance or in examining research products, and the AIRS algorithm development team. The Support Product will be generated at all locations as Standard Products. An AIRS granule has been set as 6 minutes of data. This will normally correspond to approximately 1/15 of an orbit but exactly 45 scanlines of AMSU-A data or 135 scanlines of AIRS and HSB data.
                              
                              (The Shortname for this product is AIRS2SUP).
The AIRS Level 3 8-Day Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts.  Each file covers an 8-day period, or one-half of the Aqua orbit repeat cycle. The mean values are simply the arithmetic means of the daily products, weighted by the number of input counts for each day in that grid box.

The geophysical parameters have been averaged and binned into 1 x 1 deg grid cells, from -180.0 to +180.0 deg longitude and from -90.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean and can be used to generate custom multi-day maps from the daily gridded products.
            
The thermodynamic parameters are: Skin Temperature (land and sea surface), Air Temperature at the surface, Profiles of Air Temperature and Water Vapor, Tropopause Characteristics, Column Precipitable Water, Cloud Amount/Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Reflectance, Emissivity, Surface Pressure, Cloud Vertical Distribution. 

The trace gases parameters are: Total Amounts and Vertical Profiles of Carbon Monoxide, Methane, and Ozone.

The actual names of the variables in the data files should be inferred from the Processing File Description document.

(The Shortname for this product is AIRX3ST8).
The AIRS Only Level 3 8-Day Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts.  Each file covers an 8-day period, or one-half of the Aqua orbit repeat cycle. The mean values are simply the arithmetic means of the daily products, weighted by the number of input counts for each day in that grid box.
      
The geophysical parameters have been averaged and binned into 1 x 1 deg grid cells, from -180.0 to +180.0 deg longitude and from -90.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean and can be used to generate custom multi-day maps from the daily gridded products.
      
The thermodynamic parameters are: Skin Temperature (land and sea surface), Air Temperature at the surface, Profiles of Air Temperature and Water Vapor, Tropopause Characteristics, Column Precipitable Water, Cloud Amount/Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Reflectance, Emissivity, Surface Pressure, Cloud Vertical Distribution. 

The trace gases parameters are: Total Amounts and Vertical Profiles of Carbon Monoxide, Methane, and Ozone.

The actual names of the variables in the data files should be inferred from the Processing File Description document.

(The Shortname for this product is AIRS3ST8).
AIRS/Aqua Level 3 monthly quantization product in physical units (AIRS Only).
      
The quantization products (QP) are distributional summaries derived from the Level-2 standard retrieval products (of swath type) to provide a more comprehensive set of statistical summaries than the traditional means and standard deviation. The QP products combine the Level 2 standard data parameters over grid cells of 5 x 5 deg spatial extent for temporal periods of a month. They preserve the multivariate distributional features of the original data and so provide a compressed data set that more accurately describes the disparate atmospheric states that is in the original Level-2 swath data set.
      
The geophysical parameters are: Air Temperature and Water Vapor profiles (11 levels/layers), Cloud fraction (vertical distribution).
      
(The Shortname for this product is AIRS3QPM).
The AIRS Level 3 Monthly Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts.  Each file covers a calendar month. The mean values are simply the arithmetic means of the daily products, weighted by the number of input counts for each day in that grid box.
            
The geophysical parameters have been averaged and binned into 1 x1 deg grid cells, from -180.0 to +180.0 deg longitude and from -90.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean and can be used to generate custom multi-day maps from the daily gridded products.
            
The thermodynamic parameters are: Skin Temperature (land and sea surface), Air Temperature at the surface, Profiles of Air Temperature and Water Vapor, Tropopause Characteristics, Column Precipitable Water, Cloud Amount/Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Reflectance, Emissivity, Surface Pressure, Cloud Vertical Distribution. 

The trace gases parameters are: Total Amounts and Vertical Profiles of Carbon Monoxide, Methane, and Ozone.

The actual names of the variables in the data files should be inferred from the Processing File Description document.

(The Shortname for this product is AIRX3STM).
The AIRS Only Level 3 Monthly Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts.  Each file covers a calendar month. The mean values are simply the arithmetic means of the daily products, weighted by the number of input counts for each day in that grid box.
      
The geophysical parameters have been averaged and binned into 1 x 1 deg grid cells, from -180.0 to +180.0 deg longitude and from -90.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean and can be used to generate custom multi-day maps from the daily gridded products.
      
The thermodynamic parameters are: Skin Temperature (land and sea surface), Air Temperature at the surface, Profiles of Air Temperature and Water Vapor, Tropopause Characteristics, Column Precipitable Water, Cloud Amount/Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Reflectance, Emissivity, Surface Pressure, Cloud Vertical Distribution. 

The trace gases parameters are: Total Amounts and Vertical Profiles of Carbon Monoxide, Methane, and Ozone.

The actual names of the variables in the data files should be inferred from the Processing File Description document.

(The Shortname for this product is AIRS3STM).
AIRS/Aqua Level 3 pentad quantization product in physical units (Without HSB).
      
The geophysical parameters have been averaged and binned into 1 x 1 deg grid cells, from -180.0 to +180.0 deg longitude and from -90.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean and can be used to generate custom multi-day maps from the daily gridded products.
      
The thermodynamic parameters are: Skin Temperature (land and sea surface), Air Temperature at the surface, Profiles of Air Temperature and Water Vapor, Tropopause Characteristics, Column Precipitable Water, Cloud Amount/Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Reflectance, Emissivity, Surface Pressure, Cloud Vertical Distribution. 

The trace gases parameters are: Total Amounts and Vertical Profiles of Carbon Monoxide, Methane, and Ozone.

The actual names of the variables in the data files should be inferred from the Processing File Description document.

(The Shortname for this product is AIRX3QP5).
AIRS/Aqua Level 3 monthly quantization product in physical units (Without HSB).
      
The quantization products (QP) are distributional summaries derived from the Level-2 standard retrieval products (of swath type) to provide a more comprehensive set of statistical summaries than the traditional means and standard deviation. The QP products combine the Level 2 standard data parameters over grid cells of 5 x 5 deg spatial extent for temporal periods of a month. They preserve the multivariate distributional features of the original data and so provide a compressed data set that more accurately describes the disparate atmospheric states that is in the original Level-2 swath data set.
      
The geophysical parameters are: Air Temperature and Water Vapor profiles (11 levels/layers), Cloud fraction (vertical distribution).
      
The Shortname for this product is AIRX3QPM).
The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. Because no quality control information is available for some of these fields, values from failed retrievals may be included.
The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. Because no quality control information is available for some of these fields, values from failed retrievals may be included.
The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. Because no quality control information is available for some of these fields, values from failed retrievals may be included.
The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. Because no quality control information is available for some of these fields, values from failed retrievals may be included.
The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. Because no quality control information is available for some of these fields, values from failed retrievals may be included.
The Atmospheric Infrared Sounder (AIRS) is a facility instrument aboard the second Earth Observing System (EOS) polar-orbiting  platform, EOS Aqua. In combination with the Advanced Microwave  Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible,  infrared, and microwave sensors. AIRS data will be generated continuously. Global coverage will be obtained twice daily (day and night) on a 1:30pm sun synchronous orbit from a 705-km altitude.
                                          
The AIRS Standard Retrieval Product consists of retrieved estimates of  cloud and surface properties, plus profiles of retrieved temperature,  water vapor, ozone, carbon monoxide and methane. Estimates of the errors associated with these quantities will also be part of the Standard Product. The temperature profile vertical resolution is 28 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at 14 atmospheric layers between 1100 mb and 50 mb. The horizontal resolution is 50 km. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track.
                                          
(The Shortname for this product is AIRX2RET_NRT).
The Atmospheric Infrared Sounder (AIRS) is a facility instrument  aboard the second Earth Observing System (EOS) polar-orbiting  platform, EOS Aqua. In combination with the Advanced Microwave  Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible,  infrared, and microwave sensors. AIRS data will be generated continuously. Global coverage will be obtained twice daily (day and night) on a 1:30pm sun synchronous orbit from a 705-km altitude.
                              
The Support Product includes higher vertical resolution profiles of the quantities found in the Standard Product plus intermediate output (e.g., microwave-only retrieval), research products such as the abundance of trace gases, and detailed quality assessment information. The Support Product profiles contain 100 pressure levels between 1100 and .016 mb; this higher resolution will simplify the generation of radiances using forward models, though the vertical information content is no greater than in the Standard Product profiles. The horizontal resolution is 50 km. The intended users of the Support Product are researchers interested in generating forward radiance, or in examining research products, and the AIRS algorithm development team. The Support Product will be generated at all locations as Standard Products. An AIRS granule has been set as 6 minutes of data. This will normally correspond to approximately 1/15 of an orbit but exactly 45 scanlines of AMSU-A data or 135 scanlines of AIRS and HSB data.
                              
(The Shortname for this product is AIRX2SUP_NRT).
AIRS/Aqua Level 3 pentad quantization product in physical units (AIRS Only).
      
The quantization products (QP) are distributional summaries derived from the Level-2 standard retrieval products (of swath type) to provide a more comprehensive set of statistical summaries than the traditional means and standard deviation. The QP products combine the Level 2 standard data parameters over grid cells of 5 x 5 deg spatial extent for temporal periods of five days. Pentads always start on the 1st, 6th, 11th, 16th, 21st, and 26th days of the month and may contain as few as 3 days of data or as much as 6 days. They preserve the multivariate distributional features of the original data and so provide a compressed data set that more accurately describes the disparate atmospheric states that is in the original Level-2 swath data set.
      
The geophysical parameters are: Air Temperature and Water Vapor profiles (11 levels/layers), Cloud fraction (vertical distribution).
      
(The Shortname for this product is AIRS3QP5)
Aquarius Level-2 Swath Single Orbit Soil Moisture Data
Aquarius Level-3 Gridded 1-Degree Annual Soil Moisture Data
Aquarius Level-3 Gridded 1-Degree Daily Soil Moisture Data
Aquarius Level-3 Gridded 1-Degree Monthly Soil Moisture Data
Aquarius Level-3 Gridded 1-Degree Seasonal Soil Moisture Data
Aquarius Level-3 Gridded 1-Degree Weekly Soil Moisture Data
The 1980 SAS Transport Files portion of the Archive of Census Related Products (ACRP) contains housing and population demographics from the 1980 Summary Tape File (STF3A) database and are organized by state. The population data includes education levels, ethnicity, income distribution, nativity, labor force status, means of transportation and family structure while the housing data embodies size, state and structure of housing unit, value of the unit, tenure and occupancy status in housing unit, source of water, sewage disposal, availability of telephone, heating and air conditioning, kitchen facilities, rent, mortgage status and monthly owner costs. This portion of the ACRP is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).
The 1980 SAS Transport Files portion of the Archive of Census Related Products (ACRP) contains housing and population demographics from the 1980 Summary Tape File (STF3A) database and are organized by state. The population data includes education levels, ethnicity, income distribution, nativity, labor force status, means of transportation and family structure while the housing data embodies size, state and structure of housing unit, value of the unit, tenure and occupancy status in housing unit, source of water, sewage disposal, availability of telephone, heating and air conditioning, kitchen facilities, rent, mortgage status and monthly owner costs. This portion of the ACRP is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).
The 1980 SAS Transport Files portion of the Archive of Census Related Products (ACRP) contains housing and population demographics from the 1980 Summary Tape File (STF3A) database and are organized by state. The population data includes education levels, ethnicity, income distribution, nativity, labor force status, means of transportation and family structure while the housing data embodies size, state and structure of housing unit, value of the unit, tenure and occupancy status in housing unit, source of water, sewage disposal, availability of telephone, heating and air conditioning, kitchen facilities, rent, mortgage status and monthly owner costs. This portion of the ACRP is produced by the Columbia University Center for International Earth Science Information Network (CIESIN). (Suggested Usage: To provide access to demographic data for the U.S.)
The 1990 Census Block Statistics portion of the Archive of Census Related Products (ACRP) contains population and housing data from the U.S. Census Bureau's 1990 Summary Tape File (STF1B). The population data includes total population, age, race, and hispanic origin, while the housing data comprises number of housing units, tenure, room density, mean contract rent, mean value, and mean number of rooms. Additional data includes land area, water area, centroids, Metropolitan Statistical Area (MSA) codes, place codes, and special area codes. This portion of the ACRP is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).
The 1990 Census Block Statistics portion of the Archive of Census Related Products (ACRP) contains population and housing data from the U.S. Census Bureau's 1990 Summary Tape File (STF1B). The population data includes total population, age, race, and hispanic origin, while the housing data comprises number of housing units, tenure, room density, mean contract rent, mean value, and mean number of rooms. Additional data includes land area, water area, centroids, Metropolitan Statistical Area (MSA) codes, place codes, and special area codes. This portion of the ACRP is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).
The 1990 Census Block Statistics portion of the Archive of Census Related Products (ACRP) contains population and housing data from the U.S. Census Bureau's 1990 Summary Tape File (STF1B). The population data includes total population, age, race, and hispanic origin, while the housing data comprises number of housing units, tenure, room density, mean contract rent, mean value, and mean number of rooms. Additional data includes land area, water area, centroids, Metropolitan Statistical Area (MSA) codes, place codes, and special area codes. This portion of the ACRP is produced by the Columbia University Center for International Earth Science Information Network (CIESIN). (Suggested Usage: To provide population and housing data for the U.S.)
The 1990 Enhanced Migration Files portion of the Archive of Census Related Products (ACRP) contains migration data derived from the U.S. Census Bureau's Summary Tape File (STP-28). The data includes counts by race and hispanic origin within each county, county to county, and state to state. This portion of the ACRP is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).
The 1990 Enhanced Migration Files portion of the Archive of Census Related Products (ACRP) contains migration data derived from the U.S. Census Bureau's Summary Tape File (STP-28). The data includes counts by race and hispanic origin within each county, county to county, and state to state. This portion of the ACRP is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).
The 1990 Enhanced Migration Files portion of the Archive of Census Related Products (ACRP) contains migration data derived from the U.S. Census Bureau's Summary Tape File (STP-28). The data includes counts by race and hispanic origin within each county, county to county, and state to state. This portion of the ACRP is produced by the Columbia University Center for International Earth Science Information Network (CIESIN). (Suggested Usage: To provide migration data at the county and state levels for the U.S.)
The 1990 Public Use Microdata Sample Areas (PUMA) Boundary Files portion of the Archive of Census Related Products (ACRP) consists of 5% sample (apuma) and 1% sample (bpuma) areas for the mapping of 1990 PUMS data covering the continental United States, Alaska, and Hawaii. These boundary files are created based on equivalency files generated by the Geographic Correspondence Engine (GeoCorr). A national census tract to PUMA geography correspondence file is used in merging the two files resulting in the PUMA geographies. An additional file is also available consisting of geographic centroids for the PUMA coverages calculated by UIC (Urban Information Center/Office of Computing, University of Missouri). This portion of the ACRP is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).
The 1990 Public Use Microdata Sample Areas (PUMA) Boundary Files portion of the Archive of Census Related Products (ACRP) consists of 5% sample (apuma) and 1% sample (bpuma) areas for the mapping of 1990 PUMS data covering the continental United States, Alaska, and Hawaii. These boundary files are created based on equivalency files generated by the Geographic Correspondence Engine (GeoCorr). A national census tract to PUMA geography correspondence file is used in merging the two files resulting in the PUMA geographies. An additional file is also available consisting of geographic centroids for the PUMA coverages calculated by UIC (Urban Information Center/Office of Computing, University of Missouri). This portion of the ACRP is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).
The 1990 Public Use Microdata Sample Areas (PUMA) Boundary Files portion of the Archive of Census Related Products (ACRP) consists of 5% sample (apuma) and 1% sample (bpuma) areas for the mapping of 1990 PUMS data covering the continental United States, Alaska, and Hawaii. These boundary files are created based on equivalency files generated by the Geographic Correspondence Engine (GeoCorr). A national census tract to PUMA geography correspondence file is used in merging the two files resulting in the PUMA geographies. An additional file is also available consisting of geographic centroids for the PUMA coverages calculated by UIC (Urban Information Center/Office of Computing, University of Missouri). This portion of the ACRP is produced by the Columbia University Center for International Earth Science Information Network (CIESIN). (Suggested Usage: To provide an authoritative boundary layer for the geographies associated with the Public Use Micro Sample (PUMS) data files.)
The 1990 SAS Transport Files portion of the Archive of Census Related Products (ACRP) contains housing and population data from the U.S. Census Bureau's 1990 Summary tape File (STF3A) database. The data are available by state and county, county subdivision/mcd, blockgroup, and places, as well as Indian reservations, tribal districts and congressional districts. This portion of the ACRP is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).
The 1990 SAS Transport Files portion of the Archive of Census Related Products (ACRP) contains housing and population data from the U.S. Census Bureau's 1990 Summary tape File (STF3A) database. The data are available by state and county, county subdivision/mcd, blockgroup, and places, as well as Indian reservations, tribal districts and congressional districts. This portion of the ACRP is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).
The 1990 SAS Transport Files portion of the Archive of Census Related Products (ACRP) contains housing and population data from the U.S. Census Bureau's 1990 Summary tape File (STF3A) database. The data are available by state and county, county subdivision/mcd, blockgroup, and places, as well as Indian reservations, tribal districts and congressional districts. This portion of the ACRP is produced by the Columbia University Center for International Earth Science Information Network (CIESIN). (Suggested Usage: To provide demographic data for the U.S.)
The 1990 Standard Extract Files portion of the Archive of Census Related Products (ACRP) contains population and housing data derived from the U.S. Census Bureau's 1990 STF3A database. The population data includes age distribution, education levels, ethnicity, income distribution, labor force status and family structure, while the housing data embodies size and state of the housing unit, value of the unit, water, sewage, heating, and monthly owner costs. The data are available by county, county subdivisions, place within-county, tract, block numbering area (bna), and blockgroup. Each file contains a unique POLygon IDentification (POLID) field which matches a similar field in the corresponding TIGER-based boundary file. Tract and blockgroup files are grouped by MSA (Metropolitan Statistical Area) and CMSA (Consolidated Metropolitan Statistical Area). This portion of the ACRP is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).
The 1990 Standard Extract Files portion of the Archive of Census Related Products (ACRP) contains population and housing data derived from the U.S. Census Bureau's 1990 STF3A database. The population data includes age distribution, education levels, ethnicity, income distribution, labor force status and family structure, while the housing data embodies size and state of the housing unit, value of the unit, water, sewage, heating, and monthly owner costs. The data are available by county, county subdivisions, place within-county, tract, block numbering area (bna), and blockgroup. Each file contains a unique POLygon IDentification (POLID) field which matches a similar field in the corresponding TIGER-based boundary file. Tract and blockgroup files are grouped by MSA (Metropolitan Statistical Area) and CMSA (Consolidated Metropolitan Statistical Area). This portion of the ACRP is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).
The 1990 Standard Extract Files portion of the Archive of Census Related Products (ACRP) contains population and housing data derived from the U.S. Census Bureau's 1990 STF3A database. The population data includes age distribution, education levels, ethnicity, income distribution, labor force status and family structure, while the housing data embodies size and state of the housing unit, value of the unit, water, sewage, heating, and monthly owner costs. The data are available by county, county subdivisions, place within-county, tract, block numbering area (bna), and blockgroup. Each file contains a unique POLygon IDentification (POLID) field which matches a similar field in the corresponding TIGER-based boundary file. Tract and blockgroup files are grouped by MSA (Metropolitan Statistical Area) and CMSA (Consolidated Metropolitan Statistical Area). This portion of the ACRP is produced by the Columbia University Center for International Earth Science Information Network (CIESIN). (Suggested Usage: To provide population and housing data for the U.S.)
The 1990 Street Intersections portion of the Archive of Census Related Products (ACRP) contains the latitude and longitude of street intersections for each county in the United States. The data includes the names of both streets that intersect, as well as the location and unique node number for streets which frequently intersect each other. This portion of the ACRP is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).
The 1990 Street Intersections portion of the Archive of Census Related Products (ACRP) contains the latitude and longitude of street intersections for each county in the United States. The data includes the names of both streets that intersect, as well as the location and unique node number for streets which frequently intersect each other. This portion of the ACRP is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).
The 1990 Street Intersections portion of the Archive of Census Related Products (ACRP) contains the latitude and longitude of street intersections for each county in the United States. The data includes the names of both streets that intersect, as well as the location and unique node number for streets which frequently intersect each other. This portion of the ACRP is produced by the Columbia University Center for International Earth Science Information Network (CIESIN). (Suggested Usage: To provide street intersection data for each country in the U.S.)
The 1990 Summary Tape File (STF1B) portion of the Archive of Census Related Products (ACRP) contains population and housing data, along with additional demographic data for the U.S. The population data includes age, race, sex, marital status, Hispanic origin, and household type, while the housing data encompasses tenure, number of units, value, number of rooms per unit, and the use of the unit. The data are available by county, county subdivision, place within county, tract/block numbering area (bna), blockgroup, and block. This portion of the ACRP is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).
The 1990 Summary Tape File (STF1B) portion of the Archive of Census Related Products (ACRP) contains population and housing data, along with additional demographic data for the U.S. The population data includes age, race, sex, marital status, Hispanic origin, and household type, while the housing data encompasses tenure, number of units, value, number of rooms per unit, and the use of the unit. The data are available by county, county subdivision, place within county, tract/block numbering area (bna), blockgroup, and block. This portion of the ACRP is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).
The 1990 Summary Tape File (STF1B) portion of the Archive of Census Related Products (ACRP) contains population and housing data, along with additional demographic data for the U.S. The population data includes age, race, sex, marital status, Hispanic origin, and household type, while the housing data encompasses tenure, number of units, value, number of rooms per unit, and the use of the unit. The data are available by county, county subdivision, place within county, tract/block numbering area (bna), blockgroup, and block. This portion of the ACRP is produced by the Columbia University Center for International Earth Science Information Network (CIESIN). (Suggested Usage: To provide access to demographic data for the U.S.)
The 1990 Zip Equivalency Files portion of the Archive of Census Related Products (ACRP) contains population and housing data derived from the U.S. Census Bureau's 1990 STF3B header file. The data are available at the census block and census blockgroup levels, and encompass 5-digit zip codes, POLygon IDentification (POLID) block fields that correspond to the POLID of the Tiger-based boundary file, and population centroids. This portion of the ACRP is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).
The 1990 Zip Equivalency Files portion of the Archive of Census Related Products (ACRP) contains population and housing data derived from the U.S. Census Bureau's 1990 STF3B header file. The data are available at the census block and census blockgroup levels, and encompass 5-digit zip codes, POLygon IDentification (POLID) block fields that correspond to the POLID of the Tiger-based boundary file, and population centroids. This portion of the ACRP is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).
The 1990 Zip Equivalency Files portion of the Archive of Census Related Products (ACRP) contains population and housing data derived from the U.S. Census Bureau's 1990 STF3B header file. The data are available at the census block and census blockgroup levels, and encompass 5-digit zip codes, POLygon IDentification (POLID) block fields that correspond to the POLID of the Tiger-based boundary file, and population centroids. This portion of the ACRP is produced by the Columbia University Center for International Earth Science Information Network (CIESIN). (Suggested Usage: To provide population and housing data for the U.S.)
The 1992 Boundary Files portion of the Archive of Census Related Products (ACRP) consists of 1992 boundary data from the U.S. Census Bureau's Topologically Integrated Geographic Encoding and Referencing (TIGER) system. The data have been processed to create the state boundary files for county, county subdivision (mcd), place, 1990 tract/block numbering area, blockgroup, and 1980 tract and mcd's (if available). The tract and blockgroup files are grouped by MSA (Metropolitan Statistical Area) and CMSA (Consolidated Metropolitan Statistical Area). County boundary files have also been created for each census block, distinguished by its unique POLygon IDentification (POLID) field, matching the corresponding POLID in the 1990 Summary Tape File (STF) data. This portion of the ACRP is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).
The 1992 Boundary Files portion of the Archive of Census Related Products (ACRP) consists of 1992 boundary data from the U.S. Census Bureau's Topologically Integrated Geographic Encoding and Referencing (TIGER) system. The data have been processed to create the state boundary files for county, county subdivision (mcd), place, 1990 tract/block numbering area, blockgroup, and 1980 tract and mcd's (if available). The tract and blockgroup files are grouped by MSA (Metropolitan Statistical Area) and CMSA (Consolidated Metropolitan Statistical Area). County boundary files have also been created for each census block, distinguished by its unique POLygon IDentification (POLID) field, matching the corresponding POLID in the 1990 Summary Tape File (STF) data. This portion of the ACRP is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).
The 1992 Boundary Files portion of the Archive of Census Related Products (ACRP) consists of 1992 boundary data from the U.S. Census Bureau's Topologically Integrated Geographic Encoding and Referencing (TIGER) system. The data have been processed to create the state boundary files for county, county subdivision (mcd), place, 1990 tract/block numbering area, blockgroup, and 1980 tract and mcd's (if available). The tract and blockgroup files are grouped by MSA (Metropolitan Statistical Area) and CMSA (Consolidated Metropolitan Statistical Area). County boundary files have also been created for each census block, distinguished by its unique POLygon IDentification (POLID) field, matching the corresponding POLID in the 1990 Summary Tape File (STF) data. This portion of the ACRP is produced by the Columbia University Center for International Earth Science Information Network (CIESIN). (Suggested Usage: To provide county and state boundary data for the U.S.)
CO2 and water vapor fluxes and ecosystem characteristics were measured at 24 sites along a 317-km transect from the Arctic coast to the latitudinal treeline in Alaska during the growing seasons of 1994-1996.
CO2 and water vapor fluxes and ecosystem characteristics were measured at 24 sites along a 317-km transect from the Arctic coast to the latitudinal treeline in Alaska during the growing seasons of 1994-1996.
ABSTRACT: CO2 and water vapor fluxes and ecosystem characteristics were measured at 24 sites along a 317-km transect from the Arctic coast to the latitudinal treeline in Alaska during the growing seasons of 1994-1996.
Average percent coverage and standard deviation of each canopy stratum from subplots at each aspen site during the SNF study in the Superior National Forest, Minnesota
Average percent coverage and standard deviation of each canopy stratum from subplots at each aspen site during the SNF study in the Superior National Forest, Minnesota
ABSTRACT: Average percent coverage and standard deviation of each canopy stratum from subplots at each aspen site during the SNF study in the Superior National Forest, Minnesota
Expedited ASTER level-1A data are reconstructed, unprocessed instrument digital counts with radiometric and geometric coefficients included.
Expedited ASTER level-1B data have had system radiometric corrections and geometric coefficients applied and are reprojected to a given map projection.
ASTER Global Digital Elevation Model GDEM DEM
ASTER Global Emissivity Database, 1 kilometer, Binary
ASTER Global Emissivity Database, 1 kilometer, HDF5
ASTER Global Emissivity Database, 100 meter, Binary
ASTER Global Emissivity Database, 100 meter, HDF5
ASTER Global Emissivity Database, North America, Summer, 1 kilometer, Binary (Suggested Usage: Science Research)
ASTER Global Emissivity Database, North America, Summer, 1 kilometer, HDF5 (Suggested Usage: Science Research)
ASTER Global Emissivity Database, North America, Summer, 100 meter, Binary (Suggested Usage: Science Research)
ASTER Global Emissivity Database, North America, Summer, 100 meter, HDF5 (Suggested Usage: Science Research)
ASTER Global Emissivity Database, North America, Winter, 1 kilometer, Binary (Suggested Usage: Science Research)
ASTER Global Emissivity Database, North America, Winter, 1 kilometer, HDF5 (Suggested Usage: Science Research)
ASTER Global Emissivity Database, North America, Winter, 100 meter, Binary (Suggested Usage: Science Research)
ASTER Global Emissivity Database, North America, Winter, 100 meter, HDF5 (Suggested Usage: Science Research)
ASTER level-1A data are the reconstructed, unprocessed instrument digital counts with radiometric and geometric coefficients included.
ASTER level-1B data have had system radiometric corrections and geometric coefficients applied and are reprojected to a given map projection.
NCDC radiosonde atmospheric profile data from stations near FIFE
NCDC radiosonde atmospheric profile data from stations near FIFE
ABSTRACT: NCDC radiosonde atmospheric profile data from stations near FIFE
Derived (5mb interval) radiosonde observations from Wilf Brutsaert's data
Derived (5mb interval) radiosonde observations from Wilf Brutsaert's data
ABSTRACT: Derived (5mb interval) radiosonde observations from Wilf Brutsaert's data
Radiosonde observations from Wilf Brutsaert
Radiosonde observations from Wilf Brutsaert
ABSTRACT: Radiosonde observations from Wilf Brutsaert
TOVS data received by FIFE
TOVS data received by FIFE
ABSTRACT: TOVS data received by FIFE
This data set consists of AVHRR imagery selected from hard copy 'quick
look' images to provide the best coverage possible over the Arctic
approximately every three days for a three-year period. Level-1B data
from NOAA/SDSD have been calibrated and mapped to earth locations,
then gridded to 1 km pixels on a basin scale to the polar stereographic
projection. The projection is similar to that used by NSIDC to produce
DMSP SSM/I polar brightness temperature and sea ice products. Each
image was ranked for areal coverage of particular seas and for degree
of cloud coverage. Passes covering a large area are generally favored over shorter passes with less cloud cover. The data set was developed
in support of the Office Of Naval Research Arctic Leads Accelerated
Research Initiative (Arctic Leads ARI). The aim of the Initiative was
to develop a more thorough understanding of the oceanography,
meteorology, and ice dynamics surrounding formation and evolution of
leads in sea ice. The leads ARI field experiment took place from March
to April 1992.

A spreadsheet containing the image rankings is available in hard copy
(NOARL Tech. Note 118, April 1991); paper copies of the spreadsheet
are available on request. Data set information is available
on-line. Data are available via FTP.
This data set consists of AVHRR imagery selected from hard copy 'quick
look' images to provide the best coverage possible over the Arctic
approximately every three days for a three-year period. Level-1B data
from NOAA/SDSD have been calibrated and mapped to earth locations,
then gridded to 1 km pixels on a basin scale to the polar stereographic
projection. The projection is similar to that used by NSIDC to produce
DMSP SSM/I polar brightness temperature and sea ice products. Each
image was ranked for areal coverage of particular seas and for degree
of cloud coverage. Passes covering a large area are generally favored over shorter passes with less cloud cover. The data set was developed
in support of the Office Of Naval Research Arctic Leads Accelerated
Research Initiative (Arctic Leads ARI). The aim of the Initiative was
to develop a more thorough understanding of the oceanography,
meteorology, and ice dynamics surrounding formation and evolution of
leads in sea ice. The leads ARI field experiment took place from March
to April 1992.

A spreadsheet containing the image rankings is available in hard copy
(NOARL Tech. Note 118, April 1991); paper copies of the spreadsheet
are available on request. Data set information is available
on-line. Data are available via FTP.
This data set consists of AVHRR imagery selected from hard copy 'quick
look' images to provide the best coverage possible over the Arctic
approximately every three days for a three-year period. Level-1B data
from NOAA/SDSD have been calibrated and mapped to earth locations,
then gridded to 1 km pixels on a basin scale to the polar stereographic
projection. The projection is similar to that used by NSIDC to produce
DMSP SSM/I polar brightness temperature and sea ice products. Each
image was ranked for areal coverage of particular seas and for degree
of cloud coverage. Passes covering a large area are generally favored over shorter passes with less cloud cover. The data set was developed
in support of the Office Of Naval Research Arctic Leads Accelerated
Research Initiative (Arctic Leads ARI). The aim of the Initiative was
to develop a more thorough understanding of the oceanography,
meteorology, and ice dynamics surrounding formation and evolution of
leads in sea ice. The leads ARI field experiment took place from March
to April 1992.

A spreadsheet containing the image rankings is available in hard copy
(NOARL Tech. Note 118, April 1991); paper copies of the spreadsheet
are available on request. Data set information is available
on-line. Data are available via FTP.
<h2 class='newsdate'>Please note that the machine on which these AVHRR data are processed has reached its life expectancy and will no longer be available as of 02 June 2008 until further notice.</h2>
NSIDC collects and archives 1.1 km High Resolution Picture Transmission (HRPT) and Local Area Coverage (LAC) Advanced Very High Resolution Radiometer (AVHRR) imagery. Original data are from recorded and rebroadcast LAC images for both polar regions and from several HRPT receiving stations, including McMurdo, Palmer, Casey, Fairbanks, Prince Albert, and Tromso. NSIDC receives these data from a polar subset of the EROS Data Center (EDC) Global Land 1 km AVHRR Data Set and from the Scripps Institute of Oceanography, which collects Antarctic HRPT data from the McMurdo and Palmer stations. NSIDC received LAC data for the Southern Hemisphere through 1998 from a University of Colorado Domestic Satellite (DOMSAT) receiver and from the National Oceanic and Atmospheric Administration (NOAA) Satellite Active Archive (SAA) after 1998. These sources provide nearly complete coverage of sea ice, land ice, and polar land in the north and south polar regions, at 1.1 km resolution for all five bands of the AVHRR sensor. Data extend from April 1992 through the present for the Southern Hemisphere, and from August 1993 through the present for the Northern Hemisphere.

Archived scenes, browse imagery, and information on location, time of acquisition, image size, and data quality are available via the AVHRR Polar 1 Km Level 1B Data Set Product Web site.
<h2 class='newsdate'>Please note that the machine on which these AVHRR data are processed has reached its life expectancy and will no longer be available as of 02 June 2008 until further notice.</h2>
NSIDC collects and archives 1.1 km High Resolution Picture Transmission (HRPT) and Local Area Coverage (LAC) Advanced Very High Resolution Radiometer (AVHRR) imagery. Original data are from recorded and rebroadcast LAC images for both polar regions and from several HRPT receiving stations, including McMurdo, Palmer, Casey, Fairbanks, Prince Albert, and Tromso. NSIDC receives these data from a polar subset of the EROS Data Center (EDC) Global Land 1 km AVHRR Data Set and from the Scripps Institute of Oceanography, which collects Antarctic HRPT data from the McMurdo and Palmer stations. NSIDC received LAC data for the Southern Hemisphere through 1998 from a University of Colorado Domestic Satellite (DOMSAT) receiver and from the National Oceanic and Atmospheric Administration (NOAA) Satellite Active Archive (SAA) after 1998. These sources provide nearly complete coverage of sea ice, land ice, and polar land in the north and south polar regions, at 1.1 km resolution for all five bands of the AVHRR sensor. Data extend from April 1992 through the present for the Southern Hemisphere, and from August 1993 through the present for the Northern Hemisphere.

Archived scenes, browse imagery, and information on location, time of acquisition, image size, and data quality are available via the AVHRR Polar 1 Km Level 1B Data Set Product Web site.
<h2 class='newsdate'>Please note that the machine on which these AVHRR data are processed has reached its life expectancy and will no longer be available as of 02 June 2008 until further notice.</h2>
NSIDC collects and archives 1.1 km High Resolution Picture Transmission (HRPT) and Local Area Coverage (LAC) Advanced Very High Resolution Radiometer (AVHRR) imagery. Original data are from recorded and rebroadcast LAC images for both polar regions and from several HRPT receiving stations, including McMurdo, Palmer, Casey, Fairbanks, Prince Albert, and Tromso. NSIDC receives these data from a polar subset of the EROS Data Center (EDC) Global Land 1 km AVHRR Data Set and from the Scripps Institute of Oceanography, which collects Antarctic HRPT data from the McMurdo and Palmer stations. NSIDC received LAC data for the Southern Hemisphere through 1998 from a University of Colorado Domestic Satellite (DOMSAT) receiver and from the National Oceanic and Atmospheric Administration (NOAA) Satellite Active Archive (SAA) after 1998. These sources provide nearly complete coverage of sea ice, land ice, and polar land in the north and south polar regions, at 1.1 km resolution for all five bands of the AVHRR sensor. Data extend from April 1992 through the present for the Southern Hemisphere, and from August 1993 through the present for the Northern Hemisphere.

Archived scenes, browse imagery, and information on location, time of acquisition, image size, and data quality are available via the AVHRR Polar 1 Km Level 1B Data Set Product Web site.
<h2 class='newsdate'>Please note that the machine on which these AVHRR data are processed has reached its life expectancy and will no longer be available as of 02 June 2008 until further notice.</h2>

The AVHRR Polar Pathfinder Twice-Daily 1.25 km EASE-Grid Composites
are a collection of products for both poles, consisting of twice-daily
gridded and calibrated satellite channel data and derived parameters.
Data include five Advanced Very High Resolution Radiometer (AVHRR)
channels, clear sky surface broadband albedo and skin temperature,
solar zenith angle, satellite elevation angle, sun-satellite relative
azimuth angle, surface type mask, cloud mask, orbit mask, time of
acquisition, and ice motion vectors.  Data are composited onto two
grids per day based on common local solar times and scan angle. Reduced-resolution data (25 km) derived from the 1.25 km data are
available to assist users in selecting these data.  AVHRR local area coverage (LAC) and High Resolution Picture Transmission (HRPT) level
1b data are used to generate the Polar Pathfinder products at grid
spacings of 1.25 km. AVHRR Polar Pathfinder data extend poleward from
48.4 degrees north and 53.2 degrees south latitudes, from August 1993
through December 1998 for the Northern Hemisphere, and from April 1992
through January 1996 for the Southern Hemisphere.  Data are in 1-byte and 2-byte integer grid format.  Ice motion vectors are in ASCII text
format. Data are available on 8-mm tape or by ftp. Distribution formats available: North: 104 MB (2-byte), 52 MB
(1-byte), 8 MB (ice motion) South: 82 MB (2-byte), 41 MB (1-byte), 6.3
MB (ice motion).
<h2 class='newsdate'>Please note that the machine on which these AVHRR data are processed has reached its life expectancy and will no longer be available as of 02 June 2008 until further notice.</h2>

The AVHRR Polar Pathfinder Twice-Daily 1.25 km EASE-Grid Composites
are a collection of products for both poles, consisting of twice-daily
gridded and calibrated satellite channel data and derived parameters.
Data include five Advanced Very High Resolution Radiometer (AVHRR)
channels, clear sky surface broadband albedo and skin temperature,
solar zenith angle, satellite elevation angle, sun-satellite relative
azimuth angle, surface type mask, cloud mask, orbit mask, time of
acquisition, and ice motion vectors.  Data are composited onto two
grids per day based on common local solar times and scan angle. Reduced-resolution data (25 km) derived from the 1.25 km data are
available to assist users in selecting these data.  AVHRR local area coverage (LAC) and High Resolution Picture Transmission (HRPT) level
1b data are used to generate the Polar Pathfinder products at grid
spacings of 1.25 km. AVHRR Polar Pathfinder data extend poleward from
48.4 degrees north and 53.2 degrees south latitudes, from August 1993
through December 1998 for the Northern Hemisphere, and from April 1992
through January 1996 for the Southern Hemisphere.  Data are in 1-byte and 2-byte integer grid format.  Ice motion vectors are in ASCII text
format. Data are available on 8-mm tape or by ftp. Distribution formats available: North: 104 MB (2-byte), 52 MB
(1-byte), 8 MB (ice motion) South: 82 MB (2-byte), 41 MB (1-byte), 6.3
MB (ice motion).
<h2 class='newsdate'>Please note that the machine on which these AVHRR data are processed has reached its life expectancy and will no longer be available as of 02 June 2008 until further notice.</h2>

The AVHRR Polar Pathfinder Twice-Daily 1.25 km EASE-Grid Composites
are a collection of products for both poles, consisting of twice-daily
gridded and calibrated satellite channel data and derived parameters.
Data include five Advanced Very High Resolution Radiometer (AVHRR)
channels, clear sky surface broadband albedo and skin temperature,
solar zenith angle, satellite elevation angle, sun-satellite relative
azimuth angle, surface type mask, cloud mask, orbit mask, time of
acquisition, and ice motion vectors.  Data are composited onto two
grids per day based on common local solar times and scan angle. Reduced-resolution data (25 km) derived from the 1.25 km data are
available to assist users in selecting these data.  AVHRR local area coverage (LAC) and High Resolution Picture Transmission (HRPT) level
1b data are used to generate the Polar Pathfinder products at grid
spacings of 1.25 km. AVHRR Polar Pathfinder data extend poleward from
48.4 degrees north and 53.2 degrees south latitudes, from August 1993
through December 1998 for the Northern Hemisphere, and from April 1992
through January 1996 for the Southern Hemisphere.  Data are in 1-byte and 2-byte integer grid format.  Ice motion vectors are in ASCII text
format. Data are available on 8-mm tape or by ftp. Distribution formats available: North: 104 MB (2-byte), 52 MB
(1-byte), 8 MB (ice motion) South: 82 MB (2-byte), 41 MB (1-byte), 6.3
MB (ice motion).
This data set reports stable isotope ratio data of CO2 (13C/12C and 18O/16O) associated with photosynthetic and respiratory exchanges across the biosphere-atmosphere boundary. Measurements were made at selected AmeriFlux sites including Harvard Forest, Howland Forest, Rannells Flint Hills Prairie, Niwot Ridge Forest, and the Wind River Canopy Crane Site, which span the dominant ecosystem types of the United States. These data were collected periodically from 2001 through 2004 and are available as an ASCII comma separated file.The goal of this Terrestrial Carbon Processes (TCP) project  is to better capture isotopic effects of ecosystem-atmosphere interaction at diurnal, seasonal and interannual time scales by long-term monitoring 13C of CO2 exchange with the atmosphere at weekly intervals. Photosynthesis and respiration in terrestrial ecosystems have opposite effects on diurnal and seasonal patterns on atmospheric CO2 concentration and isotope ratios. This isotopic variation contains information about the functioning of different terrestrial ecosystems.
This data set reports stable isotope ratio data of CO2 (13C/12C and 18O/16O) associated with photosynthetic and respiratory exchanges across the biosphere-atmosphere boundary. Measurements were made at selected AmeriFlux sites including Harvard Forest, Howland Forest, Rannells Flint Hills Prairie, Niwot Ridge Forest, and the Wind River Canopy Crane Site, which span the dominant ecosystem types of the United States. These data were collected periodically from 2001 through 2004 and are available as an ASCII comma separated file.The goal of this Terrestrial Carbon Processes (TCP) project  is to better capture isotopic effects of ecosystem-atmosphere interaction at diurnal, seasonal and interannual time scales by long-term monitoring 13C of CO2 exchange with the atmosphere at weekly intervals. Photosynthesis and respiration in terrestrial ecosystems have opposite effects on diurnal and seasonal patterns on atmospheric CO2 concentration and isotope ratios. This isotopic variation contains information about the functioning of different terrestrial ecosystems.
ABSTRACT: This data set reports stable isotope ratio data of CO2 (13C/12C and 18O/16O) associated with photosynthetic and respiratory exchanges across the biosphere-atmosphere boundary. Measurements were made at selected AmeriFlux sites including Harvard Forest, Howland Forest, Rannells Flint Hills Prairie, Niwot Ridge Forest, and the Wind River Canopy Crane Site, which span the dominant ecosystem types of the United States. These data were collected periodically from 2001 through 2004 and are available as an ASCII comma separated file.The goal of this Terrestrial Carbon Processes (TCP) project  is to better capture isotopic effects of ecosystem-atmosphere interaction at diurnal, seasonal and interannual time scales by long-term monitoring 13C of CO2 exchange with the atmosphere at weekly intervals. Photosynthesis and respiration in terrestrial ecosystems have opposite effects on diurnal and seasonal patterns on atmospheric CO2 concentration and isotope ratios. This isotopic variation contains information about the functioning of different terrestrial ecosystems.
The BigFoot project gathered Gross Primary Production (GPP) data for nine EOS Land Validation Sites located from Alaska to Brazil from 2000 to 2004.  Each site is representative of one or two distinct biomes, including the Arctic tundra; boreal evergreen needleleaf forest; temperate cropland, grassland, evergreen needleleaf forest, and deciduous broadleaf forest; desert grassland and shrubland; and tropical evergreen broadleaf forest. BigFoot was funded by NASA's Terrestrial Ecology Program.For more details on the BigFoot Project, please visit the website: http://www.fsl.orst.edu/larse/bigfoot/index.html.
The BigFoot project gathered Gross Primary Production (GPP) data for nine EOS Land Validation Sites located from Alaska to Brazil from 2000 to 2004.  Each site is representative of one or two distinct biomes, including the Arctic tundra; boreal evergreen needleleaf forest; temperate cropland, grassland, evergreen needleleaf forest, and deciduous broadleaf forest; desert grassland and shrubland; and tropical evergreen broadleaf forest. BigFoot was funded by NASA's Terrestrial Ecology Program.For more details on the BigFoot Project, please visit the website: http://www.fsl.orst.edu/larse/bigfoot/index.html.
ABSTRACT: The BigFoot project gathered Gross Primary Production (GPP) data for nine EOS Land Validation Sites located from Alaska to Brazil from 2000 to 2004.  Each site is representative of one or two distinct biomes, including the Arctic tundra; boreal evergreen needleleaf forest; temperate cropland, grassland, evergreen needleleaf forest, and deciduous broadleaf forest; desert grassland and shrubland; and tropical evergreen broadleaf forest. BigFoot was funded by NASA's Terrestrial Ecology Program.For more details on the BigFoot Project, please visit the website: http://www.fsl.orst.edu/larse/bigfoot/index.html.
The BigFoot project gathered data for nine EOS Land Validation Sites located from Alaska to Brazil from 2000 to 2003. Each site is representative of one or two distinct biomes, including the Arctic tundra; boreal evergreen needleleaf forest; temperate cropland, grassland, evergreen needleleaf forest, and deciduous broadleaf forest; desert grassland and shrubland; and tropical evergreen broadleaf forest. These surfaces were produced from Landsat ETM+ imagery to explicitly characterize the land cover at the BigFoot Sites to provide validation of the MODIS land cover product. BigFoot was funded by NASA's Terrestrial Ecology Program.
The BigFoot project gathered data for nine EOS Land Validation Sites located from Alaska to Brazil from 2000 to 2003. Each site is representative of one or two distinct biomes, including the Arctic tundra; boreal evergreen needleleaf forest; temperate cropland, grassland, evergreen needleleaf forest, and deciduous broadleaf forest; desert grassland and shrubland; and tropical evergreen broadleaf forest. These surfaces were produced from Landsat ETM+ imagery to explicitly characterize the land cover at the BigFoot Sites to provide validation of the MODIS land cover product. BigFoot was funded by NASA's Terrestrial Ecology Program.
ABSTRACT: The BigFoot project gathered data for nine EOS Land Validation Sites located from Alaska to Brazil from 2000 to 2003. Each site is representative of one or two distinct biomes, including the Arctic tundra; boreal evergreen needleleaf forest; temperate cropland, grassland, evergreen needleleaf forest, and deciduous broadleaf forest; desert grassland and shrubland; and tropical evergreen broadleaf forest. These surfaces were produced from Landsat ETM+ imagery to explicitly characterize the land cover at the BigFoot Sites to provide validation of the MODIS land cover product. BigFoot was funded by NASA's Terrestrial Ecology Program.
The BigFoot project gathered leaf area index (LAI) data for nine EOS Land Validation Sites located from Alaska to Brazil from 2000 to 2003.  Each site is representative of one or two distinct biomes, including the Arctic tundra; boreal evergreen needleleaf forest; temperate cropland, grassland, evergreen needleleaf forest, and deciduous broadleaf forest; desert grassland and shrubland; and tropical evergreen broadleaf forest. LAI was measured at plots within each site for at least two years using standard direct and optical methods at each site. BigFoot was funded by NASA's Terrestrial Ecology Program.
The BigFoot project gathered leaf area index (LAI) data for nine EOS Land Validation Sites located from Alaska to Brazil from 2000 to 2003.  Each site is representative of one or two distinct biomes, including the Arctic tundra; boreal evergreen needleleaf forest; temperate cropland, grassland, evergreen needleleaf forest, and deciduous broadleaf forest; desert grassland and shrubland; and tropical evergreen broadleaf forest. LAI was measured at plots within each site for at least two years using standard direct and optical methods at each site. BigFoot was funded by NASA's Terrestrial Ecology Program.
ABSTRACT: The BigFoot project gathered leaf area index (LAI) data for nine EOS Land Validation Sites located from Alaska to Brazil from 2000 to 2003.  Each site is representative of one or two distinct biomes, including the Arctic tundra; boreal evergreen needleleaf forest; temperate cropland, grassland, evergreen needleleaf forest, and deciduous broadleaf forest; desert grassland and shrubland; and tropical evergreen broadleaf forest. LAI was measured at plots within each site for at least two years using standard direct and optical methods at each site. BigFoot was funded by NASA's Terrestrial Ecology Program.
The BigFoot Project has compiled daily meteorological measurements for nine EOS Land Validation Sites located from Alaska to Brazil from 1991 to 2004. Each site is representative of one or two distinct biomes, including the Arctic tundra; boreal evergreen needleleaf forest; temperate cropland, grassland, evergreen needleleaf forest, and deciduous broadleaf forest; desert grassland and shrubland; and tropical evergreen broadleaf forest.The BigFoot Project needed meteorological data to run the ecosystem process models used for scaling GPP and NPP products, for monitoring interannual variability, and for model testing. Meteorological data were obtained from various agencies collecting data in the vicinity of the BigFoot sites and for more recent years, collected on co-located CO2 flux measurement towers. A comparable set of original measurements from all sites were aggregated to a common daily time step for use in the BIOME-BGC model.
The BigFoot Project has compiled daily meteorological measurements for nine EOS Land Validation Sites located from Alaska to Brazil from 1991 to 2004. Each site is representative of one or two distinct biomes, including the Arctic tundra; boreal evergreen needleleaf forest; temperate cropland, grassland, evergreen needleleaf forest, and deciduous broadleaf forest; desert grassland and shrubland; and tropical evergreen broadleaf forest.The BigFoot Project needed meteorological data to run the ecosystem process models used for scaling GPP and NPP products, for monitoring interannual variability, and for model testing. Meteorological data were obtained from various agencies collecting data in the vicinity of the BigFoot sites and for more recent years, collected on co-located CO2 flux measurement towers. A comparable set of original measurements from all sites were aggregated to a common daily time step for use in the BIOME-BGC model.
ABSTRACT: The BigFoot Project has compiled daily meteorological measurements for nine EOS Land Validation Sites located from Alaska to Brazil from 1991 to 2004. Each site is representative of one or two distinct biomes, including the Arctic tundra; boreal evergreen needleleaf forest; temperate cropland, grassland, evergreen needleleaf forest, and deciduous broadleaf forest; desert grassland and shrubland; and tropical evergreen broadleaf forest.The BigFoot Project needed meteorological data to run the ecosystem process models used for scaling GPP and NPP products, for monitoring interannual variability, and for model testing. Meteorological data were obtained from various agencies collecting data in the vicinity of the BigFoot sites and for more recent years, collected on co-located CO2 flux measurement towers. A comparable set of original measurements from all sites were aggregated to a common daily time step for use in the BIOME-BGC model.
The BigFoot project gathered Net Primary Production (NPP) data for nine EOS Land Validation Sites located from Alaska to Brazil from 2000 to 2004. Each site is representative of one or two distinct biomes, including the Arctic tundra; boreal evergreen needleleaf forest; temperate cropland, grassland, evergreen needleleaf forest, and deciduous broadleaf forest; desert grassland and shrubland; and tropical evergreen broadleaf forest. BigFoot was funded by NASA's Terrestrial Ecology Program.For more details on the BigFoot Project, please visit the website: http://www.fsl.orst.edu/larse/bigfoot/index.html.
The BigFoot project gathered Net Primary Production (NPP) data for nine EOS Land Validation Sites located from Alaska to Brazil from 2000 to 2004. Each site is representative of one or two distinct biomes, including the Arctic tundra; boreal evergreen needleleaf forest; temperate cropland, grassland, evergreen needleleaf forest, and deciduous broadleaf forest; desert grassland and shrubland; and tropical evergreen broadleaf forest. BigFoot was funded by NASA's Terrestrial Ecology Program.For more details on the BigFoot Project, please visit the website: http://www.fsl.orst.edu/larse/bigfoot/index.html.
ABSTRACT: The BigFoot project gathered Net Primary Production (NPP) data for nine EOS Land Validation Sites located from Alaska to Brazil from 2000 to 2004. Each site is representative of one or two distinct biomes, including the Arctic tundra; boreal evergreen needleleaf forest; temperate cropland, grassland, evergreen needleleaf forest, and deciduous broadleaf forest; desert grassland and shrubland; and tropical evergreen broadleaf forest. BigFoot was funded by NASA's Terrestrial Ecology Program.For more details on the BigFoot Project, please visit the website: http://www.fsl.orst.edu/larse/bigfoot/index.html.
This data set of leaf, stem, and root biomass for various plant taxa was compiled from the primary literature of the 20th century with a significant portion derived from Cannell (1982). Recent allometric  additions include measurements made by Niklas and colleagues (Niklas, 2003). This is a unique data set with which to evaluate allometric patterns of standing biomass within and across the broad spectrum of vascular plant species. Despite its importance to ecology, global climate research, and evolutionary and ecological theory, the general principles underlying how plant metabolic production is allocated to above- and below-ground biomass remain unclear. The resulting uncertainty severely limits the accuracy of models for many ecologically and evolutionarily important phenomena across taxonomically diverse communities. Thus, although quantitative assessments of biomass allocation patterns are central to biology, theoretical or empirical assessments of these patterns remain contentious
This data set of leaf, stem, and root biomass for various plant taxa was compiled from the primary literature of the 20th century with a significant portion derived from Cannell (1982). Recent allometric  additions include measurements made by Niklas and colleagues (Niklas, 2003). This is a unique data set with which to evaluate allometric patterns of standing biomass within and across the broad spectrum of vascular plant species. Despite its importance to ecology, global climate research, and evolutionary and ecological theory, the general principles underlying how plant metabolic production is allocated to above- and below-ground biomass remain unclear. The resulting uncertainty severely limits the accuracy of models for many ecologically and evolutionarily important phenomena across taxonomically diverse communities. Thus, although quantitative assessments of biomass allocation patterns are central to biology, theoretical or empirical assessments of these patterns remain contentious
ABSTRACT: This data set of leaf, stem, and root biomass for various plant taxa was compiled from the primary literature of the 20th century with a significant portion derived from Cannell (1982). Recent allometric  additions include measurements made by Niklas and colleagues (Niklas, 2003). This is a unique data set with which to evaluate allometric patterns of standing biomass within and across the broad spectrum of vascular plant species. Despite its importance to ecology, global climate research, and evolutionary and ecological theory, the general principles underlying how plant metabolic production is allocated to above- and below-ground biomass remain unclear. The resulting uncertainty severely limits the accuracy of models for many ecologically and evolutionarily important phenomena across taxonomically diverse communities. Thus, although quantitative assessments of biomass allocation patterns are central to biology, theoretical or empirical assessments of these patterns remain contentious
Dimension analysis (diameter at breast high, tree height, depth of crown), estimated leaf area, and total aboveground biomass for sacrificed spruce and aspens in Superior National Forest, MN
Dimension analysis (diameter at breast high, tree height, depth of crown), estimated leaf area, and total aboveground biomass for sacrificed spruce and aspens in Superior National Forest, MN
ABSTRACT: Dimension analysis (diameter at breast high, tree height, depth of crown), estimated leaf area, and total aboveground biomass for sacrificed spruce and aspens in Superior National Forest, MN
This archived model product contains the directions, executables, and procedures for running Biome-BGC, Version 4.1.2, to recreate the results of the following article: Law, B. E., O. J. Sun, J. Campbell, S. Van Tuyl, and P. E. Thornton. 2003. Changes in carbon storage and fluxes in a chronosequence of ponderosa pine. Global Change Biology, 9(4), 510-514. Abstract excerpt: Forest development following stand-replacing disturbance influences a variety of ecosystem processes including carbon exchange with the atmosphere.  On a chronosequence of ponderosa pine (Pinius ponderosa var. Laws.) stands in central Oregon, U.S.A., we used biological measurements to estimate carbon storage in vegetation and soil pools, net primary productivity (NPP), and net ecosystem productivity (NEP) in relation to stand age. Measurements were made in 2000 on a suite of 12 ponderosa pine stands ranging in age from 9 to >300 years. Total ecosystem carbon storage and the fraction of ecosystem carbon in aboveground wood mass increased rapidly until 150-200 years and did not decline in older stands. Forest inventory data on 950 ponderosa pine plots in Oregon show that the greatest proportion of plots exist in stands ~100 years old, indicating that a majority of stands are approaching maximum carbon storage and net carbon uptake. Our data suggest that NEP averages ~70 g C m-2 year-1 for ponderosa pine forests in Oregon. About 85% of the total carbon storage in biomass on the survey plots exists in stands greater than 100 years, which has implications for managing forests for carbon sequestration. To investigate variation in carbon storage and fluxes with disturbance, simulation with process models requires a dynamic parameterization for biomass allocation that depends on stand age and should include a representation of competition between multiple plant functional types for space, water, and nutrients.
This archived model product contains the directions, executables, and procedures for running Biome-BGC, Version 4.1.2, to recreate the results of the following article: Law, B. E., O. J. Sun, J. Campbell, S. Van Tuyl, and P. E. Thornton. 2003. Changes in carbon storage and fluxes in a chronosequence of ponderosa pine. Global Change Biology, 9(4), 510-514. Abstract excerpt: Forest development following stand-replacing disturbance influences a variety of ecosystem processes including carbon exchange with the atmosphere.  On a chronosequence of ponderosa pine (Pinius ponderosa var. Laws.) stands in central Oregon, U.S.A., we used biological measurements to estimate carbon storage in vegetation and soil pools, net primary productivity (NPP), and net ecosystem productivity (NEP) in relation to stand age. Measurements were made in 2000 on a suite of 12 ponderosa pine stands ranging in age from 9 to >300 years. Total ecosystem carbon storage and the fraction of ecosystem carbon in aboveground wood mass increased rapidly until 150-200 years and did not decline in older stands. Forest inventory data on 950 ponderosa pine plots in Oregon show that the greatest proportion of plots exist in stands ~100 years old, indicating that a majority of stands are approaching maximum carbon storage and net carbon uptake. Our data suggest that NEP averages ~70 g C m-2 year-1 for ponderosa pine forests in Oregon. About 85% of the total carbon storage in biomass on the survey plots exists in stands greater than 100 years, which has implications for managing forests for carbon sequestration. To investigate variation in carbon storage and fluxes with disturbance, simulation with process models requires a dynamic parameterization for biomass allocation that depends on stand age and should include a representation of competition between multiple plant functional types for space, water, and nutrients.
ABSTRACT: This archived model product contains the directions, executables, and procedures for running Biome-BGC, Version 4.1.2, to recreate the results of the following article: Law, B. E., O. J. Sun, J. Campbell, S. Van Tuyl, and P. E. Thornton. 2003. Changes in carbon storage and fluxes in a chronosequence of ponderosa pine. Global Change Biology, 9(4), 510-514. Abstract excerpt: Forest development following stand-replacing disturbance influences a variety of ecosystem processes including carbon exchange with the atmosphere.  On a chronosequence of ponderosa pine (Pinius ponderosa var. Laws.) stands in central Oregon, U.S.A., we used biological measurements to estimate carbon storage in vegetation and soil pools, net primary productivity (NPP), and net ecosystem productivity (NEP) in relation to stand age. Measurements were made in 2000 on a suite of 12 ponderosa pine stands ranging in age from 9 to >300 years. Total ecosystem carbon storage and the fraction of ecosystem carbon in aboveground wood mass increased rapidly until 150-200 years and did not decline in older stands. Forest inventory data on 950 ponderosa pine plots in Oregon show that the greatest proportion of plots exist in stands ~100 years old, indicating that a majority of stands are approaching maximum carbon storage and net carbon uptake. Our data suggest that NEP averages ~70 g C m-2 year-1 for ponderosa pine forests in Oregon. About 85% of the total carbon storage in biomass on the survey plots exists in stands greater than 100 years, which has implications for managing forests for carbon sequestration. To investigate variation in carbon storage and fluxes with disturbance, simulation with process models requires a dynamic parameterization for biomass allocation that depends on stand age and should include a representation of competition between multiple plant functional types for space, water, and nutrients.
This archived model product contains the directions, executables, and procedures for running Biome-BGC, Version 4.1.1, to recreate the results of the following article: Thornton, P. E., B. E. Law, H. L. Gholz, K. L. Clark, E. Falge, D. S. Ellsworth, A. H. Goldstein, R. K. Monson,  D. Hollinger, M. Falk, J. Chen, and J. P. Sparks. 2002. Modeling and measuring the effects of disturbance history and climate on carbon and water budgets in evergreen needleleaf forests. Agricultural and Forest Meteorology 113:185-222. Abstract: The effects of disturbance history, climate, and changes in atmospheric carbon dioxide (CO2) concentration and nitrogen deposition (Ndep) on carbon and water fluxes in seven North American evergreen forests are assessed using a coupled water, carbon, nitrogen model, canopy-scale flux observations, and descriptions of the vegetation type, management practices, and disturbance histories at each site. The effects of interannual climate variability, disturbance history, and vegetation ecophysiology on carbon and water fluxes and storage are integrated by the ecosystem process model Biome-BGC, with results compared to site biometric analyses and eddy covariance observations aggregated by month and year.  The model produced good estimates of between-site variation in leaf area index, with mixed performance for between- and within-site variation in evapotranspiration. There is a model bias toward smaller annual carbon sinks at five sites, with a seasonal model bias toward smaller warm-season sink strength at all sites.
This archived model product contains the directions, executables, and procedures for running Biome-BGC, Version 4.1.1, to recreate the results of the following article: Thornton, P. E., B. E. Law, H. L. Gholz, K. L. Clark, E. Falge, D. S. Ellsworth, A. H. Goldstein, R. K. Monson,  D. Hollinger, M. Falk, J. Chen, and J. P. Sparks. 2002. Modeling and measuring the effects of disturbance history and climate on carbon and water budgets in evergreen needleleaf forests. Agricultural and Forest Meteorology 113:185-222. Abstract: The effects of disturbance history, climate, and changes in atmospheric carbon dioxide (CO2) concentration and nitrogen deposition (Ndep) on carbon and water fluxes in seven North American evergreen forests are assessed using a coupled water, carbon, nitrogen model, canopy-scale flux observations, and descriptions of the vegetation type, management practices, and disturbance histories at each site. The effects of interannual climate variability, disturbance history, and vegetation ecophysiology on carbon and water fluxes and storage are integrated by the ecosystem process model Biome-BGC, with results compared to site biometric analyses and eddy covariance observations aggregated by month and year.  The model produced good estimates of between-site variation in leaf area index, with mixed performance for between- and within-site variation in evapotranspiration. There is a model bias toward smaller annual carbon sinks at five sites, with a seasonal model bias toward smaller warm-season sink strength at all sites.
ABSTRACT: This archived model product contains the directions, executables, and procedures for running Biome-BGC, Version 4.1.1, to recreate the results of the following article: Thornton, P. E., B. E. Law, H. L. Gholz, K. L. Clark, E. Falge, D. S. Ellsworth, A. H. Goldstein, R. K. Monson,  D. Hollinger, M. Falk, J. Chen, and J. P. Sparks. 2002. Modeling and measuring the effects of disturbance history and climate on carbon and water budgets in evergreen needleleaf forests. Agricultural and Forest Meteorology 113:185-222. Abstract: The effects of disturbance history, climate, and changes in atmospheric carbon dioxide (CO2) concentration and nitrogen deposition (Ndep) on carbon and water fluxes in seven North American evergreen forests are assessed using a coupled water, carbon, nitrogen model, canopy-scale flux observations, and descriptions of the vegetation type, management practices, and disturbance histories at each site. The effects of interannual climate variability, disturbance history, and vegetation ecophysiology on carbon and water fluxes and storage are integrated by the ecosystem process model Biome-BGC, with results compared to site biometric analyses and eddy covariance observations aggregated by month and year.  The model produced good estimates of between-site variation in leaf area index, with mixed performance for between- and within-site variation in evapotranspiration. There is a model bias toward smaller annual carbon sinks at five sites, with a seasonal model bias toward smaller warm-season sink strength at all sites.
Biome-BGC is a computer program that estimates fluxes and storage of energy, water, carbon, and nitrogen for the vegetation and soil components of terrestrial ecosystems. The primary model purpose is to study global and regional interactions between climate, disturbance, and biogeochemical cycles. Biome-BGC represents physical and biological processes that control fluxes of energy and mass. These processes include new leaf growth and old leaf litterfall, sunlight interception by leaves and penetration to the ground, precipitation routing to leaves and soil, snow accumulation and melting, drainage and runoff of soil water, evaporation of water from soil and wet leaves, transpiration of soil water through leaf stomata, photosynthetic fixation of carbon from CO2 in the air, uptake of nitrogen from the soil, distribution of carbon and nitrogen to growing plant parts, decomposition of fresh plant litter and old soil organic matter, plant mortality, and fire. The model uses a daily time-step, meaning that each flux is estimated for a one-day period. Between days, the program updates its memory of the mass stored in different components of the vegetation, litter, and soil. Weather is the most important control on vegetation processes. Flux estimates in Biome-BGC depend strongly on daily weather conditions. Model behavior over time depends on climate--the history of these weather conditions. A companion file with more information about Biome-BGC and its components is available. Biome-BGC, Version 4.1.1, was developed and is maintained by the  Numerical Terradynamic Simulation Group, School of Forestry, the University of Montana, Missoula, Montana, U.S.A.  Additional information can be found on their web site at: http://www.ntsg.umt.edu/.
Biome-BGC is a computer program that estimates fluxes and storage of energy, water, carbon, and nitrogen for the vegetation and soil components of terrestrial ecosystems. The primary model purpose is to study global and regional interactions between climate, disturbance, and biogeochemical cycles. Biome-BGC represents physical and biological processes that control fluxes of energy and mass. These processes include new leaf growth and old leaf litterfall, sunlight interception by leaves and penetration to the ground, precipitation routing to leaves and soil, snow accumulation and melting, drainage and runoff of soil water, evaporation of water from soil and wet leaves, transpiration of soil water through leaf stomata, photosynthetic fixation of carbon from CO2 in the air, uptake of nitrogen from the soil, distribution of carbon and nitrogen to growing plant parts, decomposition of fresh plant litter and old soil organic matter, plant mortality, and fire. The model uses a daily time-step, meaning that each flux is estimated for a one-day period. Between days, the program updates its memory of the mass stored in different components of the vegetation, litter, and soil. Weather is the most important control on vegetation processes. Flux estimates in Biome-BGC depend strongly on daily weather conditions. Model behavior over time depends on climate--the history of these weather conditions. A companion file with more information about Biome-BGC and its components is available. Biome-BGC, Version 4.1.1, was developed and is maintained by the  Numerical Terradynamic Simulation Group, School of Forestry, the University of Montana, Missoula, Montana, U.S.A.  Additional information can be found on their web site at: http://www.ntsg.umt.edu/.
ABSTRACT: Biome-BGC is a computer program that estimates fluxes and storage of energy, water, carbon, and nitrogen for the vegetation and soil components of terrestrial ecosystems. The primary model purpose is to study global and regional interactions between climate, disturbance, and biogeochemical cycles. Biome-BGC represents physical and biological processes that control fluxes of energy and mass. These processes include new leaf growth and old leaf litterfall, sunlight interception by leaves and penetration to the ground, precipitation routing to leaves and soil, snow accumulation and melting, drainage and runoff of soil water, evaporation of water from soil and wet leaves, transpiration of soil water through leaf stomata, photosynthetic fixation of carbon from CO2 in the air, uptake of nitrogen from the soil, distribution of carbon and nitrogen to growing plant parts, decomposition of fresh plant litter and old soil organic matter, plant mortality, and fire. The model uses a daily time-step, meaning that each flux is estimated for a one-day period. Between days, the program updates its memory of the mass stored in different components of the vegetation, litter, and soil. Weather is the most important control on vegetation processes. Flux estimates in Biome-BGC depend strongly on daily weather conditions. Model behavior over time depends on climate--the history of these weather conditions. A companion file with more information about Biome-BGC and its components is available. Biome-BGC, Version 4.1.1, was developed and is maintained by the  Numerical Terradynamic Simulation Group, School of Forestry, the University of Montana, Missoula, Montana, U.S.A.  Additional information can be found on their web site at: http://www.ntsg.umt.edu/.
Contains the Belfort rain gauge data that was collected by the HYD09 group at various locations.
Contains the Belfort rain gauge data that was collected by the HYD09 group at various locations.
ABSTRACT: Contains the Belfort rain gauge data that was collected by the HYD09 group at various locations.
Contains the Tipping Bucket rain gauge data that was collected by the HYD09 group at various locations.
Contains the Tipping Bucket rain gauge data that was collected by the HYD09 group at various locations.
ABSTRACT: Contains the Tipping Bucket rain gauge data that was collected by the HYD09 group at various locations.
Contains the Belfort rain gauge data that was collected by the HYD09 group at various locations.
Contains the Belfort rain gauge data that was collected by the HYD09 group at various locations.
ABSTRACT: Contains the Belfort rain gauge data that was collected by the HYD09 group at various locations.
Contains the Tipping Bucket rain gauge data that was collected by the HYD09 group at various locations.
Contains the Tipping Bucket rain gauge data that was collected by the HYD09 group at various locations.
ABSTRACT: Contains the Tipping Bucket rain gauge data that was collected by the HYD09 group at various locations.
Contains the Belfort rain gauge data that was collected by the HYD09 group at various locations.
Contains the Belfort rain gauge data that was collected by the HYD09 group at various locations.
ABSTRACT: Contains the Belfort rain gauge data that was collected by the HYD09 group at various locations.
Contains the Tipping Bucket rain gauge data that was collected by the HYD09 group at various locations.
Contains the Tipping Bucket rain gauge data that was collected by the HYD09 group at various locations.
ABSTRACT: Contains the Tipping Bucket rain gauge data that was collected by the HYD09 group at various locations.
Contains 5-day averages of hourly and daily data from 23 meteorological stations across Canada along with full-resolution upper air measurements from 1 station in The Pas, Manitoba.
Contains 5-day averages of hourly and daily data from 23 meteorological stations across Canada along with full-resolution upper air measurements from 1 station in The Pas, Manitoba.
ABSTRACT: Contains 5-day averages of hourly and daily data from 23 meteorological stations across Canada along with full-resolution upper air measurements from 1 station in The Pas, Manitoba.
Data include aircraft altitude, wind direction, wind speed, air temperature, potential temperature, water mixing ratio, U and V components of wind velocity, static pressure, surface radiative temperature, downwelling and upwelling total radiation, downwelling and upwelling longwave radiation, net radiation, downwelling and upwelling PAR, greenness index, CO2 concentration, O3 concentration, and CH4 concentration.
Data include aircraft altitude, wind direction, wind speed, air temperature, potential temperature, water mixing ratio, U and V components of wind velocity, static pressure, surface radiative temperature, downwelling and upwelling total radiation, downwelling and upwelling longwave radiation, net radiation, downwelling and upwelling PAR, greenness index, CO2 concentration, O3 concentration, and CH4 concentration.
ABSTRACT: Data include aircraft altitude, wind direction, wind speed, air temperature, potential temperature, water mixing ratio, U and V components of wind velocity, static pressure, surface radiative temperature, downwelling and upwelling total radiation, downwelling and upwelling longwave radiation, net radiation, downwelling and upwelling PAR, greenness index, CO2 concentration, O3 concentration, and CH4 concentration.
Contains mission information and moving window data for AFM-01 BOREAS flux aircraft runs during 1994. Contains mission information and data for AFM-02 BOREAS flux aircraft runs during 1994.
Contains mission information and moving window data for AFM-01 BOREAS flux aircraft runs during 1994. Contains mission information and data for AFM-02 BOREAS flux aircraft runs during 1994.
ABSTRACT: Contains mission information and moving window data for AFM-01 BOREAS flux aircraft runs during 1994. Contains mission information and data for AFM-02 BOREAS flux aircraft runs during 1994.
Parameters include wind direction, wind speed, west wind component (u), south wind component (v), static pressure, air dry bulb temperature, potential temperature, dewpoint, temperature, water vapor mixing ratio, and CO2 concentration.
Parameters include wind direction, wind speed, west wind component (u), south wind component (v), static pressure, air dry bulb temperature, potential temperature, dewpoint, temperature, water vapor mixing ratio, and CO2 concentration.
ABSTRACT: Parameters include wind direction, wind speed, west wind component (u), south wind component (v), static pressure, air dry bulb temperature, potential temperature, dewpoint, temperature, water vapor mixing ratio, and CO2 concentration.
Measurements of the fluxes of momentum, sensible and latent heat, carbon dioxide, and ozone over the entire BOREAS region to tie together measurements made in both the SSA and the NSA.
Measurements of the fluxes of momentum, sensible and latent heat, carbon dioxide, and ozone over the entire BOREAS region to tie together measurements made in both the SSA and the NSA.
ABSTRACT: Measurements of the fluxes of momentum, sensible and latent heat, carbon dioxide, and ozone over the entire BOREAS region to tie together measurements made in both the SSA and the NSA.
Measurements of wind speed and direction, air pressure and temperature, potential temperature, dewpoint, mixing ratio of H2O, CO2 concentration, and ozone concentration over the NSA, SSA, and the transect during BOREAS IFCs 1, 2, and 3 during 1994.
Measurements of wind speed and direction, air pressure and temperature, potential temperature, dewpoint, mixing ratio of H2O, CO2 concentration, and ozone concentration over the NSA, SSA, and the transect during BOREAS IFCs 1, 2, and 3 during 1994.
ABSTRACT: Measurements of wind speed and direction, air pressure and temperature, potential temperature, dewpoint, mixing ratio of H2O, CO2 concentration, and ozone concentration over the NSA, SSA, and the transect during BOREAS IFCs 1, 2, and 3 during 1994.
Measurements in the boundary layer of the fluxes of sensible and latent heat, momentum, ozone, methane, and carbon dioxide, plus supporting meteorological parameters such as temperature, humidity, and wind speed and direction.
Measurements in the boundary layer of the fluxes of sensible and latent heat, momentum, ozone, methane, and carbon dioxide, plus supporting meteorological parameters such as temperature, humidity, and wind speed and direction.
ABSTRACT: Measurements in the boundary layer of the fluxes of sensible and latent heat, momentum, ozone, methane, and carbon dioxide, plus supporting meteorological parameters such as temperature, humidity, and wind speed and direction.
Measurements include concentrations of carbon dioxide and ozone, atmospheric pressure, dry bulb temperature, potential temperature, dewpoint temperature, calculated mixing ratio, and wind speed and direction at both the NSA and the SSA in 1994 and 1996.
Measurements include concentrations of carbon dioxide and ozone, atmospheric pressure, dry bulb temperature, potential temperature, dewpoint temperature, calculated mixing ratio, and wind speed and direction at both the NSA and the SSA in 1994 and 1996.
ABSTRACT: Measurements include concentrations of carbon dioxide and ozone, atmospheric pressure, dry bulb temperature, potential temperature, dewpoint temperature, calculated mixing ratio, and wind speed and direction at both the NSA and the SSA in 1994 and 1996.
Contains basic upper-air parameters collected by the AFM-05 team from the network of upper-air stations during the 1993, 1994, and 1996 field campaigns over the entire study region.
Contains basic upper-air parameters collected by the AFM-05 team from the network of upper-air stations during the 1993, 1994, and 1996 field campaigns over the entire study region.
ABSTRACT: Contains basic upper-air parameters collected by the AFM-05 team from the network of upper-air stations during the 1993, 1994, and 1996 field campaigns over the entire study region.
Basic upper-air parameters interpolated at 0.5 kiloPascal increments of atmospheric pressure from the network of upper-air stations during the 1993, 1994, and 1996 field campaigns over the entire study region.
Basic upper-air parameters interpolated at 0.5 kiloPascal increments of atmospheric pressure from the network of upper-air stations during the 1993, 1994, and 1996 field campaigns over the entire study region.
ABSTRACT: Basic upper-air parameters interpolated at 0.5 kiloPascal increments of atmospheric pressure from the network of upper-air stations during the 1993, 1994, and 1996 field campaigns over the entire study region.
Contains AFM-06 hourly inversion height measurements.
Contains AFM-06 hourly inversion height measurements.
ABSTRACT: Contains AFM-06 hourly inversion height measurements.
Contains the AFM-06 temperature profiler data near the Old Jack Pine site in the Southern Study Area.
Contains the AFM-06 temperature profiler data near the Old Jack Pine site in the Southern Study Area.
ABSTRACT: Contains the AFM-06 temperature profiler data near the Old Jack Pine site in the Southern Study Area.
Contains the AFM-06 wind profiler data near the Old Jack Pine site in the Southern Study Area.
Contains the AFM-06 wind profiler data near the Old Jack Pine site in the Southern Study Area.
ABSTRACT: Contains the AFM-06 wind profiler data near the Old Jack Pine site in the Southern Study Area.
The BOREAS AFM-06 team from the National Oceanic and Atmospheric Administration Environmental Technology Laboratory (NOAA/ETL) operated a 35 GHz cloud-sensing radar in the Northern Study Area (NSA) near the Old Jack Pine (OJP) tower from 16-Jul-1994 to 08-Aug-1994.
The BOREAS AFM-06 team from the National Oceanic and Atmospheric Administration Environmental Technology Laboratory (NOAA/ETL) operated a 35 GHz cloud-sensing radar in the Northern Study Area (NSA) near the Old Jack Pine (OJP) tower from 16-Jul-1994 to 08-Aug-1994.
ABSTRACT: The BOREAS AFM-06 team from the National Oceanic and Atmospheric Administration Environmental Technology Laboratory (NOAA/ETL) operated a 35 GHz cloud-sensing radar in the Northern Study Area (NSA) near the Old Jack Pine (OJP) tower from 16-Jul-1994 to 08-Aug-1994.
Contains the AFM-06 surface meteorological data near the Old Jack Pine site in the Southern Study Area.
Contains the AFM-06 surface meteorological data near the Old Jack Pine site in the Southern Study Area.
ABSTRACT: Contains the AFM-06 surface meteorological data near the Old Jack Pine site in the Southern Study Area.
Hourly data from the ECMWF operational model from below the surface to the top of the atmosphere, including the model fluxes at the surface, at Candle Lake, Saskatchewan, in the SSA and Thompson, Manitoba, in the NSA 13-May-1994 to 30-Sept-1994 and 01-Mar-1996 to 31-Mar-1997.
Hourly data from the ECMWF operational model from below the surface to the top of the atmosphere, including the model fluxes at the surface, at Candle Lake, Saskatchewan, in the SSA and Thompson, Manitoba, in the NSA 13-May-1994 to 30-Sept-1994 and 01-Mar-1996 to 31-Mar-1997.
ABSTRACT: Hourly data from the ECMWF operational model from below the surface to the top of the atmosphere, including the model fluxes at the surface, at Candle Lake, Saskatchewan, in the SSA and Thompson, Manitoba, in the NSA 13-May-1994 to 30-Sept-1994 and 01-Mar-1996 to 31-Mar-1997.
This regional land cover data set was developed as part of a multitemporal 1-km AVHRR land cover analysis approach that was used as the basis for regional land cover mapping, fire disturbance-regeneration, and multiresolution land cover scaling studies in the boreal forest ecosystem of central Canada (Steyaert et al., 1997).
This regional land cover data set was developed as part of a multitemporal 1-km AVHRR land cover analysis approach that was used as the basis for regional land cover mapping, fire disturbance-regeneration, and multiresolution land cover scaling studies in the boreal forest ecosystem of central Canada (Steyaert et al., 1997).
ABSTRACT: This regional land cover data set was developed as part of a multitemporal 1-km AVHRR land cover analysis approach that was used as the basis for regional land cover mapping, fire disturbance-regeneration, and multiresolution land cover scaling studies in the boreal forest ecosystem of central Canada (Steyaert et al., 1997).
Not technically a data set, this is a report of the cross-comparison of data collected by various flux aircraft. Reports of these analyses are available as TIF and ASCII files.
Not technically a data set, this is a report of the cross-comparison of data collected by various flux aircraft. Reports of these analyses are available as TIF and ASCII files.
ABSTRACT: Not technically a data set, this is a report of the cross-comparison of data collected by various flux aircraft. Reports of these analyses are available as TIF and ASCII files.
The level-0 Daedalus TMS imagery, along with the other remotely sensed images, was collected to provide spatially extensive information about radiant energy over the primary BOREAS study areas.  This information includes detailed land cover and biophysical parameter maps such as fPAR and LAI.
The level-0 Daedalus TMS imagery, along with the other remotely sensed images, was collected to provide spatially extensive information about radiant energy over the primary BOREAS study areas.  This information includes detailed land cover and biophysical parameter maps such as fPAR and LAI.
ABSTRACT: The level-0 Daedalus TMS imagery, along with the other remotely sensed images, was collected to provide spatially extensive information about radiant energy over the primary BOREAS study areas.  This information includes detailed land cover and biophysical parameter maps such as fPAR and LAI.
AEAC projection of the original DEMs produced by the BOREAS HYD-08 team.
AEAC projection of the original DEMs produced by the BOREAS HYD-08 team.
ABSTRACT: AEAC projection of the original DEMs produced by the BOREAS HYD-08 team.
Contains surface meteorology data merged/interpolated from four BOREAS sites for the years 1994, 1995, and 1996.
Contains surface meteorology data merged/interpolated from four BOREAS sites for the years 1994, 1995, and 1996.
ABSTRACT: Contains surface meteorology data merged/interpolated from four BOREAS sites for the years 1994, 1995, and 1996.
Elevation contours over the NSA and SSA in ARC/Info Generate Format.  Data cover portions of the BOREAS Northern Study Area (NSA) and Southern Study Area (SSA) and are on a scale of 1:50,000.
Elevation contours over the NSA and SSA in ARC/Info Generate Format.  Data cover portions of the BOREAS Northern Study Area (NSA) and Southern Study Area (SSA) and are on a scale of 1:50,000.
ABSTRACT: Elevation contours over the NSA and SSA in ARC/Info Generate Format.  Data cover portions of the BOREAS Northern Study Area (NSA) and Southern Study Area (SSA) and are on a scale of 1:50,000.
The objective of this land cover mosaic is to provide a data product that characterizes the detailed land cover of a significant portion of the BOREAS Region. Seven Landsat-5 TM images have been assembled to completely cover the BOREAS Transect.
The objective of this land cover mosaic is to provide a data product that characterizes the detailed land cover of a significant portion of the BOREAS Region. Seven Landsat-5 TM images have been assembled to completely cover the BOREAS Transect.
ABSTRACT: The objective of this land cover mosaic is to provide a data product that characterizes the detailed land cover of a significant portion of the BOREAS Region. Seven Landsat-5 TM images have been assembled to completely cover the BOREAS Transect.
This land cover product was produced by NBIOME to generate an up-to-date, spatially and temporally consistent land cover map of the landmass of Canada for use by scientists and other users interested in environmental information at national and regional scales. This data set is gridded and was produced from 10-day composite data of surface parameters.
This land cover product was produced by NBIOME to generate an up-to-date, spatially and temporally consistent land cover map of the landmass of Canada for use by scientists and other users interested in environmental information at national and regional scales. This data set is gridded and was produced from 10-day composite data of surface parameters.
ABSTRACT: This land cover product was produced by NBIOME to generate an up-to-date, spatially and temporally consistent land cover map of the landmass of Canada for use by scientists and other users interested in environmental information at national and regional scales. This data set is gridded and was produced from 10-day composite data of surface parameters.
The BOREAS DSP-4 team acquired and analyzed imaging radar data from the ESA's ERS-1 over a complete annual cycle at the BOREAS sites in Canada in 1994 to detect shifts in radar backscatter related to varying environmental conditions. Two independent transitions correlating with snow melt and soil thaw onset, and possible canopy thaw were revealed by the data.
The BOREAS DSP-4 team acquired and analyzed imaging radar data from the ESA's ERS-1 over a complete annual cycle at the BOREAS sites in Canada in 1994 to detect shifts in radar backscatter related to varying environmental conditions. Two independent transitions correlating with snow melt and soil thaw onset, and possible canopy thaw were revealed by the data.
ABSTRACT: The BOREAS DSP-4 team acquired and analyzed imaging radar data from the ESA's ERS-1 over a complete annual cycle at the BOREAS sites in Canada in 1994 to detect shifts in radar backscatter related to varying environmental conditions. Two independent transitions correlating with snow melt and soil thaw onset, and possible canopy thaw were revealed by the data.
The BOREAS DSP-5 team generated a NPP image over the BOREAS region from a process-based ecosystem model, the Boreal Ecosystem Productivity Simulator (BEPS). The NPP image was created from a series of composited AVHRR images from April 11 - September 10, 1994. This document describes how the NPP is generated . The NPP data are stored in a binary image file.
The BOREAS DSP-5 team generated a NPP image over the BOREAS region from a process-based ecosystem model, the Boreal Ecosystem Productivity Simulator (BEPS). The NPP image was created from a series of composited AVHRR images from April 11 - September 10, 1994. This document describes how the NPP is generated . The NPP data are stored in a binary image file.
ABSTRACT: The BOREAS DSP-5 team generated a NPP image over the BOREAS region from a process-based ecosystem model, the Boreal Ecosystem Productivity Simulator (BEPS). The NPP image was created from a series of composited AVHRR images from April 11 - September 10, 1994. This document describes how the NPP is generated . The NPP data are stored in a binary image file.
The LAI and canopy closure images over the BOREAS conifer flux tower sites were produced at a spatial resolution of 30 m using the Forest-Light Interaction Model.  The data used were obtained by the CASI instrument in high spatial resolution mode in the winter of 1994.  Additional high resolution LAI and canopy closure images were produced over the two black spruce flux tower sites using the FLIM-CLUS algorithm.
The LAI and canopy closure images over the BOREAS conifer flux tower sites were produced at a spatial resolution of 30 m using the Forest-Light Interaction Model.  The data used were obtained by the CASI instrument in high spatial resolution mode in the winter of 1994.  Additional high resolution LAI and canopy closure images were produced over the two black spruce flux tower sites using the FLIM-CLUS algorithm.
ABSTRACT: The LAI and canopy closure images over the BOREAS conifer flux tower sites were produced at a spatial resolution of 30 m using the Forest-Light Interaction Model.  The data used were obtained by the CASI instrument in high spatial resolution mode in the winter of 1994.  Additional high resolution LAI and canopy closure images were produced over the two black spruce flux tower sites using the FLIM-CLUS algorithm.
This data set contains maps of surface reflectance and vegetation parameters derived from imagery collected by the POLDER instrument over BOREAS conifer tower sites in the Southern Study Area (SSA) during 1994. The POLDER imagery provided in this data set was collected on June 1 and July 21, 1994, from the NASA C-130 aircraft platform.
This data set contains maps of surface reflectance and vegetation parameters derived from imagery collected by the POLDER instrument over BOREAS conifer tower sites in the Southern Study Area (SSA) during 1994. The POLDER imagery provided in this data set was collected on June 1 and July 21, 1994, from the NASA C-130 aircraft platform.
ABSTRACT: This data set contains maps of surface reflectance and vegetation parameters derived from imagery collected by the POLDER instrument over BOREAS conifer tower sites in the Southern Study Area (SSA) during 1994. The POLDER imagery provided in this data set was collected on June 1 and July 21, 1994, from the NASA C-130 aircraft platform.
BOREAS follow-on group DSP-9 mapped surface moss type at three scales (1 km, 30 m, and 10 m) based on observed associations between moss cover and land cover type.
BOREAS follow-on group DSP-9 mapped surface moss type at three scales (1 km, 30 m, and 10 m) based on observed associations between moss cover and land cover type.
ABSTRACT: BOREAS follow-on group DSP-9 mapped surface moss type at three scales (1 km, 30 m, and 10 m) based on observed associations between moss cover and land cover type.
This data set contains a pair of raster images and a spreadsheet chronicling the most recent fire history of Saskatchewan from 1945 to 1996.  This data set was developed from a series of ARC/INFO export files of annual fire data  compiled and provided by the Saskatchewan Environment and Resource Management (SERM) Wildlife Branch.
This data set contains a pair of raster images and a spreadsheet chronicling the most recent fire history of Saskatchewan from 1945 to 1996.  This data set was developed from a series of ARC/INFO export files of annual fire data  compiled and provided by the Saskatchewan Environment and Resource Management (SERM) Wildlife Branch.
ABSTRACT: This data set contains a pair of raster images and a spreadsheet chronicling the most recent fire history of Saskatchewan from 1945 to 1996.  This data set was developed from a series of ARC/INFO export files of annual fire data  compiled and provided by the Saskatchewan Environment and Resource Management (SERM) Wildlife Branch.
These images were produced by aggregating a reclassified version of the 30-m land cover Thematic Mapper classification by CCRS and are now available at multiple resolutions (10x5 minutes, and 30 minutes).
These images were produced by aggregating a reclassified version of the 30-m land cover Thematic Mapper classification by CCRS and are now available at multiple resolutions (10x5 minutes, and 30 minutes).
ABSTRACT: These images were produced by aggregating a reclassified version of the 30-m land cover Thematic Mapper classification by CCRS and are now available at multiple resolutions (10x5 minutes, and 30 minutes).
Existing TM and AVHRR based landcover maps, AVHRR-FPAR maps, AVHRR-LAI maps, moss cover maps, and a new peatland distribution map were regridded to scales of 2 km, 10 by 5 minute, and half-degree grids for use by the BOREAS Follow-On Carbon and Hydro-Meteorological modeling groups.
Existing TM and AVHRR based landcover maps, AVHRR-FPAR maps, AVHRR-LAI maps, moss cover maps, and a new peatland distribution map were regridded to scales of 2 km, 10 by 5 minute, and half-degree grids for use by the BOREAS Follow-On Carbon and Hydro-Meteorological modeling groups.
ABSTRACT: Existing TM and AVHRR based landcover maps, AVHRR-FPAR maps, AVHRR-LAI maps, moss cover maps, and a new peatland distribution map were regridded to scales of 2 km, 10 by 5 minute, and half-degree grids for use by the BOREAS Follow-On Carbon and Hydro-Meteorological modeling groups.
These images were produced by aggregating the 1-km land cover classification by Lou Steyaert at multiple resolutions (2 km, 10x5 minutes, and 0.5 degree). These data were regridded for use by the BOREAS Follow-on Carbon and Hydro-Meteorological modeling groups to have a number of data sets available in common grid projections and scales for intercomparison studies.
These images were produced by aggregating the 1-km land cover classification by Lou Steyaert at multiple resolutions (2 km, 10x5 minutes, and 0.5 degree). These data were regridded for use by the BOREAS Follow-on Carbon and Hydro-Meteorological modeling groups to have a number of data sets available in common grid projections and scales for intercomparison studies.
ABSTRACT: These images were produced by aggregating the 1-km land cover classification by Lou Steyaert at multiple resolutions (2 km, 10x5 minutes, and 0.5 degree). These data were regridded for use by the BOREAS Follow-on Carbon and Hydro-Meteorological modeling groups to have a number of data sets available in common grid projections and scales for intercomparison studies.
Existing 1-km moss cover classifications were reprocessed and are now available at multiple resolutions (2 km, 10x5 min, and 0.5 degree).  These data were regridded for use by the BOREAS Follow-on Carbon and Hydro-Meteorological modeling groups to have a number of data sets available in common grid projections and scales for intercomparison studies.
Existing 1-km moss cover classifications were reprocessed and are now available at multiple resolutions (2 km, 10x5 min, and 0.5 degree).  These data were regridded for use by the BOREAS Follow-on Carbon and Hydro-Meteorological modeling groups to have a number of data sets available in common grid projections and scales for intercomparison studies.
ABSTRACT: Existing 1-km moss cover classifications were reprocessed and are now available at multiple resolutions (2 km, 10x5 min, and 0.5 degree).  These data were regridded for use by the BOREAS Follow-on Carbon and Hydro-Meteorological modeling groups to have a number of data sets available in common grid projections and scales for intercomparison studies.
These images were produced by averaging the 1-km FASIR-NDVI maps by Jing Chen to a 10' (horizontal) by 5' (vertical) pixel size in a straight latitude/longitude grid.  Each pixel represents the average NDVI of the 1-km pixels that fall in each 10' by 5' pixel, where more than 50% of the 1-km pixels in the 10' by 5' area are not cloud and are not missing.  If more than 50% of the 1-km pixels are missing or cloudy, a value of 0 is assigned to the 10' by 5' pixel.
These images were produced by averaging the 1-km FASIR-NDVI maps by Jing Chen to a 10' (horizontal) by 5' (vertical) pixel size in a straight latitude/longitude grid.  Each pixel represents the average NDVI of the 1-km pixels that fall in each 10' by 5' pixel, where more than 50% of the 1-km pixels in the 10' by 5' area are not cloud and are not missing.  If more than 50% of the 1-km pixels are missing or cloudy, a value of 0 is assigned to the 10' by 5' pixel.
ABSTRACT: These images were produced by averaging the 1-km FASIR-NDVI maps by Jing Chen to a 10' (horizontal) by 5' (vertical) pixel size in a straight latitude/longitude grid.  Each pixel represents the average NDVI of the 1-km pixels that fall in each 10' by 5' pixel, where more than 50% of the 1-km pixels in the 10' by 5' area are not cloud and are not missing.  If more than 50% of the 1-km pixels are missing or cloudy, a value of 0 is assigned to the 10' by 5' pixel.
These images were produced by aggregating 1' gridded data layers derived from the polygon-based Peatlands of Canada Database (Tarnocai et al., 2000) to 10' (horizontal) by 5' (vertical) and to 0.5 degree by 0.5 degree (or 30' by 30') pixel sizes in straight latitude/longitude grids.
These images were produced by aggregating 1' gridded data layers derived from the polygon-based Peatlands of Canada Database (Tarnocai et al., 2000) to 10' (horizontal) by 5' (vertical) and to 0.5 degree by 0.5 degree (or 30' by 30') pixel sizes in straight latitude/longitude grids.
ABSTRACT: These images were produced by aggregating 1' gridded data layers derived from the polygon-based Peatlands of Canada Database (Tarnocai et al., 2000) to 10' (horizontal) by 5' (vertical) and to 0.5 degree by 0.5 degree (or 30' by 30') pixel sizes in straight latitude/longitude grids.
Existing 30-m land cover Thematic Mapper classification by CCRS was aggregated and reprocessed and are now available at multiple resolutions (10x5 minutes and 30 minutes).
Existing 30-m land cover Thematic Mapper classification by CCRS was aggregated and reprocessed and are now available at multiple resolutions (10x5 minutes and 30 minutes).
ABSTRACT: Existing 30-m land cover Thematic Mapper classification by CCRS was aggregated and reprocessed and are now available at multiple resolutions (10x5 minutes and 30 minutes).
The BOREAS Follow-On FLX-01 team derived NEE, GEE, and Respiration using measured tower C02 flux measurements taken at the NSA-OBS site.  The data provided contain half-hourly measurements as well as 4 and 5 day binned data sets.  The derived data covers the period from March 1994 through the end of 1998.
The BOREAS Follow-On FLX-01 team derived NEE, GEE, and Respiration using measured tower C02 flux measurements taken at the NSA-OBS site.  The data provided contain half-hourly measurements as well as 4 and 5 day binned data sets.  The derived data covers the period from March 1994 through the end of 1998.
ABSTRACT: The BOREAS Follow-On FLX-01 team derived NEE, GEE, and Respiration using measured tower C02 flux measurements taken at the NSA-OBS site.  The data provided contain half-hourly measurements as well as 4 and 5 day binned data sets.  The derived data covers the period from March 1994 through the end of 1998.
The BOREAS Follow-On FLX-01 team collected tower flux, surface meteorological, and soil temperature data at the BOREAS NSA-OBS site continuously from March 1994 through December 1998.  Data from March 1994 through October 1996 are included in the BOREAS TF-03 effort while data from the end of October 1996 through December 1998 are included in the BOREAS Follow-on FLX-01 effort.
The BOREAS Follow-On FLX-01 team collected tower flux, surface meteorological, and soil temperature data at the BOREAS NSA-OBS site continuously from March 1994 through December 1998.  Data from March 1994 through October 1996 are included in the BOREAS TF-03 effort while data from the end of October 1996 through December 1998 are included in the BOREAS Follow-on FLX-01 effort.
ABSTRACT: The BOREAS Follow-On FLX-01 team collected tower flux, surface meteorological, and soil temperature data at the BOREAS NSA-OBS site continuously from March 1994 through December 1998.  Data from March 1994 through October 1996 are included in the BOREAS TF-03 effort while data from the end of October 1996 through December 1998 are included in the BOREAS Follow-on FLX-01 effort.
Calculations of area-averaged fluxes using extracted flux data from BORIS.
Calculations of area-averaged fluxes using extracted flux data from BORIS.
ABSTRACT: Calculations of area-averaged fluxes using extracted flux data from BORIS.
Tower flux and meteorological data were collected above 4 black spruce forest sites in the NSA that experienced stand-replacing wildfires in 1989,1981,1964 and 1930.  At each site, 4-6 weeks of data were collected during the peak growing season (June-September) in either 1999 or 2000.  Fluxes were measured using paired portable solar powered eddy flux systems. The data are part of an ongoing age sequence study that will result in year round eddy flux and meteorological measurements in seven sites that burned between 2 and 150 years ago.
Tower flux and meteorological data were collected above 4 black spruce forest sites in the NSA that experienced stand-replacing wildfires in 1989,1981,1964 and 1930.  At each site, 4-6 weeks of data were collected during the peak growing season (June-September) in either 1999 or 2000.  Fluxes were measured using paired portable solar powered eddy flux systems. The data are part of an ongoing age sequence study that will result in year round eddy flux and meteorological measurements in seven sites that burned between 2 and 150 years ago.
ABSTRACT: Tower flux and meteorological data were collected above 4 black spruce forest sites in the NSA that experienced stand-replacing wildfires in 1989,1981,1964 and 1930.  At each site, 4-6 weeks of data were collected during the peak growing season (June-September) in either 1999 or 2000.  Fluxes were measured using paired portable solar powered eddy flux systems. The data are part of an ongoing age sequence study that will result in year round eddy flux and meteorological measurements in seven sites that burned between 2 and 150 years ago.
The BOREAS RSS-14 team collected and processed several Level-1 GOES-7 and GOES-8 image data sets for 1994-1996, and GOES-7 Level-2 for 1994 over the BOREAS study region.  This data set contains shortwave and longwave radiation images at the surface and top of the atmosphere derived from collected GOES-8 data. These GOES-8 Level-2 data cover the period from 12-Feb-1996 to 22-Oct-1996.
The BOREAS RSS-14 team collected and processed several Level-1 GOES-7 and GOES-8 image data sets for 1994-1996, and GOES-7 Level-2 for 1994 over the BOREAS study region.  This data set contains shortwave and longwave radiation images at the surface and top of the atmosphere derived from collected GOES-8 data. These GOES-8 Level-2 data cover the period from 12-Feb-1996 to 22-Oct-1996.
ABSTRACT: The BOREAS RSS-14 team collected and processed several Level-1 GOES-7 and GOES-8 image data sets for 1994-1996, and GOES-7 Level-2 for 1994 over the BOREAS study region.  This data set contains shortwave and longwave radiation images at the surface and top of the atmosphere derived from collected GOES-8 data. These GOES-8 Level-2 data cover the period from 12-Feb-1996 to 22-Oct-1996.
A gridded data set has been assembled over the BOREAS hydro-meteorological study region that combines a precipitation data set based on a rain gauge network with precipitation estimates based on SSM/I satellite images.  The result is an hourly precipitation data set covering 122 consecutive days beginning on June 1, 1996.
A gridded data set has been assembled over the BOREAS hydro-meteorological study region that combines a precipitation data set based on a rain gauge network with precipitation estimates based on SSM/I satellite images.  The result is an hourly precipitation data set covering 122 consecutive days beginning on June 1, 1996.
ABSTRACT: A gridded data set has been assembled over the BOREAS hydro-meteorological study region that combines a precipitation data set based on a rain gauge network with precipitation estimates based on SSM/I satellite images.  The result is an hourly precipitation data set covering 122 consecutive days beginning on June 1, 1996.
Phase II and III gridded data sets have been generated by an objective analysis scheme using all of the surface meteorological station data over BOREAS region for 1994-1996. The meteorological variables in this data set are surface air pressure, air temperature, dew point temperature, wind speed, wind direction, precipitation, incoming solar (shortwave) radiation, and incoming infrared (longwave) radiation.
Phase II and III gridded data sets have been generated by an objective analysis scheme using all of the surface meteorological station data over BOREAS region for 1994-1996. The meteorological variables in this data set are surface air pressure, air temperature, dew point temperature, wind speed, wind direction, precipitation, incoming solar (shortwave) radiation, and incoming infrared (longwave) radiation.
ABSTRACT: Phase II and III gridded data sets have been generated by an objective analysis scheme using all of the surface meteorological station data over BOREAS region for 1994-1996. The meteorological variables in this data set are surface air pressure, air temperature, dew point temperature, wind speed, wind direction, precipitation, incoming solar (shortwave) radiation, and incoming infrared (longwave) radiation.
Point data developed from in situ observations at four flux tower sites were combined to produce continuous, above the canopy, meteorological forcing data sets. Meteorological variables of interest are surface air pressure, air temperature, dew point temperature, wind speed, wind direction, precipitation, incoming solar (shortwave) radiation, and incoming infrared (longwave) radiation.
Point data developed from in situ observations at four flux tower sites were combined to produce continuous, above the canopy, meteorological forcing data sets. Meteorological variables of interest are surface air pressure, air temperature, dew point temperature, wind speed, wind direction, precipitation, incoming solar (shortwave) radiation, and incoming infrared (longwave) radiation.
ABSTRACT: Point data developed from in situ observations at four flux tower sites were combined to produce continuous, above the canopy, meteorological forcing data sets. Meteorological variables of interest are surface air pressure, air temperature, dew point temperature, wind speed, wind direction, precipitation, incoming solar (shortwave) radiation, and incoming infrared (longwave) radiation.
As part of the BOREAS Follow-on, an extended period of data collection was supported in the NSA because of the continued efforts at the NSA-OBS site.  This data set contains near-surface meteorological data collected and averaged over 15 minute intervals from two sites in the NSA, the SRC tower at the Thompson airport (YTH) and a temporary walkup wooden tower at the Old Black Spruce (OBS) tower site.
As part of the BOREAS Follow-on, an extended period of data collection was supported in the NSA because of the continued efforts at the NSA-OBS site.  This data set contains near-surface meteorological data collected and averaged over 15 minute intervals from two sites in the NSA, the SRC tower at the Thompson airport (YTH) and a temporary walkup wooden tower at the Old Black Spruce (OBS) tower site.
ABSTRACT: As part of the BOREAS Follow-on, an extended period of data collection was supported in the NSA because of the continued efforts at the NSA-OBS site.  This data set contains near-surface meteorological data collected and averaged over 15 minute intervals from two sites in the NSA, the SRC tower at the Thompson airport (YTH) and a temporary walkup wooden tower at the Old Black Spruce (OBS) tower site.
The Phase 3 gridded data sets provided by HMet-01 on an hourly time step have been converted to averaged daily files by the MOD-01 group to reduce the size and number of files used for input to some of the carbon models.
The Phase 3 gridded data sets provided by HMet-01 on an hourly time step have been converted to averaged daily files by the MOD-01 group to reduce the size and number of files used for input to some of the carbon models.
ABSTRACT: The Phase 3 gridded data sets provided by HMet-01 on an hourly time step have been converted to averaged daily files by the MOD-01 group to reduce the size and number of files used for input to some of the carbon models.
Raster files created by processing original vector data.  Data include information of forest parameters for the BOREAS SSA MSA.
Raster files created by processing original vector data.  Data include information of forest parameters for the BOREAS SSA MSA.
ABSTRACT: Raster files created by processing original vector data.  Data include information of forest parameters for the BOREAS SSA MSA.
Processed by BORIS staff from the original vector data of species, crown closure, cutting class, and site classification/subtype into raster files.
Processed by BORIS staff from the original vector data of species, crown closure, cutting class, and site classification/subtype into raster files.
ABSTRACT: Processed by BORIS staff from the original vector data of species, crown closure, cutting class, and site classification/subtype into raster files.
Contains the hydraulic properties of the soil at each tower flux site determined by the HYD-01 science team.
Contains the hydraulic properties of the soil at each tower flux site determined by the HYD-01 science team.
ABSTRACT: Contains the hydraulic properties of the soil at each tower flux site determined by the HYD-01 science team.
Contains the under canopy precipitation data that was collected in 1994, 1995, 1996 and 1997 by the HYD-01 group at the various tower sites.
Contains the under canopy precipitation data that was collected in 1994, 1995, 1996 and 1997 by the HYD-01 group at the various tower sites.
ABSTRACT: Contains the under canopy precipitation data that was collected in 1994, 1995, 1996 and 1997 by the HYD-01 group at the various tower sites.
Contains the percent soil moisture by volume data that was collected by the HYD-01 group at the various tower sites.
Contains the percent soil moisture by volume data that was collected by the HYD-01 group at the various tower sites.
ABSTRACT: Contains the percent soil moisture by volume data that was collected by the HYD-01 group at the various tower sites.
Contains HYD-02 snow water equivalent derived from microwave measurements from aircraft.
Contains HYD-02 snow water equivalent derived from microwave measurements from aircraft.
ABSTRACT: Contains HYD-02 snow water equivalent derived from microwave measurements from aircraft.
The BOREAS HYD-03 team collected several data sets related to the hydrology of forested areas. This data set contains measurements of stem density from a variety of sites. Stem density measurements were made during the FFC-W 1996 in the SSA only using standard techniques. This study was undertaken to predict spatial distributions of energy transfer, snow properties important to the hydrology, remote sensing signatures, and transmissivity of gases through the snow and their relation to forests in boreal ecosystems.
The BOREAS HYD-03 team collected several data sets related to the hydrology of forested areas. This data set contains measurements of stem density from a variety of sites. Stem density measurements were made during the FFC-W 1996 in the SSA only using standard techniques. This study was undertaken to predict spatial distributions of energy transfer, snow properties important to the hydrology, remote sensing signatures, and transmissivity of gases through the snow and their relation to forests in boreal ecosystems.
ABSTRACT: The BOREAS HYD-03 team collected several data sets related to the hydrology of forested areas. This data set contains measurements of stem density from a variety of sites. Stem density measurements were made during the FFC-W 1996 in the SSA only using standard techniques. This study was undertaken to predict spatial distributions of energy transfer, snow properties important to the hydrology, remote sensing signatures, and transmissivity of gases through the snow and their relation to forests in boreal ecosystems.
The BOREAS HYD-03 team collected several data sets related to the hydrology of forested areas. This data set contains measurements of canopy density (closure) from a variety of sites. Canopy density measurements were made during the FFC-W and FFC-T 1994 in both the SSA and NSA using a forest densiometer. This study was undertaken to predict spatial distributions of energy transfer, snow properties important to the hydrology, remote sensing signatures, and transmissivity of gases through the snow and their relation to forests in boreal ecosystems.
The BOREAS HYD-03 team collected several data sets related to the hydrology of forested areas. This data set contains measurements of canopy density (closure) from a variety of sites. Canopy density measurements were made during the FFC-W and FFC-T 1994 in both the SSA and NSA using a forest densiometer. This study was undertaken to predict spatial distributions of energy transfer, snow properties important to the hydrology, remote sensing signatures, and transmissivity of gases through the snow and their relation to forests in boreal ecosystems.
ABSTRACT: The BOREAS HYD-03 team collected several data sets related to the hydrology of forested areas. This data set contains measurements of canopy density (closure) from a variety of sites. Canopy density measurements were made during the FFC-W and FFC-T 1994 in both the SSA and NSA using a forest densiometer. This study was undertaken to predict spatial distributions of energy transfer, snow properties important to the hydrology, remote sensing signatures, and transmissivity of gases through the snow and their relation to forests in boreal ecosystems.
This table contains snow depth measurements made by HYD-3 in 1996.
This table contains snow depth measurements made by HYD-3 in 1996.
ABSTRACT: This table contains snow depth measurements made by HYD-3 in 1996.
This table contains snow pit measurements made by HYD-3 in 1996.
This table contains snow pit measurements made by HYD-3 in 1996.
ABSTRACT: This table contains snow pit measurements made by HYD-3 in 1996.
This is a snow temperature table for HYD03. The snow temperature is given for various snow heights at various sites.
This is a snow temperature table for HYD03. The snow temperature is given for various snow heights at various sites.
ABSTRACT: This is a snow temperature table for HYD03. The snow temperature is given for various snow heights at various sites.
Contains the snow water equivalent that was calculated from measurements of snow pack density and snow depth.
Contains the snow water equivalent that was calculated from measurements of snow pack density and snow depth.
ABSTRACT: Contains the snow water equivalent that was calculated from measurements of snow pack density and snow depth.
Contains the snow water equivalent, snow_depth, and snow density measurements made by HYD-3 with the Canadian snow sampler in 1996.
Contains the snow water equivalent, snow_depth, and snow density measurements made by HYD-3 with the Canadian snow sampler in 1996.
ABSTRACT: Contains the snow water equivalent, snow_depth, and snow density measurements made by HYD-3 with the Canadian snow sampler in 1996.
The BOREAS HYD-03 team collected several data sets related to the hydrology of forested areas. This data set contains measurements of tree diameter at breast height (DBH) from a variety of sites. This study was undertaken to predict spatial distributions of energy transfer, snow properties important to the hydrology, remote sensing signatures, and transmissivity of gases through the snow and their relation to forests in boreal ecosystems.
The BOREAS HYD-03 team collected several data sets related to the hydrology of forested areas. This data set contains measurements of tree diameter at breast height (DBH) from a variety of sites. This study was undertaken to predict spatial distributions of energy transfer, snow properties important to the hydrology, remote sensing signatures, and transmissivity of gases through the snow and their relation to forests in boreal ecosystems.
ABSTRACT: The BOREAS HYD-03 team collected several data sets related to the hydrology of forested areas. This data set contains measurements of tree diameter at breast height (DBH) from a variety of sites. This study was undertaken to predict spatial distributions of energy transfer, snow properties important to the hydrology, remote sensing signatures, and transmissivity of gases through the snow and their relation to forests in boreal ecosystems.
This table contains the sub-canopy meteorological data collected by HYD-3.
This table contains the sub-canopy meteorological data collected by HYD-3.
ABSTRACT: This table contains the sub-canopy meteorological data collected by HYD-3.
This table contains the sub-canopy radiation data collected by HYD-3.
This table contains the sub-canopy radiation data collected by HYD-3.
ABSTRACT: This table contains the sub-canopy radiation data collected by HYD-3.
Contains the areal snow survey data collected by HYD-04.  The flight line numbers that are included correspond to the flight lines from HYD-06 measurements.
Contains the areal snow survey data collected by HYD-04.  The flight line numbers that are included correspond to the flight lines from HYD-06 measurements.
ABSTRACT: Contains the areal snow survey data collected by HYD-04.  The flight line numbers that are included correspond to the flight lines from HYD-06 measurements.
Contains the standard snow course data collected at various sites in the NSA and SSA by HYD-04.
Contains the standard snow course data collected at various sites in the NSA and SSA by HYD-04.
ABSTRACT: Contains the standard snow course data collected at various sites in the NSA and SSA by HYD-04.
Contains the HYD-05 flux, and meteorological measurements from Bear Trap Forest, Saskatchewan in the winter of 1994. Contains the HYD-05 flux, meteorological, and infrared thermometer measurements from Namekus Lake, Saskatchewan in the winters of 1994 and
Contains the HYD-05 flux, and meteorological measurements from Bear Trap Forest, Saskatchewan in the winter of 1994. Contains the HYD-05 flux, meteorological, and infrared thermometer measurements from Namekus Lake, Saskatchewan in the winters of 1994 and
ABSTRACT: Contains the HYD-05 flux, and meteorological measurements from Bear Trap Forest, Saskatchewan in the winter of 1994. Contains the HYD-05 flux, meteorological, and infrared thermometer measurements from Namekus Lake, Saskatchewan in the winters of 1994 and
Contains the aircraft estimates of soil moisture measured by the gamma ray instrument from HYD06.
Contains the aircraft estimates of soil moisture measured by the gamma ray instrument from HYD06.
ABSTRACT: Contains the aircraft estimates of soil moisture measured by the gamma ray instrument from HYD06.
Contains the measurements of soil moisture that were made at various sites on the ground by HYD-06.
Contains the measurements of soil moisture that were made at various sites on the ground by HYD-06.
ABSTRACT: Contains the measurements of soil moisture that were made at various sites on the ground by HYD-06.
This table contains the measurements of water content of the moss/humus layer that were made at various sites on the ground by HYD-06.
This table contains the measurements of water content of the moss/humus layer that were made at various sites on the ground by HYD-06.
ABSTRACT: This table contains the measurements of water content of the moss/humus layer that were made at various sites on the ground by HYD-06.
Contains the gravimetric moss data collected by HYD-08 at the Black Spruce and Joey Lake sites.  It contains the weights of moss turves under two different conditions.
Contains the gravimetric moss data collected by HYD-08 at the Black Spruce and Joey Lake sites.  It contains the weights of moss turves under two different conditions.
ABSTRACT: Contains the gravimetric moss data collected by HYD-08 at the Black Spruce and Joey Lake sites.  It contains the weights of moss turves under two different conditions.
Contains the HYD-08 weights of the dried moss samples. These weights do not include the weight of the tray in which the sample was contained. Contains the HYD-08 moss measurements of water equivalent made at the moss lysimeter sites near the SSA-OBS in 19
Contains the HYD-08 weights of the dried moss samples. These weights do not include the weight of the tray in which the sample was contained. Contains the HYD-08 moss measurements of water equivalent made at the moss lysimeter sites near the SSA-OBS in 19
ABSTRACT: Contains the HYD-08 weights of the dried moss samples. These weights do not include the weight of the tray in which the sample was contained. Contains the HYD-08 moss measurements of water equivalent made at the moss lysimeter sites near the SSA-OBS in 19
Contains the HYD-08 mean gross precipitation measurements made at 2 gauges at the SSA-OBS.
Contains the HYD-08 mean gross precipitation measurements made at 2 gauges at the SSA-OBS.
ABSTRACT: Contains the HYD-08 mean gross precipitation measurements made at 2 gauges at the SSA-OBS.
DEMs produced from digitized contours at a cell resolution of 100 meters.
DEMs produced from digitized contours at a cell resolution of 100 meters.
ABSTRACT: DEMs produced from digitized contours at a cell resolution of 100 meters.
Contains the moss lysimeter measurements made by HYD-08.
Contains the moss lysimeter measurements made by HYD-08.
ABSTRACT: Contains the moss lysimeter measurements made by HYD-08.
The BOREAS HYD-09 team collected data on precipitation and streamflow over portions of the NSA and SSA.  This data set contains Cartesian maps of rain accumulation for 1-hour and daily periods during the summer of 1994 over the SSA only (not the full view of the radar)
The BOREAS HYD-09 team collected data on precipitation and streamflow over portions of the NSA and SSA.  This data set contains Cartesian maps of rain accumulation for 1-hour and daily periods during the summer of 1994 over the SSA only (not the full view of the radar)
ABSTRACT: The BOREAS HYD-09 team collected data on precipitation and streamflow over portions of the NSA and SSA.  This data set contains Cartesian maps of rain accumulation for 1-hour and daily periods during the summer of 1994 over the SSA only (not the full view of the radar)
Contains the stream gauge data that was collected by the HYD09 group.
Contains the stream gauge data that was collected by the HYD09 group.
ABSTRACT: Contains the stream gauge data that was collected by the HYD09 group.
A set of MSS images from Landsat satellites 1, 2, 4 and 5 covering the dates of 21-Aug-1972 to 05-Sep-1988.
A set of MSS images from Landsat satellites 1, 2, 4 and 5 covering the dates of 21-Aug-1972 to 05-Sep-1988.
ABSTRACT: A set of MSS images from Landsat satellites 1, 2, 4 and 5 covering the dates of 21-Aug-1972 to 05-Sep-1988.
The level-0 AOCI imagery, along with the other remotely sensed images, was collected to provide spatially extensive information about radiant energy over the primary BOREAS study areas.  The AOCI was the only remote sensing instrument flown with wavelength bands specific to the investigation of various aquatic parameters such as chlorophyll content and turbidity.
The level-0 AOCI imagery, along with the other remotely sensed images, was collected to provide spatially extensive information about radiant energy over the primary BOREAS study areas.  The AOCI was the only remote sensing instrument flown with wavelength bands specific to the investigation of various aquatic parameters such as chlorophyll content and turbidity.
ABSTRACT: The level-0 AOCI imagery, along with the other remotely sensed images, was collected to provide spatially extensive information about radiant energy over the primary BOREAS study areas.  The AOCI was the only remote sensing instrument flown with wavelength bands specific to the investigation of various aquatic parameters such as chlorophyll content and turbidity.
The TIMS imagery, along with the other remotely sensed images, was collected to provide spatially extensive information over the primary study areas.  This information includes detailed land cover, biophysical parameter maps such as fraction of photosynthetically active radiation (fPAR), leaf area index (LAI), and surface thermal properties.
The TIMS imagery, along with the other remotely sensed images, was collected to provide spatially extensive information over the primary study areas.  This information includes detailed land cover, biophysical parameter maps such as fraction of photosynthetically active radiation (fPAR), leaf area index (LAI), and surface thermal properties.
ABSTRACT: The TIMS imagery, along with the other remotely sensed images, was collected to provide spatially extensive information over the primary study areas.  This information includes detailed land cover, biophysical parameter maps such as fraction of photosynthetically active radiation (fPAR), leaf area index (LAI), and surface thermal properties.
MAS images, along with the other remotely sensed data, were collected to provide spatially extensive information over the primary study areas. This information includes detailed land cover and biophysical parameter maps such as fPAR (fraction of Photosynthetically Active Radiation) and LAI (Leaf Area Index).
MAS images, along with the other remotely sensed data, were collected to provide spatially extensive information over the primary study areas. This information includes detailed land cover and biophysical parameter maps such as fPAR (fraction of Photosynthetically Active Radiation) and LAI (Leaf Area Index).
ABSTRACT: MAS images, along with the other remotely sensed data, were collected to provide spatially extensive information over the primary study areas. This information includes detailed land cover and biophysical parameter maps such as fPAR (fraction of Photosynthetically Active Radiation) and LAI (Leaf Area Index).
TIMS imagery, along with other aircraft images, was collected to provide spatially extensive information over the primary study areas.  The level-1B TIMS images cover the time periods of 16-Apr-1994 to 20-Apr-1994 and 06-Sep-1994 to 17-Sep-1994.
TIMS imagery, along with other aircraft images, was collected to provide spatially extensive information over the primary study areas.  The level-1B TIMS images cover the time periods of 16-Apr-1994 to 20-Apr-1994 and 06-Sep-1994 to 17-Sep-1994.
ABSTRACT: TIMS imagery, along with other aircraft images, was collected to provide spatially extensive information over the primary study areas.  The level-1B TIMS images cover the time periods of 16-Apr-1994 to 20-Apr-1994 and 06-Sep-1994 to 17-Sep-1994.
MAS images, along with other remotely sensed data, were collected to provide spatially extensive information over the primary study areas. This information includes biophysical parameter maps such as surface reflectance and temperature. Collection of the MAS images occurred over the study areas during the 1994 field campaigns.
MAS images, along with other remotely sensed data, were collected to provide spatially extensive information over the primary study areas. This information includes biophysical parameter maps such as surface reflectance and temperature. Collection of the MAS images occurred over the study areas during the 1994 field campaigns.
ABSTRACT: MAS images, along with other remotely sensed data, were collected to provide spatially extensive information over the primary study areas. This information includes biophysical parameter maps such as surface reflectance and temperature. Collection of the MAS images occurred over the study areas during the 1994 field campaigns.
Data acquired from the AVHRR instrument on the NOAA-9, -11, -12, and -14 satellites were processed and archived. A few winter acquisitions are available, but the archive contains primarily growing season imagery.  These gridded, at-sensor radiance image data cover the period of 30-Jan-1994 to 18-Sep-1996.  Geographically, the data cover the entire 1000 km x 1000 km BOREAS Region.
Data acquired from the AVHRR instrument on the NOAA-9, -11, -12, and -14 satellites were processed and archived. A few winter acquisitions are available, but the archive contains primarily growing season imagery.  These gridded, at-sensor radiance image data cover the period of 30-Jan-1994 to 18-Sep-1996.  Geographically, the data cover the entire 1000 km x 1000 km BOREAS Region.
ABSTRACT: Data acquired from the AVHRR instrument on the NOAA-9, -11, -12, and -14 satellites were processed and archived. A few winter acquisitions are available, but the archive contains primarily growing season imagery.  These gridded, at-sensor radiance image data cover the period of 30-Jan-1994 to 18-Sep-1996.  Geographically, the data cover the entire 1000 km x 1000 km BOREAS Region.
These AVHRR level-4b data are gridded, 10-day composites of at-sensor radiance values produced from sets of single-day images.  Temporally, the 10-day compositing periods begin 11-Apr-1994 and end 10-Sep-1994.  Spatially, the data cover the entire BOREAS region.
These AVHRR level-4b data are gridded, 10-day composites of at-sensor radiance values produced from sets of single-day images.  Temporally, the 10-day compositing periods begin 11-Apr-1994 and end 10-Sep-1994.  Spatially, the data cover the entire BOREAS region.
ABSTRACT: These AVHRR level-4b data are gridded, 10-day composites of at-sensor radiance values produced from sets of single-day images.  Temporally, the 10-day compositing periods begin 11-Apr-1994 and end 10-Sep-1994.  Spatially, the data cover the entire BOREAS region.
These AVHRR level-4c data are gridded, 10-day composites of surface parameters produced from sets of single-day images.  Temporally, the 10-day compositing periods begin 11-Apr-1994 and end 10-Sep-1994.  Spatially, the data cover the entire BOREAS region.
These AVHRR level-4c data are gridded, 10-day composites of surface parameters produced from sets of single-day images.  Temporally, the 10-day compositing periods begin 11-Apr-1994 and end 10-Sep-1994.  Spatially, the data cover the entire BOREAS region.
ABSTRACT: These AVHRR level-4c data are gridded, 10-day composites of surface parameters produced from sets of single-day images.  Temporally, the 10-day compositing periods begin 11-Apr-1994 and end 10-Sep-1994.  Spatially, the data cover the entire BOREAS region.
The NS001 TMS imagery, along with the other remotely sensed images, was collected in order to provide spatially extensive information over the primary study areas.  This information includes detailed land cover and biophysical parameter maps such as fPAR and LAI.  Data collections occurred over the study areas during the 1994 field campaigns.
The NS001 TMS imagery, along with the other remotely sensed images, was collected in order to provide spatially extensive information over the primary study areas.  This information includes detailed land cover and biophysical parameter maps such as fPAR and LAI.  Data collections occurred over the study areas during the 1994 field campaigns.
ABSTRACT: The NS001 TMS imagery, along with the other remotely sensed images, was collected in order to provide spatially extensive information over the primary study areas.  This information includes detailed land cover and biophysical parameter maps such as fPAR and LAI.  Data collections occurred over the study areas during the 1994 field campaigns.
This information includes detailed land cover and biophysical parameter maps such as fPAR and LAI.  Collection of the NS001 images occurred over the study areas during the 1994 field campaigns.  The Level-2 NS001 data are atmospherically corrected versions of some of the best original NS001 imagery and cover the dates of 19-Apr-1994, 07-Jun-1994, 21-Jul-1994, 08-Aug-1994, and 16-Sep-1994.
This information includes detailed land cover and biophysical parameter maps such as fPAR and LAI.  Collection of the NS001 images occurred over the study areas during the 1994 field campaigns.  The Level-2 NS001 data are atmospherically corrected versions of some of the best original NS001 imagery and cover the dates of 19-Apr-1994, 07-Jun-1994, 21-Jul-1994, 08-Aug-1994, and 16-Sep-1994.
ABSTRACT: This information includes detailed land cover and biophysical parameter maps such as fPAR and LAI.  Collection of the NS001 images occurred over the study areas during the 1994 field campaigns.  The Level-2 NS001 data are atmospherically corrected versions of some of the best original NS001 imagery and cover the dates of 19-Apr-1994, 07-Jun-1994, 21-Jul-1994, 08-Aug-1994, and 16-Sep-1994.
Detailed canopy, understory, and ground cover, height, density, and condition information for PANP in the western part of the BOREAS SSA in vector form.
Detailed canopy, understory, and ground cover, height, density, and condition information for PANP in the western part of the BOREAS SSA in vector form.
ABSTRACT: Detailed canopy, understory, and ground cover, height, density, and condition information for PANP in the western part of the BOREAS SSA in vector form.
Based on the GTOPO30 DEM produced by the USGS EDC.  The BOREAS region was extracted from the GTOPO30 data and reprojected by BOREAS staff into the AEAC projection.
Based on the GTOPO30 DEM produced by the USGS EDC.  The BOREAS region was extracted from the GTOPO30 data and reprojected by BOREAS staff into the AEAC projection.
ABSTRACT: Based on the GTOPO30 DEM produced by the USGS EDC.  The BOREAS region was extracted from the GTOPO30 data and reprojected by BOREAS staff into the AEAC projection.
This data set was gridded by BORIS staff from a vector data set received from Canadian Soil Information System (CanSIS).  Data were gridded into the Albers Equal-Area Conic (AEAC) projection.
This data set was gridded by BORIS staff from a vector data set received from Canadian Soil Information System (CanSIS).  Data were gridded into the Albers Equal-Area Conic (AEAC) projection.
ABSTRACT: This data set was gridded by BORIS staff from a vector data set received from Canadian Soil Information System (CanSIS).  Data were gridded into the Albers Equal-Area Conic (AEAC) projection.
Contains the RSS-01 PARABOLA data sets averaged by 15 degree common view zenith and azimuth angles collected during the BOREAS field campaigns in 1994.
Contains the RSS-01 PARABOLA data sets averaged by 15 degree common view zenith and azimuth angles collected during the BOREAS field campaigns in 1994.
ABSTRACT: Contains the RSS-01 PARABOLA data sets averaged by 15 degree common view zenith and azimuth angles collected during the BOREAS field campaigns in 1994.
Contains calculated bidirectional reflectance factor means derived from extractions of C130-based ASAS measurements made during BOREAS.
Contains calculated bidirectional reflectance factor means derived from extractions of C130-based ASAS measurements made during BOREAS.
ABSTRACT: Contains calculated bidirectional reflectance factor means derived from extractions of C130-based ASAS measurements made during BOREAS.
The BOREAS RSS-02 team used the ASAS instrument, mounted on the NASA C-130 aircraft, to create at-sensor radiance images of various sites as a function of spectral wavelength, view geometry (combinations of view zenith angle, view azimuth angle, solar zenith angle, and solar azimuth angle), and altitude.  The level-1b ASAS images of the BOREAS study areas were collected from April to September 1994 and March to July 1996.
The BOREAS RSS-02 team used the ASAS instrument, mounted on the NASA C-130 aircraft, to create at-sensor radiance images of various sites as a function of spectral wavelength, view geometry (combinations of view zenith angle, view azimuth angle, solar zenith angle, and solar azimuth angle), and altitude.  The level-1b ASAS images of the BOREAS study areas were collected from April to September 1994 and March to July 1996.
ABSTRACT: The BOREAS RSS-02 team used the ASAS instrument, mounted on the NASA C-130 aircraft, to create at-sensor radiance images of various sites as a function of spectral wavelength, view geometry (combinations of view zenith angle, view azimuth angle, solar zenith angle, and solar azimuth angle), and altitude.  The level-1b ASAS images of the BOREAS study areas were collected from April to September 1994 and March to July 1996.
Contains Helicopter-based measurements of atmospheric conditions acquired during the BOReal Ecosystem-Atmosphere Study (BOREAS) processed to estimates of aerosol optical thickness and atmospheric water vapor.
Contains Helicopter-based measurements of atmospheric conditions acquired during the BOReal Ecosystem-Atmosphere Study (BOREAS) processed to estimates of aerosol optical thickness and atmospheric water vapor.
ABSTRACT: Contains Helicopter-based measurements of atmospheric conditions acquired during the BOReal Ecosystem-Atmosphere Study (BOREAS) processed to estimates of aerosol optical thickness and atmospheric water vapor.
Contains site averaged reflected radiance and reflectance values from barnes MMR taken from helicopter.
Contains site averaged reflected radiance and reflectance values from barnes MMR taken from helicopter.
ABSTRACT: Contains site averaged reflected radiance and reflectance values from barnes MMR taken from helicopter.
Contains helicopter SE-590 measurements taken by RSS-03.
Contains helicopter SE-590 measurements taken by RSS-03.
ABSTRACT: Contains helicopter SE-590 measurements taken by RSS-03.
Contains sample values and mean values of needle biochemistry data taken at jack pine sites in the SSA.
Contains sample values and mean values of needle biochemistry data taken at jack pine sites in the SSA.
ABSTRACT: Contains sample values and mean values of needle biochemistry data taken at jack pine sites in the SSA.
Contains Decagon Ceptometer estimates of LAI and fPAR. Contains LI-COR LAI-2000 estimates of leaf area index and mean tip angle.
Contains Decagon Ceptometer estimates of LAI and fPAR. Contains LI-COR LAI-2000 estimates of leaf area index and mean tip angle.
ABSTRACT: Contains Decagon Ceptometer estimates of LAI and fPAR. Contains LI-COR LAI-2000 estimates of leaf area index and mean tip angle.
Contains daily green fpar, LAI, needle-to-shoot area ratio, clumping index, PAI, and foliage-to-total area index for tower and auxiliary sites for IFC's 1, 2, and 3. Also contains effective LAI measurements acquired along RSS-07 transects in the BOREAS study area.
Contains daily green fpar, LAI, needle-to-shoot area ratio, clumping index, PAI, and foliage-to-total area index for tower and auxiliary sites for IFC's 1, 2, and 3. Also contains effective LAI measurements acquired along RSS-07 transects in the BOREAS study area.
ABSTRACT: Contains daily green fpar, LAI, needle-to-shoot area ratio, clumping index, PAI, and foliage-to-total area index for tower and auxiliary sites for IFC's 1, 2, and 3. Also contains effective LAI measurements acquired along RSS-07 transects in the BOREAS study area.
The BOREAS RSS-07 team used Landsat TM images processed at CCRS to produce images of LAI for the BOREAS study areas.  Two images acquired on June 6 and August 9, 1991 were used for the SSA, and one image acquired on June 9, 1994 was used for the NSA.  The LAI images are based on ground measurements and Landsat TM RSR images.
The BOREAS RSS-07 team used Landsat TM images processed at CCRS to produce images of LAI for the BOREAS study areas.  Two images acquired on June 6 and August 9, 1991 were used for the SSA, and one image acquired on June 9, 1994 was used for the NSA.  The LAI images are based on ground measurements and Landsat TM RSR images.
ABSTRACT: The BOREAS RSS-07 team used Landsat TM images processed at CCRS to produce images of LAI for the BOREAS study areas.  Two images acquired on June 6 and August 9, 1991 were used for the SSA, and one image acquired on June 9, 1994 was used for the NSA.  The LAI images are based on ground measurements and Landsat TM RSR images.
The BOREAS RSS-07 team collected various data sets to develop and validate an algorithm to allow the retrieval of the spatial distribution of LAI from remotely sensed images.  Ground measurements of LAI and FPAR absorbed by the plant canopy were made using the LAI-2000 and TRAC optical instruments during focused periods from 09-AUG-1993 to 19-SEP-1994.
The BOREAS RSS-07 team collected various data sets to develop and validate an algorithm to allow the retrieval of the spatial distribution of LAI from remotely sensed images.  Ground measurements of LAI and FPAR absorbed by the plant canopy were made using the LAI-2000 and TRAC optical instruments during focused periods from 09-AUG-1993 to 19-SEP-1994.
ABSTRACT: The BOREAS RSS-07 team collected various data sets to develop and validate an algorithm to allow the retrieval of the spatial distribution of LAI from remotely sensed images.  Ground measurements of LAI and FPAR absorbed by the plant canopy were made using the LAI-2000 and TRAC optical instruments during focused periods from 09-AUG-1993 to 19-SEP-1994.
BIOME-BGC is a general ecosystem process model designed to simulate biogeochemical and hydrologic processes across multiple scales.  BIOME-BGC is used to estimate daily water and carbon budgets for the BOREAS tower flux sites for 1994.
BIOME-BGC is a general ecosystem process model designed to simulate biogeochemical and hydrologic processes across multiple scales.  BIOME-BGC is used to estimate daily water and carbon budgets for the BOREAS tower flux sites for 1994.
ABSTRACT: BIOME-BGC is a general ecosystem process model designed to simulate biogeochemical and hydrologic processes across multiple scales.  BIOME-BGC is used to estimate daily water and carbon budgets for the BOREAS tower flux sites for 1994.
Derived maps of landcover type and crown and stem biomass as model inputs to determine annual evapotranspiration, gross primary production, autotrophic respiration and net primary productivity within the BOREAS SSA-MSA, at a 30 m spatial resolution.  Mode
Derived maps of landcover type and crown and stem biomass as model inputs to determine annual evapotranspiration, gross primary production, autotrophic respiration and net primary productivity within the BOREAS SSA-MSA, at a 30 m spatial resolution.  Mode
ABSTRACT: Derived maps of landcover type and crown and stem biomass as model inputs to determine annual evapotranspiration, gross primary production, autotrophic respiration and net primary productivity within the BOREAS SSA-MSA, at a 30 m spatial resolution.  Mode
Ground BRDF measurements were acquired by the Remote Sensing Science (RSS)-08 team to aid in the development of advanced spectral vegetation indices.  The RSS-08 team measured reflectances at the double-scaffold towers in the Southern Study Area (SSA) Old Black Spruce (OBS) and Old Aspend (OA) sites during IFC-3 in 1994.
Ground BRDF measurements were acquired by the Remote Sensing Science (RSS)-08 team to aid in the development of advanced spectral vegetation indices.  The RSS-08 team measured reflectances at the double-scaffold towers in the Southern Study Area (SSA) Old Black Spruce (OBS) and Old Aspend (OA) sites during IFC-3 in 1994.
ABSTRACT: Ground BRDF measurements were acquired by the Remote Sensing Science (RSS)-08 team to aid in the development of advanced spectral vegetation indices.  The RSS-08 team measured reflectances at the double-scaffold towers in the Southern Study Area (SSA) Old Black Spruce (OBS) and Old Aspend (OA) sites during IFC-3 in 1994.
The BOREAS RSS-08 team utilized Landsat TM images to perform mapping of snow extent over the SSA.  This data set consists of two Landsat TM images which were used to determine the snow-covered pixels over the BOREAS SSA on 18-Jan-1993 and on 06-Feb-1994.
The BOREAS RSS-08 team utilized Landsat TM images to perform mapping of snow extent over the SSA.  This data set consists of two Landsat TM images which were used to determine the snow-covered pixels over the BOREAS SSA on 18-Jan-1993 and on 06-Feb-1994.
ABSTRACT: The BOREAS RSS-08 team utilized Landsat TM images to perform mapping of snow extent over the SSA.  This data set consists of two Landsat TM images which were used to determine the snow-covered pixels over the BOREAS SSA on 18-Jan-1993 and on 06-Feb-1994.
The RSS08 team acquired stereo photography from the double-scaffold towers at the Southern Study Area (SSA), Old Black Spruce (OBS), Old Aspen (OA), and Old Jack Pine (OJP) sites during IFC-3 in 1994.  The imagery of the canopy was taken from various perspectives.  The RSS08 team also measured BRDF at the SSA-OA and -OBS sites during IFC-3.
The RSS08 team acquired stereo photography from the double-scaffold towers at the Southern Study Area (SSA), Old Black Spruce (OBS), Old Aspen (OA), and Old Jack Pine (OJP) sites during IFC-3 in 1994.  The imagery of the canopy was taken from various perspectives.  The RSS08 team also measured BRDF at the SSA-OA and -OBS sites during IFC-3.
ABSTRACT: The RSS08 team acquired stereo photography from the double-scaffold towers at the Southern Study Area (SSA), Old Black Spruce (OBS), Old Aspen (OA), and Old Jack Pine (OJP) sites during IFC-3 in 1994.  The imagery of the canopy was taken from various perspectives.  The RSS08 team also measured BRDF at the SSA-OA and -OBS sites during IFC-3.
The BOREAS RSS-10 team investigated the magnitude of daily, seasonal, and yearly variations of PAR from ground and satellite observations.  This data set contains satellite estimates of surface-incident photosynthetically active radiation (PAR, 400-700 nm, MJ m-2) at 1 degree spatial resolution. The spatial coverage is circumpolar from latitudes of 41 to 66 degrees N latitude.
The BOREAS RSS-10 team investigated the magnitude of daily, seasonal, and yearly variations of PAR from ground and satellite observations.  This data set contains satellite estimates of surface-incident photosynthetically active radiation (PAR, 400-700 nm, MJ m-2) at 1 degree spatial resolution. The spatial coverage is circumpolar from latitudes of 41 to 66 degrees N latitude.
ABSTRACT: The BOREAS RSS-10 team investigated the magnitude of daily, seasonal, and yearly variations of PAR from ground and satellite observations.  This data set contains satellite estimates of surface-incident photosynthetically active radiation (PAR, 400-700 nm, MJ m-2) at 1 degree spatial resolution. The spatial coverage is circumpolar from latitudes of 41 to 66 degrees N latitude.
This table contains measurements from the automatic sun photometers operated by RSS-11 (Markham).
This table contains measurements from the automatic sun photometers operated by RSS-11 (Markham).
ABSTRACT: This table contains measurements from the automatic sun photometers operated by RSS-11 (Markham).
Contains measurements from the airborne auto tracking sun photometers on board the NASA Ames C-130 aircraft, operated by RSS12 (Wrigley).
Contains measurements from the airborne auto tracking sun photometers on board the NASA Ames C-130 aircraft, operated by RSS12 (Wrigley).
ABSTRACT: Contains measurements from the airborne auto tracking sun photometers on board the NASA Ames C-130 aircraft, operated by RSS12 (Wrigley).
This table contains measurements from the ground sun photometers operated by RSS12 (Wrigley).
This table contains measurements from the ground sun photometers operated by RSS12 (Wrigley).
ABSTRACT: This table contains measurements from the ground sun photometers operated by RSS12 (Wrigley).
The level-1 BOREAS GOES-7 image data were collected by Remote Sensing Science Team 14 (RSS-14) personnel at Florida State University (FSU) and delivered to BORIS. The data cover the period of 01-Jan-1994 through 08-Jul-1995, with partial to complete coverage on the majority of the days. The data include three bands with eight-bit pixel values.
The level-1 BOREAS GOES-7 image data were collected by Remote Sensing Science Team 14 (RSS-14) personnel at Florida State University (FSU) and delivered to BORIS. The data cover the period of 01-Jan-1994 through 08-Jul-1995, with partial to complete coverage on the majority of the days. The data include three bands with eight-bit pixel values.
ABSTRACT: The level-1 BOREAS GOES-7 image data were collected by Remote Sensing Science Team 14 (RSS-14) personnel at Florida State University (FSU) and delivered to BORIS. The data cover the period of 01-Jan-1994 through 08-Jul-1995, with partial to complete coverage on the majority of the days. The data include three bands with eight-bit pixel values.
The level-1a BOREAS GOES-7 image data was collected by Remote Sensing Science Team-14 (RSS-14) personnel at Florida State University and processed to level-1a products by BORIS personnel.  The data cover the period 01-JAN-1994 through 08-JUL-1995.
The level-1a BOREAS GOES-7 image data was collected by Remote Sensing Science Team-14 (RSS-14) personnel at Florida State University and processed to level-1a products by BORIS personnel.  The data cover the period 01-JAN-1994 through 08-JUL-1995.
ABSTRACT: The level-1a BOREAS GOES-7 image data was collected by Remote Sensing Science Team-14 (RSS-14) personnel at Florida State University and processed to level-1a products by BORIS personnel.  The data cover the period 01-JAN-1994 through 08-JUL-1995.
This data set contains images of shortwave and longwave radiation at the surface and top of the atmosphere derived from collected GOES-7 data.  The data cover the time period of 05-Feb-1994 to 20-Sep-1994.  The images missing from the temporal series were zero-filled to create a consistent sequence of files.
This data set contains images of shortwave and longwave radiation at the surface and top of the atmosphere derived from collected GOES-7 data.  The data cover the time period of 05-Feb-1994 to 20-Sep-1994.  The images missing from the temporal series were zero-filled to create a consistent sequence of files.
ABSTRACT: This data set contains images of shortwave and longwave radiation at the surface and top of the atmosphere derived from collected GOES-7 data.  The data cover the time period of 05-Feb-1994 to 20-Sep-1994.  The images missing from the temporal series were zero-filled to create a consistent sequence of files.
The level-1 BOREAS GOES-8 images are raw data values collected by RSS-14 personnel at FSU and delivered to BORIS.  The data cover 14-Jul-1995 to 21-Sep-1995 and 01-Jan-1996 to 03-Oct- 1996. The data start out containing three 8-bit spectral bands and end up containing five 10-bit spectral bands.
The level-1 BOREAS GOES-8 images are raw data values collected by RSS-14 personnel at FSU and delivered to BORIS.  The data cover 14-Jul-1995 to 21-Sep-1995 and 01-Jan-1996 to 03-Oct- 1996. The data start out containing three 8-bit spectral bands and end up containing five 10-bit spectral bands.
ABSTRACT: The level-1 BOREAS GOES-8 images are raw data values collected by RSS-14 personnel at FSU and delivered to BORIS.  The data cover 14-Jul-1995 to 21-Sep-1995 and 01-Jan-1996 to 03-Oct- 1996. The data start out containing three 8-bit spectral bands and end up containing five 10-bit spectral bands.
The level-1a GOES-8 images cover 14-Jul-1995 to 21-Sep-1995 and 12-Feb-1996 to 03-Oct-1996. The data start out as three bands with 8-bit pixel values and end up as five bands with 10-bit pixel values. The differences between the level-1 and level-1a GOES-8 data are the formatting and packaging of the data. The images missing from the temporal series of level-1 GOES-8 images were zero-filled to create files consistent in size and format.
The level-1a GOES-8 images cover 14-Jul-1995 to 21-Sep-1995 and 12-Feb-1996 to 03-Oct-1996. The data start out as three bands with 8-bit pixel values and end up as five bands with 10-bit pixel values. The differences between the level-1 and level-1a GOES-8 data are the formatting and packaging of the data. The images missing from the temporal series of level-1 GOES-8 images were zero-filled to create files consistent in size and format.
ABSTRACT: The level-1a GOES-8 images cover 14-Jul-1995 to 21-Sep-1995 and 12-Feb-1996 to 03-Oct-1996. The data start out as three bands with 8-bit pixel values and end up as five bands with 10-bit pixel values. The differences between the level-1 and level-1a GOES-8 data are the formatting and packaging of the data. The images missing from the temporal series of level-1 GOES-8 images were zero-filled to create files consistent in size and format.
The BOREAS RSS-14 team collected and processed GOES-7 and -8 images of the BOREAS region as part of their effort to characterize the incoming, reflected and emitted radiation at regional scales. This data set contains surface radiation parameters, such as net radiation and net solar radiation, that have been interpolated from GOES-7 images and AMS data onto the standard BOREAS mapping grid at a resolution of 5 km N-S and E-W.
The BOREAS RSS-14 team collected and processed GOES-7 and -8 images of the BOREAS region as part of their effort to characterize the incoming, reflected and emitted radiation at regional scales. This data set contains surface radiation parameters, such as net radiation and net solar radiation, that have been interpolated from GOES-7 images and AMS data onto the standard BOREAS mapping grid at a resolution of 5 km N-S and E-W.
ABSTRACT: The BOREAS RSS-14 team collected and processed GOES-7 and -8 images of the BOREAS region as part of their effort to characterize the incoming, reflected and emitted radiation at regional scales. This data set contains surface radiation parameters, such as net radiation and net solar radiation, that have been interpolated from GOES-7 images and AMS data onto the standard BOREAS mapping grid at a resolution of 5 km N-S and E-W.
The RSS-15 team conducted an investigation using SIR-C , X-SAR and Landsat TM data for estimating total above-ground dry biomass for the SSA and NSA modeling grids and component biomass for the SSA. Relationships of backscatter to total biomass and total biomass to foliage, branch, and bole biomass were used to estimate biomass density across the landscape.
The RSS-15 team conducted an investigation using SIR-C , X-SAR and Landsat TM data for estimating total above-ground dry biomass for the SSA and NSA modeling grids and component biomass for the SSA. Relationships of backscatter to total biomass and total biomass to foliage, branch, and bole biomass were used to estimate biomass density across the landscape.
ABSTRACT: The RSS-15 team conducted an investigation using SIR-C , X-SAR and Landsat TM data for estimating total above-ground dry biomass for the SSA and NSA modeling grids and component biomass for the SSA. Relationships of backscatter to total biomass and total biomass to foliage, branch, and bole biomass were used to estimate biomass density across the landscape.
Satellite and aircraft SAR data used in conjunction with various ground measurements to determine the moisture regime of the boreal forest. The NASA JPL AIRSAR is a side-looking imaging radar system that utilizes the SAR principle to obtain high-resolution images that represent the radar backscatter of the imaged surface at different frequencies and polarizations. The information contained in each pixel of the AIRSAR data represents the radar backscatter for all possible combinations of horizontal and vertical transmit and receive polarizations (i.e., HH, HV, VH, and VV).
Satellite and aircraft SAR data used in conjunction with various ground measurements to determine the moisture regime of the boreal forest. The NASA JPL AIRSAR is a side-looking imaging radar system that utilizes the SAR principle to obtain high-resolution images that represent the radar backscatter of the imaged surface at different frequencies and polarizations. The information contained in each pixel of the AIRSAR data represents the radar backscatter for all possible combinations of horizontal and vertical transmit and receive polarizations (i.e., HH, HV, VH, and VV).
ABSTRACT: Satellite and aircraft SAR data used in conjunction with various ground measurements to determine the moisture regime of the boreal forest. The NASA JPL AIRSAR is a side-looking imaging radar system that utilizes the SAR principle to obtain high-resolution images that represent the radar backscatter of the imaged surface at different frequencies and polarizations. The information contained in each pixel of the AIRSAR data represents the radar backscatter for all possible combinations of horizontal and vertical transmit and receive polarizations (i.e., HH, HV, VH, and VV).
Satellite and aircraft SAR data used in conjunction with various ground measurements to determine the moisture regime of the boreal forest. The NASA JPL AIRSAR is a side-looking imaging radar system that utilizes the SAR principle to obtain high resolution images that represent the radar backscatter of the imaged surface atdifferent frequencies and polarizations. The information contained in each pixel of the AIRSAR data represents the radar backscatter for all possible combinations of horizontal and vertical transmit and receive polarizations (i.e., HH, HV, VH, and VV). The level-3b AIRSAR SY data are the JPL synoptic product and contain 3 of the 12 total frequency and polarization combinations that are possible.
Satellite and aircraft SAR data used in conjunction with various ground measurements to determine the moisture regime of the boreal forest. The NASA JPL AIRSAR is a side-looking imaging radar system that utilizes the SAR principle to obtain high resolution images that represent the radar backscatter of the imaged surface atdifferent frequencies and polarizations. The information contained in each pixel of the AIRSAR data represents the radar backscatter for all possible combinations of horizontal and vertical transmit and receive polarizations (i.e., HH, HV, VH, and VV). The level-3b AIRSAR SY data are the JPL synoptic product and contain 3 of the 12 total frequency and polarization combinations that are possible.
ABSTRACT: Satellite and aircraft SAR data used in conjunction with various ground measurements to determine the moisture regime of the boreal forest. The NASA JPL AIRSAR is a side-looking imaging radar system that utilizes the SAR principle to obtain high resolution images that represent the radar backscatter of the imaged surface atdifferent frequencies and polarizations. The information contained in each pixel of the AIRSAR data represents the radar backscatter for all possible combinations of horizontal and vertical transmit and receive polarizations (i.e., HH, HV, VH, and VV). The level-3b AIRSAR SY data are the JPL synoptic product and contain 3 of the 12 total frequency and polarization combinations that are possible.
Contains dielectric profile  measurements taken by RSS-17 at NSA and SSA treed tower sites.
Contains dielectric profile  measurements taken by RSS-17 at NSA and SSA treed tower sites.
ABSTRACT: Contains dielectric profile  measurements taken by RSS-17 at NSA and SSA treed tower sites.
Contains the soil and stem temperature measurements collected by RSS17 at various flux sites:  (1) Southern Study Area (SSA) at the Old Black Spruce (OBS), Old Jack Pine (OJP), Old Aspen (OA), and Young Jack Pine (YJP); and (2) Northern Study Area (NSA) O
Contains the soil and stem temperature measurements collected by RSS17 at various flux sites:  (1) Southern Study Area (SSA) at the Old Black Spruce (OBS), Old Jack Pine (OJP), Old Aspen (OA), and Young Jack Pine (YJP); and (2) Northern Study Area (NSA) O
ABSTRACT: Contains the soil and stem temperature measurements collected by RSS17 at various flux sites:  (1) Southern Study Area (SSA) at the Old Black Spruce (OBS), Old Jack Pine (OJP), Old Aspen (OA), and Young Jack Pine (YJP); and (2) Northern Study Area (NSA) O
Contains xylem flux density  measurements taken by RSS-17 at SSA-OBS.
Contains xylem flux density  measurements taken by RSS-17 at SSA-OBS.
ABSTRACT: Contains xylem flux density  measurements taken by RSS-17 at SSA-OBS.
Data were acquired for BOREAS with NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). This optical sensor measures images that consist of spectra from 400 to 2500 nm at 10 nm sampling. These spectra are acquired as images with 20 meter spatial resolution, 11 km swath width and up to 800 km length.  The measurements are spectrally, radiometrically, geometrically calibrated.
Data were acquired for BOREAS with NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). This optical sensor measures images that consist of spectra from 400 to 2500 nm at 10 nm sampling. These spectra are acquired as images with 20 meter spatial resolution, 11 km swath width and up to 800 km length.  The measurements are spectrally, radiometrically, geometrically calibrated.
ABSTRACT: Data were acquired for BOREAS with NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). This optical sensor measures images that consist of spectra from 400 to 2500 nm at 10 nm sampling. These spectra are acquired as images with 20 meter spatial resolution, 11 km swath width and up to 800 km length.  The measurements are spectrally, radiometrically, geometrically calibrated.
Ground-based sunphotometer data collected in support of AVIRIS remote sensing activities at the SSA. The following information was compiled by staff members of the BOReal Ecosystem-Atmosphere Study (BOREAS)Information System (BORIS) as part of their data documentation efforts.
Ground-based sunphotometer data collected in support of AVIRIS remote sensing activities at the SSA. The following information was compiled by staff members of the BOReal Ecosystem-Atmosphere Study (BOREAS)Information System (BORIS) as part of their data documentation efforts.
ABSTRACT: Ground-based sunphotometer data collected in support of AVIRIS remote sensing activities at the SSA. The following information was compiled by staff members of the BOReal Ecosystem-Atmosphere Study (BOREAS)Information System (BORIS) as part of their data documentation efforts.
CASI images from the Chieftain Navaho aircraft taken in order to observe the seasonal change in the radiometric reflectance properties of the boreal forest landscape. CASI data include the following: 1) canopy bidirectional reflectance,2) canopy biochemistry, 3) spatial variability, and 4) estimates of up and downwelling PAR and spectral albedo.
CASI images from the Chieftain Navaho aircraft taken in order to observe the seasonal change in the radiometric reflectance properties of the boreal forest landscape. CASI data include the following: 1) canopy bidirectional reflectance,2) canopy biochemistry, 3) spatial variability, and 4) estimates of up and downwelling PAR and spectral albedo.
ABSTRACT: CASI images from the Chieftain Navaho aircraft taken in order to observe the seasonal change in the radiometric reflectance properties of the boreal forest landscape. CASI data include the following: 1) canopy bidirectional reflectance,2) canopy biochemistry, 3) spatial variability, and 4) estimates of up and downwelling PAR and spectral albedo.
Average spectral reflectance measurements of the ground surface of BOREAS flux tower sites. Measurements made along a transect with the instrument held at approximately one meter above the ground.
Average spectral reflectance measurements of the ground surface of BOREAS flux tower sites. Measurements made along a transect with the instrument held at approximately one meter above the ground.
ABSTRACT: Average spectral reflectance measurements of the ground surface of BOREAS flux tower sites. Measurements made along a transect with the instrument held at approximately one meter above the ground.
CASI images from the Chieftain Navaho aircraft collected in order to observe the seasonal change in the radiometric reflectance properties of the boreal forest landscape. The overall objective of the CASI deployment was to observe the seasonal change in the radiometric reflectance properties of the boreal forest landscape. CASI data include the following: 1) canopy bidirectional reflectance, 2) canopy biochemistry, 3) spatial variability, and 4) estimates of up and downwelling PAR spectral albedo.
CASI images from the Chieftain Navaho aircraft collected in order to observe the seasonal change in the radiometric reflectance properties of the boreal forest landscape. The overall objective of the CASI deployment was to observe the seasonal change in the radiometric reflectance properties of the boreal forest landscape. CASI data include the following: 1) canopy bidirectional reflectance, 2) canopy biochemistry, 3) spatial variability, and 4) estimates of up and downwelling PAR spectral albedo.
ABSTRACT: CASI images from the Chieftain Navaho aircraft collected in order to observe the seasonal change in the radiometric reflectance properties of the boreal forest landscape. The overall objective of the CASI deployment was to observe the seasonal change in the radiometric reflectance properties of the boreal forest landscape. CASI data include the following: 1) canopy bidirectional reflectance, 2) canopy biochemistry, 3) spatial variability, and 4) estimates of up and downwelling PAR spectral albedo.
Contains the POLDER data collected from the C130 platform during IFCs 1 and 2 in 1994.
Contains the POLDER data collected from the C130 platform during IFCs 1 and 2 in 1994.
ABSTRACT: Contains the POLDER data collected from the C130 platform during IFCs 1 and 2 in 1994.
Contains POLDER data collected from the helicopter platform during IFCs 1 and 3 in 1994.
Contains POLDER data collected from the helicopter platform during IFCs 1 and 3 in 1994.
ABSTRACT: Contains POLDER data collected from the helicopter platform during IFCs 1 and 3 in 1994.
A subset of images collected by the POLDER instrument mounted on the NASA/Ames C-130 aircraft over tower sites in the BOREAS study areas during the IFCs in 1994.
A subset of images collected by the POLDER instrument mounted on the NASA/Ames C-130 aircraft over tower sites in the BOREAS study areas during the IFCs in 1994.
ABSTRACT: A subset of images collected by the POLDER instrument mounted on the NASA/Ames C-130 aircraft over tower sites in the BOREAS study areas during the IFCs in 1994.
Contains 1994 and 1995 hourly data from various forestry meteorology stations.
Contains 1994 and 1995 hourly data from various forestry meteorology stations.
ABSTRACT: Contains 1994 and 1995 hourly data from various forestry meteorology stations.
This data set was prepared by the SERM-FBIU.  The data include information on forest parameters and cover the area in and near the BOREAS SSA, excluding the PANP.
This data set was prepared by the SERM-FBIU.  The data include information on forest parameters and cover the area in and near the BOREAS SSA, excluding the PANP.
ABSTRACT: This data set was prepared by the SERM-FBIU.  The data include information on forest parameters and cover the area in and near the BOREAS SSA, excluding the PANP.
A condensed forest cover type digital map of Saskatchewan and is a product of the Saskatchewan Environment and Resource Management, Forestry Branch-Inventory Unit (SERM-FBIU).  Map was generalized from SERM township maps of vegetation cover at an approximate scale of 1:63,000 (1 in. = 1 mile).  The cover information was iteratively generalized until it was compiled on a 1:1,000,000 scale map base.
A condensed forest cover type digital map of Saskatchewan and is a product of the Saskatchewan Environment and Resource Management, Forestry Branch-Inventory Unit (SERM-FBIU).  Map was generalized from SERM township maps of vegetation cover at an approximate scale of 1:63,000 (1 in. = 1 mile).  The cover information was iteratively generalized until it was compiled on a 1:1,000,000 scale map base.
ABSTRACT: A condensed forest cover type digital map of Saskatchewan and is a product of the Saskatchewan Environment and Resource Management, Forestry Branch-Inventory Unit (SERM-FBIU).  Map was generalized from SERM township maps of vegetation cover at an approximate scale of 1:63,000 (1 in. = 1 mile).  The cover information was iteratively generalized until it was compiled on a 1:1,000,000 scale map base.
Series of ARC/INFO export files of the fire history of Saskatchewan by year from 1945 to 1996, with a few missing years.
Series of ARC/INFO export files of the fire history of Saskatchewan by year from 1945 to 1996, with a few missing years.
ABSTRACT: Series of ARC/INFO export files of the fire history of Saskatchewan by year from 1945 to 1996, with a few missing years.
Geographic coordinate and other site information from several sources throughout the experiment period. The final set of information is organized into two data sets that provide geographic coordinate and site characteristic information for single sites and corner coordinates for standard geographic areas.
Geographic coordinate and other site information from several sources throughout the experiment period. The final set of information is organized into two data sets that provide geographic coordinate and site characteristic information for single sites and corner coordinates for standard geographic areas.
ABSTRACT: Geographic coordinate and other site information from several sources throughout the experiment period. The final set of information is organized into two data sets that provide geographic coordinate and site characteristic information for single sites and corner coordinates for standard geographic areas.
Contains the TE-01 CH4 flux data for the southern study area old aspen site.
Contains the TE-01 CH4 flux data for the southern study area old aspen site.
ABSTRACT: Contains the TE-01 CH4 flux data for the southern study area old aspen site.
Contains the TE-01 CH4 and CO2 flux data for the southern study area OBS site.
Contains the TE-01 CH4 and CO2 flux data for the southern study area OBS site.
ABSTRACT: Contains the TE-01 CH4 and CO2 flux data for the southern study area OBS site.
Gridded from vector layers of soil maps that were received from Dr. Darwin Anderson TE-01, who did the original soil mapping in the field during 1994. The vector layers were gridded into raster files that cover approximately 1 square kilometer over each of the SSA tower sites.
Gridded from vector layers of soil maps that were received from Dr. Darwin Anderson TE-01, who did the original soil mapping in the field during 1994. The vector layers were gridded into raster files that cover approximately 1 square kilometer over each of the SSA tower sites.
ABSTRACT: Gridded from vector layers of soil maps that were received from Dr. Darwin Anderson TE-01, who did the original soil mapping in the field during 1994. The vector layers were gridded into raster files that cover approximately 1 square kilometer over each of the SSA tower sites.
This data set provides a set of soil properties for the SSA. The soil samples were collected at sets of soil pits. Major soil properties include soil horizon; dry soil color; pH; bulk density; total, organic, and inorganic carbon; electric conductivity; cation exchange capacity; exchangeable sodium, potassium, calcium, magnesium, and hydrogen; water content at 0.01, 0.033, and 1.5 MPascals; nitrogen; phosphorus; particle size distribution; texture; pH of the mineral soil and of the organic soil; extractable acid; and sulfur.
This data set provides a set of soil properties for the SSA. The soil samples were collected at sets of soil pits. Major soil properties include soil horizon; dry soil color; pH; bulk density; total, organic, and inorganic carbon; electric conductivity; cation exchange capacity; exchangeable sodium, potassium, calcium, magnesium, and hydrogen; water content at 0.01, 0.033, and 1.5 MPascals; nitrogen; phosphorus; particle size distribution; texture; pH of the mineral soil and of the organic soil; extractable acid; and sulfur.
ABSTRACT: This data set provides a set of soil properties for the SSA. The soil samples were collected at sets of soil pits. Major soil properties include soil horizon; dry soil color; pH; bulk density; total, organic, and inorganic carbon; electric conductivity; cation exchange capacity; exchangeable sodium, potassium, calcium, magnesium, and hydrogen; water content at 0.01, 0.033, and 1.5 MPascals; nitrogen; phosphorus; particle size distribution; texture; pH of the mineral soil and of the organic soil; extractable acid; and sulfur.
Contains TE-01 methane flux and inorganic concentrations at the southern study area fen site.
Contains TE-01 methane flux and inorganic concentrations at the southern study area fen site.
ABSTRACT: Contains TE-01 methane flux and inorganic concentrations at the southern study area fen site.
Contains wood respiration continuous measurement data collected by TE-02.
Contains wood respiration continuous measurement data collected by TE-02.
ABSTRACT: Contains wood respiration continuous measurement data collected by TE-02.
Contains foliage respiration data collected by TE-02.
Contains foliage respiration data collected by TE-02.
ABSTRACT: Contains foliage respiration data collected by TE-02.
Contains fine root respiration data collected by TE-02.
Contains fine root respiration data collected by TE-02.
ABSTRACT: Contains fine root respiration data collected by TE-02.
Contains stem growth and sapwood data collected by TE-02.
Contains stem growth and sapwood data collected by TE-02.
ABSTRACT: Contains stem growth and sapwood data collected by TE-02.
Contains wood respiration data collected by TE-02.
Contains wood respiration data collected by TE-02.
ABSTRACT: Contains wood respiration data collected by TE-02.
Contains 1996 TE-04 data of branch bag studies of photosynthesis, respiration and stomatal conductance of boreal forest species using the open MPH-1000 system.
Contains 1996 TE-04 data of branch bag studies of photosynthesis, respiration and stomatal conductance of boreal forest species using the open MPH-1000 system.
ABSTRACT: Contains 1996 TE-04 data of branch bag studies of photosynthesis, respiration and stomatal conductance of boreal forest species using the open MPH-1000 system.
Contains TE-04 data on gas exchange studies of photosynthesis, respiration and stomatal conductance of boreal forest species using the MPH-1000 system.
Contains TE-04 data on gas exchange studies of photosynthesis, respiration and stomatal conductance of boreal forest species using the MPH-1000 system.
ABSTRACT: Contains TE-04 data on gas exchange studies of photosynthesis, respiration and stomatal conductance of boreal forest species using the MPH-1000 system.
This table contains data collected by TE05  in the NSA and SSA on air stable istope measurements.
This table contains data collected by TE05  in the NSA and SSA on air stable istope measurements.
ABSTRACT: This table contains data collected by TE05  in the NSA and SSA on air stable istope measurements.
Contains the CO2 profile concentration measurements made by the TE-05 BOREAS team in the NSA and SSA.
Contains the CO2 profile concentration measurements made by the TE-05 BOREAS team in the NSA and SSA.
ABSTRACT: Contains the CO2 profile concentration measurements made by the TE-05 BOREAS team in the NSA and SSA.
Contains leaf carbon isotope data collected by TE-05 during 1994.
Contains leaf carbon isotope data collected by TE-05 during 1994.
ABSTRACT: Contains leaf carbon isotope data collected by TE-05 during 1994.
Contains the leaf gas exchange data collected by TE-05 in the NSA and SSA.
Contains the leaf gas exchange data collected by TE-05 in the NSA and SSA.
ABSTRACT: Contains the leaf gas exchange data collected by TE-05 in the NSA and SSA.
Contains data collected by TE-05 on soil gas exchange data in the NSA and SSA in 1994.
Contains data collected by TE-05 on soil gas exchange data in the NSA and SSA in 1994.
ABSTRACT: Contains data collected by TE-05 on soil gas exchange data in the NSA and SSA in 1994.
Contains meteorological data collected during field campaigns by TE-05.
Contains meteorological data collected during field campaigns by TE-05.
ABSTRACT: Contains meteorological data collected during field campaigns by TE-05.
Contains tree ring width and C13 isotope cellulose ratio data collected by TE-05.
Contains tree ring width and C13 isotope cellulose ratio data collected by TE-05.
ABSTRACT: Contains tree ring width and C13 isotope cellulose ratio data collected by TE-05.
Contains soil temperature data collected by TE-06 during the BOREAS project.
Contains soil temperature data collected by TE-06 during the BOREAS project.
ABSTRACT: Contains soil temperature data collected by TE-06 during the BOREAS project.
Contains allometry data collected by TE-06.
Contains allometry data collected by TE-06.
ABSTRACT: Contains allometry data collected by TE-06.
Contains biomass data collected by TE-06.
Contains biomass data collected by TE-06.
ABSTRACT: Contains biomass data collected by TE-06.
Describes the average data values derived from the multi-vegetation imager used by TE-06 during the BOREAS project.Describes the single point data values derived from the multi-vegetation imager used by TE-06 during the BOREAS project.
Describes the average data values derived from the multi-vegetation imager used by TE-06 during the BOREAS project.Describes the single point data values derived from the multi-vegetation imager used by TE-06 during the BOREAS project.
ABSTRACT: Describes the average data values derived from the multi-vegetation imager used by TE-06 during the BOREAS project.Describes the single point data values derived from the multi-vegetation imager used by TE-06 during the BOREAS project.
Contains Net primary production data gathered by the TE-06 group in the NSA and SSA.
Contains Net primary production data gathered by the TE-06 group in the NSA and SSA.
ABSTRACT: Contains Net primary production data gathered by the TE-06 group in the NSA and SSA.
Contains data collected by TE-06 on pre-dawn leaf water potential and foliage moisture content.
Contains data collected by TE-06 on pre-dawn leaf water potential and foliage moisture content.
ABSTRACT: Contains data collected by TE-06 on pre-dawn leaf water potential and foliage moisture content.
Contains tree summary information for the TE-07 dendrology data.
Contains tree summary information for the TE-07 dendrology data.
ABSTRACT: Contains tree summary information for the TE-07 dendrology data.
Contains sap flow data collected by TE-07.
Contains sap flow data collected by TE-07.
ABSTRACT: Contains sap flow data collected by TE-07.
Contains bark biochemical data collected by TE-08.
Contains bark biochemical data collected by TE-08.
ABSTRACT: Contains bark biochemical data collected by TE-08.
Contains bark opical data collected by the TE-08 team during the BOREAS field campaign.
Contains bark opical data collected by the TE-08 team during the BOREAS field campaign.
ABSTRACT: Contains bark opical data collected by the TE-08 team during the BOREAS field campaign.
Contains in situ diurnal gas exchange and water potential data for forest stand in the Northern Study Area collected by TE-09.
Contains in situ diurnal gas exchange and water potential data for forest stand in the Northern Study Area collected by TE-09.
ABSTRACT: Contains in situ diurnal gas exchange and water potential data for forest stand in the Northern Study Area collected by TE-09.
Contains forest understory spectral reflectance data collected by BOREAS TE-09 at the ground level in the Old Jack Pine, Young Jack Pine nd Young Aspen boreal forest sites in the NSA.
Contains forest understory spectral reflectance data collected by BOREAS TE-09 at the ground level in the Old Jack Pine, Young Jack Pine nd Young Aspen boreal forest sites in the NSA.
ABSTRACT: Contains forest understory spectral reflectance data collected by BOREAS TE-09 at the ground level in the Old Jack Pine, Young Jack Pine nd Young Aspen boreal forest sites in the NSA.
Contains values of canopy biochemistry derived by the TE-09 team.
Contains values of canopy biochemistry derived by the TE-09 team.
ABSTRACT: Contains values of canopy biochemistry derived by the TE-09 team.
Contains sample measurements of the canopy biochemistry measured by the TE-09 team.
Contains sample measurements of the canopy biochemistry measured by the TE-09 team.
ABSTRACT: Contains sample measurements of the canopy biochemistry measured by the TE-09 team.
Contains foliar chlorophyll measurements collected by the TE-09 team.
Contains foliar chlorophyll measurements collected by the TE-09 team.
ABSTRACT: Contains foliar chlorophyll measurements collected by the TE-09 team.
This data table contains data collected by TE09 on the relationship between PAR and foliage nitrogen in the canopies of NSA-OBS, NSA-UBS, NSA-YJP, NSA-OJP and NSA-OASP.
This data table contains data collected by TE09 on the relationship between PAR and foliage nitrogen in the canopies of NSA-OBS, NSA-UBS, NSA-YJP, NSA-OJP and NSA-OASP.
ABSTRACT: This data table contains data collected by TE09 on the relationship between PAR and foliage nitrogen in the canopies of NSA-OBS, NSA-UBS, NSA-YJP, NSA-OJP and NSA-OASP.
Contains TE-09 data on the response of photosynthetic capacity to foliage nitrogen concentration and photosynthetic capacity in the canopies of Norther Study Area sites.
Contains TE-09 data on the response of photosynthetic capacity to foliage nitrogen concentration and photosynthetic capacity in the canopies of Norther Study Area sites.
ABSTRACT: Contains TE-09 data on the response of photosynthetic capacity to foliage nitrogen concentration and photosynthetic capacity in the canopies of Norther Study Area sites.
Contains photosynthetic response of boreal tree species to varied parameters.  Collected by TE-09 BOREAS group in the Northern Study Area.
Contains photosynthetic response of boreal tree species to varied parameters.  Collected by TE-09 BOREAS group in the Northern Study Area.
ABSTRACT: Contains photosynthetic response of boreal tree species to varied parameters.  Collected by TE-09 BOREAS group in the Northern Study Area.
Leaf chemistry data collected by TE-10. Contains 3 granules: 1) biochemical data; 2) biochemical data on a per dry weight basis; and 3) biochemical carbon, hydrogen, and nitrogen data given on a per dry weight basis and on a per area basis.
Leaf chemistry data collected by TE-10. Contains 3 granules: 1) biochemical data; 2) biochemical data on a per dry weight basis; and 3) biochemical carbon, hydrogen, and nitrogen data given on a per dry weight basis and on a per area basis.
ABSTRACT: Leaf chemistry data collected by TE-10. Contains 3 granules: 1) biochemical data; 2) biochemical data on a per dry weight basis; and 3) biochemical carbon, hydrogen, and nitrogen data given on a per dry weight basis and on a per area basis.
Contains gas exchange data collected by TE-10 during the BOREAS project.
Contains gas exchange data collected by TE-10 during the BOREAS project.
ABSTRACT: Contains gas exchange data collected by TE-10 during the BOREAS project.
Data collected in support of efforts to characterize and interpret information on the reflectance, transmittance, gas exchange, oxygen evolution, and biochemical properties of boreal vegetation. This data set describes the spectral optical properties (reflectance and transmittance) of boreal forest conifers and broadleaf tree leaves as measured with a Spectron Engineering SE590 spectroradiometer at the SSA OBS, OJP, YJP, OA, OA-AUX, YA-AUX, and YA sites.  The data were collected during the growing seasons of 1994 and 1996.
Data collected in support of efforts to characterize and interpret information on the reflectance, transmittance, gas exchange, oxygen evolution, and biochemical properties of boreal vegetation. This data set describes the spectral optical properties (reflectance and transmittance) of boreal forest conifers and broadleaf tree leaves as measured with a Spectron Engineering SE590 spectroradiometer at the SSA OBS, OJP, YJP, OA, OA-AUX, YA-AUX, and YA sites.  The data were collected during the growing seasons of 1994 and 1996.
ABSTRACT: Data collected in support of efforts to characterize and interpret information on the reflectance, transmittance, gas exchange, oxygen evolution, and biochemical properties of boreal vegetation. This data set describes the spectral optical properties (reflectance and transmittance) of boreal forest conifers and broadleaf tree leaves as measured with a Spectron Engineering SE590 spectroradiometer at the SSA OBS, OJP, YJP, OA, OA-AUX, YA-AUX, and YA sites.  The data were collected during the growing seasons of 1994 and 1996.
Contains the 1994 oxygen evolution and leaf characteristics data collected by TE-10.
Contains the 1994 oxygen evolution and leaf characteristics data collected by TE-10.
ABSTRACT: Contains the 1994 oxygen evolution and leaf characteristics data collected by TE-10.
Contains data collected by BOREAS TE-11 team on CO2 assimilation and transpiration in the SSA.
Contains data collected by BOREAS TE-11 team on CO2 assimilation and transpiration in the SSA.
ABSTRACT: Contains data collected by BOREAS TE-11 team on CO2 assimilation and transpiration in the SSA.
Contains sap flow data collected by TE-11.
Contains sap flow data collected by TE-11.
ABSTRACT: Contains sap flow data collected by TE-11.
Data collected in support of efforts to characterize and interpret information on the sapflow, gas exchange, and lichen photosynthesis.
Data collected in support of efforts to characterize and interpret information on the sapflow, gas exchange, and lichen photosynthesis.
ABSTRACT: Data collected in support of efforts to characterize and interpret information on the sapflow, gas exchange, and lichen photosynthesis.
Contains measurements of average incoming PAR through the forest canopy by TE-12.
Contains measurements of average incoming PAR through the forest canopy by TE-12.
ABSTRACT: Contains measurements of average incoming PAR through the forest canopy by TE-12.
Contains data collected by TE-12 of single leaf gas exchange properties of dominant vascular plant species in the SSA in 1994 and 1995.
Contains data collected by TE-12 of single leaf gas exchange properties of dominant vascular plant species in the SSA in 1994 and 1995.
ABSTRACT: Contains data collected by TE-12 of single leaf gas exchange properties of dominant vascular plant species in the SSA in 1994 and 1995.
Contains leaf optical properties data collected by the BOREAS TE-12 team.
Contains leaf optical properties data collected by the BOREAS TE-12 team.
ABSTRACT: Contains leaf optical properties data collected by the BOREAS TE-12 team.
Contains the shoot geometry data for both coniferous and deciduous samples taken by BOREAS TE-12.
Contains the shoot geometry data for both coniferous and deciduous samples taken by BOREAS TE-12.
ABSTRACT: Contains the shoot geometry data for both coniferous and deciduous samples taken by BOREAS TE-12.
Contains data on water potential for the Southern Study Area, Fen, Young Jack Pine, Young Aspen, and Old Black Spruce sites.
Contains data on water potential for the Southern Study Area, Fen, Young Jack Pine, Young Aspen, and Old Black Spruce sites.
ABSTRACT: Contains data on water potential for the Southern Study Area, Fen, Young Jack Pine, Young Aspen, and Old Black Spruce sites.
A BOREAS version of the Global Production Efficiency Model(www.inform.umd.edu/glopem) was developed by TE-17 to generate maps of gross and net primary production, autotrophic respiration, and light use efficiency for the BOREAS region.
A BOREAS version of the Global Production Efficiency Model(www.inform.umd.edu/glopem) was developed by TE-17 to generate maps of gross and net primary production, autotrophic respiration, and light use efficiency for the BOREAS region.
ABSTRACT: A BOREAS version of the Global Production Efficiency Model(www.inform.umd.edu/glopem) was developed by TE-17 to generate maps of gross and net primary production, autotrophic respiration, and light use efficiency for the BOREAS region.
This biomass density image covers almost the entire BOREAS SSA.  The pixels for which biomass density is computed include areas that are in conifer land cover classes only.  The biomass density values represent the amount of overstory biomass (i.e., tree biomass only) per unit area.  It is derived from a Landsat-5 TM image collected on 02-Sep-1994.  The technique that was used to create this image is very similar to the technique that was used to create the physical classification of the SSA.
This biomass density image covers almost the entire BOREAS SSA.  The pixels for which biomass density is computed include areas that are in conifer land cover classes only.  The biomass density values represent the amount of overstory biomass (i.e., tree biomass only) per unit area.  It is derived from a Landsat-5 TM image collected on 02-Sep-1994.  The technique that was used to create this image is very similar to the technique that was used to create the physical classification of the SSA.
ABSTRACT: This biomass density image covers almost the entire BOREAS SSA.  The pixels for which biomass density is computed include areas that are in conifer land cover classes only.  The biomass density values represent the amount of overstory biomass (i.e., tree biomass only) per unit area.  It is derived from a Landsat-5 TM image collected on 02-Sep-1994.  The technique that was used to create this image is very similar to the technique that was used to create the physical classification of the SSA.
The GEOSAIL model was created by combining the SAIL (Scattering from Arbitrarily Inclined Leaves) model with the Jasinski geometric model to simulate canopy spectral reflectance and absorption of photosynthetically active radiation for discontinuous canopies. Tree shapes are described by cylinders or cones distributed over a plane. Spectral reflectance and transmittance of trees are calculated from the SAIL model to determine the reflectance of the three components used in the geometric model: illuminated canopy, illuminated background, shadowed canopy, and shadowed background.
The GEOSAIL model was created by combining the SAIL (Scattering from Arbitrarily Inclined Leaves) model with the Jasinski geometric model to simulate canopy spectral reflectance and absorption of photosynthetically active radiation for discontinuous canopies. Tree shapes are described by cylinders or cones distributed over a plane. Spectral reflectance and transmittance of trees are calculated from the SAIL model to determine the reflectance of the three components used in the geometric model: illuminated canopy, illuminated background, shadowed canopy, and shadowed background.
ABSTRACT: The GEOSAIL model was created by combining the SAIL (Scattering from Arbitrarily Inclined Leaves) model with the Jasinski geometric model to simulate canopy spectral reflectance and absorption of photosynthetically active radiation for discontinuous canopies. Tree shapes are described by cylinders or cones distributed over a plane. Spectral reflectance and transmittance of trees are calculated from the SAIL model to determine the reflectance of the three components used in the geometric model: illuminated canopy, illuminated background, shadowed canopy, and shadowed background.
The objective of this classification is to provide the BOREAS investigators with a data product that characterizes the land cover of the NSA. A Landsat-5 TM image from 20-Aug-1988 was used to derive this classification.  A standard supervised maximum likelihood classification approach was used to produce this classification.
The objective of this classification is to provide the BOREAS investigators with a data product that characterizes the land cover of the NSA. A Landsat-5 TM image from 20-Aug-1988 was used to derive this classification.  A standard supervised maximum likelihood classification approach was used to produce this classification.
ABSTRACT: The objective of this classification is to provide the BOREAS investigators with a data product that characterizes the land cover of the NSA. A Landsat-5 TM image from 20-Aug-1988 was used to derive this classification.  A standard supervised maximum likelihood classification approach was used to produce this classification.
A Landsat-5 TM image from 06-Aug-1990 was used to derive this classification, the objective of which is to provide BOREAS investigators with a data product that characterizes the land cover of the SSA. A standard supervised maximum likelihood classification approach was used to produce this classification.
A Landsat-5 TM image from 06-Aug-1990 was used to derive this classification, the objective of which is to provide BOREAS investigators with a data product that characterizes the land cover of the SSA. A standard supervised maximum likelihood classification approach was used to produce this classification.
ABSTRACT: A Landsat-5 TM image from 06-Aug-1990 was used to derive this classification, the objective of which is to provide BOREAS investigators with a data product that characterizes the land cover of the SSA. A standard supervised maximum likelihood classification approach was used to produce this classification.
The objective of this classification is to provide BOREAS investigators with a data product that characterizes the land cover of the NSA. A Landsat-5 TM image from 21-Jun-1995 was used to derive the classification. A technique was implemented that uses reflectances of various land cover types along with a geometric optical canopy model to produce spectral trajectories. These trajectories are used in a way that is similar to training  data to classify the image into the different land cover classes.
The objective of this classification is to provide BOREAS investigators with a data product that characterizes the land cover of the NSA. A Landsat-5 TM image from 21-Jun-1995 was used to derive the classification. A technique was implemented that uses reflectances of various land cover types along with a geometric optical canopy model to produce spectral trajectories. These trajectories are used in a way that is similar to training  data to classify the image into the different land cover classes.
ABSTRACT: The objective of this classification is to provide BOREAS investigators with a data product that characterizes the land cover of the NSA. A Landsat-5 TM image from 21-Jun-1995 was used to derive the classification. A technique was implemented that uses reflectances of various land cover types along with a geometric optical canopy model to produce spectral trajectories. These trajectories are used in a way that is similar to training  data to classify the image into the different land cover classes.
The objective of this classification is to provide BOREAS investigators with a data product that characterizes the land cover of the SSA. A Landsat-5 TM image from 02-Sep-1994 was used to derive the classification. A technique was implemented that uses reflectances of various land cover types along with a geometric optical canopy model to produce spectral trajectories. These trajectories are used as training data to classify the image into the different land cover classes.
The objective of this classification is to provide BOREAS investigators with a data product that characterizes the land cover of the SSA. A Landsat-5 TM image from 02-Sep-1994 was used to derive the classification. A technique was implemented that uses reflectances of various land cover types along with a geometric optical canopy model to produce spectral trajectories. These trajectories are used as training data to classify the image into the different land cover classes.
ABSTRACT: The objective of this classification is to provide BOREAS investigators with a data product that characterizes the land cover of the SSA. A Landsat-5 TM image from 02-Sep-1994 was used to derive the classification. A technique was implemented that uses reflectances of various land cover types along with a geometric optical canopy model to produce spectral trajectories. These trajectories are used as training data to classify the image into the different land cover classes.
The BOREAS TE-18 team used a radiometric rectification process to produce standardized DN values for a series of Landsat TM images of the BOREAS SSA and NSA in order to compare images that were collected under different atmospheric conditions. The images for each study area were referenced to an image that had very clear atmospheric qualities.
The BOREAS TE-18 team used a radiometric rectification process to produce standardized DN values for a series of Landsat TM images of the BOREAS SSA and NSA in order to compare images that were collected under different atmospheric conditions. The images for each study area were referenced to an image that had very clear atmospheric qualities.
ABSTRACT: The BOREAS TE-18 team used a radiometric rectification process to produce standardized DN values for a series of Landsat TM images of the BOREAS SSA and NSA in order to compare images that were collected under different atmospheric conditions. The images for each study area were referenced to an image that had very clear atmospheric qualities.
The Spruce and Moss Model (SPAM) was designed to simulate the daily carbon balance of a black spruce/moss boreal forest ecosystem. It is driven by daily weather conditions and consists of four components: (1) soil climate; (2) tree photosynthesis and respiration; (3) moss photosynthesis and respiration; and (4) litter decomposition and associated heterotrophic respiration. The model can be used to generate predictions of total site net ecosystem exchange of carbon (NEE), total soil dark respiration (live roots + heterotrophs + live moss), spruce and moss net productivity, and net carbon accumulation in the soil.
The Spruce and Moss Model (SPAM) was designed to simulate the daily carbon balance of a black spruce/moss boreal forest ecosystem. It is driven by daily weather conditions and consists of four components: (1) soil climate; (2) tree photosynthesis and respiration; (3) moss photosynthesis and respiration; and (4) litter decomposition and associated heterotrophic respiration. The model can be used to generate predictions of total site net ecosystem exchange of carbon (NEE), total soil dark respiration (live roots + heterotrophs + live moss), spruce and moss net productivity, and net carbon accumulation in the soil.
ABSTRACT: The Spruce and Moss Model (SPAM) was designed to simulate the daily carbon balance of a black spruce/moss boreal forest ecosystem. It is driven by daily weather conditions and consists of four components: (1) soil climate; (2) tree photosynthesis and respiration; (3) moss photosynthesis and respiration; and (4) litter decomposition and associated heterotrophic respiration. The model can be used to generate predictions of total site net ecosystem exchange of carbon (NEE), total soil dark respiration (live roots + heterotrophs + live moss), spruce and moss net productivity, and net carbon accumulation in the soil.
Contains the soils lab data that were collected by TE-20 in 1994.
Contains the soils lab data that were collected by TE-20 in 1994.
ABSTRACT: Contains the soils lab data that were collected by TE-20 in 1994.
The BOREAS TE-20 team collected several data sets for use in developing and testing models of forest ecosystem dynamics. This data set contains measurements of site characteristics conducted in the SSA from 18-Jul-1994 to 30-Jul-1994.
The BOREAS TE-20 team collected several data sets for use in developing and testing models of forest ecosystem dynamics. This data set contains measurements of site characteristics conducted in the SSA from 18-Jul-1994 to 30-Jul-1994.
ABSTRACT: The BOREAS TE-20 team collected several data sets for use in developing and testing models of forest ecosystem dynamics. This data set contains measurements of site characteristics conducted in the SSA from 18-Jul-1994 to 30-Jul-1994.
Gridded from vector layers of soil maps received from Dr. Hugo Veldhuis, who did the original mapping in the field during 1994.The vector layers were gridded into raster files that cover the NSA-MSA and tower sites.
Gridded from vector layers of soil maps received from Dr. Hugo Veldhuis, who did the original mapping in the field during 1994.The vector layers were gridded into raster files that cover the NSA-MSA and tower sites.
ABSTRACT: Gridded from vector layers of soil maps received from Dr. Hugo Veldhuis, who did the original mapping in the field during 1994.The vector layers were gridded into raster files that cover the NSA-MSA and tower sites.
Vector layers of soil maps. The vector layers were converted to ARC/INFO EXPORT files.  These data cover 1-km diameters around each of the NSA tower sites and another layer covers the NSA-MSA.
Vector layers of soil maps. The vector layers were converted to ARC/INFO EXPORT files.  These data cover 1-km diameters around each of the NSA tower sites and another layer covers the NSA-MSA.
ABSTRACT: Vector layers of soil maps. The vector layers were converted to ARC/INFO EXPORT files.  These data cover 1-km diameters around each of the NSA tower sites and another layer covers the NSA-MSA.
Data for use in developing and testing models of forest ecosystem dynamics. The data set describes the soils and landscape characteristics of the NSA-MSA and tower sites.
Data for use in developing and testing models of forest ecosystem dynamics. The data set describes the soils and landscape characteristics of the NSA-MSA and tower sites.
ABSTRACT: Data for use in developing and testing models of forest ecosystem dynamics. The data set describes the soils and landscape characteristics of the NSA-MSA and tower sites.
Contains meteorological measurement data collected by TE-21.
Contains meteorological measurement data collected by TE-21.
ABSTRACT: Contains meteorological measurement data collected by TE-21.
The BOREAS TE-22 team collected data sets in support of its efforts to characterize and interpret information on the forest structure of boreal vegetation in the SSA and NSA during the 1994 growing season.
The BOREAS TE-22 team collected data sets in support of its efforts to characterize and interpret information on the forest structure of boreal vegetation in the SSA and NSA during the 1994 growing season.
ABSTRACT: The BOREAS TE-22 team collected data sets in support of its efforts to characterize and interpret information on the forest structure of boreal vegetation in the SSA and NSA during the 1994 growing season.
Tree core  data from several sites in the SSA and NSA collected in order to perform historical growth studies and relate the information to their modeling activities. The cores were collected during the summer of 1994 in the Northern and Southern Study Areas. A sample of the file types resulting from the analysis of the tree cores is provided.
Tree core  data from several sites in the SSA and NSA collected in order to perform historical growth studies and relate the information to their modeling activities. The cores were collected during the summer of 1994 in the Northern and Southern Study Areas. A sample of the file types resulting from the analysis of the tree cores is provided.
ABSTRACT: Tree core  data from several sites in the SSA and NSA collected in order to perform historical growth studies and relate the information to their modeling activities. The cores were collected during the summer of 1994 in the Northern and Southern Study Areas. A sample of the file types resulting from the analysis of the tree cores is provided.
Hemispherical photographs collected in support of the effort to characterize and interpret information on estimates of canopy architecture and radiative transfer properties for most BOREAS study sites. Various OA, OBS, OJP, YJP, and YA sites in the boreal forest were measured from May to August 1994. The hemispherical photographs were used to derive values of LAI, Leaf angle, Gap fraction, and Clumping index.
Hemispherical photographs collected in support of the effort to characterize and interpret information on estimates of canopy architecture and radiative transfer properties for most BOREAS study sites. Various OA, OBS, OJP, YJP, and YA sites in the boreal forest were measured from May to August 1994. The hemispherical photographs were used to derive values of LAI, Leaf angle, Gap fraction, and Clumping index.
ABSTRACT: Hemispherical photographs collected in support of the effort to characterize and interpret information on estimates of canopy architecture and radiative transfer properties for most BOREAS study sites. Various OA, OBS, OJP, YJP, and YA sites in the boreal forest were measured from May to August 1994. The hemispherical photographs were used to derive values of LAI, Leaf angle, Gap fraction, and Clumping index.
Describes the mapped plot data and the mapped plot site data taken by TE-23.
Describes the mapped plot data and the mapped plot site data taken by TE-23.
ABSTRACT: Describes the mapped plot data and the mapped plot site data taken by TE-23.
Data collected in support of the effort to characterize and interpret soil information at the SSA-OA tower site in 1994. Data collected include soil respiration, temperature, moisture, and gravimetric data.
Data collected in support of the effort to characterize and interpret soil information at the SSA-OA tower site in 1994. Data collected include soil respiration, temperature, moisture, and gravimetric data.
ABSTRACT: Data collected in support of the effort to characterize and interpret soil information at the SSA-OA tower site in 1994. Data collected include soil respiration, temperature, moisture, and gravimetric data.
Energy, cargon dioxide, and momentum flux data collected above the canopy along with meteorological and soils data at the BOREAS SSA-OA site
Energy, cargon dioxide, and momentum flux data collected above the canopy along with meteorological and soils data at the BOREAS SSA-OA site
ABSTRACT: Energy, cargon dioxide, and momentum flux data collected above the canopy along with meteorological and soils data at the BOREAS SSA-OA site
Energy, carbon dioxide, and momentum flux data collected under the canopy along with meteorological and soils data at the BOREAS SSA-OA site from mid-October to mid-November of 1993 and throughout all of 1994.
Energy, carbon dioxide, and momentum flux data collected under the canopy along with meteorological and soils data at the BOREAS SSA-OA site from mid-October to mid-November of 1993 and throughout all of 1994.
ABSTRACT: Energy, carbon dioxide, and momentum flux data collected under the canopy along with meteorological and soils data at the BOREAS SSA-OA site from mid-October to mid-November of 1993 and throughout all of 1994.
This data set contains methane (CH4) and nitrous oxide (N2O)fluxes that were measured at the BOREAS SSA-OA site
This data set contains methane (CH4) and nitrous oxide (N2O)fluxes that were measured at the BOREAS SSA-OA site
ABSTRACT: This data set contains methane (CH4) and nitrous oxide (N2O)fluxes that were measured at the BOREAS SSA-OA site
The BOREAS TF-02 team collected various trace gas and energy flux data along with meteorological parameters at the SSA-OA site. This data set contains meteorological and ozone measurements from instruments mounted below a tethered balloon. These data were collected at the SSA-OA site to extend meteorological and ozone measurements made from the flux tower to heights of 300 m.
The BOREAS TF-02 team collected various trace gas and energy flux data along with meteorological parameters at the SSA-OA site. This data set contains meteorological and ozone measurements from instruments mounted below a tethered balloon. These data were collected at the SSA-OA site to extend meteorological and ozone measurements made from the flux tower to heights of 300 m.
ABSTRACT: The BOREAS TF-02 team collected various trace gas and energy flux data along with meteorological parameters at the SSA-OA site. This data set contains meteorological and ozone measurements from instruments mounted below a tethered balloon. These data were collected at the SSA-OA site to extend meteorological and ozone measurements made from the flux tower to heights of 300 m.
Energy, carbon dioxide, water vaport, and momentum flux data collected above the canopy and in profiles through the canopy, along with meteorological data at the BOREAS SSA-OA site. Daily precipitation data from several gauges were also collected.
Energy, carbon dioxide, water vaport, and momentum flux data collected above the canopy and in profiles through the canopy, along with meteorological data at the BOREAS SSA-OA site. Daily precipitation data from several gauges were also collected.
ABSTRACT: Energy, carbon dioxide, water vaport, and momentum flux data collected above the canopy and in profiles through the canopy, along with meteorological data at the BOREAS SSA-OA site. Daily precipitation data from several gauges were also collected.
Automated measurements of CO2 exchange at the moss surface of NSA-OBS.
Automated measurements of CO2 exchange at the moss surface of NSA-OBS.
ABSTRACT: Automated measurements of CO2 exchange at the moss surface of NSA-OBS.
The flux and ancillary data collected at the NSA-OBS tower flux site by the TF-03 group.
The flux and ancillary data collected at the NSA-OBS tower flux site by the TF-03 group.
ABSTRACT: The flux and ancillary data collected at the NSA-OBS tower flux site by the TF-03 group.
Contains fluxes of carbon dioxide and methane across the soil-air interface in four ages of jack pine forest at the Southern Study Area.  Gross and net flux of CO2 and flux of CH4 between soil and air are presented for 24 chamber sites in mature jack pine stands.
Contains fluxes of carbon dioxide and methane across the soil-air interface in four ages of jack pine forest at the Southern Study Area.  Gross and net flux of CO2 and flux of CH4 between soil and air are presented for 24 chamber sites in mature jack pine stands.
ABSTRACT: Contains fluxes of carbon dioxide and methane across the soil-air interface in four ages of jack pine forest at the Southern Study Area.  Gross and net flux of CO2 and flux of CH4 between soil and air are presented for 24 chamber sites in mature jack pine stands.
The TF-04 Soil profile CH4 concentration data.
The TF-04 Soil profile CH4 concentration data.
ABSTRACT: The TF-04 Soil profile CH4 concentration data.
The flux and ancillary data collected at the SSA-YJP towerflux site by the TF-04 group.
The flux and ancillary data collected at the SSA-YJP towerflux site by the TF-04 group.
ABSTRACT: The flux and ancillary data collected at the SSA-YJP towerflux site by the TF-04 group.
The flux and ancillary data collected at the SSA-OJP tower flux site by the TF-05 group.
The flux and ancillary data collected at the SSA-OJP tower flux site by the TF-05 group.
ABSTRACT: The flux and ancillary data collected at the SSA-OJP tower flux site by the TF-05 group.
Contains meteorology data collected at the SSA-YA tower flux site by the TF6 group. These data were reported at 10 minute intervals. The flux and ancillary data collected at the SSA-YA tower flux site by the TF6 group.
Contains meteorology data collected at the SSA-YA tower flux site by the TF6 group. These data were reported at 10 minute intervals. The flux and ancillary data collected at the SSA-YA tower flux site by the TF6 group.
ABSTRACT: Contains meteorology data collected at the SSA-YA tower flux site by the TF6 group. These data were reported at 10 minute intervals. The flux and ancillary data collected at the SSA-YA tower flux site by the TF6 group.
The flux and ancillary data collected at the SSA-OBS towerflux site by the TF-07 group.
The flux and ancillary data collected at the SSA-OBS towerflux site by the TF-07 group.
ABSTRACT: The flux and ancillary data collected at the SSA-OBS towerflux site by the TF-07 group.
Ceilometer measurements of cloud characteristics made by the TF-08 team at the NSA-OJP and SSA-OBS sites.
Ceilometer measurements of cloud characteristics made by the TF-08 team at the NSA-OJP and SSA-OBS sites.
ABSTRACT: Ceilometer measurements of cloud characteristics made by the TF-08 team at the NSA-OJP and SSA-OBS sites.
Energy, carbon dioxide, and water vapor flux data collected by the BOREAS TF-08 team.
Energy, carbon dioxide, and water vapor flux data collected by the BOREAS TF-08 team.
ABSTRACT: Energy, carbon dioxide, and water vapor flux data collected by the BOREAS TF-08 team.
CO2 and H2O vapor exchange and ancillary data collected from enclosures around the black spruce branches.  Data collected by the TF-09 group at the SSA-OBS site.
CO2 and H2O vapor exchange and ancillary data collected from enclosures around the black spruce branches.  Data collected by the TF-09 group at the SSA-OBS site.
ABSTRACT: CO2 and H2O vapor exchange and ancillary data collected from enclosures around the black spruce branches.  Data collected by the TF-09 group at the SSA-OBS site.
Soil temperature and heat flux data from the SSA-OBS site, collected by the TF-09 group; and the flux and ancillary data collected at the SSA-OBS tower flux site by the TF-09 group.
Soil temperature and heat flux data from the SSA-OBS site, collected by the TF-09 group; and the flux and ancillary data collected at the SSA-OBS tower flux site by the TF-09 group.
ABSTRACT: Soil temperature and heat flux data from the SSA-OBS site, collected by the TF-09 group; and the flux and ancillary data collected at the SSA-OBS tower flux site by the TF-09 group.
ABSTRACT: The flux and ancillary data collected at the NSA-Fen tower flux site by the TF-10 group.
ABSTRACT: The flux and ancillary data collected at the NSA-Fen tower flux site by the TF-10 group.
ABSTRACT: The flux and ancillary data collected at the NSA-Fen tower flux site by the TF-10 group.
The flux and ancillary data collected at the NSA-YJP tower flux site by the TF-10 group.  Measurements of stomatal conductance collected at the NSA-YJP site by the TF-10 team.
The flux and ancillary data collected at the NSA-YJP tower flux site by the TF-10 group.  Measurements of stomatal conductance collected at the NSA-YJP site by the TF-10 team.
ABSTRACT: The flux and ancillary data collected at the NSA-YJP tower flux site by the TF-10 group.  Measurements of stomatal conductance collected at the NSA-YJP site by the TF-10 team.
Contains plant coverage, plant biomass, and estimated net primary productivity collected by TF-11.
Contains plant coverage, plant biomass, and estimated net primary productivity collected by TF-11.
ABSTRACT: Contains plant coverage, plant biomass, and estimated net primary productivity collected by TF-11.
Contains temperature, pH, and CH4 and CO2 concentration profiles in the surface 50 cm of peat.
Contains temperature, pH, and CH4 and CO2 concentration profiles in the surface 50 cm of peat.
ABSTRACT: Contains temperature, pH, and CH4 and CO2 concentration profiles in the surface 50 cm of peat.
Contains CH4 and CO2 static chamber fluxes at the SSA-FEN.
Contains CH4 and CO2 static chamber fluxes at the SSA-FEN.
ABSTRACT: Contains CH4 and CO2 static chamber fluxes at the SSA-FEN.
Contains decomposition rates of a standard substrate (wheat straw).
Contains decomposition rates of a standard substrate (wheat straw).
ABSTRACT: Contains decomposition rates of a standard substrate (wheat straw).
Contains the TF-11 CO2 chamber flux measurements made with the LI-6200 under water surface film conditions and  the TF-11 CO2 chamber concentration measurements made using the Gas Chromatograph-Flame Ionization Detector.
Contains the TF-11 CO2 chamber flux measurements made with the LI-6200 under water surface film conditions and  the TF-11 CO2 chamber concentration measurements made using the Gas Chromatograph-Flame Ionization Detector.
ABSTRACT: Contains the TF-11 CO2 chamber flux measurements made with the LI-6200 under water surface film conditions and  the TF-11 CO2 chamber concentration measurements made using the Gas Chromatograph-Flame Ionization Detector.
Contains the TF-11 leaf gas exchange data made with the LI-6200 and LI-6400 systems.
Contains the TF-11 leaf gas exchange data made with the LI-6200 and LI-6400 systems.
ABSTRACT: Contains the TF-11 leaf gas exchange data made with the LI-6200 and LI-6400 systems.
Contains the TF-11 soil surface CO2 flux data that were measured using a portable gas exchange system.
Contains the TF-11 soil surface CO2 flux data that were measured using a portable gas exchange system.
ABSTRACT: Contains the TF-11 soil surface CO2 flux data that were measured using a portable gas exchange system.
Contains the TF-11 Leaf Area Index (LAI) data collected at the SSA Fen site.
Contains the TF-11 Leaf Area Index (LAI) data collected at the SSA Fen site.
ABSTRACT: Contains the TF-11 Leaf Area Index (LAI) data collected at the SSA Fen site.
The flux and ancillary data collected at the SSA-Fen tower flux site by the TF-11 group.
The flux and ancillary data collected at the SSA-Fen tower flux site by the TF-11 group.
ABSTRACT: The flux and ancillary data collected at the SSA-Fen tower flux site by the TF-11 group.
Contains TGB-01 carbon chamber flux data for Northern Study Area.
Contains TGB-01 carbon chamber flux data for Northern Study Area.
ABSTRACT: Contains TGB-01 carbon chamber flux data for Northern Study Area.
Contains CH4 tower flux data collected by BOREAS science group TGB01 in the Northern Study Area.
Contains CH4 tower flux data collected by BOREAS science group TGB01 in the Northern Study Area.
ABSTRACT: Contains CH4 tower flux data collected by BOREAS science group TGB01 in the Northern Study Area.
Contains the TGB-01 SF6 flux data for the Northern Study Area OJP and YJP sites.
Contains the TGB-01 SF6 flux data for the Northern Study Area OJP and YJP sites.
ABSTRACT: Contains the TGB-01 SF6 flux data for the Northern Study Area OJP and YJP sites.
Contains TGB-03 methane flux data for fens in the Northern Study Area.
Contains TGB-03 methane flux data for fens in the Northern Study Area.
ABSTRACT: Contains TGB-03 methane flux data for fens in the Northern Study Area.
Contains TGB-03 NET Ecosystem Exchange data from the combined TGB-01 and TGB-03 teams.
Contains TGB-03 NET Ecosystem Exchange data from the combined TGB-01 and TGB-03 teams.
ABSTRACT: Contains TGB-03 NET Ecosystem Exchange data from the combined TGB-01 and TGB-03 teams.
Contains water table and peat temperature data collected by the TGB-03 team for sites in the northern study area.
Contains water table and peat temperature data collected by the TGB-03 team for sites in the northern study area.
ABSTRACT: Contains water table and peat temperature data collected by the TGB-03 team for sites in the northern study area.
Contains TGB-03 methane flux and CO2 flux data for uplands in the northern study area.
Contains TGB-03 methane flux and CO2 flux data for uplands in the northern study area.
ABSTRACT: Contains TGB-03 methane flux and CO2 flux data for uplands in the northern study area.
Contains TGB-03 dissolved organic carbon concentration data for northern study area.
Contains TGB-03 dissolved organic carbon concentration data for northern study area.
ABSTRACT: Contains TGB-03 dissolved organic carbon concentration data for northern study area.
Composition of plant species that were within the collars used to measure net ecosystem exchange (NEE).
Composition of plant species that were within the collars used to measure net ecosystem exchange (NEE).
ABSTRACT: Composition of plant species that were within the collars used to measure net ecosystem exchange (NEE).
The flux and ancillary data collected at the NSA-BP tower flux site by the TGB-04 group.
The flux and ancillary data collected at the NSA-BP tower flux site by the TGB-04 group.
ABSTRACT: The flux and ancillary data collected at the NSA-BP tower flux site by the TGB-04 group.
Contains TGB-04  water and sediment temperature data for northern study area (tower beaver pond site).
Contains TGB-04  water and sediment temperature data for northern study area (tower beaver pond site).
ABSTRACT: Contains TGB-04  water and sediment temperature data for northern study area (tower beaver pond site).
Contains the TGB-05 NO and N2O flux data for sites in the Northern Study Area.
Contains the TGB-05 NO and N2O flux data for sites in the Northern Study Area.
ABSTRACT: Contains the TGB-05 NO and N2O flux data for sites in the Northern Study Area.
Contains the TGB-05 CO, CO2, and CH4 flux data for sites in the Boreas Northern Study Area.
Contains the TGB-05 CO, CO2, and CH4 flux data for sites in the Boreas Northern Study Area.
ABSTRACT: Contains the TGB-05 CO, CO2, and CH4 flux data for sites in the Boreas Northern Study Area.
Contains the TGB-05 dissolved organic carbon data for the Northern Study Area beaver pond site.
Contains the TGB-05 dissolved organic carbon data for the Northern Study Area beaver pond site.
ABSTRACT: Contains the TGB-05 dissolved organic carbon data for the Northern Study Area beaver pond site.
Raster format data set covering the province of Manitoba and produced by Forestry Canada from hand-drawn boundaries of fires on photocopies of 1:250,000 scale maps.
Raster format data set covering the province of Manitoba and produced by Forestry Canada from hand-drawn boundaries of fires on photocopies of 1:250,000 scale maps.
ABSTRACT: Raster format data set covering the province of Manitoba and produced by Forestry Canada from hand-drawn boundaries of fires on photocopies of 1:250,000 scale maps.
Vector format data set covering the province of Manitoba and produced by Forestry Canada from hand-drawn boundaries of fires on photocopies of 1:250,000 scale maps.
Vector format data set covering the province of Manitoba and produced by Forestry Canada from hand-drawn boundaries of fires on photocopies of 1:250,000 scale maps.
ABSTRACT: Vector format data set covering the province of Manitoba and produced by Forestry Canada from hand-drawn boundaries of fires on photocopies of 1:250,000 scale maps.
Contains 1993, 1994, and 1996 methane concentrations from TGB-06 in the NSA and SSA.
Contains 1993, 1994, and 1996 methane concentrations from TGB-06 in the NSA and SSA.
ABSTRACT: Contains 1993, 1994, and 1996 methane concentrations from TGB-06 in the NSA and SSA.
Contains herbicide and organic chlorine concentrations in ambient air samples for TGB-07 in the SSA.
Contains herbicide and organic chlorine concentrations in ambient air samples for TGB-07 in the SSA.
ABSTRACT: Contains herbicide and organic chlorine concentrations in ambient air samples for TGB-07 in the SSA.
Contains herbicide and organic chlorine fluxes in dry deposition samples for TGB-07 in the SSA.
Contains herbicide and organic chlorine fluxes in dry deposition samples for TGB-07 in the SSA.
ABSTRACT: Contains herbicide and organic chlorine fluxes in dry deposition samples for TGB-07 in the SSA.
Contains 1993 and 1994 herbicide concentrations in rain samples for TGB-07 in the SSA.
Contains 1993 and 1994 herbicide concentrations in rain samples for TGB-07 in the SSA.
ABSTRACT: Contains 1993 and 1994 herbicide concentrations in rain samples for TGB-07 in the SSA.
Contains monoterpene emission and concentration data collected by the TGB-08 BOREAS science group.
Contains monoterpene emission and concentration data collected by the TGB-08 BOREAS science group.
ABSTRACT: Contains monoterpene emission and concentration data collected by the TGB-08 BOREAS science group.
Contains TGB-08 Photosynthesis data measured at 30C by month.
Contains TGB-08 Photosynthesis data measured at 30C by month.
ABSTRACT: Contains TGB-08 Photosynthesis data measured at 30C by month.
Contains starch concentration data collected by TGB-08 from the SSA-OBS and SSA-OJP BOREAS sites in 1994.
Contains starch concentration data collected by TGB-08 from the SSA-OBS and SSA-OJP BOREAS sites in 1994.
ABSTRACT: Contains starch concentration data collected by TGB-08 from the SSA-OBS and SSA-OJP BOREAS sites in 1994.
Contains the mixing ratio and concentration of Non-Methane HydroCarbons (NMHC) taken by the TGB-09 group.
Contains the mixing ratio and concentration of Non-Methane HydroCarbons (NMHC) taken by the TGB-09 group.
ABSTRACT: Contains the mixing ratio and concentration of Non-Methane HydroCarbons (NMHC) taken by the TGB-09 group.
Contains oxidant (O3, H2O2, ROOH) concentration data collected by TGB-10 for the Southern Study Area.
Contains oxidant (O3, H2O2, ROOH) concentration data collected by TGB-10 for the Southern Study Area.
ABSTRACT: Contains oxidant (O3, H2O2, ROOH) concentration data collected by TGB-10 for the Southern Study Area.
Contains oxidant flux data collected by TGB-10 for sites in the Southern Study Area.
Contains oxidant flux data collected by TGB-10 for sites in the Southern Study Area.
ABSTRACT: Contains oxidant flux data collected by TGB-10 for sites in the Southern Study Area.
Contains biogenic VOC data collected by TGB-10 during the summer of 1994.
Contains biogenic VOC data collected by TGB-10 during the summer of 1994.
ABSTRACT: Contains biogenic VOC data collected by TGB-10 during the summer of 1994.
Contains TGB-12 soil CO2 flux data for sites in the northern study area.
Contains TGB-12 soil CO2 flux data for sites in the northern study area.
ABSTRACT: Contains TGB-12 soil CO2 flux data for sites in the northern study area.
Contains RADON-222 activity in soil gas collected by TGB-12 at 5 BOREAS auxiliary sites in the northern study area.
Contains RADON-222 activity in soil gas collected by TGB-12 at 5 BOREAS auxiliary sites in the northern study area.
ABSTRACT: Contains RADON-222 activity in soil gas collected by TGB-12 at 5 BOREAS auxiliary sites in the northern study area.
Contains RADON-222 flux data data collected by TGB-12 in the northern study area.
Contains RADON-222 flux data data collected by TGB-12 in the northern study area.
ABSTRACT: Contains RADON-222 flux data data collected by TGB-12 in the northern study area.
This data set contains (1) estimates of soil carbon stocks by horizon based on soil survey data and analyses of data from individual soil profiles; (2) estimates of soil carbon fluxes based on stocks, fire history, drainage, and soil C inputs and decomposition constants based on field work using radiocarbon analyses; (3) fire history data estimating age ranges of time since last fire; (4) a raster image and an associated soils table file from which area-weighted maps of soil carbon and fluxes and fire history may be generated.
This data set contains (1) estimates of soil carbon stocks by horizon based on soil survey data and analyses of data from individual soil profiles; (2) estimates of soil carbon fluxes based on stocks, fire history, drainage, and soil C inputs and decomposition constants based on field work using radiocarbon analyses; (3) fire history data estimating age ranges of time since last fire; (4) a raster image and an associated soils table file from which area-weighted maps of soil carbon and fluxes and fire history may be generated.
ABSTRACT: This data set contains (1) estimates of soil carbon stocks by horizon based on soil survey data and analyses of data from individual soil profiles; (2) estimates of soil carbon fluxes based on stocks, fire history, drainage, and soil C inputs and decomposition constants based on field work using radiocarbon analyses; (3) fire history data estimating age ranges of time since last fire; (4) a raster image and an associated soils table file from which area-weighted maps of soil carbon and fluxes and fire history may be generated.
Contains the soil carbon data collected by TGB-12 in the northern study area.
Contains the soil carbon data collected by TGB-12 in the northern study area.
ABSTRACT: Contains the soil carbon data collected by TGB-12 in the northern study area.
Data collected to support analysis of soil carbon content in the NSA. Other ancillary information was stored and provided in two sets of soil pit description and surface vegetation transect files.  In addition, a site description file provides more information on the positioning of the sampling sites.
Data collected to support analysis of soil carbon content in the NSA. Other ancillary information was stored and provided in two sets of soil pit description and surface vegetation transect files.  In addition, a site description file provides more information on the positioning of the sampling sites.
ABSTRACT: Data collected to support analysis of soil carbon content in the NSA. Other ancillary information was stored and provided in two sets of soil pit description and surface vegetation transect files.  In addition, a site description file provides more information on the positioning of the sampling sites.
Contains data from 1993 from the Atmospheric Environment Service Campbell Scientific autostations collecting continuous fifteen minute data for BOREAS.
Contains data from 1993 from the Atmospheric Environment Service Campbell Scientific autostations collecting continuous fifteen minute data for BOREAS.
ABSTRACT: Contains data from 1993 from the Atmospheric Environment Service Campbell Scientific autostations collecting continuous fifteen minute data for BOREAS.
Contains data from 1994 from the Atmospheric Environment Service Campbell Scientific autostations collecting continuous fifteen minute data for BOREAS.
Contains data from 1994 from the Atmospheric Environment Service Campbell Scientific autostations collecting continuous fifteen minute data for BOREAS.
ABSTRACT: Contains data from 1994 from the Atmospheric Environment Service Campbell Scientific autostations collecting continuous fifteen minute data for BOREAS.
Contains data from 1995 from the Atmospheric Environment Service Campbell Scientific autostations collecting continuous fifteen minute data for BOREAS.
Contains data from 1995 from the Atmospheric Environment Service Campbell Scientific autostations collecting continuous fifteen minute data for BOREAS.
ABSTRACT: Contains data from 1995 from the Atmospheric Environment Service Campbell Scientific autostations collecting continuous fifteen minute data for BOREAS.
Contains data from 1996 from the Atmospheric Environment Service Campbell Scientific autostations collecting continuous fifteen minute data for BOREAS.
Contains data from 1996 from the Atmospheric Environment Service Campbell Scientific autostations collecting continuous fifteen minute data for BOREAS.
ABSTRACT: Contains data from 1996 from the Atmospheric Environment Service Campbell Scientific autostations collecting continuous fifteen minute data for BOREAS.
Contains 15 minute surface meteorology data collected during the 1994 field campaigns by the Atmospheric Environment Service Meteorological Automatic Reporting System II autostations.
Contains 15 minute surface meteorology data collected during the 1994 field campaigns by the Atmospheric Environment Service Meteorological Automatic Reporting System II autostations.
ABSTRACT: Contains 15 minute surface meteorology data collected during the 1994 field campaigns by the Atmospheric Environment Service Meteorological Automatic Reporting System II autostations.
Contains 15 minute surface meteorology data collected during the 1994 field campaigns by the Atmospheric Environment Service Remote Environmental Automatic Data Acquisition Concept (READAC) autostations.
Contains 15 minute surface meteorology data collected during the 1994 field campaigns by the Atmospheric Environment Service Remote Environmental Automatic Data Acquisition Concept (READAC) autostations.
ABSTRACT: Contains 15 minute surface meteorology data collected during the 1994 field campaigns by the Atmospheric Environment Service Remote Environmental Automatic Data Acquisition Concept (READAC) autostations.
Contains the data collected in 1994 by the AMS suite A instrument set operated by SRC and provided to BORIS.
Contains the data collected in 1994 by the AMS suite A instrument set operated by SRC and provided to BORIS.
ABSTRACT: Contains the data collected in 1994 by the AMS suite A instrument set operated by SRC and provided to BORIS.
Contains the data collected in 1995 by the AMS suite A instrument set operated by SRC and provided to BORIS.
Contains the data collected in 1995 by the AMS suite A instrument set operated by SRC and provided to BORIS.
ABSTRACT: Contains the data collected in 1995 by the AMS suite A instrument set operated by SRC and provided to BORIS.
Contains the data collected in 1996 by the AMS suite A instrument set operated by SRC and provided to BORIS.
Contains the data collected in 1996 by the AMS suite A instrument set operated by SRC and provided to BORIS.
ABSTRACT: Contains the data collected in 1996 by the AMS suite A instrument set operated by SRC and provided to BORIS.
Contains the data collected in 1994 by the AMS suite B instrument set operated by SRC and provided to BORIS.
Contains the data collected in 1994 by the AMS suite B instrument set operated by SRC and provided to BORIS.
ABSTRACT: Contains the data collected in 1994 by the AMS suite B instrument set operated by SRC and provided to BORIS.
Contains the data collected in 1995 by the AMS suite B instrument set operated by SRC and provided to BORIS.
Contains the data collected in 1995 by the AMS suite B instrument set operated by SRC and provided to BORIS.
ABSTRACT: Contains the data collected in 1995 by the AMS suite B instrument set operated by SRC and provided to BORIS.
Contains the data collected in 1996 by the AMS suite B instrument set operated by SRC and provided to BORIS.
Contains the data collected in 1996 by the AMS suite B instrument set operated by SRC and provided to BORIS.
ABSTRACT: Contains the data collected in 1996 by the AMS suite B instrument set operated by SRC and provided to BORIS.
Height of the mixed layer gas for each LIDAR shot in volume scan, then averaged
Height of the mixed layer gas for each LIDAR shot in volume scan, then averaged
ABSTRACT: Height of the mixed layer gas for each LIDAR shot in volume scan, then averaged
Inversion heights as measured using a Sodar by R.L. Coulter & M.L. Wesely
Inversion heights as measured using a Sodar by R.L. Coulter & M.L. Wesely
ABSTRACT: Inversion heights as measured using a Sodar by R.L. Coulter & M.L. Wesely
Evaluation of surface radiation and energy budget stations for FIFE
Evaluation of surface radiation and energy budget stations for FIFE
ABSTRACT: Evaluation of surface radiation and energy budget stations for FIFE
Surface flux measurements by Bowen Ratio technique during FIFE
Surface flux measurements by Bowen Ratio technique during FIFE
ABSTRACT: Surface flux measurements by Bowen Ratio technique during FIFE
Assessing the effects of annual burning & topography on surface energy exchanges
Assessing the effects of annual burning & topography on surface energy exchanges
ABSTRACT: Assessing the effects of annual burning & topography on surface energy exchanges
Retrieval of surface fluxes from a combination of satellite & surface platforms
Retrieval of surface fluxes from a combination of satellite & surface platforms
ABSTRACT: Retrieval of surface fluxes from a combination of satellite & surface platforms
Latent & sensible heat flux by Bowen Ratio & aerodynamic characterization of vegetation
Latent & sensible heat flux by Bowen Ratio & aerodynamic characterization of vegetation
ABSTRACT: Latent & sensible heat flux by Bowen Ratio & aerodynamic characterization of vegetation
Surface flux measurements by Bowen Ratio technique during FIFE
Surface flux measurements by Bowen Ratio technique during FIFE
ABSTRACT: Surface flux measurements by Bowen Ratio technique during FIFE
The Solar Backscattered Ultra Violet (SBUV) from Nimbus-4 Level-2 daily product (BUVN04L2) contains ozone nadir profile and total column data from retrievals generated from the v8.6 SBUV algorithm. The v8.6 SBUV algorithm estimates the ozone nadir profile and total column from SBUV measurements using 1) the Brion-Daumont-Malicet ozone cross sections, 2) an OMI-derived cloud-height climatology, 3) a revised a priori ozone climatology, and 4) inter-instrument calibration based on comparisons with no local time difference.

The BUVN04L2 product is written as daily files using the HDF5 format, with file sizes ranging from about 1 to 5 Mbytes. Data are available from May 1970 through April 1976. The BUVN04L2 data product was used as input in creating the BUVN04L3zm monthly zonal mean data product.
CALIPSO Infrared Imaging Radiometer Level 1B radiance data
CALIPSO Infrared Imaging Radiometer Level 1B radiance data
CALIPSO Infrared Imaging Radiometer Level 1B radiance data
CALIPSO Infrared Imaging Radiometer emissivity and cloud particle data assigned to IIR pixels on a 1 km grid centered on the Lidar track
CALIPSO Infrared Imaging Radiometer emissivity and cloud particle
CALIPSO Infrared Imaging Radiometer emissivity and cloud particle
CALIPSO Infrared Imaging Radiometer emissivity and cloud particle data related to pixels colocated to the Lidar track
CALIPSO Infrared Imaging Radiometer emissivity and cloud particle data related to pixels colocated to the Lidar track
CALIPSO Infrared Imaging Radiometer emissivity and cloud particle data related to pixels colocated to the Lidar track
CALIPSO Lidar Level 1B profile data
CALIPSO Lidar Level 1B profile data
CALIPSO Lidar Level 1B profile data
CALIPSO Lidar Level 1B profile data
CALIPSO Lidar Level 2 1 km cloud layer data
CALIPSO Lidar Level 2 1 km cloud layer data
CALIPSO Lidar Level 2 1 km cloud layer data
CALIPSO Lidar Level 2 5 km aerosol layer data
CALIPSO Lidar Level 2 5 km aerosol layer data
CALIPSO Lidar Level 2 5 km aerosol layer data
CALIPSO Lidar Level 2 aerosol profile data using the CALIPSO Lidar Ratio selection algorithm
CALIPSO Lidar Level 2 aerosol profile data using the CALIPSO Lidar Ratio selection algorithm
CALIPSO Lidar Level 2 aerosol profile data using the CALIPSO Lidar Ratio selection algorithm
CALIPSO Lidar Level 2 5 km cloud layer data
CALIPSO Lidar Level 2 5 km cloud layer data
CALIPSO Lidar Level 2 5 km cloud layer data
CALIPSO Lidar Level 2 Cloud Profile data
CALIPSO Lidar Level 2 Cloud Profile data
CALIPSO Lidar Level 2 Cloud Profile data
CALIPSO Lidar Level 2 1/3 km cloud layer data
CALIPSO Lidar Level 2 1/3 km cloud layer data
CALIPSO Lidar Level 2 1/3 km cloud layer data
CALIPSO Lidar Level 2 Polar Stratospheric Clouds presents, composition, and optical properties
CALIPSO Lidar Level 2 Vertical Feature Mask data
CALIPSO Lidar Level 2 Vertical Feature Mask data
CALIPSO Lidar Level 2 Vertical Feature Mask data
CALIPSO Lidar Level 3 aerosol profile all-sky data, beta
CALIPSO Lidar Level 3 aerosol profile combined data, beta
CALIPSO Wide Field Camera Level 1A 125 m Native Science data
CALIPSO Wide Field Camera Level 1A 125 m Native Science data
CALIPSO Wide Field Camera Level 1A 1 km Native Science data
CALIPSO Wide Field Camera Level 1A 1 km Native Science data
CALIPSO Wide Field Camera Level 1A 1 km Registered Science data
CALIPSO Wide Field Camera Level 1A 1 km Registered Science data
Study plot canopy chemistry values were calculated from leaf chemistry and litterfall weight values. Average leaf concentrations of nitrogen and carbon were used to investigate how reflectance varies with chemistry.
Study plot canopy chemistry values were calculated from leaf chemistry and litterfall weight values. Average leaf concentrations of nitrogen and carbon were used to investigate how reflectance varies with chemistry.
ABSTRACT: Study plot canopy chemistry values were calculated from leaf chemistry and litterfall weight values. Average leaf concentrations of nitrogen and carbon were used to investigate how reflectance varies with chemistry.
The CAMEX-3 DC-8 Airborne Vertical Atmosphere Profiling System (AVAPS) uses dropwinsonde and Global Positioning System (GPS) receivers to measure the atmospheric state parameters (temp, humidity, windspeed/direction pressure) and location in 3 dimensional space during the sonde's descent once each half second.  Measurements are transmitted to the aircraft from the time of release until impact with the ocean's surface.
The CAMEX-3 DC-8 Airborne Vertical Atmosphere Profiling System (AVAPS) uses dropwinsonde and Global Positioning System (GPS) receivers to measure the atmospheric state parameters (temp, humidity, windspeed/direction pressure) and location in 3 dimensional space during the sonde's descent once each half second.  Measurements are transmitted to the aircraft from the time of release until impact with the ocean's surface.
The CAMEX-3 DC-8 Airborne Vertical Atmosphere Profiling System (AVAPS) uses dropwinsonde and Global Positioning System (GPS) receivers to measure the atmospheric state parameters (temp, humidity, windspeed/direction pressure) and location in 3 dimensional space during the sonde's descent once each half second.  Measurements are transmitted to the aircraft from the time of release until impact with the ocean's surface.
The Advanced Microwave Precipitation Radiometer (AMPR) was deployed during the Third Convection and Moisture Experiment (CAMEX-3). AMPR data were collected at four microwave frequencies (10.7, 19.35, 37.1 and 85.5 GHz) for the period of 31 July 1999 through 27 September 1999. The CAMEX-3 mission was to study hurricanes over land and ocean in the U.S., Gulf of Mexico, Carribean, and Western Atlantic Ocean in coordination with multiple aircraft and research-quality radar, lightning, radiosonde and raingage sites.
The Advanced Microwave Precipitation Radiometer (AMPR) was deployed during the Third Convection and Moisture Experiment (CAMEX-3). AMPR data were collected at four microwave frequencies (10.7, 19.35, 37.1 and 85.5 GHz) for the period of 31 July 1999 through 27 September 1999. The CAMEX-3 mission was to study hurricanes over land and ocean in the U.S., Gulf of Mexico, Carribean, and Western Atlantic Ocean in coordination with multiple aircraft and research-quality radar, lightning, radiosonde and raingage sites.
The Advanced Microwave Precipitation Radiometer (AMPR) was deployed during the Third Convection and Moisture Experiment (CAMEX-3). AMPR data were collected at four microwave frequencies (10.7, 19.35, 37.1 and 85.5 GHz) for the period of 31 July 1999 through 27 September 1999. The CAMEX-3 mission was to study hurricanes over land and ocean in the U.S., Gulf of Mexico, Carribean, and Western Atlantic Ocean in coordination with multiple aircraft and research-quality radar, lightning, radiosonde and raingage sites.
In support of CAMEX-3, numerous Radiosonde and Rawinsondes were launched from Andros Island. These consisted of instruments manufactured by VIS and Vaisala. Some sondes were GPS or LORAN located so that winds aloft could be determined without ground based tracking systems. Data from the sondes was used to verify several ground based instruments observing the lower troposphere.
In support of CAMEX-3, numerous Radiosonde and Rawinsondes were launched from Andros Island. These consisted of instruments manufactured by VIS and Vaisala. Some sondes were GPS or LORAN located so that winds aloft could be determined without ground based tracking systems. Data from the sondes was used to verify several ground based instruments observing the lower troposphere.
In support of CAMEX-3, numerous Radiosonde and Rawinsondes were launched from Andros Island. These consisted of instruments manufactured by VIS and Vaisala. Some sondes were GPS or LORAN located so that winds aloft could be determined without ground based tracking systems. Data from the sondes was used to verify several ground based instruments observing the lower troposphere.
The Atmospheric Emitted Radiance Interferometer (AERI) was used to make atmospheric temperature and moisture retrievals. AERI provides absolutely calibrated radiances which can be used for forward calculation comparisons of radiosonde and LIDAR (for CAMEX-3, the SRL) profiles and provides a reference to the airborne and ground based remote sensing instruments. Additionally, AERI radiances contain valuable temperature and water vapor information which can be used to retrieve planetary boundary layer thermodynamics.
The Atmospheric Emitted Radiance Interferometer (AERI) was used to make atmospheric temperature and moisture retrievals. AERI provides absolutely calibrated radiances which can be used for forward calculation comparisons of radiosonde and LIDAR (for CAMEX-3, the SRL) profiles and provides a reference to the airborne and ground based remote sensing instruments. Additionally, AERI radiances contain valuable temperature and water vapor information which can be used to retrieve planetary boundary layer thermodynamics.
The Atmospheric Emitted Radiance Interferometer (AERI) was used to make atmospheric temperature and moisture retrievals. AERI provides absolutely calibrated radiances which can be used for forward calculation comparisons of radiosonde and LIDAR (for CAMEX-3, the SRL) profiles and provides a reference to the airborne and ground based remote sensing instruments. Additionally, AERI radiances contain valuable temperature and water vapor information which can be used to retrieve planetary boundary layer thermodynamics.
Cloud and aerosol particles are exposed to laser light to measure particle size from 0.3 micrometer to 6.4 millimeter, and both size and shape between 40 micrometer and 6.4 millimeter particle diameter as function of particle size.  The size distributions thus determined will be integrated to yield particle surface area, and ice and liquid water contents in clouds and precipitation.
Cloud and aerosol particles are exposed to laser light to measure particle size from 0.3 micrometer to 6.4 millimeter, and both size and shape between 40 micrometer and 6.4 millimeter particle diameter as function of particle size.  The size distributions thus determined will be integrated to yield particle surface area, and ice and liquid water contents in clouds and precipitation.
Cloud and aerosol particles are exposed to laser light to measure particle size from 0.3 micrometer to 6.4 millimeter, and both size and shape between 40 micrometer and 6.4 millimeter particle diameter as function of particle size.  The size distributions thus determined will be integrated to yield particle surface area, and ice and liquid water contents in clouds and precipitation.
Cloud and aerosol particles are exposed to laser light to measure particle size from 0.3 micrometer to 6.4 millimeter, and both size and shape between 40 micrometer and 6.4 millimeter particle diameter as function of particle size.  The size distributions thus determined will be integrated to yield particle surface area, and ice and liquid water contents in clouds and precipitation.
The CAMEX-3 Meteorological Measurement System (MMS) aboard the NASA DC-8 consists of three major systems: an air-motion sensing system to measure air velocity with respect to the aircraft, an aircraft-motion sensing system to measure the aircraft velocity with respect to the earth, and a data acquisition system to sample, process, and record the measured quantities.
The CAMEX-3 Meteorological Measurement System (MMS) aboard the NASA DC-8 consists of three major systems: an air-motion sensing system to measure air velocity with respect to the aircraft, an aircraft-motion sensing system to measure the aircraft velocity with respect to the earth, and a data acquisition system to sample, process, and record the measured quantities.
The CAMEX-3 Meteorological Measurement System (MMS) aboard the NASA DC-8 consists of three major systems: an air-motion sensing system to measure air velocity with respect to the aircraft, an aircraft-motion sensing system to measure the aircraft velocity with respect to the earth, and a data acquisition system to sample, process, and record the measured quantities.
The CAMEX-3 DC-8 Data Aquisition and Distribution System (DADS) Navigation data files contain information recorded by on board navigation and data collection systems. In addition to typical navigation data (e.g. date, time, lat/lon and altitude), it contains outside meteorological parameters such as wind speed and direction, temperature, cabin altitude, vertical speed, saturation vapor pressure of water and other parameters. These ascii text files are recorded every second for the length of the sortie.  In addition, graphical representations of these measured parameters are shown in .gif files.
The CAMEX-3 DC-8 Data Aquisition and Distribution System (DADS) Navigation data files contain information recorded by on board navigation and data collection systems. In addition to typical navigation data (e.g. date, time, lat/lon and altitude), it contains outside meteorological parameters such as wind speed and direction, temperature, cabin altitude, vertical speed, saturation vapor pressure of water and other parameters. These ascii text files are recorded every second for the length of the sortie.  In addition, graphical representations of these measured parameters are shown in .gif files.
The CAMEX-3 DC-8 Data Aquisition and Distribution System (DADS) Navigation data files contain information recorded by on board navigation and data collection systems. In addition to typical navigation data (e.g. date, time, lat/lon and altitude), it contains outside meteorological parameters such as wind speed and direction, temperature, cabin altitude, vertical speed, saturation vapor pressure of water and other parameters. These ascii text files are recorded every second for the length of the sortie.  In addition, graphical representations of these measured parameters are shown in .gif files.
The NPOESS Aircraft Sounder Testbed - Microwave Temperature Sounder (NAST-MTS) dataset contains information gathered during the Third Convection And Moisture EXperiment (CAMEX -3).  NAST-MTS contains two microwave radiometer systems covering the spectral ranges of 50-56 Ghz with nine double-sideband channels centered on the 118.75 Ghz oxygen line, and provides a temperature profile of the atmosphere from the flight altitude (approximately 20km) to the surface.
The NPOESS Aircraft Sounder Testbed - Microwave Temperature Sounder (NAST-MTS) dataset contains information gathered during the Third Convection And Moisture EXperiment (CAMEX -3).  NAST-MTS contains two microwave radiometer systems covering the spectral ranges of 50-56 Ghz with nine double-sideband channels centered on the 118.75 Ghz oxygen line, and provides a temperature profile of the atmosphere from the flight altitude (approximately 20km) to the surface.
The NPOESS Aircraft Sounder Testbed - Microwave Temperature Sounder (NAST-MTS) dataset contains information gathered during the Third Convection And Moisture EXperiment (CAMEX -3).  NAST-MTS contains two microwave radiometer systems covering the spectral ranges of 50-56 Ghz with nine double-sideband channels centered on the 118.75 Ghz oxygen line, and provides a temperature profile of the atmosphere from the flight altitude (approximately 20km) to the surface.
The CAMEX-3 ER-2 Navigation data files contain information recorded by on board navigation and data collection systems. In addition to typical navigation data (e.g. date, time, lat/lon and altitude) it contains outside meteorological parameters such as wind speed and direction and temperature. These ascii text files are recorded every second for the length of the sortie. In addition, graphical representations of these measured parameters are shown in .gif files.
The CAMEX-3 ER-2 Navigation data files contain information recorded by on board navigation and data collection systems. In addition to typical navigation data (e.g. date, time, lat/lon and altitude) it contains outside meteorological parameters such as wind speed and direction and temperature. These ascii text files are recorded every second for the length of the sortie. In addition, graphical representations of these measured parameters are shown in .gif files.
The CAMEX-3 ER-2 Navigation data files contain information recorded by on board navigation and data collection systems. In addition to typical navigation data (e.g. date, time, lat/lon and altitude) it contains outside meteorological parameters such as wind speed and direction and temperature. These ascii text files are recorded every second for the length of the sortie. In addition, graphical representations of these measured parameters are shown in .gif files.
In support of the third Convection and Moisture Experiment (CAMEX-3), imagery from the Geostationary Operational Environmental Satellite 8 (GOES-8) was collected and archived.Three channels were archived: channel 1-- visible (0.65 microns), channel 2-- infrared (11 microns) and channel 3-- known as the water vapor channel (6.75 microns).
In support of the third Convection and Moisture Experiment (CAMEX-3), imagery from the Geostationary Operational Environmental Satellite 8 (GOES-8) was collected and archived.Three channels were archived: channel 1-- visible (0.65 microns), channel 2-- infrared (11 microns) and channel 3-- known as the water vapor channel (6.75 microns).
In support of the third Convection and Moisture Experiment (CAMEX-3), imagery from the Geostationary Operational Environmental Satellite 8 (GOES-8) was collected and archived.Three channels were archived: channel 1-- visible (0.65 microns), channel 2-- infrared (11 microns) and channel 3-- known as the water vapor channel (6.75 microns).
This dataset was collected during CAMEX-3 in Aug and Sep of 1998, and consists of timeline measurements (one timeline per flight) of water vapor content during each of the DC-8 flights.  Using the JPL Laser Hygrometer making measurements out of the right side of the forward fuselage into the free airstream beyond the boundary layer, in situ moisture measurements were made of the atmosphere in immediate proximity to the aircraft along the flight track.
This dataset was collected during CAMEX-3 in Aug and Sep of 1998, and consists of timeline measurements (one timeline per flight) of water vapor content during each of the DC-8 flights.  Using the JPL Laser Hygrometer making measurements out of the right side of the forward fuselage into the free airstream beyond the boundary layer, in situ moisture measurements were made of the atmosphere in immediate proximity to the aircraft along the flight track.
This dataset was collected during CAMEX-3 in Aug and Sep of 1998, and consists of timeline measurements (one timeline per flight) of water vapor content during each of the DC-8 flights.  Using the JPL Laser Hygrometer making measurements out of the right side of the forward fuselage into the free airstream beyond the boundary layer, in situ moisture measurements were made of the atmosphere in immediate proximity to the aircraft along the flight track.
This dataset was collected during CAMEX-3 in Aug and Sep of 1998, and consists of timeline measurements (one timeline per flight) of water vapor content during
each of the DC-8 flights using the JPL SAW instrument.
This dataset was collected during CAMEX-3 in Aug and Sep of 1998, and consists of timeline measurements (one timeline per flight) of water vapor content during
each of the DC-8 flights using the JPL SAW instrument.
This dataset was collected during CAMEX-3 in Aug and Sep of 1998, and consists of timeline measurements (one timeline per flight) of water vapor content during
each of the DC-8 flights using the JPL SAW instrument.
The ER-2 Lightning Instrument Package (LIP) allows the  vector components of the electric field (i.e, Ex,Ey, Ez )to be readily obtained, and thus, greatly improves our knowledge of the electrical structure of storms overflown.  The field mills measure the components of the electric field over a wide dynamic range extending from fair weather electric fields, (i.e., a few to tens of V/m) to larger thunderstorm fields (I.e., tens of kV/m). Total lightning (i.e., cloud-to-ground, intracloud) is identified from the abrupt electric field changes in the data. The  conductivity probe measures the air conductivity at the aircraft flight altitude. Storm electric currents can be derived using the electric field and air conductivity measurements.
The ER-2 Lightning Instrument Package (LIP) allows the  vector components of the electric field (i.e, Ex,Ey, Ez )to be readily obtained, and thus, greatly improves our knowledge of the electrical structure of storms overflown.  The field mills measure the components of the electric field over a wide dynamic range extending from fair weather electric fields, (i.e., a few to tens of V/m) to larger thunderstorm fields (I.e., tens of kV/m). Total lightning (i.e., cloud-to-ground, intracloud) is identified from the abrupt electric field changes in the data. The  conductivity probe measures the air conductivity at the aircraft flight altitude. Storm electric currents can be derived using the electric field and air conductivity measurements.
The ER-2 Lightning Instrument Package (LIP) allows the  vector components of the electric field (i.e, Ex,Ey, Ez )to be readily obtained, and thus, greatly improves our knowledge of the electrical structure of storms overflown.  The field mills measure the components of the electric field over a wide dynamic range extending from fair weather electric fields, (i.e., a few to tens of V/m) to larger thunderstorm fields (I.e., tens of kV/m). Total lightning (i.e., cloud-to-ground, intracloud) is identified from the abrupt electric field changes in the data. The  conductivity probe measures the air conductivity at the aircraft flight altitude. Storm electric currents can be derived using the electric field and air conductivity measurements.
The Multi-center Airborne Coherent Atmospheric Wind Sensor (MACAWS) was deployed during the Third Convection and Moisture Experiment (CAMEX_3). Line-of-sight velocity and intensity as as function of range, backscattered intensity, turbulence approximation, and complex covariance data for the period of 3 August 1998 through 22 September 1998. The CAMEX-3 mission was to study hurricanes over land and ocean in the U.S., Gulf of Mexico, Carribbean, and Western Atlantic ocean in coordination with multiple aircraft and reserach-quality radar, lightning,radiosonde, and raingauge sites.
The Multi-center Airborne Coherent Atmospheric Wind Sensor (MACAWS) was deployed during the Third Convection and Moisture Experiment (CAMEX_3). Line-of-sight velocity and intensity as as function of range, backscattered intensity, turbulence approximation, and complex covariance data for the period of 3 August 1998 through 22 September 1998. The CAMEX-3 mission was to study hurricanes over land and ocean in the U.S., Gulf of Mexico, Carribbean, and Western Atlantic ocean in coordination with multiple aircraft and reserach-quality radar, lightning,radiosonde, and raingauge sites.
The Multi-center Airborne Coherent Atmospheric Wind Sensor (MACAWS) was deployed during the Third Convection and Moisture Experiment (CAMEX_3). Line-of-sight velocity and intensity as as function of range, backscattered intensity, turbulence approximation, and complex covariance data for the period of 3 August 1998 through 22 September 1998. The CAMEX-3 mission was to study hurricanes over land and ocean in the U.S., Gulf of Mexico, Carribbean, and Western Atlantic ocean in coordination with multiple aircraft and reserach-quality radar, lightning,radiosonde, and raingauge sites.
The Convection And Moisture EXperiment (CAMEX)-3 Mission Reports were filed every day that an aircraft flew in support of the experiment. The reports include a short description of the day's mission, its objective, and notes.
The Convection And Moisture EXperiment (CAMEX)-3 Mission Reports were filed every day that an aircraft flew in support of the experiment. The reports include a short description of the day's mission, its objective, and notes.
The Convection And Moisture EXperiment (CAMEX)-3 Mission Reports were filed every day that an aircraft flew in support of the experiment. The reports include a short description of the day's mission, its objective, and notes.
MAMS is a multispectral scanner which measures reflected radiation from the Earth's surface and clouds in eight visible/near-infrared bands, and thermal emission from the Earth' surface, clouds, and atmospheric constituents (primarily water vapor) in four infrared bands. The 5.0 mRaperture of MAMS produces an instantaneous field-of-view (IFOV) resolution of 100 m at nadir from the nominal ER-2 altitude of 20 km. The width of the entire cross path field-of-view scanned by the sensor is 37 km, thereby providing detailed resolution of atmospheric and surface features across the swath width and along the aircraft flight track. For clouds and thunderstorm features the IFOV decreases with increasing cloud height by a factor of (Z-20)/20, where Z is the cloud height  in kilometers.
MAMS is a multispectral scanner which measures reflected radiation from the Earth's surface and clouds in eight visible/near-infrared bands, and thermal emission from the Earth' surface, clouds, and atmospheric constituents (primarily water vapor) in four infrared bands. The 5.0 mRaperture of MAMS produces an instantaneous field-of-view (IFOV) resolution of 100 m at nadir from the nominal ER-2 altitude of 20 km. The width of the entire cross path field-of-view scanned by the sensor is 37 km, thereby providing detailed resolution of atmospheric and surface features across the swath width and along the aircraft flight track. For clouds and thunderstorm features the IFOV decreases with increasing cloud height by a factor of (Z-20)/20, where Z is the cloud height  in kilometers.
MAMS is a multispectral scanner which measures reflected radiation from the Earth's surface and clouds in eight visible/near-infrared bands, and thermal emission from the Earth' surface, clouds, and atmospheric constituents (primarily water vapor) in four infrared bands. The 5.0 mRaperture of MAMS produces an instantaneous field-of-view (IFOV) resolution of 100 m at nadir from the nominal ER-2 altitude of 20 km. The width of the entire cross path field-of-view scanned by the sensor is 37 km, thereby providing detailed resolution of atmospheric and surface features across the swath width and along the aircraft flight track. For clouds and thunderstorm features the IFOV decreases with increasing cloud height by a factor of (Z-20)/20, where Z is the cloud height  in kilometers.
The National Polar-orbiting Operational Environmental Satellite System (NPOESS) Atmospheric Sounding Testbed (NAST) is a suite of airborne infrared and microwave spectrometers, being developed for the Integrated Program Office (IPO), that will be flown on the NASA high altitude ER-2 aircraft as part of the risk reduction effort for NPOESS. In addition to their stand-alone scientific value, data from these airborne instruments will be used to simulate possible satellite-based radiance measurements, therefore enabling experimental validation of instrument system specifications and data processing techniques for future advanced atmospheric remote sensors (e.g., the proposed sounder component for NPOESS).
The National Polar-orbiting Operational Environmental Satellite System (NPOESS) Atmospheric Sounding Testbed (NAST) is a suite of airborne infrared and microwave spectrometers, being developed for the Integrated Program Office (IPO), that will be flown on the NASA high altitude ER-2 aircraft as part of the risk reduction effort for NPOESS. In addition to their stand-alone scientific value, data from these airborne instruments will be used to simulate possible satellite-based radiance measurements, therefore enabling experimental validation of instrument system specifications and data processing techniques for future advanced atmospheric remote sensors (e.g., the proposed sounder component for NPOESS).
The National Polar-orbiting Operational Environmental Satellite System (NPOESS) Atmospheric Sounding Testbed (NAST) is a suite of airborne infrared and microwave spectrometers, being developed for the Integrated Program Office (IPO), that will be flown on the NASA high altitude ER-2 aircraft as part of the risk reduction effort for NPOESS. In addition to their stand-alone scientific value, data from these airborne instruments will be used to simulate possible satellite-based radiance measurements, therefore enabling experimental validation of instrument system specifications and data processing techniques for future advanced atmospheric remote sensors (e.g., the proposed sounder component for NPOESS).
The Polarimetric Scanning Radiometer (PSR) is a versatile airborne microwave imaging radiometer developed by the Georgia Institute of Technology and the NOAA Environmental Technology Laboratory for the purpose of obtaining polarimetric microwave emission imagery of the Earth's oceans, land, ice, clouds, and precipitation.
The Polarimetric Scanning Radiometer (PSR) is a versatile airborne microwave imaging radiometer developed by the Georgia Institute of Technology and the NOAA Environmental Technology Laboratory for the purpose of obtaining polarimetric microwave emission imagery of the Earth's oceans, land, ice, clouds, and precipitation.
The Polarimetric Scanning Radiometer (PSR) is a versatile airborne microwave imaging radiometer developed by the Georgia Institute of Technology and the NOAA Environmental Technology Laboratory for the purpose of obtaining polarimetric microwave emission imagery of the Earth's oceans, land, ice, clouds, and precipitation.
The Scanning Raman Lidar (Light Detection and Ranging) dataset collected data during the CAMEX-3 campaign on Andros Island during the period 6 August - 20 September 1998 The SRL instrument is designed to determine the composition and vertical distribution of several atmospheric constituents, specifically water vapor and aerosols.
The Scanning Raman Lidar (Light Detection and Ranging) dataset collected data during the CAMEX-3 campaign on Andros Island during the period 6 August - 20 September 1998 The SRL instrument is designed to determine the composition and vertical distribution of several atmospheric constituents, specifically water vapor and aerosols.
The Scanning Raman Lidar (Light Detection and Ranging) dataset collected data during the CAMEX-3 campaign on Andros Island during the period 6 August - 20 September 1998 The SRL instrument is designed to determine the composition and vertical distribution of several atmospheric constituents, specifically water vapor and aerosols.
The Second Generation Precipitation Radar (PR-2) is a dual-frequency, Doppler, dual-polarization radar system that includes digital, real-time pulse compression, extremely compact RF electronics, and a large deployable dual-frequency cylindrical parabolic antenna subsystem.
The Second Generation Precipitation Radar (PR-2) is a dual-frequency, Doppler, dual-polarization radar system that includes digital, real-time pulse compression, extremely compact RF electronics, and a large deployable dual-frequency cylindrical parabolic antenna subsystem.
The Second Generation Precipitation Radar (PR-2) is a dual-frequency, Doppler, dual-polarization radar system that includes digital, real-time pulse compression, extremely compact RF electronics, and a large deployable dual-frequency cylindrical parabolic antenna subsystem.
The Aerosonde is an unmanned aerial vehicle with a wingspan of a 2.9 meters (~9 feet) weighing approximately 14 Kg (~31 lbs). Carrying a payload of air pressure, temperature and humidity probes, the aircraft can fly at altitudes from near the surface to 21,000 feet at speeds of 50-95 mph for periods of up to 30 hours. Controlled by dual computers and navigated by GPS, the Aerosonde is designed to economically collect meteorological data over a wide area.
The Aerosonde is an unmanned aerial vehicle with a wingspan of a 2.9 meters (~9 feet) weighing approximately 14 Kg (~31 lbs). Carrying a payload of air pressure, temperature and humidity probes, the aircraft can fly at altitudes from near the surface to 21,000 feet at speeds of 50-95 mph for periods of up to 30 hours. Controlled by dual computers and navigated by GPS, the Aerosonde is designed to economically collect meteorological data over a wide area.
The Aerosonde is an unmanned aerial vehicle with a wingspan of a 2.9 meters (~9 feet) weighing approximately 14 Kg (~31 lbs). Carrying a payload of air pressure, temperature and humidity probes, the aircraft can fly at altitudes from near the surface to 21,000 feet at speeds of 50-95 mph for periods of up to 30 hours. Controlled by dual computers and navigated by GPS, the Aerosonde is designed to economically collect meteorological data over a wide area.
The Advanced Microwave Precipitation Radiometer (AMPR) was deployed during the Fourth Convection and Moisture Experiment (CAMEX-4). AMPR data were collected at four microwave frequencies (10.7, 19.35, 37.1 and 85.5 GHz) for the period of 8 Aug 2001 through 24 Sept 2001. The CAMEX-4 mission was to study hurricanes over land and ocean in the U.S., Gulf of Mexico, Carribean, and Western Atlantic Ocean in coordination with multiple aircraft and research-quality radar, lightning, and radiosonde sites.
The Advanced Microwave Precipitation Radiometer (AMPR) was deployed during the Fourth Convection and Moisture Experiment (CAMEX-4). AMPR data were collected at four microwave frequencies (10.7, 19.35, 37.1 and 85.5 GHz) for the period of 8 Aug 2001 through 24 Sept 2001. The CAMEX-4 mission was to study hurricanes over land and ocean in the U.S., Gulf of Mexico, Carribean, and Western Atlantic Ocean in coordination with multiple aircraft and research-quality radar, lightning, and radiosonde sites.
The Advanced Microwave Precipitation Radiometer (AMPR) was deployed during the Fourth Convection and Moisture Experiment (CAMEX-4). AMPR data were collected at four microwave frequencies (10.7, 19.35, 37.1 and 85.5 GHz) for the period of 8 Aug 2001 through 24 Sept 2001. The CAMEX-4 mission was to study hurricanes over land and ocean in the U.S., Gulf of Mexico, Carribean, and Western Atlantic Ocean in coordination with multiple aircraft and research-quality radar, lightning, and radiosonde sites.
Numerous  Rawinsondes (radiosondes) were launched from Andros Island in support of CAMEX-4. These sondes provided Atmospheric soundings of temperature, pressure, relative humidity, wind, and altitude.
Numerous  Rawinsondes (radiosondes) were launched from Andros Island in support of CAMEX-4. These sondes provided Atmospheric soundings of temperature, pressure, relative humidity, wind, and altitude.
Numerous  Rawinsondes (radiosondes) were launched from Andros Island in support of CAMEX-4. These sondes provided Atmospheric soundings of temperature, pressure, relative humidity, wind, and altitude.
The Cloud Microphysics dataset consists of particle size distributions from three instruments, the 2D-P (two dimensional precipitation probe), the 2D-C (two dimensional cloud probe) and the FSSP (Forward Scattering Spectrometer Probe). These three instruments yield precipitation, hydrometeor and aerosol sizes ranging from 0.3-6400 micrometers. Data is in the form of images and ascii tables.
The Cloud Microphysics dataset consists of particle size distributions from three instruments, the 2D-P (two dimensional precipitation probe), the 2D-C (two dimensional cloud probe) and the FSSP (Forward Scattering Spectrometer Probe). These three instruments yield precipitation, hydrometeor and aerosol sizes ranging from 0.3-6400 micrometers. Data is in the form of images and ascii tables.
The Cloud Microphysics dataset consists of particle size distributions from three instruments, the 2D-P (two dimensional precipitation probe), the 2D-C (two dimensional cloud probe) and the FSSP (Forward Scattering Spectrometer Probe). These three instruments yield precipitation, hydrometeor and aerosol sizes ranging from 0.3-6400 micrometers. Data is in the form of images and ascii tables.
The Conically-Scanning Two-look Airborne Radiometer (C-STAR) was deployed during the Fourth Convection and Moisture Experiment (CAMEX-4). C-STAR data were collected at 37GHz (in the microwave part of the electromagnetic spectrum) for the period of 8 Aug 2001 through 24 Sept 2001. The CAMEX-4 missions studied hurricanes over land and ocean in the U.S., Gulf of Mexico, Carribean, and Western Atlantic Ocean, and made use of multiple aircraft and research-quality radar, lightning, and radiosonde sites.
The Conically-Scanning Two-look Airborne Radiometer (C-STAR) was deployed during the Fourth Convection and Moisture Experiment (CAMEX-4). C-STAR data were collected at 37GHz (in the microwave part of the electromagnetic spectrum) for the period of 8 Aug 2001 through 24 Sept 2001. The CAMEX-4 missions studied hurricanes over land and ocean in the U.S., Gulf of Mexico, Carribean, and Western Atlantic Ocean, and made use of multiple aircraft and research-quality radar, lightning, and radiosonde sites.
The Conically-Scanning Two-look Airborne Radiometer (C-STAR) was deployed during the Fourth Convection and Moisture Experiment (CAMEX-4). C-STAR data were collected at 37GHz (in the microwave part of the electromagnetic spectrum) for the period of 8 Aug 2001 through 24 Sept 2001. The CAMEX-4 missions studied hurricanes over land and ocean in the U.S., Gulf of Mexico, Carribean, and Western Atlantic Ocean, and made use of multiple aircraft and research-quality radar, lightning, and radiosonde sites.
The counterflow virtual impactor (CVI) was used to measure condensed water content (liquid water or ice in particles about 8 microns in diameter and up) and cloud condensation nuclei (CCN) on the DC-8 during CAMEX-4. Water content was measured after evaporation with a dual-wavelength Lyman-alpha hygrometer.
The counterflow virtual impactor (CVI) was used to measure condensed water content (liquid water or ice in particles about 8 microns in diameter and up) and cloud condensation nuclei (CCN) on the DC-8 during CAMEX-4. Water content was measured after evaporation with a dual-wavelength Lyman-alpha hygrometer.
The counterflow virtual impactor (CVI) was used to measure condensed water content (liquid water or ice in particles about 8 microns in diameter and up) and cloud condensation nuclei (CCN) on the DC-8 during CAMEX-4. Water content was measured after evaporation with a dual-wavelength Lyman-alpha hygrometer.
The DC-8 Dropsonde System (D8D) uses dropwinsonde and Global Positioning System (GPS) receivers to measure the atmospheric state parameters (temp, humidity, windspeed/direction pressure) and location in 3 dimensional space during the sonde's descent once each half second. Measurements are transmitted to the aircraft from the time of release until impact with the ocean's surface.
The DC-8 Dropsonde System (D8D) uses dropwinsonde and Global Positioning System (GPS) receivers to measure the atmospheric state parameters (temp, humidity, windspeed/direction pressure) and location in 3 dimensional space during the sonde's descent once each half second. Measurements are transmitted to the aircraft from the time of release until impact with the ocean's surface.
The DC-8 Dropsonde System (D8D) uses dropwinsonde and Global Positioning System (GPS) receivers to measure the atmospheric state parameters (temp, humidity, windspeed/direction pressure) and location in 3 dimensional space during the sonde's descent once each half second. Measurements are transmitted to the aircraft from the time of release until impact with the ocean's surface.
This dataset consists of VHS tapes which record the forward and nadir views from the NASA DC-8 aircraft on flights during CAMEX-4. The tapes have timestamps, and record the voice channels of the scientists and mission managers in the aircraft. Occasionally pilot and radio calls are heard.
The Camex-4 DC-8 Information Collection and Transmission System (ICATS) is designed to:  1) interface and process avionics and environmental paramaters from the Navigational Management System, GPS, Central Air Data Computer, Embedded GPS/INS, and analog voltage sources from aircraft and experimenters,  2) Furnish engineering unit values of selected parameters and computed functions for real-time video display, and archive ASCII data at experimenter stations,  3) Archive engineering unit values of 'Appendix A' (to the ICATS document included with dataset documentation) on data storage for post flight retrieval.
The Camex-4 DC-8 Information Collection and Transmission System (ICATS) is designed to:  1) interface and process avionics and environmental paramaters from the Navigational Management System, GPS, Central Air Data Computer, Embedded GPS/INS, and analog voltage sources from aircraft and experimenters,  2) Furnish engineering unit values of selected parameters and computed functions for real-time video display, and archive ASCII data at experimenter stations,  3) Archive engineering unit values of 'Appendix A' (to the ICATS document included with dataset documentation) on data storage for post flight retrieval.
The Camex-4 DC-8 Information Collection and Transmission System (ICATS) is designed to:  1) interface and process avionics and environmental paramaters from the Navigational Management System, GPS, Central Air Data Computer, Embedded GPS/INS, and analog voltage sources from aircraft and experimenters,  2) Furnish engineering unit values of selected parameters and computed functions for real-time video display, and archive ASCII data at experimenter stations,  3) Archive engineering unit values of 'Appendix A' (to the ICATS document included with dataset documentation) on data storage for post flight retrieval.
The DC-8 Lightning Instrument Package (LIP) allows the vector components of the electric field (i.e, Ex,Ey, Ez )to be readily obtained, and thus, greatly improves our knowledge of the electrical structure of storms overflown. The field mills measure the components of the electric field over a wide dynamic range extending from fair weather electric fields, (i.e., a few to tens of V/m) to larger thunderstorm fields (I.e., tens of kV/m). Total lightning (i.e., cloud-to-ground, intracloud) is identified from the abrupt electric field changes in the data. The conductivity probe measures the air conductivity at the aircraft flight altitude. Storm electric currents can be derived using the electric field and air conductivity measurements.
The DC-8 Lightning Instrument Package (LIP) allows the vector components of the electric field (i.e, Ex,Ey, Ez )to be readily obtained, and thus, greatly improves our knowledge of the electrical structure of storms overflown. The field mills measure the components of the electric field over a wide dynamic range extending from fair weather electric fields, (i.e., a few to tens of V/m) to larger thunderstorm fields (I.e., tens of kV/m). Total lightning (i.e., cloud-to-ground, intracloud) is identified from the abrupt electric field changes in the data. The conductivity probe measures the air conductivity at the aircraft flight altitude. Storm electric currents can be derived using the electric field and air conductivity measurements.
The DC-8 Lightning Instrument Package (LIP) allows the vector components of the electric field (i.e, Ex,Ey, Ez )to be readily obtained, and thus, greatly improves our knowledge of the electrical structure of storms overflown. The field mills measure the components of the electric field over a wide dynamic range extending from fair weather electric fields, (i.e., a few to tens of V/m) to larger thunderstorm fields (I.e., tens of kV/m). Total lightning (i.e., cloud-to-ground, intracloud) is identified from the abrupt electric field changes in the data. The conductivity probe measures the air conductivity at the aircraft flight altitude. Storm electric currents can be derived using the electric field and air conductivity measurements.
The NASA DC-8 Meteorological Measurement System consists of three major systems: an air-motion sensing system to measure air velocity with respect to the aircraft, an aircraft-motion sensing system to measure the aircraft velocity with respect to the earth, and a data acquisition system to sample, process, and record the measured quantities.
The NASA DC-8 Meteorological Measurement System consists of three major systems: an air-motion sensing system to measure air velocity with respect to the aircraft, an aircraft-motion sensing system to measure the aircraft velocity with respect to the earth, and a data acquisition system to sample, process, and record the measured quantities.
The NASA DC-8 Meteorological Measurement System consists of three major systems: an air-motion sensing system to measure air velocity with respect to the aircraft, an aircraft-motion sensing system to measure the aircraft velocity with respect to the earth, and a data acquisition system to sample, process, and record the measured quantities.
The Microwave Temperature Profiler (MTP) is a passive microwave radiometer which measures the thermal emmission from oxygen molecules in the atmosphere for a selection of elevation angles. Current observing frequencies are 56.6 and 58.8 GHz. Measured brightness temperatues versus elevation angle is converted to air temperature versus altitude using a statistical retrieval procedure. An altitude temperature profile is produced every three km along the flight path.
The Microwave Temperature Profiler (MTP) is a passive microwave radiometer which measures the thermal emmission from oxygen molecules in the atmosphere for a selection of elevation angles. Current observing frequencies are 56.6 and 58.8 GHz. Measured brightness temperatues versus elevation angle is converted to air temperature versus altitude using a statistical retrieval procedure. An altitude temperature profile is produced every three km along the flight path.
The Microwave Temperature Profiler (MTP) is a passive microwave radiometer which measures the thermal emmission from oxygen molecules in the atmosphere for a selection of elevation angles. Current observing frequencies are 56.6 and 58.8 GHz. Measured brightness temperatues versus elevation angle is converted to air temperature versus altitude using a statistical retrieval procedure. An altitude temperature profile is produced every three km along the flight path.
The Nevzorov probe is an instrument that measures the total water content of the sample of air which passes through it. It flew on the NASA DC-8 during the CAMEX-4 experiment. The Nevzorov probe is a so-called hot-wire device. Two resistors are heated to evaporate all hydrometeors that touch their surfaces during the flight. Due to their shape they are able to catch small droplets or droplets and ice crystals. The amount of energy necessary to evaporate particles is a direct measure of the liquid water content of the hydrometeor (liquid or frozen) and also gives an indication of the water vapor present.
The Nevzorov probe is an instrument that measures the total water content of the sample of air which passes through it. It flew on the NASA DC-8 during the CAMEX-4 experiment. The Nevzorov probe is a so-called hot-wire device. Two resistors are heated to evaporate all hydrometeors that touch their surfaces during the flight. Due to their shape they are able to catch small droplets or droplets and ice crystals. The amount of energy necessary to evaporate particles is a direct measure of the liquid water content of the hydrometeor (liquid or frozen) and also gives an indication of the water vapor present.
The Nevzorov probe is an instrument that measures the total water content of the sample of air which passes through it. It flew on the NASA DC-8 during the CAMEX-4 experiment. The Nevzorov probe is a so-called hot-wire device. Two resistors are heated to evaporate all hydrometeors that touch their surfaces during the flight. Due to their shape they are able to catch small droplets or droplets and ice crystals. The amount of energy necessary to evaporate particles is a direct measure of the liquid water content of the hydrometeor (liquid or frozen) and also gives an indication of the water vapor present.
Ozone is measured in situ using a photometer consisting of a mercury lamp, two sample chambers that can be peridically scrubbed of ozone, and two detectors that measure the 254-nm radtion transmitted through the chamber from the lamp. The ozone number density is calculated using the ozone absorption cross-section at 254-nm and the Beer-Lambert Law. The one second data collection rate the minimum detectable concentration of ozone (one sigma) is 1.5 x 1010 molecules/cm3.
Ozone is measured in situ using a photometer consisting of a mercury lamp, two sample chambers that can be peridically scrubbed of ozone, and two detectors that measure the 254-nm radtion transmitted through the chamber from the lamp. The ozone number density is calculated using the ozone absorption cross-section at 254-nm and the Beer-Lambert Law. The one second data collection rate the minimum detectable concentration of ozone (one sigma) is 1.5 x 1010 molecules/cm3.
Ozone is measured in situ using a photometer consisting of a mercury lamp, two sample chambers that can be peridically scrubbed of ozone, and two detectors that measure the 254-nm radtion transmitted through the chamber from the lamp. The ozone number density is calculated using the ozone absorption cross-section at 254-nm and the Beer-Lambert Law. The one second data collection rate the minimum detectable concentration of ozone (one sigma) is 1.5 x 1010 molecules/cm3.
The ER-2 Doppler radar (EDOP) is an X-band (9.6 GHz) Doppler radar mounted in the nose of ER-2. The instrument has two fixed antennas, one pointing at nadir and the second pointing approximately 33 degree ahead of nadir. The beam width of the antenna is 3 degree in the vertical and horizontal directions which, for a 20 km altitude, yields a nadir footprint a the surface of 1 km.
The ER-2 Doppler radar (EDOP) is an X-band (9.6 GHz) Doppler radar mounted in the nose of ER-2. The instrument has two fixed antennas, one pointing at nadir and the second pointing approximately 33 degree ahead of nadir. The beam width of the antenna is 3 degree in the vertical and horizontal directions which, for a 20 km altitude, yields a nadir footprint a the surface of 1 km.
The ER-2 Doppler radar (EDOP) is an X-band (9.6 GHz) Doppler radar mounted in the nose of ER-2. The instrument has two fixed antennas, one pointing at nadir and the second pointing approximately 33 degree ahead of nadir. The beam width of the antenna is 3 degree in the vertical and horizontal directions which, for a 20 km altitude, yields a nadir footprint a the surface of 1 km.
The ER-2 Hight Altitude Dropsonde System (EHAD) uses dropwinsonde fitted with Global Positioning System (GPS) receivers to measure the atmospheric state parameters (temp, humidity, windspeed/direction pressure) and location in 3 dimensional space during the sonde's descent once each half second. Measurements are transmitted to the aircraft from the time of release until impact with the ocean's surface.
The ER-2 Hight Altitude Dropsonde System (EHAD) uses dropwinsonde fitted with Global Positioning System (GPS) receivers to measure the atmospheric state parameters (temp, humidity, windspeed/direction pressure) and location in 3 dimensional space during the sonde's descent once each half second. Measurements are transmitted to the aircraft from the time of release until impact with the ocean's surface.
The ER-2 Hight Altitude Dropsonde System (EHAD) uses dropwinsonde fitted with Global Positioning System (GPS) receivers to measure the atmospheric state parameters (temp, humidity, windspeed/direction pressure) and location in 3 dimensional space during the sonde's descent once each half second. Measurements are transmitted to the aircraft from the time of release until impact with the ocean's surface.
The ER-2 Lightning Instrument Package (LIP) allows the vector components of the electric field (i.e, Ex, Ey, Ez )to be readily obtained, and thus, greatly improves our knowledge of the electrical structure of storms overflown. The field mills measure the components of the electric field over a wide dynamic range extending from fair weather electric fields, (i.e., a few to tens of V/m) to larger thunderstorm fields (I.e., tens of kV/m). Total lightning (i.e., cloud-to-ground, intracloud) is identified from the abrupt electric field changes in the data. The conductivity probe measures the air conductivity at the aircraft flight altitude. Storm electric currents can be derived using the electric field and air conductivity measurements.
The ER-2 Lightning Instrument Package (LIP) allows the vector components of the electric field (i.e, Ex, Ey, Ez )to be readily obtained, and thus, greatly improves our knowledge of the electrical structure of storms overflown. The field mills measure the components of the electric field over a wide dynamic range extending from fair weather electric fields, (i.e., a few to tens of V/m) to larger thunderstorm fields (I.e., tens of kV/m). Total lightning (i.e., cloud-to-ground, intracloud) is identified from the abrupt electric field changes in the data. The conductivity probe measures the air conductivity at the aircraft flight altitude. Storm electric currents can be derived using the electric field and air conductivity measurements.
The ER-2 Lightning Instrument Package (LIP) allows the vector components of the electric field (i.e, Ex, Ey, Ez )to be readily obtained, and thus, greatly improves our knowledge of the electrical structure of storms overflown. The field mills measure the components of the electric field over a wide dynamic range extending from fair weather electric fields, (i.e., a few to tens of V/m) to larger thunderstorm fields (I.e., tens of kV/m). Total lightning (i.e., cloud-to-ground, intracloud) is identified from the abrupt electric field changes in the data. The conductivity probe measures the air conductivity at the aircraft flight altitude. Storm electric currents can be derived using the electric field and air conductivity measurements.
The Microwave Temperature Profiler (MTP) is a passive microwave radiometer which measures the thermal emmission from oxygen molecules in the atmosphere for a selection of elevation angles. Current observing frequencies are 56.6 and 58.8 GHz. Measured brightness temperature versus elevation angle is converted to air temperature versus altitude using a statistical retrieval procedure. An altitude temperature profile is produced every three km along the flight path. Theses data were collected during the CAMEX-4 campaign August 16-September 25, 2002 from Jacksonville Naval Air Station, Florida.
The Microwave Temperature Profiler (MTP) is a passive microwave radiometer which measures the thermal emmission from oxygen molecules in the atmosphere for a selection of elevation angles. Current observing frequencies are 56.6 and 58.8 GHz. Measured brightness temperature versus elevation angle is converted to air temperature versus altitude using a statistical retrieval procedure. An altitude temperature profile is produced every three km along the flight path. Theses data were collected during the CAMEX-4 campaign August 16-September 25, 2002 from Jacksonville Naval Air Station, Florida.
The Microwave Temperature Profiler (MTP) is a passive microwave radiometer which measures the thermal emmission from oxygen molecules in the atmosphere for a selection of elevation angles. Current observing frequencies are 56.6 and 58.8 GHz. Measured brightness temperature versus elevation angle is converted to air temperature versus altitude using a statistical retrieval procedure. An altitude temperature profile is produced every three km along the flight path. Theses data were collected during the CAMEX-4 campaign August 16-September 25, 2002 from Jacksonville Naval Air Station, Florida.
The MODIS Airborne Simulator (MAS) is an airborne scanning spectrometer that acquires high spatial resolution imagery of cloud and surface features from its vantage point on-board a NASA ER-2 high-altitude research aircraft.  This dataset has visible and infrared imagery calibrated to at-sensor radiance.  Included are many associated browse files including the flight line tracks, and also text files of nadir brightness temperature and radiance for selected bands.
The MODIS Airborne Simulator (MAS) is an airborne scanning spectrometer that acquires high spatial resolution imagery of cloud and surface features from its vantage point on-board a NASA ER-2 high-altitude research aircraft.  This dataset has visible and infrared imagery calibrated to at-sensor radiance.  Included are many associated browse files including the flight line tracks, and also text files of nadir brightness temperature and radiance for selected bands.
The MODIS Airborne Simulator (MAS) is an airborne scanning spectrometer that acquires high spatial resolution imagery of cloud and surface features from its vantage point on-board a NASA ER-2 high-altitude research aircraft.  This dataset has visible and infrared imagery calibrated to at-sensor radiance.  Included are many associated browse files including the flight line tracks, and also text files of nadir brightness temperature and radiance for selected bands.
The CAMEX-4 ER-2 Navigation data files contain information recorded by on board navigation and data collection systems. In addition to typical navigation data (e.g. date, time, lat/lon and altitude) it contains outside meteorological parameters such as wind speed and direction and temperature. These ascii text files are recorded every second for the length of the sortie. Additionally, graphical representations of these measured parameters are shown in .gif files.
The CAMEX-4 ER-2 Navigation data files contain information recorded by on board navigation and data collection systems. In addition to typical navigation data (e.g. date, time, lat/lon and altitude) it contains outside meteorological parameters such as wind speed and direction and temperature. These ascii text files are recorded every second for the length of the sortie. Additionally, graphical representations of these measured parameters are shown in .gif files.
The CAMEX-4 ER-2 Navigation data files contain information recorded by on board navigation and data collection systems. In addition to typical navigation data (e.g. date, time, lat/lon and altitude) it contains outside meteorological parameters such as wind speed and direction and temperature. These ascii text files are recorded every second for the length of the sortie. Additionally, graphical representations of these measured parameters are shown in .gif files.
In support of the fourth Convection and Moisture Experiment (CAMEX-4), imagery from the Geostationary Operational Environmental Satellite 8 (GOES-8) was collected and archived. Three channels were archived: channel 1-- visible (0.65 microns), channel 2-- infrared (11 microns) and channel 3-- known as the water vapor channel (6.75 microns). Data files in McIDAS format as well as browse images were created.
In support of the fourth Convection and Moisture Experiment (CAMEX-4), imagery from the Geostationary Operational Environmental Satellite 8 (GOES-8) was collected and archived. Three channels were archived: channel 1-- visible (0.65 microns), channel 2-- infrared (11 microns) and channel 3-- known as the water vapor channel (6.75 microns). Data files in McIDAS format as well as browse images were created.
In support of the fourth Convection and Moisture Experiment (CAMEX-4), imagery from the Geostationary Operational Environmental Satellite 8 (GOES-8) was collected and archived. Three channels were archived: channel 1-- visible (0.65 microns), channel 2-- infrared (11 microns) and channel 3-- known as the water vapor channel (6.75 microns). Data files in McIDAS format as well as browse images were created.
The High Altitude monolithic microwave integrated Circuit (MMIC) Sounding Radiometer (HAMSR) is a microwave atmospheric sounder recently developed by JPL under the NASA Instrument Incubator Program. Operating with 25 spectral channels in the 50-190 HGz region, it provides measurements that can be used to infer the 3-D distribution of temperature, water vapor, and liquid water in the atmosphere, even in the presence of clouds.  HAMSR was mounted in a wing pod of a NASA ER-2 research aircraft.
The High Altitude monolithic microwave integrated Circuit (MMIC) Sounding Radiometer (HAMSR) is a microwave atmospheric sounder recently developed by JPL under the NASA Instrument Incubator Program. Operating with 25 spectral channels in the 50-190 HGz region, it provides measurements that can be used to infer the 3-D distribution of temperature, water vapor, and liquid water in the atmosphere, even in the presence of clouds.  HAMSR was mounted in a wing pod of a NASA ER-2 research aircraft.
The High Altitude monolithic microwave integrated Circuit (MMIC) Sounding Radiometer (HAMSR) is a microwave atmospheric sounder recently developed by JPL under the NASA Instrument Incubator Program. Operating with 25 spectral channels in the 50-190 HGz region, it provides measurements that can be used to infer the 3-D distribution of temperature, water vapor, and liquid water in the atmosphere, even in the presence of clouds.  HAMSR was mounted in a wing pod of a NASA ER-2 research aircraft.
The Laser Hygrometer measures in situ water vapor content using a tuneable laser emitting at 1.37 microns. Absorption at that wavelength is a function of water vapor content. Measuring the amount of absorption in an open path beyond the aircraft boundary layer, a value of water vapor pressure is made. The maximum sampling rate is 8 Hz, but the instrument is normally configured through the software for a 1Hz sampling rate.
The Laser Hygrometer measures in situ water vapor content using a tuneable laser emitting at 1.37 microns. Absorption at that wavelength is a function of water vapor content. Measuring the amount of absorption in an open path beyond the aircraft boundary layer, a value of water vapor pressure is made. The maximum sampling rate is 8 Hz, but the instrument is normally configured through the software for a 1Hz sampling rate.
The Laser Hygrometer measures in situ water vapor content using a tuneable laser emitting at 1.37 microns. Absorption at that wavelength is a function of water vapor content. Measuring the amount of absorption in an open path beyond the aircraft boundary layer, a value of water vapor pressure is made. The maximum sampling rate is 8 Hz, but the instrument is normally configured through the software for a 1Hz sampling rate.
LASE is an airborne DIAL (Differential Absorption Lidar) system used to measure water vapor, aerosols, and clouds throughout the troposphere.  LASE operates by locking to a strong water vapor line and electronically tuning to any spectral position on the absorption line to choose the suitable absorption cross-section for optimum measurements over a range of water vapor concentrations in the atmosphere. During CAMEX-4, LASE operated from the NASA DC-8 using strong and weak water vapor lines in both the nadir and zenith modes, thereby simultaneously acquiring data below and above the aircraft.
LASE is an airborne DIAL (Differential Absorption Lidar) system used to measure water vapor, aerosols, and clouds throughout the troposphere.  LASE operates by locking to a strong water vapor line and electronically tuning to any spectral position on the absorption line to choose the suitable absorption cross-section for optimum measurements over a range of water vapor concentrations in the atmosphere. During CAMEX-4, LASE operated from the NASA DC-8 using strong and weak water vapor lines in both the nadir and zenith modes, thereby simultaneously acquiring data below and above the aircraft.
LASE is an airborne DIAL (Differential Absorption Lidar) system used to measure water vapor, aerosols, and clouds throughout the troposphere.  LASE operates by locking to a strong water vapor line and electronically tuning to any spectral position on the absorption line to choose the suitable absorption cross-section for optimum measurements over a range of water vapor concentrations in the atmosphere. During CAMEX-4, LASE operated from the NASA DC-8 using strong and weak water vapor lines in both the nadir and zenith modes, thereby simultaneously acquiring data below and above the aircraft.
The University of Alabama in Huntsville (UAH)  Mobile Integrated Profiling System (MIPS) is a mobile atmospheric profiling system. It includes a 915 MHz Doppler profiler, lidar ceilometer, 12 channel microwave profiling radiometer, Doppler Sodar, Radio Acoustic Sounding System (RASS), Field Mills, and surface observing station. This dataset contains 15 minute averaged 3-D wind profiles. Additionally, data from each of the 5 beams (including radial velocity and backscatter intensity) are contained in the dataset.
The University of Alabama in Huntsville (UAH)  Mobile Integrated Profiling System (MIPS) is a mobile atmospheric profiling system. It includes a 915 MHz Doppler profiler, lidar ceilometer, 12 channel microwave profiling radiometer, Doppler Sodar, Radio Acoustic Sounding System (RASS), Field Mills, and surface observing station. This dataset contains 15 minute averaged 3-D wind profiles. Additionally, data from each of the 5 beams (including radial velocity and backscatter intensity) are contained in the dataset.
The University of Alabama in Huntsville (UAH)  Mobile Integrated Profiling System (MIPS) is a mobile atmospheric profiling system. It includes a 915 MHz Doppler profiler, lidar ceilometer, 12 channel microwave profiling radiometer, Doppler Sodar, Radio Acoustic Sounding System (RASS), Field Mills, and surface observing station. This dataset contains 15 minute averaged 3-D wind profiles. Additionally, data from each of the 5 beams (including radial velocity and backscatter intensity) are contained in the dataset.
The University of Alabama in Huntsville (UAH) Mobile Integrated Profiling System (MIPS) is a mobile atmospheric profiling system. It includes a 915 MHz Doppler profiler, lidar ceilometer, 12 channel microwave profiling radiometer, Doppler Sodar, Radio Acoustic Sounding System (RASS), Field Mills, and surface observing station. The ceilometer data collection gathers backscatter power and up to three cloud base heights.
The University of Alabama in Huntsville (UAH) Mobile Integrated Profiling System (MIPS) is a mobile atmospheric profiling system. It includes a 915 MHz Doppler profiler, lidar ceilometer, 12 channel microwave profiling radiometer, Doppler Sodar, Radio Acoustic Sounding System (RASS), Field Mills, and surface observing station. The ceilometer data collection gathers backscatter power and up to three cloud base heights.
The University of Alabama in Huntsville (UAH) Mobile Integrated Profiling System (MIPS) is a mobile atmospheric profiling system. It includes a 915 MHz Doppler profiler, lidar ceilometer, 12 channel microwave profiling radiometer, Doppler Sodar, Radio Acoustic Sounding System (RASS), Field Mills, and surface observing station. The ceilometer data collection gathers backscatter power and up to three cloud base heights.
The University of Alabama in Huntsville (UAH) Mobile Integrated Profiling System (MIPS) is a mobile atmospheric profiling system. It includes a 915 MHz Doppler profiler, lidar ceilometer, 12 channel microwave profiling radiometer, Doppler Sodar, Radio Acoustic Sounding System (RASS), Field Mills, and surface observing station. This dataset consists of data from the Electric Field Mills which yield information about the atmospheric electrical fields above the instruments.
The University of Alabama in Huntsville (UAH) Mobile Integrated Profiling System (MIPS) is a mobile atmospheric profiling system. It includes a 915 MHz Doppler profiler, lidar ceilometer, 12 channel microwave profiling radiometer, Doppler Sodar, Radio Acoustic Sounding System (RASS), Field Mills, and surface observing station. This dataset consists of data from the Electric Field Mills which yield information about the atmospheric electrical fields above the instruments.
The University of Alabama in Huntsville (UAH) Mobile Integrated Profiling System (MIPS) is a mobile atmospheric profiling system. It includes a 915 MHz Doppler profiler, lidar ceilometer, 12 channel microwave profiling radiometer, Doppler Sodar, Radio Acoustic Sounding System (RASS), Field Mills, and surface observing station. This dataset consists of data from the Electric Field Mills which yield information about the atmospheric electrical fields above the instruments.
The University of Alabama in Huntsville (UAH) Mobile Integrated Profiling System (MIPS) is a mobile atmospheric profiling system. It includes a 915 MHz Doppler profiler, lidar ceilometer, 12 channel microwave profiling radiometer, Doppler Sodar, Radio Acoustic Sounding System (RASS), Field Mills, and surface observing station. The 12 channel microwave profiling radiometer provides profiles of temperature, water vapor and liquid water and integrated values of water vapor and liquid water from the surface to 10km every ~ 15 minutes. Cloud base temperature is also measured.
The University of Alabama in Huntsville (UAH) Mobile Integrated Profiling System (MIPS) is a mobile atmospheric profiling system. It includes a 915 MHz Doppler profiler, lidar ceilometer, 12 channel microwave profiling radiometer, Doppler Sodar, Radio Acoustic Sounding System (RASS), Field Mills, and surface observing station. The 12 channel microwave profiling radiometer provides profiles of temperature, water vapor and liquid water and integrated values of water vapor and liquid water from the surface to 10km every ~ 15 minutes. Cloud base temperature is also measured.
The University of Alabama in Huntsville (UAH) Mobile Integrated Profiling System (MIPS) is a mobile atmospheric profiling system. It includes a 915 MHz Doppler profiler, lidar ceilometer, 12 channel microwave profiling radiometer, Doppler Sodar, Radio Acoustic Sounding System (RASS), Field Mills, and surface observing station. The 12 channel microwave profiling radiometer provides profiles of temperature, water vapor and liquid water and integrated values of water vapor and liquid water from the surface to 10km every ~ 15 minutes. Cloud base temperature is also measured.
The University of Alabama in Huntsville (UAH) Mobile Integrated Profiling System (MIPS) is a mobile atmospheric profiling system.  It includes a 915 MHz Doppler profiler, lidar ceilometer, 12 channel microwave profiling radiometer, Doppler Sodar, Radio Acoustic Sounding System (RASS), Field Mills, and surface observing station. This dataset consists of cdf and mom files tarred together for a day. The 'cdf' file collects 15 minute average 3-D wind profiles from the Doppler Sodar starting at the beginning of each day. The 'mom' file contains data from each beam about radial velocity and backscattter intensity. Each horizontal beam is approximately 7 seconds apart; vertical beams are approximately 21 seconds apart.
The University of Alabama in Huntsville (UAH) Mobile Integrated Profiling System (MIPS) is a mobile atmospheric profiling system.  It includes a 915 MHz Doppler profiler, lidar ceilometer, 12 channel microwave profiling radiometer, Doppler Sodar, Radio Acoustic Sounding System (RASS), Field Mills, and surface observing station. This dataset consists of cdf and mom files tarred together for a day. The 'cdf' file collects 15 minute average 3-D wind profiles from the Doppler Sodar starting at the beginning of each day. The 'mom' file contains data from each beam about radial velocity and backscattter intensity. Each horizontal beam is approximately 7 seconds apart; vertical beams are approximately 21 seconds apart.
The University of Alabama in Huntsville (UAH) Mobile Integrated Profiling System (MIPS) is a mobile atmospheric profiling system.  It includes a 915 MHz Doppler profiler, lidar ceilometer, 12 channel microwave profiling radiometer, Doppler Sodar, Radio Acoustic Sounding System (RASS), Field Mills, and surface observing station. This dataset consists of cdf and mom files tarred together for a day. The 'cdf' file collects 15 minute average 3-D wind profiles from the Doppler Sodar starting at the beginning of each day. The 'mom' file contains data from each beam about radial velocity and backscattter intensity. Each horizontal beam is approximately 7 seconds apart; vertical beams are approximately 21 seconds apart.
The University of Alabama in Huntsville (UAH) Mobile Integrated Profiling System (MIPS) is a mobile atmospheric profiling system.  It includes a 915 MHz Doppler profiler, lidar ceilometer, 12 channel microwave profiling radiometer, Doppler Sodar, Radio Acoustic Sounding System (RASS), Field Mills, and surface observing station. This dataset consists of data from Surface Station One containing multiple instruments including an anemometer, rain gauge, thermometer, pyranometer and barometer.  Information, collected at 1Hz, includes windspeed and direction as well as precipitation, temperature/humidity, solar radiation, and atmospheric pressure.
The University of Alabama in Huntsville (UAH) Mobile Integrated Profiling System (MIPS) is a mobile atmospheric profiling system.  It includes a 915 MHz Doppler profiler, lidar ceilometer, 12 channel microwave profiling radiometer, Doppler Sodar, Radio Acoustic Sounding System (RASS), Field Mills, and surface observing station. This dataset consists of data from Surface Station One containing multiple instruments including an anemometer, rain gauge, thermometer, pyranometer and barometer.  Information, collected at 1Hz, includes windspeed and direction as well as precipitation, temperature/humidity, solar radiation, and atmospheric pressure.
The University of Alabama in Huntsville (UAH) Mobile Integrated Profiling System (MIPS) is a mobile atmospheric profiling system.  It includes a 915 MHz Doppler profiler, lidar ceilometer, 12 channel microwave profiling radiometer, Doppler Sodar, Radio Acoustic Sounding System (RASS), Field Mills, and surface observing station. This dataset consists of data from Surface Station One containing multiple instruments including an anemometer, rain gauge, thermometer, pyranometer and barometer.  Information, collected at 1Hz, includes windspeed and direction as well as precipitation, temperature/humidity, solar radiation, and atmospheric pressure.
The University of Alabama in Huntsville (UAH) Mobile Integrated Profiling System (MIPS) is a mobile atmospheric profiling system. It includes a 915 MHz Doppler profiler, lidar ceilometer, 12 channel microwave profiling radiometer, Doppler Sodar, Radio Acoustic Sounding System (RASS), Field Mills, and surface observing station. This dataset consists of data from Surface Station Two which contained multiple instruments including an anemometer, rain gauge, thermometer, pyranometer and barometer.  Information, collected at 0.5 Hz, includes windspeed and direction as well as precipitation, temperature/humidity, solar radiation, and atmospheric pressure.
The University of Alabama in Huntsville (UAH) Mobile Integrated Profiling System (MIPS) is a mobile atmospheric profiling system. It includes a 915 MHz Doppler profiler, lidar ceilometer, 12 channel microwave profiling radiometer, Doppler Sodar, Radio Acoustic Sounding System (RASS), Field Mills, and surface observing station. This dataset consists of data from Surface Station Two which contained multiple instruments including an anemometer, rain gauge, thermometer, pyranometer and barometer.  Information, collected at 0.5 Hz, includes windspeed and direction as well as precipitation, temperature/humidity, solar radiation, and atmospheric pressure.
The University of Alabama in Huntsville (UAH) Mobile Integrated Profiling System (MIPS) is a mobile atmospheric profiling system. It includes a 915 MHz Doppler profiler, lidar ceilometer, 12 channel microwave profiling radiometer, Doppler Sodar, Radio Acoustic Sounding System (RASS), Field Mills, and surface observing station. This dataset consists of data from Surface Station Two which contained multiple instruments including an anemometer, rain gauge, thermometer, pyranometer and barometer.  Information, collected at 0.5 Hz, includes windspeed and direction as well as precipitation, temperature/humidity, solar radiation, and atmospheric pressure.
The Convection And Moisture EXperiment (CAMEX)-4 Mission Reports were filed every day that an aircraft flew in support of the experiment. The reports include a short description of the day's mission, its objective, and notes.
The Convection And Moisture EXperiment (CAMEX)-4 Mission Reports were filed every day that an aircraft flew in support of the experiment. The reports include a short description of the day's mission, its objective, and notes.
The Convection And Moisture EXperiment (CAMEX)-4 Mission Reports were filed every day that an aircraft flew in support of the experiment. The reports include a short description of the day's mission, its objective, and notes.
Mobile X-band Polarimetric Weather Radar on Wheels (X-POW)is a Doppler scanning radar operating at 9.3 GHz.with horizontal and vertical polarization. Used for detection and detailing surface rainfall rate and precipitation classification fields, 3D precipitation microphysical retrievals including water/frozen hydrometeor contents and drop size distribution profiles, X-POW was located in the Florida Keys during the CAMEX-4 field experiment.
Mobile X-band Polarimetric Weather Radar on Wheels (X-POW)is a Doppler scanning radar operating at 9.3 GHz.with horizontal and vertical polarization. Used for detection and detailing surface rainfall rate and precipitation classification fields, 3D precipitation microphysical retrievals including water/frozen hydrometeor contents and drop size distribution profiles, X-POW was located in the Florida Keys during the CAMEX-4 field experiment.
Mobile X-band Polarimetric Weather Radar on Wheels (X-POW)is a Doppler scanning radar operating at 9.3 GHz.with horizontal and vertical polarization. Used for detection and detailing surface rainfall rate and precipitation classification fields, 3D precipitation microphysical retrievals including water/frozen hydrometeor contents and drop size distribution profiles, X-POW was located in the Florida Keys during the CAMEX-4 field experiment.
The NASA Portable S-band Multiparameter Weather Research Radar (NPOL) is a Doppler S-band radar that when used continuous operation provides a full volume scan every ten minutes. Scans may be either 300 Km long range scans or 150 Km range for most high resolution data scans. Products available include real time PPI scans of reflectivities and velocities, and near real time displays of other radar products, including RHI's, CAPPI's, and Polarimetric products. Browse imagery is available for PPI reflectivities.
The NASA Portable S-band Multiparameter Weather Research Radar (NPOL) is a Doppler S-band radar that when used continuous operation provides a full volume scan every ten minutes. Scans may be either 300 Km long range scans or 150 Km range for most high resolution data scans. Products available include real time PPI scans of reflectivities and velocities, and near real time displays of other radar products, including RHI's, CAPPI's, and Polarimetric products. Browse imagery is available for PPI reflectivities.
The NASA Portable S-band Multiparameter Weather Research Radar (NPOL) is a Doppler S-band radar that when used continuous operation provides a full volume scan every ten minutes. Scans may be either 300 Km long range scans or 150 Km range for most high resolution data scans. Products available include real time PPI scans of reflectivities and velocities, and near real time displays of other radar products, including RHI's, CAPPI's, and Polarimetric products. Browse imagery is available for PPI reflectivities.
Ozone is measured in situ using a photometer consisting of a mercury lamp, two sample chambers and two detectors.  The detectors measure the 254nm radiation transmitted through the chamber by the lamp. The ozone number density is then caculated using the ozone absorbtion cross section and the Beer-Lambert Law.
Ozone is measured in situ using a photometer consisting of a mercury lamp, two sample chambers and two detectors.  The detectors measure the 254nm radiation transmitted through the chamber by the lamp. The ozone number density is then caculated using the ozone absorbtion cross section and the Beer-Lambert Law.
Ozone is measured in situ using a photometer consisting of a mercury lamp, two sample chambers and two detectors.  The detectors measure the 254nm radiation transmitted through the chamber by the lamp. The ozone number density is then caculated using the ozone absorbtion cross section and the Beer-Lambert Law.
The NOAA WP-3D Orion aircraft has multiple meteorological and microphysical sensors. These include, for example, cloud particle imagers and temperature and dewpoint probes. This dataset includes navigation data as well as the meteorological and microphysical data for each mission.
The NOAA WP-3D Orion aircraft collects numerous in-situ meteorological measurements along with navigation and aircraft state parameters during each flight (these data are similar to that collected with the NASA DC-8 ICATS  system). The WP-3D data are encoded on 8mm tape in what is called the 'AOC Standard Tape Format'. Examples of meteorological data include total temperature, dew point, liquid water content and dynamic pressure (from several sensors). Aircraft parameters include angle of attack, airspeed, slip angle, etc.
The NOAA WP-3D Orion aircraft has two separate research radars to collect meteorological data. One is mounted on the lower fuselage (C-band), and the other is located in the tail (X-band). Data from these radars consists of reflectivity in range and azimuth coordinates collected either in the horizontal (lower fuselage) or vertical (tail radar) planes.  Doppler radial velocity is also collected by the tail radar.
This dataset is comprised of VHS video tapes created during the NOAA WP-3D flights during CAMEX-4.
The Shared Mobile Atmospheric Research and Teaching Radar (SMART-R) is a portable 5 cm Doppler radar.  All equipment- antenna, power generator, processors and readout computers are truck mounted to provide maximum transportability. Originally located in the Florida Keys during CAMEX-4, the radar was moved to the Venice Florida area for landfall of TS Gabrielle on 14 Sept.
The Shared Mobile Atmospheric Research and Teaching Radar (SMART-R) is a portable 5 cm Doppler radar.  All equipment- antenna, power generator, processors and readout computers are truck mounted to provide maximum transportability. Originally located in the Florida Keys during CAMEX-4, the radar was moved to the Venice Florida area for landfall of TS Gabrielle on 14 Sept.
The Shared Mobile Atmospheric Research and Teaching Radar (SMART-R) is a portable 5 cm Doppler radar.  All equipment- antenna, power generator, processors and readout computers are truck mounted to provide maximum transportability. Originally located in the Florida Keys during CAMEX-4, the radar was moved to the Venice Florida area for landfall of TS Gabrielle on 14 Sept.
The TOGA radar dataset consists of browse and radar data collected from the TOGA radar during the CAMEX-4 experiment. TOGA is a C-band linear polarized doppler radar using 500KW of radiated power. Products available include real time PPI scans of reflectivities and Doppler velocities.
The TOGA radar dataset consists of browse and radar data collected from the TOGA radar during the CAMEX-4 experiment. TOGA is a C-band linear polarized doppler radar using 500KW of radiated power. Products available include real time PPI scans of reflectivities and Doppler velocities.
The TOGA radar dataset consists of browse and radar data collected from the TOGA radar during the CAMEX-4 experiment. TOGA is a C-band linear polarized doppler radar using 500KW of radiated power. Products available include real time PPI scans of reflectivities and Doppler velocities.
Canopy characteristics: leaf chemistry, specific leaf area, LAI, PAR, IPAR, NPP, standing biomass--see also: Meteorology (OTTER) for associated meteorological conditions
Canopy characteristics: leaf chemistry, specific leaf area, LAI, PAR, IPAR, NPP, standing biomass--see also: Meteorology (OTTER) for associated meteorological conditions
ABSTRACT: Canopy characteristics: leaf chemistry, specific leaf area, LAI, PAR, IPAR, NPP, standing biomass--see also: Meteorology (OTTER) for associated meteorological conditions
Canopy photosynthesis rates and resistance chamber measurements during FIFE
Canopy photosynthesis rates and resistance chamber measurements during FIFE
ABSTRACT: Canopy photosynthesis rates and resistance chamber measurements during FIFE
The Doppler Orbitography by Radiopositioning Integrated on Satellite (DORIS) was developed by the Centre National d'Etudes Spatiales (CNES) with cooperation from other French government agencies. The system was developed to provide precise orbit determination and high accuracy location of ground beacons for point positioning. DORIS is a dual-frequency Doppler system that has been included as an experiment on various space missions such as TOPEX/Poseidon, SPOT-2, -3, -4, and -5, Envisat, and Jason satellites. Unlike many other navigation systems, DORIS is based on an uplink device. The receivers are on board the satellite with the transmitters are on the ground. This creates a centralized system in which the complete set of observations is downloaded by the satellite to the ground center, from where they are distributed after editing and processing. An accurate measurment is made of the Doppler shift on radiofrequency signals emitted by the ground beacons and received on the spacecraft.
The Doppler Orbitography by Radiopositioning Integrated on Satellite (DORIS) was developed by the Centre National d'Etudes Spatiales (CNES) with cooperation from other French government agencies. The system was developed to provide precise orbit determination and high accuracy location of ground beacons for point positioning. DORIS is a dual-frequency Doppler system that has been included as an experiment on various space missions such as TOPEX/Poseidon, SPOT-2, -3, -4, and -5, Envisat, and Jason satellites. Unlike many other navigation systems, DORIS is based on an uplink device. The receivers are on board the satellite with the transmitters are on the ground. This creates a centralized system in which the complete set of observations is downloaded by the satellite to the ground center, from where they are distributed after editing and processing. An accurate measurment is made of the Doppler shift on radiofrequency signals emitted by the ground beacons and received on the spacecraft.
The Doppler Orbitography by Radiopositioning Integrated on Satellite (DORIS) was developed by the Centre National d'Etudes Spatiales (CNES) with cooperation from other French government agencies. The system was developed to provide precise orbit determination and high accuracy location of ground beacons for point positioning. DORIS is a dual-frequency Doppler system that has been included as an experiment on various space missions such as TOPEX/Poseidon, SPOT-2, -3, -4, and -5, Envisat, and Jason satellites. Unlike many other navigation systems, DORIS is based on an uplink device. The receivers are on board the satellite with the transmitters are on the ground. This creates a centralized system in which the complete set of observations is downloaded by the satellite to the ground center, from where they are distributed after editing and processing. An accurate measurment is made of the Doppler shift on radiofrequency signals emitted by the ground beacons and received on the spacecraft.
The Doppler Orbitography by Radiopositioning Integrated on Satellite (DORIS) was developed by the Centre National d'Etudes Spatiales (CNES) with cooperation from other French government agencies. The system was developed to provide precise orbit determination and high accuracy location of ground beacons for point positioning. DORIS is a dual-frequency Doppler system that has been included as an experiment on various space missions such as TOPEX/Poseidon, SPOT-2, -3, -4, and -5, Envisat, and Jason satellites. Unlike many other navigation systems, DORIS is based on an uplink device. The receivers are on board the satellite with the transmitters are on the ground. This creates a centralized system in which the complete set of observations is downloaded by the satellite to the ground center, from where they are distributed after editing and processing. An accurate measurment is made of the Doppler shift on radiofrequency signals emitted by the ground beacons and received on the spacecraft.
The Doppler Orbitography by Radiopositioning Integrated on Satellite (DORIS) was developed by the Centre National d'Etudes Spatiales (CNES) with cooperation from other French government agencies. The system was developed to provide precise orbit determination and high accuracy location of ground beacons for point positioning. DORIS is a dual-frequency Doppler system that has been included as an experiment on various space missions such as TOPEX/Poseidon, SPOT-2, -3, -4, and -5, Envisat, and Jason satellites. Unlike many other navigation systems, DORIS is based on an uplink device. The receivers are on board the satellite with the transmitters are on the ground. This creates a centralized system in which the complete set of observations is downloaded by the satellite to the ground center, from where they are distributed after editing and processing. An accurate measurment is made of the Doppler shift on radiofrequency signals emitted by the ground beacons and received on the spacecraft.
The Doppler Orbitography by Radiopositioning Integrated on Satellite (DORIS) was developed by the Centre National d'Etudes Spatiales (CNES) with cooperation from other French government agencies. The system was developed to provide precise orbit determination and high accuracy location of ground beacons for point positioning. DORIS is a dual-frequency Doppler system that has been included as an experiment on various space missions such as TOPEX/Poseidon, SPOT-2, -3, -4, and -5, Envisat, and Jason satellites. Unlike many other navigation systems, DORIS is based on an uplink device. The receivers are on board the satellite with the transmitters are on the ground. This creates a centralized system in which the complete set of observations is downloaded by the satellite to the ground center, from where they are distributed after editing and processing. An accurate measurment is made of the Doppler shift on radiofrequency signals emitted by the ground beacons and received on the spacecraft.
Geocenter determination solutions derived from analysis of  Doppler Orbitography by Radiopositioning Integrated on Satellite (DORIS) data. These products are the generated by analysis centers in support of the International DORIS Service (IDS).
Geocenter determination solutions derived from analysis of  Doppler Orbitography by Radiopositioning Integrated on Satellite (DORIS) data. These products are the generated by analysis centers in support of the International DORIS Service (IDS).
Geocenter determination solutions derived from analysis of  Doppler Orbitography by Radiopositioning Integrated on Satellite (DORIS) data. These products are the generated by analysis centers in support of the International DORIS Service (IDS).
Ionosphere correction values derived from analysis of Doppler Orbitography by Radiopositioning Integrated on Satellite (DORIS) data. These products are the generated by analysis centers in support of the International DORIS Service (IDS).
Ionosphere correction values derived from analysis of Doppler Orbitography by Radiopositioning Integrated on Satellite (DORIS) data. These products are the generated by analysis centers in support of the International DORIS Service (IDS).
Ionosphere correction values derived from analysis of Doppler Orbitography by Radiopositioning Integrated on Satellite (DORIS) data. These products are the generated by analysis centers in support of the International DORIS Service (IDS).
Precise satellite orbits derived from analysis of Doppler Orbitography by Radiopositioning Integrated on Satellite (DORIS) data. These products are the generated by analysis centers in support of the International DORIS Service (IDS).
Precise satellite orbits derived from analysis of Doppler Orbitography by Radiopositioning Integrated on Satellite (DORIS) data. These products are the generated by analysis centers in support of the International DORIS Service (IDS).
Precise satellite orbits derived from analysis of Doppler Orbitography by Radiopositioning Integrated on Satellite (DORIS) data. These products are the generated by analysis centers in support of the International DORIS Service (IDS).
Station position and velocity solutions (weekly and cumulative) in Software INdependent EXchange (SINEX) format derived from analysis of Doppler Orbitography by Radiopositioning Integrated on Satellite (DORIS) data. The solutions include daily values of Earth Orientation Parameters (EOPs). These products are the generated by analysis centers in support of the International DORIS Service (IDS). Time series of station coordinate solutions in Station Coordinate Difference (STCD) are also generated by the IDS analysis centers. Weekly solutions represent the IDS contribution to the International Terrestrial Reference Frame (ITRF) determination.
Station position and velocity solutions (weekly and cumulative) in Software INdependent EXchange (SINEX) format derived from analysis of Doppler Orbitography by Radiopositioning Integrated on Satellite (DORIS) data. The solutions include daily values of Earth Orientation Parameters (EOPs). These products are the generated by analysis centers in support of the International DORIS Service (IDS). Time series of station coordinate solutions in Station Coordinate Difference (STCD) are also generated by the IDS analysis centers. Weekly solutions represent the IDS contribution to the International Terrestrial Reference Frame (ITRF) determination.
Station position and velocity solutions (weekly and cumulative) in Software INdependent EXchange (SINEX) format derived from analysis of Doppler Orbitography by Radiopositioning Integrated on Satellite (DORIS) data. The solutions include daily values of Earth Orientation Parameters (EOPs). These products are the generated by analysis centers in support of the International DORIS Service (IDS). Time series of station coordinate solutions in Station Coordinate Difference (STCD) are also generated by the IDS analysis centers. Weekly solutions represent the IDS contribution to the International Terrestrial Reference Frame (ITRF) determination.
Satellite attitude information from satellites with Doppler Orbitography by Radiopositioning Integrated on Satellite (DORIS) receivers. Files include attitude quaternions for the body of the spacecraft and solar panel angular positions.
Satellite attitude information from satellites with Doppler Orbitography by Radiopositioning Integrated on Satellite (DORIS) receivers. Files include attitude quaternions for the body of the spacecraft and solar panel angular positions.
Satellite attitude information from satellites with Doppler Orbitography by Radiopositioning Integrated on Satellite (DORIS) receivers. Files include attitude quaternions for the body of the spacecraft and solar panel angular positions.
Station position time series solutions in DORIS Station Coordinate Difference (STCD) format derived from analysis of Doppler Orbitography by Radiopositioning Integrated on Satellite (DORIS) data. These products are the generated by analysis centers in support of the International DORIS Service (IDS).
Station position time series solutions in DORIS Station Coordinate Difference (STCD) format derived from analysis of Doppler Orbitography by Radiopositioning Integrated on Satellite (DORIS) data. These products are the generated by analysis centers in support of the International DORIS Service (IDS).
Station position time series solutions in DORIS Station Coordinate Difference (STCD) format derived from analysis of Doppler Orbitography by Radiopositioning Integrated on Satellite (DORIS) data. These products are the generated by analysis centers in support of the International DORIS Service (IDS).
Global Navigation Satellite System (GNSS) data consists of the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS) (plus other international systems) data sets. The Global Positioning System, developed by the U.S. Department of Defense, has been fully operational since 1994. GPS consists of a constellation of 24 satellites and three active spares each traveling in a 12 hour circular orbit, 20,200 kilometers above the Earth. The satellites are positioned so that six are observable nearly 100 percent of the time from any point on the Earth. The GLObal NAvigation Satellite System (GLONASS), managed and deployed by the Russian Federation, is similar to the U. S. Global Positioning System (GPS) in terms of the satellite constellation, orbits, and signal structure. GNSS receivers detect, decode, and process signals from the GNSS satellites. The satellites transmit the ranging codes on two radio-frequency carriers, allowing the locations of GNSS r
Global Navigation Satellite System (GNSS) data consists of the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS) (plus other international systems) data sets. The Global Positioning System, developed by the U.S. Department of Defense, has been fully operational since 1994. GPS consists of a constellation of 24 satellites and three active spares each traveling in a 12 hour circular orbit, 20,200 kilometers above the Earth. The satellites are positioned so that six are observable nearly 100 percent of the time from any point on the Earth. The GLObal NAvigation Satellite System (GLONASS), managed and deployed by the Russian Federation, is similar to the U. S. Global Positioning System (GPS) in terms of the satellite constellation, orbits, and signal structure. GNSS receivers detect, decode, and process signals from the GNSS satellites. The satellites transmit the ranging codes on two radio-frequency carriers, allowing the locations of GNSS r
Global Navigation Satellite System (GNSS) data consists of the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS) (plus other international systems) data sets. The Global Positioning System, developed by the U.S. Department of Defense, has been fully operational since 1994. GPS consists of a constellation of 24 satellites and three active spares each traveling in a 12 hour circular orbit, 20,200 kilometers above the Earth. The satellites are positioned so that six are observable nearly 100 percent of the time from any point on the Earth. The GLObal NAvigation Satellite System (GLONASS), managed and deployed by the Russian Federation, is similar to the U. S. Global Positioning System (GPS) in terms of the satellite constellation, orbits, and signal structure. GNSS receivers detect, decode, and process signals from the GNSS satellites. The satellites transmit the ranging codes on two radio-frequency carriers, allowing the locations of GNSS r
Global Navigation Satellite System (GNSS) data consists of the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS) (plus other international systems) data sets. The Global Positioning System, developed by the U.S. Department of Defense, has been fully operational since 1994. GPS consists of a constellation of 24 satellites and three active spares each traveling in a 12 hour circular orbit, 20,200 kilometers above the Earth. The satellites are positioned so that six are observable nearly 100 percent of the time from any point on the Earth. The GLObal NAvigation Satellite System (GLONASS), managed and deployed by the Russian Federation, is similar to the U. S. Global Positioning System (GPS) in terms of the satellite constellation, orbits, and signal structure. GNSS receivers detect, decode, and process signals from the GNSS satellites. The satellites transmit the ranging codes on two radio-frequency carriers, allowing the locations of GNSS r
Global Navigation Satellite System (GNSS) data consists of the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS) (plus other international systems) data sets. The Global Positioning System, developed by the U.S. Department of Defense, has been fully operational since 1994. GPS consists of a constellation of 24 satellites and three active spares each traveling in a 12 hour circular orbit, 20,200 kilometers above the Earth. The satellites are positioned so that six are observable nearly 100 percent of the time from any point on the Earth. The GLObal NAvigation Satellite System (GLONASS), managed and deployed by the Russian Federation, is similar to the U. S. Global Positioning System (GPS) in terms of the satellite constellation, orbits, and signal structure. GNSS receivers detect, decode, and process signals from the GNSS satellites. The satellites transmit the ranging codes on two radio-frequency carriers, allowing the locations of GNSS r
Global Navigation Satellite System (GNSS) data consists of the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS) (plus other international systems) data sets. The Global Positioning System, developed by the U.S. Department of Defense, has been fully operational since 1994. GPS consists of a constellation of 24 satellites and three active spares each traveling in a 12 hour circular orbit, 20,200 kilometers above the Earth. The satellites are positioned so that six are observable nearly 100 percent of the time from any point on the Earth. The GLObal NAvigation Satellite System (GLONASS), managed and deployed by the Russian Federation, is similar to the U. S. Global Positioning System (GPS) in terms of the satellite constellation, orbits, and signal structure. GNSS receivers detect, decode, and process signals from the GNSS satellites. The satellites transmit the ranging codes on two radio-frequency carriers, allowing the locations of GNSS r
Global Navigation Satellite System (GNSS) data consists of the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS) (plus other international systems) data sets. The Global Positioning System, developed by the U.S. Department of Defense, has been fully operational since 1994. GPS consists of a constellation of 24 satellites and three active spares each traveling in a 12 hour circular orbit, 20,200 kilometers above the Earth. The satellites are positioned so that six are observable nearly 100 percent of the time from any point on the Earth. The GLObal NAvigation Satellite System (GLONASS), managed and deployed by the Russian Federation, is similar to the U. S. Global Positioning System (GPS) in terms of the satellite constellation, orbits, and signal structure. GNSS receivers detect, decode, and process signals from the GNSS satellites. The satellites transmit the ranging codes on two radio-frequency carriers, allowing the locations of GNSS r
Global Navigation Satellite System (GNSS) data consists of the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS) (plus other international systems) data sets. The Global Positioning System, developed by the U.S. Department of Defense, has been fully operational since 1994. GPS consists of a constellation of 24 satellites and three active spares each traveling in a 12 hour circular orbit, 20,200 kilometers above the Earth. The satellites are positioned so that six are observable nearly 100 percent of the time from any point on the Earth. The GLObal NAvigation Satellite System (GLONASS), managed and deployed by the Russian Federation, is similar to the U. S. Global Positioning System (GPS) in terms of the satellite constellation, orbits, and signal structure. GNSS receivers detect, decode, and process signals from the GNSS satellites. The satellites transmit the ranging codes on two radio-frequency carriers, allowing the locations of GNSS r
Global Navigation Satellite System (GNSS) data consists of the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS) (plus other international systems) data sets. The Global Positioning System, developed by the U.S. Department of Defense, has been fully operational since 1994. GPS consists of a constellation of 24 satellites and three active spares each traveling in a 12 hour circular orbit, 20,200 kilometers above the Earth. The satellites are positioned so that six are observable nearly 100 percent of the time from any point on the Earth. The GLObal NAvigation Satellite System (GLONASS), managed and deployed by the Russian Federation, is similar to the U. S. Global Positioning System (GPS) in terms of the satellite constellation, orbits, and signal structure. GNSS receivers detect, decode, and process signals from the GNSS satellites. The satellites transmit the ranging codes on two radio-frequency carriers, allowing the locations of GNSS r
Satellite and receiver clock products derived from analysis of Global Navigation Satellite System (GNSS) data. These products are the generated by analysis centers in support of the International GNSS Service (IGS) and combined by the IGS analysis coordinator to form the official IGS final clock product (weekly).
Satellite and receiver clock products derived from analysis of Global Navigation Satellite System (GNSS) data. These products are the generated by analysis centers in support of the International GNSS Service (IGS) and combined by the IGS analysis coordinator to form the official IGS final clock product (weekly).
Satellite and receiver clock products derived from analysis of Global Navigation Satellite System (GNSS) data. These products are the generated by analysis centers in support of the International GNSS Service (IGS) and combined by the IGS analysis coordinator to form the official IGS final clock product (weekly).
Satellite and receiver clock products derived from analysis of Global Navigation Satellite System (GNSS) data. These products are the generated by analysis centers in support of the International GNSS Service (IGS) and combined by the IGS analysis coordinator to form the official IGS rapid clock product (daily).
Satellite and receiver clock products derived from analysis of Global Navigation Satellite System (GNSS) data. These products are the generated by analysis centers in support of the International GNSS Service (IGS) and combined by the IGS analysis coordinator to form the official IGS rapid clock product (daily).
Satellite and receiver clock products derived from analysis of Global Navigation Satellite System (GNSS) data. These products are the generated by analysis centers in support of the International GNSS Service (IGS) and combined by the IGS analysis coordinator to form the official IGS rapid clock product (daily).
Satellite and receiver clock products derived from analysis of Global Navigation Satellite System (GNSS) data. These products are the generated by analysis centers in support of the International GNSS Service (IGS) and combined by the IGS analysis coordinator to form the official IGS real-time clock product (daily). This product is used for comparison purposes.
Satellite and receiver clock products derived from analysis of Global Navigation Satellite System (GNSS) data. These products are the generated by analysis centers in support of the International GNSS Service (IGS) and combined by the IGS analysis coordinator to form the official IGS real-time clock product (daily). This product is used for comparison purposes.
Satellite and receiver clock products derived from analysis of Global Navigation Satellite System (GNSS) data. These products are the generated by analysis centers in support of the International GNSS Service (IGS) and combined by the IGS analysis coordinator to form the official IGS real-time clock product (daily). This product is used for comparison purposes.
Earth Rotation Parameters (ERPs) derived from analysis of Global Navigation Satellite System (GNSS) data. These products are the generated by analysis centers in support of the International GNSS Service (IGS) and combined by the IGS analysis coordinator to form the official IGS ERP product (weekly).
Earth Rotation Parameters (ERPs) derived from analysis of Global Navigation Satellite System (GNSS) data. These products are the generated by analysis centers in support of the International GNSS Service (IGS) and combined by the IGS analysis coordinator to form the official IGS ERP product (weekly).
Earth Rotation Parameters (ERPs) derived from analysis of Global Navigation Satellite System (GNSS) data. These products are the generated by analysis centers in support of the International GNSS Service (IGS) and combined by the IGS analysis coordinator to form the official IGS ERP product (weekly).
Satellite and receiver clock products derived from analysis of Global Navigation Satellite System (GNSS) data. These products are the generated by analysis centers in support of the International GNSS Service (IGS) and combined by the IGS analysis coordinator to form the official IGS real-time clock product (daily). This product is used for comparison purposes.
Satellite and receiver clock products derived from analysis of Global Navigation Satellite System (GNSS) data. These products are the generated by analysis centers in support of the International GNSS Service (IGS) and combined by the IGS analysis coordinator to form the official IGS real-time clock product (daily). This product is used for comparison purposes.
Satellite and receiver clock products derived from analysis of Global Navigation Satellite System (GNSS) data. These products are the generated by analysis centers in support of the International GNSS Service (IGS) and combined by the IGS analysis coordinator to form the official IGS real-time clock product (daily). This product is used for comparison purposes.
Ionosphere Total Electron Content (TEC) grids derived from analysis of Global Navigation Satellite System (GNSS) data. These products are the generated by analysis centers in support of the International GNSS Service (IGS) and combined by the IGS ionosphere analysis coordinator to form the official IGS predicted ionopsphere product (daily).
Ionosphere Total Electron Content (TEC) grids derived from analysis of Global Navigation Satellite System (GNSS) data. These products are the generated by analysis centers in support of the International GNSS Service (IGS) and combined by the IGS ionosphere analysis coordinator to form the official IGS predicted ionopsphere product (daily).
Ionosphere Total Electron Content (TEC) grids derived from analysis of Global Navigation Satellite System (GNSS) data. These products are the generated by analysis centers in support of the International GNSS Service (IGS) and combined by the IGS ionosphere analysis coordinator to form the official IGS predicted ionopsphere product (daily).
Ionosphere Total Electron Content (TEC) grids derived from analysis of Global Navigation Satellite System (GNSS) data. These products are the generated by analysis centers in support of the International GNSS Service (IGS) and combined by the IGS ionosphere analysis coordinator to form the official IGS rapid ionosphere product (daily).
Ionosphere Total Electron Content (TEC) grids derived from analysis of Global Navigation Satellite System (GNSS) data. These products are the generated by analysis centers in support of the International GNSS Service (IGS) and combined by the IGS ionosphere analysis coordinator to form the official IGS rapid ionosphere product (daily).
Ionosphere Total Electron Content (TEC) grids derived from analysis of Global Navigation Satellite System (GNSS) data. These products are the generated by analysis centers in support of the International GNSS Service (IGS) and combined by the IGS ionosphere analysis coordinator to form the official IGS rapid ionosphere product (daily).
Precise satellite orbits derived from analysis of Global Navigation Satellite System (GNSS) data. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce precise orbits identifying the position and velocity of the GNSS satellites. These orbits are determined sub-daily (ultra-rapid generation), daily (rapid generation), and weekly (final IGS product). The IGS Analysis Center Coordinator retrieves these individual solutions and generates the official IGS combined orbit products. The orbits generated by the individual ACs and the combination products generated by the ACCs are available at the CDDIS. These orbits can be used to determine precise coordinates of the observing stations, gravity field parameters, and Earth orientation parameters. The IGS Final products are the basis for the IGS reference frame and are intended for those applications demanding high consistency and quality.
Precise satellite orbits derived from analysis of Global Navigation Satellite System (GNSS) data. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce precise orbits identifying the position and velocity of the GNSS satellites. These orbits are determined sub-daily (ultra-rapid generation), daily (rapid generation), and weekly (final IGS product). The IGS Analysis Center Coordinator retrieves these individual solutions and generates the official IGS combined orbit products. The orbits generated by the individual ACs and the combination products generated by the ACCs are available at the CDDIS. These orbits can be used to determine precise coordinates of the observing stations, gravity field parameters, and Earth orientation parameters. The IGS Final products are the basis for the IGS reference frame and are intended for those applications demanding high consistency and quality.
Precise satellite orbits derived from analysis of Global Navigation Satellite System (GNSS) data. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce precise orbits identifying the position and velocity of the GNSS satellites. These orbits are determined sub-daily (ultra-rapid generation), daily (rapid generation), and weekly (final IGS product). The IGS Analysis Center Coordinator retrieves these individual solutions and generates the official IGS combined orbit products. The orbits generated by the individual ACs and the combination products generated by the ACCs are available at the CDDIS. These orbits can be used to determine precise coordinates of the observing stations, gravity field parameters, and Earth orientation parameters. The IGS Final products are the basis for the IGS reference frame and are intended for those applications demanding high consistency and quality.
Precise satellite orbits derived from analysis of Global Navigation Satellite System (GNSS) data. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce precise orbits identifying the position and velocity of the GNSS satellites. These orbits are determined sub-daily (ultra-rapid generation), daily (rapid generation), and weekly (final IGS product). The IGS Analysis Center Coordinator retrieves these individual solutions and generates the official IGS combined orbit products. The orbits generated by the individual ACs and the combination products generated by the ACCs are available at the CDDIS. These orbits can be used to determine precise coordinates of the observing stations, gravity field parameters, and Earth orientation parameters.
Precise satellite orbits derived from analysis of Global Navigation Satellite System (GNSS) data. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce precise orbits identifying the position and velocity of the GNSS satellites. These orbits are determined sub-daily (ultra-rapid generation), daily (rapid generation), and weekly (final IGS product). The IGS Analysis Center Coordinator retrieves these individual solutions and generates the official IGS combined orbit products. The orbits generated by the individual ACs and the combination products generated by the ACCs are available at the CDDIS. These orbits can be used to determine precise coordinates of the observing stations, gravity field parameters, and Earth orientation parameters.
Precise satellite orbits derived from analysis of Global Navigation Satellite System (GNSS) data. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce precise orbits identifying the position and velocity of the GNSS satellites. These orbits are determined sub-daily (ultra-rapid generation), daily (rapid generation), and weekly (final IGS product). The IGS Analysis Center Coordinator retrieves these individual solutions and generates the official IGS combined orbit products. The orbits generated by the individual ACs and the combination products generated by the ACCs are available at the CDDIS. These orbits can be used to determine precise coordinates of the observing stations, gravity field parameters, and Earth orientation parameters.
Precise satellite orbits derived from analysis of Global Navigation Satellite System (GNSS) data. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce precise orbits identifying the position and velocity of the GNSS satellites. The orbits derived from real-time data streams are used for comparison purposes.
Precise satellite orbits derived from analysis of Global Navigation Satellite System (GNSS) data. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce precise orbits identifying the position and velocity of the GNSS satellites. The orbits derived from real-time data streams are used for comparison purposes.
Precise satellite orbits derived from analysis of Global Navigation Satellite System (GNSS) data. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce precise orbits identifying the position and velocity of the GNSS satellites. The orbits derived from real-time data streams are used for comparison purposes.
Precise satellite orbits derived from analysis of Global Navigation Satellite System (GNSS) data. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce precise orbits identifying the position and velocity of the GNSS satellites. These orbits are determined sub-daily (ultra-rapid generation), daily (rapid generation), and weekly (final IGS product). The IGS Analysis Center Coordinator retrieves these individual solutions and generates the official IGS combined orbit products. The orbits generated by the individual ACs and the combination products generated by the ACCs are available at the CDDIS. These orbits can be used to determine precise coordinates of the observing stations, gravity field parameters, and Earth orientation parameters. The Ultra-rapid products are available for real time and near real time use and include predicted orbit information.
Precise satellite orbits derived from analysis of Global Navigation Satellite System (GNSS) data. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce precise orbits identifying the position and velocity of the GNSS satellites. These orbits are determined sub-daily (ultra-rapid generation), daily (rapid generation), and weekly (final IGS product). The IGS Analysis Center Coordinator retrieves these individual solutions and generates the official IGS combined orbit products. The orbits generated by the individual ACs and the combination products generated by the ACCs are available at the CDDIS. These orbits can be used to determine precise coordinates of the observing stations, gravity field parameters, and Earth orientation parameters. The Ultra-rapid products are available for real time and near real time use and include predicted orbit information.
Precise satellite orbits derived from analysis of Global Navigation Satellite System (GNSS) data. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce precise orbits identifying the position and velocity of the GNSS satellites. These orbits are determined sub-daily (ultra-rapid generation), daily (rapid generation), and weekly (final IGS product). The IGS Analysis Center Coordinator retrieves these individual solutions and generates the official IGS combined orbit products. The orbits generated by the individual ACs and the combination products generated by the ACCs are available at the CDDIS. These orbits can be used to determine precise coordinates of the observing stations, gravity field parameters, and Earth orientation parameters. The Ultra-rapid products are available for real time and near real time use and include predicted orbit information.
Weekly station positions and velocity solutions in Software INdependent EXchange (SINEX) format derived from analysis of Global Navigation Satellite System (GNSS) data. These products are the generated by analysis centers in support of the International GNSS Service (IGS) and combined by the IGS reference frame coordinator to form the official IGS station position product (weekly).
Weekly station positions and velocity solutions in Software INdependent EXchange (SINEX) format derived from analysis of Global Navigation Satellite System (GNSS) data. These products are the generated by analysis centers in support of the International GNSS Service (IGS) and combined by the IGS reference frame coordinator to form the official IGS station position product (weekly).
Weekly station positions and velocity solutions in Software INdependent EXchange (SINEX) format derived from analysis of Global Navigation Satellite System (GNSS) data. These products are the generated by analysis centers in support of the International GNSS Service (IGS) and combined by the IGS reference frame coordinator to form the official IGS station position product (weekly).
Troposphere Zenith Path Delay (ZPD) values by site derived from analysis of Global Navigation Satellite System (GNSS) data. These products are the generated by analysis centers in support of the International GNSS Service (IGS) and combined by the IGS troposphere analysis coordinator to form the official IGS troposphere product (daily).
Troposphere Zenith Path Delay (ZPD) values by site derived from analysis of Global Navigation Satellite System (GNSS) data. These products are the generated by analysis centers in support of the International GNSS Service (IGS) and combined by the IGS troposphere analysis coordinator to form the official IGS troposphere product (daily).
Troposphere Zenith Path Delay (ZPD) values by site derived from analysis of Global Navigation Satellite System (GNSS) data. These products are the generated by analysis centers in support of the International GNSS Service (IGS) and combined by the IGS troposphere analysis coordinator to form the official IGS troposphere product (daily).
Global Navigation Satellite System (GNSS) data consists of the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS) (plus other international systems) data sets. The Global Positioning System, developed by the U.S. Department of Defense, has been fully operational since 1994. GPS consists of a constellation of 24 satellites and three active spares each traveling in a 12 hour circular orbit, 20,200 kilometers above the Earth. The satellites are positioned so that six are observable nearly 100 percent of the time from any point on the Earth. The GLObal NAvigation Satellite System (GLONASS), managed and deployed by the Russian Federation, is similar to the U. S. Global Positioning System (GPS) in terms of the satellite constellation, orbits, and signal structure. GNSS receivers detect, decode, and process signals from the GNSS satellites. The satellites transmit the ranging codes on two radio-frequency carriers, allowing the locations of GNSS r
Global Navigation Satellite System (GNSS) data consists of the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS) (plus other international systems) data sets. The Global Positioning System, developed by the U.S. Department of Defense, has been fully operational since 1994. GPS consists of a constellation of 24 satellites and three active spares each traveling in a 12 hour circular orbit, 20,200 kilometers above the Earth. The satellites are positioned so that six are observable nearly 100 percent of the time from any point on the Earth. The GLObal NAvigation Satellite System (GLONASS), managed and deployed by the Russian Federation, is similar to the U. S. Global Positioning System (GPS) in terms of the satellite constellation, orbits, and signal structure. GNSS receivers detect, decode, and process signals from the GNSS satellites. The satellites transmit the ranging codes on two radio-frequency carriers, allowing the locations of GNSS r
Global Navigation Satellite System (GNSS) data consists of the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS) (plus other international systems) data sets. The Global Positioning System, developed by the U.S. Department of Defense, has been fully operational since 1994. GPS consists of a constellation of 24 satellites and three active spares each traveling in a 12 hour circular orbit, 20,200 kilometers above the Earth. The satellites are positioned so that six are observable nearly 100 percent of the time from any point on the Earth. The GLObal NAvigation Satellite System (GLONASS), managed and deployed by the Russian Federation, is similar to the U. S. Global Positioning System (GPS) in terms of the satellite constellation, orbits, and signal structure. GNSS receivers detect, decode, and process signals from the GNSS satellites. The satellites transmit the ranging codes on two radio-frequency carriers, allowing the locations of GNSS r
Lunar Laser Ranging (LLR) measures the distance between the Earth and the Moon using laser ranging. A short pulse of coherent light generated by a laser (Light Amplification by Stimulated Emission of Radiation) is transmitted in a narrow beam to illuminate corner cube retroreflectors on the moon. The return signal, typically a few photons, is collected by a telescope and the time-of-flight is measured. Apollo astronauts and Russian rovers deployed laser ranging retroreflector arrays on the lunar surface which continue to yield fundamental scientific data. Analysis of Lunar Laser Ranging (LLR) data provides information on the lunar orbit, rotation, solid-body tides, and retroreflector locations.
Lunar Laser Ranging (LLR) measures the distance between the Earth and the Moon using laser ranging. A short pulse of coherent light generated by a laser (Light Amplification by Stimulated Emission of Radiation) is transmitted in a narrow beam to illuminate corner cube retroreflectors on the moon. The return signal, typically a few photons, is collected by a telescope and the time-of-flight is measured. Apollo astronauts and Russian rovers deployed laser ranging retroreflector arrays on the lunar surface which continue to yield fundamental scientific data. Analysis of Lunar Laser Ranging (LLR) data provides information on the lunar orbit, rotation, solid-body tides, and retroreflector locations.
Lunar Laser Ranging (LLR) measures the distance between the Earth and the Moon using laser ranging. A short pulse of coherent light generated by a laser (Light Amplification by Stimulated Emission of Radiation) is transmitted in a narrow beam to illuminate corner cube retroreflectors on the moon. The return signal, typically a few photons, is collected by a telescope and the time-of-flight is measured. Apollo astronauts and Russian rovers deployed laser ranging retroreflector arrays on the lunar surface which continue to yield fundamental scientific data. Analysis of Lunar Laser Ranging (LLR) data provides information on the lunar orbit, rotation, solid-body tides, and retroreflector locations.
Miscellaneous documentation, information, and ancillary data to be used with GNSS, laser ranging, VLBI and DORIS data and analyzed products available through the CDDIS.
Miscellaneous documentation, information, and ancillary data to be used with GNSS, laser ranging, VLBI and DORIS data and analyzed products available through the CDDIS.
Miscellaneous documentation, information, and ancillary data to be used with GNSS, laser ranging, VLBI and DORIS data and analyzed products available through the CDDIS.
In Satellite Laser Ranging (SLR), a short pulse of coherent light generated by a laser (Light Amplification by Stimulated Emission of Radiation) is transmitted in a narrow beam to illuminate corner cube retroreflectors on the satellite. The return signal, typically a few photons, is collected by a telescope and the time-of-flight is measured. Using information about the satellite's orbit, the time-of-flight, and the speed of light, the location of the ranging station can be determined. Similar data acquired by another station, many kilometers distant from the first, or on a different continent, can be used to determine the distance between stations to precisions of centimeters or better. Repetitive measurements over months and years yield the change in distance, or the motion of the Earth's crust.
In Satellite Laser Ranging (SLR), a short pulse of coherent light generated by a laser (Light Amplification by Stimulated Emission of Radiation) is transmitted in a narrow beam to illuminate corner cube retroreflectors on the satellite. The return signal, typically a few photons, is collected by a telescope and the time-of-flight is measured. Using information about the satellite's orbit, the time-of-flight, and the speed of light, the location of the ranging station can be determined. Similar data acquired by another station, many kilometers distant from the first, or on a different continent, can be used to determine the distance between stations to precisions of centimeters or better. Repetitive measurements over months and years yield the change in distance, or the motion of the Earth's crust.
In Satellite Laser Ranging (SLR), a short pulse of coherent light generated by a laser (Light Amplification by Stimulated Emission of Radiation) is transmitted in a narrow beam to illuminate corner cube retroreflectors on the satellite. The return signal, typically a few photons, is collected by a telescope and the time-of-flight is measured. Using information about the satellite's orbit, the time-of-flight, and the speed of light, the location of the ranging station can be determined. Similar data acquired by another station, many kilometers distant from the first, or on a different continent, can be used to determine the distance between stations to precisions of centimeters or better. Repetitive measurements over months and years yield the change in distance, or the motion of the Earth's crust.
Predicted satellite orbits for Satellite Laser Ranging (SLR) tracking of satellites equipped with corner cube retroreflectors. SLR stations download these prediction files and coordinate tracking schedules for satellite acquisition. The predicted orbit files typically contain orbit information for multiple days and are issued on a daily or sub-daily basis.
Predicted satellite orbits for Satellite Laser Ranging (SLR) tracking of satellites equipped with corner cube retroreflectors. SLR stations download these prediction files and coordinate tracking schedules for satellite acquisition. The predicted orbit files typically contain orbit information for multiple days and are issued on a daily or sub-daily basis.
Predicted satellite orbits for Satellite Laser Ranging (SLR) tracking of satellites equipped with corner cube retroreflectors. SLR stations download these prediction files and coordinate tracking schedules for satellite acquisition. The predicted orbit files typically contain orbit information for multiple days and are issued on a daily or sub-daily basis.
Station position and Earth Orientation Parameters (EOPs) solutions derived from analysis of Satellite Laser Ranging (SLR) data. These products are the generated by analysis centers in support of the International Laser Ranging Service (ILRS) and combined by the ILRS analysis coordinator to form the official ILRS EOP and station position product (daily and weekly).
Station position and Earth Orientation Parameters (EOPs) solutions derived from analysis of Satellite Laser Ranging (SLR) data. These products are the generated by analysis centers in support of the International Laser Ranging Service (ILRS) and combined by the ILRS analysis coordinator to form the official ILRS EOP and station position product (daily and weekly).
Station position and Earth Orientation Parameters (EOPs) solutions derived from analysis of Satellite Laser Ranging (SLR) data. These products are the generated by analysis centers in support of the International Laser Ranging Service (ILRS) and combined by the ILRS analysis coordinator to form the official ILRS EOP and station position product (daily and weekly).
Very Long Baseline Interferometry (VLBI) is a geometric technique: it measures the time difference between the arrival at two Earth-based antennas of a radio wavefront emitted by a distant quasar. VLBI precisely locates points on Earth by comparing radio signals from a quasar radio source located in deep space received at different times at different Earth ground stations. This method gives relative positions of the Earth stations. VLBI data can be used to calculate information on Earth rotation, length of day, polar motion, baseline and velocities of stations. Products generated by VLBI contribute to research in many areas, including solid Earth, tides, studies of the vertical, fundamental astronomy, and VLBI technique improvement.
Very Long Baseline Interferometry (VLBI) is a geometric technique: it measures the time difference between the arrival at two Earth-based antennas of a radio wavefront emitted by a distant quasar. VLBI precisely locates points on Earth by comparing radio signals from a quasar radio source located in deep space received at different times at different Earth ground stations. This method gives relative positions of the Earth stations. VLBI data can be used to calculate information on Earth rotation, length of day, polar motion, baseline and velocities of stations. Products generated by VLBI contribute to research in many areas, including solid Earth, tides, studies of the vertical, fundamental astronomy, and VLBI technique improvement.
Very Long Baseline Interferometry (VLBI) is a geometric technique: it measures the time difference between the arrival at two Earth-based antennas of a radio wavefront emitted by a distant quasar. VLBI precisely locates points on Earth by comparing radio signals from a quasar radio source located in deep space received at different times at different Earth ground stations. This method gives relative positions of the Earth stations. VLBI data can be used to calculate information on Earth rotation, length of day, polar motion, baseline and velocities of stations. Products generated by VLBI contribute to research in many areas, including solid Earth, tides, studies of the vertical, fundamental astronomy, and VLBI technique improvement.
Celestial Reference Frame (CRF) product derived from analysis of Very Long Baseline Interferometry (VLBI) data. These products are the generated by analysis centers in support of the International VLBI Service for Geodesy and Astrometry (IVS) and combined by the IVS analysis coordinator to form the official IVS CRF product.
Celestial Reference Frame (CRF) product derived from analysis of Very Long Baseline Interferometry (VLBI) data. These products are the generated by analysis centers in support of the International VLBI Service for Geodesy and Astrometry (IVS) and combined by the IVS analysis coordinator to form the official IVS CRF product.
Celestial Reference Frame (CRF) product derived from analysis of Very Long Baseline Interferometry (VLBI) data. These products are the generated by analysis centers in support of the International VLBI Service for Geodesy and Astrometry (IVS) and combined by the IVS analysis coordinator to form the official IVS CRF product.
Earth Orientation Parameters (EOPs) product derived from analysis of Very Long Baseline Interferometry (VLBI) data. These products are the generated by analysis centers in support of the International VLBI Service for Geodesy and Astrometry (IVS).
Earth Orientation Parameters (EOPs) product derived from analysis of Very Long Baseline Interferometry (VLBI) data. These products are the generated by analysis centers in support of the International VLBI Service for Geodesy and Astrometry (IVS).
Earth Orientation Parameters (EOPs) product derived from analysis of Very Long Baseline Interferometry (VLBI) data. These products are the generated by analysis centers in support of the International VLBI Service for Geodesy and Astrometry (IVS).
Station positions and velocity solutions in Software INdependent EXchange (SINEX) format derived from analysis of Very Long Baseline Interferometry (VLBI) data. These products are the generated by analysis centers in support of the International VLBI Service for Geodesy and Astrometry (IVS) and combined by the IVS analysis coordinator to form the official IVS station position product.
Station positions and velocity solutions in Software INdependent EXchange (SINEX) format derived from analysis of Very Long Baseline Interferometry (VLBI) data. These products are the generated by analysis centers in support of the International VLBI Service for Geodesy and Astrometry (IVS) and combined by the IVS analysis coordinator to form the official IVS station position product.
Station positions and velocity solutions in Software INdependent EXchange (SINEX) format derived from analysis of Very Long Baseline Interferometry (VLBI) data. These products are the generated by analysis centers in support of the International VLBI Service for Geodesy and Astrometry (IVS) and combined by the IVS analysis coordinator to form the official IVS station position product.
Troposphere Zenith Path Delay (ZPD) values derived from analysis of Very Long Baseline Interferometry (VLBI) data. These products are the generated by analysis centers in support of the International VLBI Service for Geodesy and Astrometry (IVS).
Troposphere Zenith Path Delay (ZPD) values derived from analysis of Very Long Baseline Interferometry (VLBI) data. These products are the generated by analysis centers in support of the International VLBI Service for Geodesy and Astrometry (IVS).
Troposphere Zenith Path Delay (ZPD) values derived from analysis of Very Long Baseline Interferometry (VLBI) data. These products are the generated by analysis centers in support of the International VLBI Service for Geodesy and Astrometry (IVS).
The Central American Vegetation/Land Cover Classification and Conservation Status consists of GIS coverages of vegetation classes (forests, woodlands, savannas, shrubs, grasslands, wetlands, rocks, sand, soils, inland waters, parks and reserves) for Central America, derived from 1-kilometer resolution Advanced Very High Resolution Radiometer (AVHRR) imagery. This dataset is produced by Proyecto Ambiental Regional de Centroamerica/Central America Protected Areas Systems (PROARCA/CAPAS), a conservation partnership of the Central American Commission on Environment and Development (CCAD), U.S. Agency for International Development (USAID), International Resources Group, Ltd. (IRG), The Nature Conservancy (TNC), Winrock International (WI), and is distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
The Central American Vegetation/Land Cover Classification and Conservation Status consists of GIS coverages of vegetation classes (forests, woodlands, savannas, shrubs, grasslands, wetlands, rocks, sand, soils, inland waters, parks and reserves) for Central America, derived from 1-kilometer resolution Advanced Very High Resolution Radiometer (AVHRR) imagery. This dataset is produced by Proyecto Ambiental Regional de Centroamerica/Central America Protected Areas Systems (PROARCA/CAPAS), a conservation partnership of the Central American Commission on Environment and Development (CCAD), U.S. Agency for International Development (USAID), International Resources Group, Ltd. (IRG), The Nature Conservancy (TNC), Winrock International (WI), and is distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
The Central American Vegetation/Land Cover Classification and Conservation Status consists of GIS coverages of vegetation classes (forests, woodlands, savannas, shrubs, grasslands, wetlands, rocks, sand, soils, inland waters, parks and reserves) for Central America, derived from 1-kilometer resolution Advanced Very High Resolution Radiometer (AVHRR) imagery. This dataset is produced by Proyecto Ambiental Regional de Centroamerica/Central America Protected Areas Systems (PROARCA/CAPAS), a conservation partnership of the Central American Commission on Environment and Development (CCAD), U.S. Agency for International Development (USAID), International Resources Group, Ltd. (IRG), The Nature Conservancy (TNC), Winrock International (WI), and is distributed by the Columbia University Center for International Earth Science Information Network (CIESIN). (Suggested Usage: To identify remaining natural vegetation types in Central America.)
The CENTURY model, Version 4, is a general model of plant-soil nutrient cycling that is being used to simulate carbon and nutrient dynamics for different types of ecosystems including grasslands, agricultural lands, forests and savannas. CENTURY is composed of a soil organic matter/ decomposition submodel, a water budget model, a grassland/crop submodel, a forest production submodel, and management and events scheduling functions.
The CENTURY model, Version 4, is a general model of plant-soil nutrient cycling that is being used to simulate carbon and nutrient dynamics for different types of ecosystems including grasslands, agricultural lands, forests and savannas. CENTURY is composed of a soil organic matter/ decomposition submodel, a water budget model, a grassland/crop submodel, a forest production submodel, and management and events scheduling functions.
ABSTRACT: The CENTURY model, Version 4, is a general model of plant-soil nutrient cycling that is being used to simulate carbon and nutrient dynamics for different types of ecosystems including grasslands, agricultural lands, forests and savannas. CENTURY is composed of a soil organic matter/ decomposition submodel, a water budget model, a grassland/crop submodel, a forest production submodel, and management and events scheduling functions.
CERES-MODIS-CALIPSO-CloudSat CCCM data set is an integrated product in HDF.
CERES BiDirectional Scans (BDS) data in HDF.
CERES BiDirectional Scans (BDS) data in HDF.
CERES BiDirectional Scans (BDS) data in HDF.
CERES BiDirectional Scans (BDS) data in HDF.
CERES BiDirectional Scans (BDS) data in HDF.
CERES BiDirectional Scans (BDS) data in HDF.
CERES BiDirectional Scans (BDS) data in HDF.
CERES BiDirectional Scans (BDS) data in HDF.
CERES BiDirectional Scans (BDS) data in HDF.
CERES BiDirectional Scans (BDS) data in HDF.
CERES Clouds and Radiative Swath (CRS) data in HDF.
CERES Clouds and Radiative Swath (CRS) data in HDF.
CERES Clouds and Radiative Swath (CRS) data in HDF.
CERES Clouds and Radiative Swath (CRS) data in HDF.
CERES Clouds and Radiative Swath (CRS) data in HDF.
CERES Clouds and Radiative Swath (CRS) data in HDF.
CERES Clouds and Radiative Swath (CRS) data in HDF.
CERES Clouds and Radiative Swath (CRS) data in HDF.
CERES ERBE-like Monthly Geographical Averages (ES-4) in HDF.
CERES ERBE-like Monthly Geographical Averages (ES-4) in HDF.
CERES ERBE-like Monthly Geographical Averages (ES-4) in HDF.
CERES ERBE-like Monthly Geographical Averages (ES-4) in HDF.
CERES ERBE-like Instantaneous TOA Estimates (ES-8) in HDF.
CERES ERBE-like Instantaneous TOA Estimates (ES-8) in HDF.
CERES ERBE-like Instantaneous TOA Estimates (ES-8) in HDF.
CERES ERBE-like Instantaneous TOA Estimates (ES-8) in HDF.
CERES ERBE-like Instantaneous TOA Estimates (ES-8) in HDF.
CERES ERBE-like Instantaneous TOA Estimates (ES-8) in HDF
CERES ERBE-like Monthly Regional Averages (ES-9) in HDF.
CERES ERBE-like Monthly Regional Averages (ES-9) in HDF.
CERES ERBE-like Monthly Regional Averages (ES-9) in HDF.
CERES ERBE-like Monthly Regional Averages (ES-9) in HDF
CERES  Monthly Gridded Radiative Fluxes and Clouds (FSW) data in HDF.
CERES Monthly Gridded Radiative Fluxes and Clouds (FSW) data in HDF.
CERES Monthly Gridded Radiative Fluxes and Clouds (FSW) data in HDF.
CERES Single Satellite Fluxes and Clouds (FSW) data in HDF.
CERES Single Satellite Fluxes and Clouds (FSW) data in HDF.
CERES Single Satellite Fluxes and Clouds (FSW) data in HDF.
CERES Single Satellite Fluxes and Clouds (FSW) data in HDF.
CERES Single Satellite Fluxes and Clouds (FSW) data in HDF.
CERES ISCCP D2 like Monthly-3Hourly Averages and Monthly Gridded Cloud Averages (ISCCP-D2like-Day) in HDF format.
CERES ISCCP D2 like Monthly-3Hourly Averages and Monthly Gridded Cloud Averages (ISCCP-D2like-Day) in HDF format.
CERES ISCCP D2 like Monthly-3Hourly and Monthly Gridded Cloud Averages (ISCCP-D2like-Day) in HDF format.
CERES ISCCP D2 like Monthly-3Hourly and Monthly Gridded Cloud Averages (ISCCP-D2like-Day) in HDF format.
CERES ISCCP-D2like-GEO Monthly-3Hourly and Monthly Gridded Cloud Averages (ISCCP-D2like-GEO) in HDF format.
CERES ISCCP-D2like-Mrg Monthly-3Hourly and Monthly Gridded Cloud Averages (ISCCP-D2like-Mrg) in HDF format.
CERES ISCCP D2 like Monthly-3Hourly and Monthly Gridded Cloud Averages (ISCCP-D2like-Nit) in HDF format.
CERES ISCCP D2 like Monthly-3Hourly and Monthly Gridded Cloud Averages (ISCCP-D2like-Nit) in HDF format.
CERES ISCCP D2 like Monhtly-3Hourly and Monthly Gridded Cloud Averages (ISCCP-D2like-Nit) in HDF format.
CERES ISCCP D2 like Monhtly-3Hourly and Monthly Gridded Cloud Averages (ISCCP-D2like-Nit) in HDF format.
CERES Monthly Gridded Single Satellite TOA and Surfaces/Clouds (SFC) data in HDF.
CERES Monthly Gridded Single Satellite TOA and Surfaces/Clouds (SFC) data in HDF.
CERES Single Scanner Satellite Footprint, TOA, Surface Fluxes and Clouds (SSF) data in HDF.
CERES Single Scanner Satellite Footprint, TOA, Surface Fluxes and Clouds (SSF) data in HDF.
CERES Single Scanner Satellite Footprint, TOA, Surface Fluxes and Clouds (SSF) data in HDF.
CERES Single Scanner Satellite Footprint, TOA, Surface Fluxes and Clouds (SSF) data in HDF.
CERES Single Scanner Satellite Footprint, TOA, Surface Fluxes and Clouds (SSF) data in HDF.
Clouds and the Earth's Radiant Energy System (CERES) Synoptic Radiative Fluxes and Clouds data in HDF.
Clouds and the Earth's Radiant Energy System (CERES) Synoptic Radiative Fluxes and Clouds data in HDF.
Clouds and the Earth's Radiant Energy System (CERES) Monthly Regional Radiative Fluxes and Clouds data in HDF.
Clouds and the Earth's Radiant Energy System (CERES) Synoptic Radiative Fluxes and Clouds data in HDF.
Clouds and the Earth's Radiant Energy System (CERES) Monthly Zonal and Global Radiative Fluxes and Clouds data in HDF.
Clouds and the Earth's Radiant Energy System (CERES) Monthly Zonal and Global Radiative Fluxes and Clouds data in HDF.
CERES Energy Balanced and Filled(EBAF) Surface Monthly means data in netCDF
CERES Energy Balanced and Filled(EBAF) TOA Monthly means data in netCDF
CERES Energy Balanced and Filled(EBAF) TOA Monthly means data in netCDF
CERES Single Scanner Satellite Footprint, TOA, Surface Fluxes and Clouds (SSF) data in HDF.
CERES Single Scanner Satellite Footprint, TOA, Surface Fluxes and Clouds (SSF) data in HDF.
CERES Single Scanner Satellite Footprint, TOA, Surface Fluxes and Clouds (SSF) data in HDF.
CERES Single Scanner Satellite Footprint, TOA, Surface Fluxes and Clouds (SSF) data in HDF.
This data set includes the soil and vegetation characteristics, herbivore estimates, and precipitation measurement data for the 854 sites described and analyzed in Sankaran et al., 2005. Savannas are globally important ecosystems of great significance to human economies. In these biomes, which are characterized by the co-dominance of trees and grasses, woody cover is a chief determinant of ecosystem properties. The availability of resources (water, nutrients) and disturbance regimes (fire, herbivory) are thought to be important in regulating woody cover but perceptions differ on which of these are the primary drivers of savanna structure. Analyses of data from 854 sites across Africa (Figure 1) showed that maximum woody cover in savannas receiving a mean annual precipitation (MAP) of less than approximately 650 mm is constrained by, and increases linearly with, MAP. These arid and semi-arid savannas may be considered stable systems in which water constrains woody cover and permits grasses to coexist, while fire, herbivory and soil properties interact to reduce woody cover below the MAP-controlled upper bound. Above a MAP of approximately 650 mm, savannas are unstable systems in which MAP is sufficient for woody canopy closure, and disturbances (fire, herbivory) are required for the coexistence of trees and grass. These results provide insights into the nature of African savannas and suggest that future changes in precipitation may considerably affect their distribution and dynamics (Sankaran et al., 2005).This data set includes the site characteristics and measurement data for the 854 sites described and analyzed in Sankaran et al., 2005.  The data are provided in two formats, *.xls and *.csv. See the data format section below for more information. A companion document composed of the supplemental documentation and figures provided with Sankaran et al., 2005 is also included (ftp://daac.ornl.gov/data/global_vegetation/african_woody_savanna/comp/Woody_Cover.pdf).
This data set includes the soil and vegetation characteristics, herbivore estimates, and precipitation measurement data for the 854 sites described and analyzed in Sankaran et al., 2005. Savannas are globally important ecosystems of great significance to human economies. In these biomes, which are characterized by the co-dominance of trees and grasses, woody cover is a chief determinant of ecosystem properties. The availability of resources (water, nutrients) and disturbance regimes (fire, herbivory) are thought to be important in regulating woody cover but perceptions differ on which of these are the primary drivers of savanna structure. Analyses of data from 854 sites across Africa (Figure 1) showed that maximum woody cover in savannas receiving a mean annual precipitation (MAP) of less than approximately 650 mm is constrained by, and increases linearly with, MAP. These arid and semi-arid savannas may be considered stable systems in which water constrains woody cover and permits grasses to coexist, while fire, herbivory and soil properties interact to reduce woody cover below the MAP-controlled upper bound. Above a MAP of approximately 650 mm, savannas are unstable systems in which MAP is sufficient for woody canopy closure, and disturbances (fire, herbivory) are required for the coexistence of trees and grass. These results provide insights into the nature of African savannas and suggest that future changes in precipitation may considerably affect their distribution and dynamics (Sankaran et al., 2005).This data set includes the site characteristics and measurement data for the 854 sites described and analyzed in Sankaran et al., 2005.  The data are provided in two formats, *.xls and *.csv. See the data format section below for more information. A companion document composed of the supplemental documentation and figures provided with Sankaran et al., 2005 is also included (ftp://daac.ornl.gov/data/global_vegetation/african_woody_savanna/comp/Woody_Cover.pdf).
Agricultural Statistics of the People's Republic of China, 1949-1990 is an historical collection of agricultural statistical data compiled by China's State Statistical Bureau (SSB). The collection contains 297 variables covering social and economic indicators, commodities, price index, production, trade, and consumption. The data are provided at the national level (1949-1990) and the provincial level (1979-1990). This dataset is produced in collaboration with the United States Department of Agriculture (USDA), SSB, and the Center for International Earth Science Information Network (CIESIN).
Agricultural Statistics of the People's Republic of China, 1949-1990 is an historical collection of agricultural statistical data compiled by China's State Statistical Bureau (SSB). The collection contains 297 variables covering social and economic indicators, commodities, price index, production, trade, and consumption. The data are provided at the national level (1949-1990) and the provincial level (1979-1990). This dataset is produced in collaboration with the United States Department of Agriculture (USDA), SSB, and the Center for International Earth Science Information Network (CIESIN).
Agricultural Statistics of the People's Republic of China, 1949-1990 is an historical collection of agricultural statistical data compiled by China's State Statistical Bureau (SSB). The collection contains 297 variables covering social and economic indicators, commodities, price index, production, trade, and consumption. The data are provided at the national level (1949-1990) and the provincial level (1979-1990). This dataset is produced in collaboration with the United States Department of Agriculture (USDA), SSB, and the Center for International Earth Science Information Network (CIESIN). (Suggested Usage: To provide historical agricultural statistics of the People's Republic of China for 1949-1990 at the provincial and national levels.)
Bibliography of Chinese Administrative Geography is a historical collection of bibliographic information on 75 published books describing the administrative geography of China. The information resides in a searchable database and includes title, author/editor, subject, spatial (national, provincial, local) and temporal coverage, publisher, description, and language, as well as location of the reference, for works published during the 1949-1994 period. This dataset is produced in collaboration with the University of Washington as part of the China in Time and Space (CITAS) project, Universities Service Center at the Chinese University of Hong Kong, and the Columbia University Center for International Earth Science Information Network (CIESIN).
Bibliography of Chinese Administrative Geography is a historical collection of bibliographic information on 75 published books describing the administrative geography of China. The information resides in a searchable database and includes title, author/editor, subject, spatial (national, provincial, local) and temporal coverage, publisher, description, and language, as well as location of the reference, for works published during the 1949-1994 period. This dataset is produced in collaboration with the University of Washington as part of the China in Time and Space (CITAS) project, Universities Service Center at the Chinese University of Hong Kong, and the Columbia University Center for International Earth Science Information Network (CIESIN).
Bibliography of Chinese Administrative Geography is a historical collection of bibliographic information on 75 published books describing the administrative geography of China. The information resides in a searchable database and includes title, author/editor, subject, spatial (national, provincial, local) and temporal coverage, publisher, description, and language, as well as location of the reference, for works published during the 1949-1994 period. This dataset is produced in collaboration with the University of Washington as part of the China in Time and Space (CITAS) project, Universities Service Center at the Chinese University of Hong Kong, and the Columbia University Center for International Earth Science Information Network (CIESIN). (Suggested Usage: To provide a database of works pertaining to the administrative geography of China, searchable by location, time, and subject matter)
China Administrative Regions GIS Data: 1:1M, County Level, 1 July 1990 consists of geographic boundary data for the administrative regions of China as of 1 July 1990. The data includes the geographical location, area, administrative division code, and county and island name. The data are at a scale of one to one million (1:1M) at the national, provincial, and county level. This dataset is produced in collaboration with the Columbia University Center for International Earth Science Information Network (CIESIN), Chinese Academy of Surveying and Mapping (CASM), and the University of Washington as part of the China in Time and Space (CITAS) project.
China Administrative Regions GIS Data: 1:1M, County Level, 1 July 1990 consists of geographic boundary data for the administrative regions of China as of 1 July 1990. The data includes the geographical location, area, administrative division code, and county and island name. The data are at a scale of one to one million (1:1M) at the national, provincial, and county level. This dataset is produced in collaboration with the Columbia University Center for International Earth Science Information Network (CIESIN), Chinese Academy of Surveying and Mapping (CASM), and the University of Washington as part of the China in Time and Space (CITAS) project.
China Administrative Regions GIS Data: 1:1M, County Level, 1 July 1990 consists of geographic boundary data for the administrative regions of China as of 1 July 1990. The data includes the geographical location, area, administrative division code, and county and island name. The data are at a scale of one to one million (1:1M) at the national, provincial, and county level. This dataset is produced in collaboration with the Columbia University Center for International Earth Science Information Network (CIESIN), Chinese Academy of Surveying and Mapping (CASM), and the University of Washington as part of the China in Time and Space (CITAS) project. (Suggested Usage: To provide information of geographical locations, areas, administrative division codes, and names for the administrative regions of China.)
China Administrative Regions GIS Data: 1:1M, County Level, 1990 consists of geographic boundary data for the administrative regions of China as of 31 December 1990. The data includes the geographical location, area, administrative division code, and county and island name. The data are at a scale of one to one million (1:1M) at the national, provincial, and county level. This dataset is produced in collaboration with the Columbia University Center for International Earth Science Information Network (CIESIN), Chinese Academy of Surveying and Mapping (CASM), and the University of Washington as part of the China in Time and Space (CITAS) project.
China Administrative Regions GIS Data: 1:1M, County Level, 1990 consists of geographic boundary data for the administrative regions of China as of 31 December 1990. The data includes the geographical location, area, administrative division code, and county and island name. The data are at a scale of one to one million (1:1M) at the national, provincial, and county level. This dataset is produced in collaboration with the Columbia University Center for International Earth Science Information Network (CIESIN), Chinese Academy of Surveying and Mapping (CASM), and the University of Washington as part of the China in Time and Space (CITAS) project.
China Administrative Regions GIS Data: 1:1M, County Level, 1990 consists of geographic boundary data for the administrative regions of China as of 31 December 1990. The data includes the geographical location, area, administrative division code, and county and island name. The data are at a scale of one to one million (1:1M) at the national, provincial, and county level. This dataset is produced in collaboration with the Columbia University Center for International Earth Science Information Network (CIESIN), Chinese Academy of Surveying and Mapping (CASM), and the University of Washington as part of the China in Time and Space (CITAS) project. (Suggested Usage: To provide information on the geographical locations, areas, administrative division codes, and names of the administrative regions of China.)
China County-Level Data on Provincial Economic Yearbooks, Keyed To 1:1M GIS Map consists of socioeconomic and boundary data for the administrative regions of China for 1990 and 1991. The socioeconomic data includes natural resources, population, employment, investment, wage, public finance, price, people's livelihood, agriculture, industry, energy, production, transportation, telecommunication, construction, trade, tourism, environmental protection, education, science, patents, culture, sports, health care, and social welfare. The boundary data are at a scale of one to one million (1:1M) at the county level. This dataset is produced in collaboration with the University of Washington as part of the China in Time and Space (CITAS) project, University of Michigan Center of China Studies (CCS), and the Columbia University Center for International Earth Science Information Network (CIESIN).
China County-Level Data on Provincial Economic Yearbooks, Keyed To 1:1M GIS Map consists of socioeconomic and boundary data for the administrative regions of China for 1990 and 1991. The socioeconomic data includes natural resources, population, employment, investment, wage, public finance, price, people's livelihood, agriculture, industry, energy, production, transportation, telecommunication, construction, trade, tourism, environmental protection, education, science, patents, culture, sports, health care, and social welfare. The boundary data are at a scale of one to one million (1:1M) at the county level. This dataset is produced in collaboration with the University of Washington as part of the China in Time and Space (CITAS) project, University of Michigan Center of China Studies (CCS), and the Columbia University Center for International Earth Science Information Network (CIESIN).
China County-Level Data on Provincial Economic Yearbooks, Keyed To 1:1M GIS Map consists of socioeconomic and boundary data for the administrative regions of China for 1990 and 1991. The socioeconomic data includes natural resources, population, employment, investment, wage, public finance, price, people's livelihood, agriculture, industry, energy, production, transportation, telecommunication, construction, trade, tourism, environmental protection, education, science, patents, culture, sports, health care, and social welfare. The boundary data are at a scale of one to one million (1:1M) at the county level. This dataset is produced in collaboration with the University of Washington as part of the China in Time and Space (CITAS) project, University of Michigan Center of China Studies (CCS), and the Columbia University Center for International Earth Science Information Network (CIESIN). (Suggested Usage: To provide county-level social information of China in a series of tables from the China Provincial Economic Yearbook data for 1990 and 1991.)
China County-Level Data on Population (Census) and Agriculture, Keyed To 1:1M GIS Map consists of census, agricultural economic, and boundary data for the administrative regions of China for 1990. The census data includes urban and rural residency, age and sex distribution, educational attainment, illiteracy, marital status, childbirth, mortality, immigration (since 1985), industrial/economic activity, occupation, and ethnicity. The agricultural economic data encompasses rural population, labor force, forestry, livestock and fishery, commodities, equipment, utilities, irrigation, and output value. The boundary data are at a scale of one to one million (1:1M) at the county level. This dataset is produced in collaboration with the University of Washington as part of the China in Time and Space (CITAS) project, University of California-Davis China in Time and Space (CITAS) project, and the Columbia University Center for International Earth Science Information Network (CIESIN).
China County-Level Data on Population (Census) and Agriculture, Keyed To 1:1M GIS Map consists of census, agricultural economic, and boundary data for the administrative regions of China for 1990. The census data includes urban and rural residency, age and sex distribution, educational attainment, illiteracy, marital status, childbirth, mortality, immigration (since 1985), industrial/economic activity, occupation, and ethnicity. The agricultural economic data encompasses rural population, labor force, forestry, livestock and fishery, commodities, equipment, utilities, irrigation, and output value. The boundary data are at a scale of one to one million (1:1M) at the county level. This dataset is produced in collaboration with the University of Washington as part of the China in Time and Space (CITAS) project, University of California-Davis China in Time and Space (CITAS) project, and the Columbia University Center for International Earth Science Information Network (CIESIN).
China County-Level Data on Population (Census) and Agriculture, Keyed To 1:1M GIS Map consists of census, agricultural economic, and boundary data for the administrative regions of China for 1990. The census data includes urban and rural residency, age and sex distribution, educational attainment, illiteracy, marital status, childbirth, mortality, immigration (since 1985), industrial/economic activity, occupation, and ethnicity. The agricultural economic data encompasses rural population, labor force, forestry, livestock and fishery, commodities, equipment, utilities, irrigation, and output value. The boundary data are at a scale of one to one million (1:1M) at the county level. This dataset is produced in collaboration with the University of Washington as part of the China in Time and Space (CITAS) project, University of California-Davis China in Time and Space (CITAS) project, and the Columbia University Center for International Earth Science Information Network (CIESIN). (Suggested Usage: To provide information on China's population and agricultural economy from the 1990 census, and geocoded 1990 China county boundary GIS data at 1:1M.)
China Maps Bibliographic Database is an historical collection of bibliographic information for more than 400 maps of China. The information resides in a searchable database and includes title, author/editor, publisher, location, projection, year, elevation, land cover type (forest, desert, marsh/swamp, grassland), vegetation, transportation (roads, railroads), rivers and lakes, spatial coverage (provincial, county, township), and language for maps published from 1765 to 1994. The information is available in both English and Chinese (GB Code for Chinese Characters). This dataset is produced in collaboration with the University of Washington as part of the China in Time and Space (CITAS) project and the Columbia University Center for International Earth Science Information Network (CIESIN).
China Maps Bibliographic Database is an historical collection of bibliographic information for more than 400 maps of China. The information resides in a searchable database and includes title, author/editor, publisher, location, projection, year, elevation, land cover type (forest, desert, marsh/swamp, grassland), vegetation, transportation (roads, railroads), rivers and lakes, spatial coverage (provincial, county, township), and language for maps published from 1765 to 1994. The information is available in both English and Chinese (GB Code for Chinese Characters). This dataset is produced in collaboration with the University of Washington as part of the China in Time and Space (CITAS) project and the Columbia University Center for International Earth Science Information Network (CIESIN).
China Maps Bibliographic Database is an historical collection of bibliographic information for more than 400 maps of China. The information resides in a searchable database and includes title, author/editor, publisher, location, projection, year, elevation, land cover type (forest, desert, marsh/swamp, grassland), vegetation, transportation (roads, railroads), rivers and lakes, spatial coverage (provincial, county, township), and language for maps published from 1765 to 1994. The information is available in both English and Chinese (GB Code for Chinese Characters). This dataset is produced in collaboration with the University of Washington as part of the China in Time and Space (CITAS) project and the Columbia University Center for International Earth Science Information Network (CIESIN). (Suggested Usage: To provide a database of public China maps, searchable by location, scale, content, publisher, and publication date)
Chinese County-Level Data on Hospitals and Epidemiology Stations, 1950-1985 consists of hospital and epidemiological station data for the administrative regions of China from 1950 to 1985. The data includes name and years of operation at a scale of one to one million (1:1M) at the county level. This dataset is produced in collaboration with the University of Washington as part of the China in Time and Space (CITAS) project and the Columbia University Center for International Earth Science Information Network (CIESIN).
Chinese County-Level Data on Hospitals and Epidemiology Stations, 1950-1985 consists of hospital and epidemiological station data for the administrative regions of China from 1950 to 1985. The data includes name and years of operation at a scale of one to one million (1:1M) at the county level. This dataset is produced in collaboration with the University of Washington as part of the China in Time and Space (CITAS) project and the Columbia University Center for International Earth Science Information Network (CIESIN).
Chinese County-Level Data on Hospitals and Epidemiology Stations, 1950-1985 consists of hospital and epidemiological station data for the administrative regions of China from 1950 to 1985. The data includes name and years of operation at a scale of one to one million (1:1M) at the county level. This dataset is produced in collaboration with the University of Washington as part of the China in Time and Space (CITAS) project and the Columbia University Center for International Earth Science Information Network (CIESIN). (Suggested Usage: To provide descriptive information on health care institutions in China between 1950 and 1985.)
Fundamental GIS: Digital Chart of China, 1:1M, Version 1 consists of vector maps of China and surrounding areas. The maps include roads, railroads, drainage systems, contours, populated places, and urbanized areas for China proper, as well as for China and neighboring countries. The maps are at a scale of one to one million (1:1M). This dataset is produced in collaboration with the University of Washington as part of the China in Time and Space (CITAS) project and the Columbia University Center for International Earth Science Information Network (CIESIN).
Fundamental GIS: Digital Chart of China, 1:1M, Version 1 consists of vector maps of China and surrounding areas. The maps include roads, railroads, drainage systems, contours, populated places, and urbanized areas for China proper, as well as for China and neighboring countries. The maps are at a scale of one to one million (1:1M). This dataset is produced in collaboration with the University of Washington as part of the China in Time and Space (CITAS) project and the Columbia University Center for International Earth Science Information Network (CIESIN).
Fundamental GIS: Digital Chart of China, 1:1M, Version 1 consists of vector maps of China and surrounding areas. The maps include roads, railroads, drainage systems, contours, populated places, and urbanized areas for China proper, as well as for China and neighboring countries. The maps are at a scale of one to one million (1:1M). This dataset is produced in collaboration with the University of Washington as part of the China in Time and Space (CITAS) project and the Columbia University Center for International Earth Science Information Network (CIESIN). (Suggested Usage: To provide fundamental GIS maps of China, and neighboring countries and regions.)
GuoBiao (GB) Codes for the Administrative Divisions of the People's Republic of China consists of geographic codes for the administrative divisions of China. The data includes provinces (autonomous regions, municipalities directly under the Central Government), prefectures (prefecture-level cities, autonomous prefectures, leagues), and counties (districts, county-level cities, autonomous counties, banners) for 1 January 1982 to 31 December 1992. This dataset is produced in collaboration with the Chinese Academy of Surveying and Mapping (CASM), University of Washington as part of the China in Time and Space (CITAS) project, and the Columbia University Center for International Earth Science Information Network (CIESIN).
GuoBiao (GB) Codes for the Administrative Divisions of the People's Republic of China consists of geographic codes for the administrative divisions of China. The data includes provinces (autonomous regions, municipalities directly under the Central Government), prefectures (prefecture-level cities, autonomous prefectures, leagues), and counties (districts, county-level cities, autonomous counties, banners) for 1 January 1982 to 31 December 1992. This dataset is produced in collaboration with the Chinese Academy of Surveying and Mapping (CASM), University of Washington as part of the China in Time and Space (CITAS) project, and the Columbia University Center for International Earth Science Information Network (CIESIN).
GuoBiao (GB) Codes for the Administrative Divisions of the People's Republic of China consists of geographic codes for the administrative divisions of China. The data includes provinces (autonomous regions, municipalities directly under the Central Government), prefectures (prefecture-level cities, autonomous prefectures, leagues), and counties (districts, county-level cities, autonomous counties, banners) for 1 January 1982 to 31 December 1992. This dataset is produced in collaboration with the Chinese Academy of Surveying and Mapping (CASM), University of Washington as part of the China in Time and Space (CITAS) project, and the Columbia University Center for International Earth Science Information Network (CIESIN). (Suggested Usage: To provide a dynamic matrix for the identification of spatially and temporally-specific data on Chinese administrative units.)
Priority Programme for China's Agenda 21 consists of full-text program descriptions supporting China's economic and social development. The descriptions represent 69 programs covering legislation, policy, education, agriculture, environment, energy, transportation, regional development, population, health, and global change research. Each description includes project scope, background, objectives, activities, inputs, and benefits. This dataset is produced in collaboration with the Administrative Center for China's Agenda 21 (ACCA21), United Nations Development Programme (UNDP), and the Columbia University Center for International Earth Science Information Network (CIESIN)
Priority Programme for China's Agenda 21 consists of full-text program descriptions supporting China's economic and social development. The descriptions represent 69 programs covering legislation, policy, education, agriculture, environment, energy, transportation, regional development, population, health, and global change research. Each description includes project scope, background, objectives, activities, inputs, and benefits. This dataset is produced in collaboration with the Administrative Center for China's Agenda 21 (ACCA21), United Nations Development Programme (UNDP), and the Columbia University Center for International Earth Science Information Network (CIESIN)
Priority Programme for China's Agenda 21 consists of full-text program descriptions supporting China's economic and social development. The descriptions represent 69 programs covering legislation, policy, education, agriculture, environment, energy, transportation, regional development, population, health, and global change research. Each description includes project scope, background, objectives, activities, inputs, and benefits. This dataset is produced in collaboration with the Administrative Center for China's Agenda 21 (ACCA21), United Nations Development Programme (UNDP), and the Columbia University Center for International Earth Science Information Network (CIESIN) (Suggested Usage: To provide information on the medium and long term plans for China's national economic and social development.)
Cloud estimates
Cloud estimates
ABSTRACT: Cloud estimates
This data set includes two sets of soil temperature profiles, two sets of soil moisture profiles, two sets of soil heat flux profiles (in dense pine and open pine areas), and one set of a snow temperature profile, air temperature, and relative humidity measurements. Measurements were made at the Local Scale Observation Site (LSOS) of the Cold Land Processes Field Experiment (CLPX) in northern Colorado. The LSOS is a 100 m x 100 m study site located within the Fraser Intensive Study Area (ISA). The study area has flat topography with a uniform pine forest, a discontinuous pine forest, and a small clearing. Data were collected between 
6 November 2002 and 29 May 2003. These data were primarily collected to support the University of Michigan microwave radiometric observations acquired at the same site (to be published in early 2004).
This data set includes two sets of soil temperature profiles, two sets of soil moisture profiles, two sets of soil heat flux profiles (in dense pine and open pine areas), and one set of a snow temperature profile, air temperature, and relative humidity measurements. Measurements were made at the Local Scale Observation Site (LSOS) of the Cold Land Processes Field Experiment (CLPX) in northern Colorado. The LSOS is a 100 m x 100 m study site located within the Fraser Intensive Study Area (ISA). The study area has flat topography with a uniform pine forest, a discontinuous pine forest, and a small clearing. Data were collected between 
6 November 2002 and 29 May 2003. These data were primarily collected to support the University of Michigan microwave radiometric observations acquired at the same site (to be published in early 2004).
This data set includes two sets of soil temperature profiles, two sets of soil moisture profiles, two sets of soil heat flux profiles (in dense pine and open pine areas), and one set of a snow temperature profile, air temperature, and relative humidity measurements. Measurements were made at the Local Scale Observation Site (LSOS) of the Cold Land Processes Field Experiment (CLPX) in northern Colorado. The LSOS is a 100 m x 100 m study site located within the Fraser Intensive Study Area (ISA). The study area has flat topography with a uniform pine forest, a discontinuous pine forest, and a small clearing. Data were collected between 
6 November 2002 and 29 May 2003. These data were primarily collected to support the University of Michigan microwave radiometric observations acquired at the same site (to be published in early 2004).
The CODATA Catalog of Roads Data Sets, Version 1 contains 367 entries describing national-level road network data sets for 147 countries and four entries describing global data sets. It was produced by the Columbia University Center for International Earth Science Information Network (CIESIN) under the oversight of the CODATA Global Roads Data Development Working Group, and as a contribution to the development of the Global Roads Open Access Data Set (gROADS).
The CODATA Catalog of Roads Data Sets, Version 1 contains 367 entries describing national-level road network data sets for 147 countries and four entries describing global data sets. It was produced by the Columbia University Center for International Earth Science Information Network (CIESIN) under the oversight of the CODATA Global Roads Data Development Working Group, and as a contribution to the development of the Global Roads Open Access Data Set (gROADS).
The CODATA Catalog of Roads Data Sets, Version 1 contains 367 entries describing national-level road network data sets for 147 countries and four entries describing global data sets. It was produced by the Columbia University Center for International Earth Science Information Network (CIESIN) under the oversight of the CODATA Global Roads Data Development Working Group, and as a contribution to the development of the Global Roads Open Access Data Set (gROADS).
The CODATA Catalog of Roads Data Sets, Version 1 contains 367 entries describing national-level road network data sets for 147 countries and four entries describing global data sets. It was produced by the Columbia University Center for International Earth Science Information Network (CIESIN) under the oversight of the CODATA Global Roads Data Development Working Group, and as a contribution to the development of the Global Roads Open Access Data Set (gROADS).  (Suggested Usage: To provide documentation on roads data sets that are potential candidates for inclusion in the Global Roads Open Access Data Set (gROADS), and which may be useful to others searching for data on road infrastructure.)
The TRMM Microwave Imager (TMI) Gridded Orbital rainfall data, a special product derived from the TRMM standard product, TMI rain profile (2A-12), and mapped to a 0.5 degree x 0.5 degree latitude/longitude grid. The spatial coverage is defined by a 760 km swath bounded by the latitudes 38 degree N and 38 degree S for a single satellite orbit. The parameters included in this data set are the mean, standard deviation, and number of samples for the conditional surface rain rate; and the cloud water at 14 vertical levels in the rain column. The document also provides the information necessary to readily access and interpret the parameters stored in the files.
Combined TRMM Microwave Imager (TMI) and Precipitation Radar (PR) gridded orbital rainfall data, is a special product derived from the TRMM standard product (2B-31) TMI and PR combined and mapped to a 0.1 degree x 0.1 degree latitude/longitude grid. The spatial coverage is defined by a 220 km swath bounded by the latitudes 38 degree N and 38 degree S for a single satellite orbit. The parameters included in this data set include the mean, standard deviation, and number of samples of the surface rain rate. Because the spatial resolution of the Combined TMI and PR data is about 5 km, the maximum number of samples per grid box in the gridded orbital data is about nine.  The metadata applicable to each orbit of data are contained in a header record in the file.
The 2004 Environmental Vulnerability Index (EVI) portion of the Compendium of Environmental Sustainability Indicator Collections contains 111 variables for 235 countries and territories. This index is designed to be used with economic and social vulnerability indices to provide insights into the processes that can negatively influence the sustainable development of countries. It was developed through consultation and collaborations with countries, institutions and experts across the globe by the South Pacific Applied Geoscience Commission (SOPAC), the United Nations Environment Programme (UNEP) and their partners. The data are distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
The 2004 Environmental Vulnerability Index (EVI) portion of the Compendium of Environmental Sustainability Indicator Collections contains 111 variables for 235 countries and territories. This index is designed to be used with economic and social vulnerability indices to provide insights into the processes that can negatively influence the sustainable development of countries. It was developed through consultation and collaborations with countries, institutions and experts across the globe by the South Pacific Applied Geoscience Commission (SOPAC), the United Nations Environment Programme (UNEP) and their partners. The data are distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
The 2004 Environmental Vulnerability Index (EVI) portion of the Compendium of Environmental Sustainability Indicator Collections contains 111 variables for 235 countries and territories. This index is designed to be used with economic and social vulnerability indices to provide insights into the processes that can negatively influence the sustainable development of countries. It was developed through consultation and collaborations with countries, institutions and experts across the globe by the South Pacific Applied Geoscience Commission (SOPAC), the United Nations Environment Programme (UNEP) and their partners. The data are distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
The 2005 Environmental Sustainability Index (ESI) portion of the Compendium of Environmental Sustainability Indicators Collection contains 103 variables for 146 countries. The variables include raw data and processed indicators and indices which are calculated on the basis of the raw data. The index was unveiled at the World Economic Forum's annual meeting in January 2005 at Davos, Switzerland. The 2005 ESI is a joint product of the Yale Center for Environmental Law and Policy (YCELP) and the Columbia University Center for International Earth Science Information Network (CIESIN), in collaboration with the World Economic Forum (WEF) and the Joint Research Centre (JRC), European Commission. The data are distributed by the Columbia University Center for International Earth Science Information Network (CIESIN). (Suggested Usage: To provide a mechanism for making environmental management more quantitative, empirically grounded and systematic. The ESI scores evaluate a country's potential to avoid major environmental deterioration. The ESI provides a policy tool for identifying issues that deserve greater attention within national environmental protection programs and across societies more generally.)
The 2005 Environmental Sustainability Index (ESI) portion of the Compendium of Environmental Sustainability Indicators Collection contains 103 variables for 146 countries. The variables include raw data and processed indicators and indices which are calculated on the basis of the raw data. The index was unveiled at the World Economic Forum's annual meeting in January 2005 at Davos, Switzerland. The 2005 ESI is a joint product of the Yale Center for Environmental Law and Policy (YCELP) and the Columbia University Center for International Earth Science Information Network (CIESIN), in collaboration with the World Economic Forum (WEF) and the Joint Research Centre (JRC), European Commission. The data are distributed by the Columbia University Center for International Earth Science Information Network (CIESIN). (Suggested Usage: To provide a mechanism for making environmental management more quantitative, empirically grounded and systematic. The ESI scores evaluate a country's potential to avoid major environmental deterioration. The ESI provides a policy tool for identifying issues that deserve greater attention within national environmental protection programs and across societies more generally.)
The 2005 Environmental Sustainability Index (ESI) portion of the Compendium of Environmental Sustainability Indicators Collection contains 103 variables for 146 countries. The variables include raw data and processed indicators and indices which are calculated on the basis of the raw data. The index was unveiled at the World Economic Forum's annual meeting in January 2005 at Davos, Switzerland. The 2005 ESI is a joint product of the Yale Center for Environmental Law and Policy (YCELP) and the Columbia University Center for International Earth Science Information Network (CIESIN), in collaboration with the World Economic Forum (WEF) and the Joint Research Centre (JRC), European Commission. The data are distributed by the Columbia University Center for International Earth Science Information Network (CIESIN). (Suggested Usage: To provide a mechanism for making environmental management more quantitative, empirically grounded and systematic. The ESI scores evaluate a country's potential to avoid major environmental deterioration. The ESI provides a policy tool for identifying issues that deserve greater attention within national environmental protection programs and across societies more generally.)
The 2006 National Footprint Accounts (NFA) portion of the Compendium of Environmental Sustainability Indicator Collections, version 1.1 is a data set that measures how much land and water area a human population requires to produce the resources it consumes and to absorb its wastes under prevailing technology and management. It includes Footprints for cropland, grazing land, carbon, nuclear, forest, built-up and fishing ground for 147 countries. It also identifies countries that are considered to have ecological deficits and reserves. These data are drawn from the National Footprint Accounts, 2006 Edition, produced by the Global Footprint Network and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
The 2006 National Footprint Accounts (NFA) portion of the Compendium of Environmental Sustainability Indicator Collections, version 1.1 is a data set that measures how much land and water area a human population requires to produce the resources it consumes and to absorb its wastes under prevailing technology and management. It includes Footprints for cropland, grazing land, carbon, nuclear, forest, built-up and fishing ground for 147 countries. It also identifies countries that are considered to have ecological deficits and reserves. These data are drawn from the National Footprint Accounts, 2006 Edition, produced by the Global Footprint Network and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
The 2006 National Footprint Accounts (NFA) portion of the Compendium of Environmental Sustainability Indicator Collections, version 1.1 is a data set that measures how much land and water area a human population requires to produce the resources it consumes and to absorb its wastes under prevailing technology and management. It includes Footprints for cropland, grazing land, carbon, nuclear, forest, built-up and fishing ground for 147 countries. It also identifies countries that are considered to have ecological deficits and reserves. These data are drawn from the National Footprint Accounts, 2006 Edition, produced by the Global Footprint Network and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN). (Suggested Usage:  To help assess a country's demand on and supply of biological capital.)
The Ancillary Data portion of the Compendium of Environmental Sustainability Indicator Collections contains 38 variables (time series data on population and gross domestic product as well as region codes, land area, and waterbody area) for 238 countries. The data are taken from the UN Population Division, the World Bank, the CIA Factbook, and CIESIN's Gridded Population of the World, and are distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
The Ancillary Data portion of the Compendium of Environmental Sustainability Indicator Collections contains 38 variables (time series data on population and gross domestic product as well as region codes, land area, and waterbody area) for 238 countries. The data are taken from the UN Population Division, the World Bank, the CIA Factbook, and CIESIN's Gridded Population of the World, and are distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
The Ancillary Data portion of the Compendium of Environmental Sustainability Indicator Collections contains 38 variables (time series data on population and gross domestic product as well as region codes, land area, and waterbody area) for 238 countries. The data are taken from the UN Population Division, the World Bank, the CIA Factbook, and CIESIN's Gridded Population of the World, and are distributed by the Columbia University Center for International Earth Science Information Network (CIESIN). (Suggested Usage: To provide population estimates, gross domestic product, and surface area data to be used in conjunction with the sustainability indicators included in the compendium.)
The Compendium of Environmental Sustainability Indicator Collections, Version 1.1 contains 426 indicators for 239 countries from five major environmental sustainability indicator efforts: the 2006 Environmental Performance Index (EPI), 2005 Environmental Sustainability Index (ESI), 2004 Environmental Vulnerability Index (EVI), the Rio to Johannesburg Dashboard, the Wellbeing of Nations, and 2006 National Footprint Accounts. It also incorporates 38 ancillary variables such as region name, dummy variables for landlocked countries and small island states, population, GDP, and land area. The collection is compiled and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
The Compendium of Environmental Sustainability Indicator Collections, Version 1.1 contains 426 indicators for 239 countries from five major environmental sustainability indicator efforts: the 2006 Environmental Performance Index (EPI), 2005 Environmental Sustainability Index (ESI), 2004 Environmental Vulnerability Index (EVI), the Rio to Johannesburg Dashboard, the Wellbeing of Nations, and 2006 National Footprint Accounts. It also incorporates 38 ancillary variables such as region name, dummy variables for landlocked countries and small island states, population, GDP, and land area. The collection is compiled and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
The Compendium of Environmental Sustainability Indicator Collections, Version 1.1 contains 426 indicators for 239 countries from five major environmental sustainability indicator efforts: the 2006 Environmental Performance Index (EPI), 2005 Environmental Sustainability Index (ESI), 2004 Environmental Vulnerability Index (EVI), the Rio to Johannesburg Dashboard, the Wellbeing of Nations, and 2006 National Footprint Accounts. It also incorporates 38 ancillary variables such as region name, dummy variables for landlocked countries and small island states, population, GDP, and land area. The collection is compiled and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN). (Suggested Usage: To make the acquisition, comparison and analysis of sustainability indicators easier by compiling them in a single database, incorporating multiple country codes, and condensing the indicator descriptions into short methodological summaries in an accompanying data dictionary.)
The Rio to Johannesburg Dashboard of Sustainable Development Indicators portion of the Compendium of Environmental Sustainability Indicator Collections contains 35 Commission on Sustainable Development (CSD) indicators for 202 countries. Commonly known as the RioJo Dashboard, indicators are from the CSD Thematic Framework from the Rio Summit (1992 UN conference on the Environment and Development) to the time of the Johannesburg Summit in 2000. The data are distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
The Rio to Johannesburg Dashboard of Sustainable Development Indicators portion of the Compendium of Environmental Sustainability Indicator Collections contains 35 Commission on Sustainable Development (CSD) indicators for 202 countries. Commonly known as the RioJo Dashboard, indicators are from the CSD Thematic Framework from the Rio Summit (1992 UN conference on the Environment and Development) to the time of the Johannesburg Summit in 2000. The data are distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
The Rio to Johannesburg Dashboard of Sustainable Development Indicators portion of the Compendium of Environmental Sustainability Indicator Collections contains 35 Commission on Sustainable Development (CSD) indicators for 202 countries. Commonly known as the RioJo Dashboard, indicators are from the CSD Thematic Framework from the Rio Summit (1992 UN conference on the Environment and Development) to the time of the Johannesburg Summit in 2000. The data are distributed by the Columbia University Center for International Earth Science Information Network (CIESIN). (Suggested Usage: To provide data to gauge broad similarities and differences of national progress on sustainability of countries, over time and relative to other nations.)
The Wellbeing of Nations portion of the Compendium of Environmental Sustainability Indicator Collections contains a subset of 123 variables assembled 

from the Wellbeing of Nations, which assesses human and ecosystem wellbeing for 183 countries. The variables selected include both raw data and processed indicators and indices created 

by the report's author, Robert Prescott-Allen. The data are distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
The Wellbeing of Nations portion of the Compendium of Environmental Sustainability Indicator Collections contains a subset of 123 variables assembled 

from the Wellbeing of Nations, which assesses human and ecosystem wellbeing for 183 countries. The variables selected include both raw data and processed indicators and indices created 

by the report's author, Robert Prescott-Allen. The data are distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
The Wellbeing of Nations portion of the Compendium of Environmental Sustainability Indicator Collections contains a subset of 123 variables assembled 

from the Wellbeing of Nations, which assesses human and ecosystem wellbeing for 183 countries. The variables selected include both raw data and processed indicators and indices created 

by the report's author, Robert Prescott-Allen. The data are distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
The Country-Level GDP and Downscaled Projections Based on the Special Report on Emissions Scenarios (SRES) A1, A2, B1, and B2 marker scenarios, 1990-2100, were developed using the 1990 base year GDP (Gross Domestic Product) from national accounts database available from the UN Statistics Division. SRES regional GDP growth rates were calculated from 1990 to 2100 based on the SRES marker model regional data and applied uniformly to each country that fell within the SRES-defined regions. This dataset is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
The Country-Level GDP and Downscaled Projections Based on the Special Report on Emissions Scenarios (SRES) A1, A2, B1, and B2 marker scenarios, 1990-2100, were developed using the 1990 base year GDP (Gross Domestic Product) from national accounts database available from the UN Statistics Division. SRES regional GDP growth rates were calculated from 1990 to 2100 based on the SRES marker model regional data and applied uniformly to each country that fell within the SRES-defined regions. This dataset is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
The Country-Level GDP and Downscaled Projections Based on the Special Report on Emissions Scenarios (SRES) A1, A2, B1, and B2 marker scenarios, 1990-2100, were developed using the 1990 base year GDP (Gross Domestic Product) from national accounts database available from the UN Statistics Division. SRES regional GDP growth rates were calculated from 1990 to 2100 based on the SRES marker model regional data and applied uniformly to each country that fell within the SRES-defined regions. This dataset is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN). (Suggested Usage: To provide data on country-level GDP per capita and downscaled projections based on the SRES A1, A2, B1, B2 marker scenarios.)
The Country-Level Population and Downscaled Projections Based on Special Report on Emissions Scenarios (SRES) A1, B1, and A2 Scenarios, 1990-2100, were adopted in 2000 from population projections realized at the International Institute for Applied Systems Analysis (IIASA) in 1996. The Intergovernmental Panel on Climate Change (IPCC) SRES A1 and B1 scenarios both used the same IIASA "rapid" fertility transition projection, which assumes low fertility and low mortality rates. The SRES A2 scenario used a corresponding IIASA "slow" fertility transition projection (high fertility and high mortality rates). Both IIASA low and high projections are performed for 13 world regions including North Africa, Sub-Saharan Africa, China and Centrally Planned Asia, Pacific Asia, Pacific OECD, Central Asia, Middle East, South Asia, Eastern Europe, European part of the former Soviet Union, Western Europe, Latin America, and North America. This data set is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
The Country-Level Population and Downscaled Projections Based on Special Report on Emissions Scenarios (SRES) A1, B1, and A2 Scenarios, 1990-2100, were adopted in 2000 from population projections realized at the International Institute for Applied Systems Analysis (IIASA) in 1996. The Intergovernmental Panel on Climate Change (IPCC) SRES A1 and B1 scenarios both used the same IIASA "rapid" fertility transition projection, which assumes low fertility and low mortality rates. The SRES A2 scenario used a corresponding IIASA "slow" fertility transition projection (high fertility and high mortality rates). Both IIASA low and high projections are performed for 13 world regions including North Africa, Sub-Saharan Africa, China and Centrally Planned Asia, Pacific Asia, Pacific OECD, Central Asia, Middle East, South Asia, Eastern Europe, European part of the former Soviet Union, Western Europe, Latin America, and North America. This data set is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
The Country-Level Population and Downscaled Projections Based on Special Report on Emissions Scenarios (SRES) A1, B1, and A2 Scenarios, 1990-2100, were adopted in 2000 from population projections realized at the International Institute for Applied Systems Analysis (IIASA) in 1996. The Intergovernmental Panel on Climate Change (IPCC) SRES A1 and B1 scenarios both used the same IIASA "rapid" fertility transition projection, which assumes low fertility and low mortality rates. The SRES A2 scenario used a corresponding IIASA "slow" fertility transition projection (high fertility and high mortality rates). Both IIASA low and high projections are performed for 13 world regions including North Africa, Sub-Saharan Africa, China and Centrally Planned Asia, Pacific Asia, Pacific OECD, Central Asia, Middle East, South Asia, Eastern Europe, European part of the former Soviet Union, Western Europe, Latin America, and North America. This data set is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN). (Suggested Usage: To provide data on country-level population and downscaled projections based on the SRES A1, B1, and A2 marker scenarios.)
The Country-Level Population and Downscaled Projections Based on Special Report on Emissions Scenarios (SRES) B2 Scenario, 1990-2100, were based on the UN 1998 Medium Long Range Projection for the years 1995 to 2100. The official version projects population for 8 regions of the world including Africa, Asia (minus India and China), India, China, Europe, Latin America, Northern America, and Oceania. This dataset is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
The Country-Level Population and Downscaled Projections Based on Special Report on Emissions Scenarios (SRES) B2 Scenario, 1990-2100, were based on the UN 1998 Medium Long Range Projection for the years 1995 to 2100. The official version projects population for 8 regions of the world including Africa, Asia (minus India and China), India, China, Europe, Latin America, Northern America, and Oceania. This dataset is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
The Country-Level Population and Downscaled Projections Based on Special Report on Emissions Scenarios (SRES) B2 Scenario, 1990-2100, were based on the UN 1998 Medium Long Range Projection for the years 1995 to 2100. The official version projects population for 8 regions of the world including Africa, Asia (minus India and China), India, China, Europe, Latin America, Northern America, and Oceania. This dataset is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN). (Suggested Usage: To provide data on country-level population and downscaled projections based on the SRES B2 marker scenario.)
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains the uncalibrated, top-of-atmosphere data products in sensor coordinates.
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains the uncalibrated, top-of-atmosphere data products in sensor coordinates.
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains the uncalibrated, top-of-atmosphere data products in sensor coordinates.
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains the geophysical data products in sensor coordinates.  The products included: chlorophyll a, remote sensing reflectance in the visible bands, aerosol optical depth at 670nm, aerosol angstrom parameter, and diffuse attenuation at 490nm.  These data were processed to Level 2 using NASA's OBPG l2gen software.
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains the geophysical data products in sensor coordinates.  The products included: chlorophyll a, remote sensing reflectance in the visible bands, aerosol optical depth at 670nm, aerosol angstrom parameter, and diffuse attenuation at 490nm.  These data were processed to Level 2 using NASA's OBPG l2gen software.
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains the geophysical data products in sensor coordinates.  The products included: chlorophyll a, remote sensing reflectance in the visible bands, aerosol optical depth at 670nm, aerosol angstrom parameter, and diffuse attenuation at 490nm.  These data were processed to Level 2 using NASA's OBPG l2gen software.
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains composite eight day Level-3 binned (Equal-Area Sinusoidal grid) data products in three variants:
            CHL- which includes chlorophyll a,
            KD490 - which includes diffuse attenuation at 490nm and
            RRS which includes the remote sensing reflectance in the visible bands, aerosol optical depth at 670nm and aerosol angstrom parameter.
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains composite eight day Level-3 binned (Equal-Area Sinusoidal grid) data products in three variants:
            CHL- which includes chlorophyll a,
            KD490 - which includes diffuse attenuation at 490nm and
            RRS which includes the remote sensing reflectance in the visible bands, aerosol optical depth at 670nm and aerosol angstrom parameter.
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains composite eight day Level-3 binned (Equal-Area Sinusoidal grid) data products in three variants:
            CHL- which includes chlorophyll a,
            KD490 - which includes diffuse attenuation at 490nm and
            RRS which includes the remote sensing reflectance in the visible bands, aerosol optical depth at 670nm and aerosol angstrom parameter.
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains the mission long cumulative Level-3 binned (Equal-Area Sinusoidal grid) data products in three variants:
            CHL- which includes chlorophyll a,
            KD490 - which includes diffuse attenuation at 490nm and
            RRS which includes the remote sensing reflectance in the visible bands, aerosol optical depth at 670nm and aerosol angstrom parameter.
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains the mission long cumulative Level-3 binned (Equal-Area Sinusoidal grid) data products in three variants:
            CHL- which includes chlorophyll a,
            KD490 - which includes diffuse attenuation at 490nm and
            RRS which includes the remote sensing reflectance in the visible bands, aerosol optical depth at 670nm and aerosol angstrom parameter.
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains the mission long cumulative Level-3 binned (Equal-Area Sinusoidal grid) data products in three variants:
            CHL- which includes chlorophyll a,
            KD490 - which includes diffuse attenuation at 490nm and
            RRS which includes the remote sensing reflectance in the visible bands, aerosol optical depth at 670nm and aerosol angstrom parameter.
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains the daily Level-3 binned (Equal-Area Sinusoidal grid) data products in three variants:
            CHL- which includes chlorophyll a,
            KD490 - which includes diffuse attenuation at 490nm and
            RRS which includes the remote sensing reflectance in the visible bands, aerosol optical depth at 670nm and aerosol angstrom parameter.
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains the daily Level-3 binned (Equal-Area Sinusoidal grid) data products in three variants:
            CHL- which includes chlorophyll a,
            KD490 - which includes diffuse attenuation at 490nm and
            RRS which includes the remote sensing reflectance in the visible bands, aerosol optical depth at 670nm and aerosol angstrom parameter.
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains the daily Level-3 binned (Equal-Area Sinusoidal grid) data products in three variants:
            CHL- which includes chlorophyll a,
            KD490 - which includes diffuse attenuation at 490nm and
            RRS which includes the remote sensing reflectance in the visible bands, aerosol optical depth at 670nm and aerosol angstrom parameter.
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains monthly climatological Level-3 binned (Equal-Area Sinusoidal grid) data products in three variants:
            CHL- which includes chlorophyll a,
            KD490 - which includes diffuse attenuation at 490nm and
            RRS which includes the remote sensing reflectance in the visible bands, aerosol optical depth at 670nm and aerosol angstrom parameter.
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains monthly climatological Level-3 binned (Equal-Area Sinusoidal grid) data products in three variants:
            CHL- which includes chlorophyll a,
            KD490 - which includes diffuse attenuation at 490nm and
            RRS which includes the remote sensing reflectance in the visible bands, aerosol optical depth at 670nm and aerosol angstrom parameter.
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains monthly climatological Level-3 binned (Equal-Area Sinusoidal grid) data products in three variants:
            CHL- which includes chlorophyll a,
            KD490 - which includes diffuse attenuation at 490nm and
            RRS which includes the remote sensing reflectance in the visible bands, aerosol optical depth at 670nm and aerosol angstrom parameter.
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains the monthly Level-3 binned (Equal-Area Sinusoidal grid) data products in three variants:
            CHL- which includes chlorophyll a,
            KD490 - which includes diffuse attenuation at 490nm and
            RRS which includes the remote sensing reflectance in the visible bands, aerosol optical depth at 670nm and aerosol angstrom parameter.
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains the monthly Level-3 binned (Equal-Area Sinusoidal grid) data products in three variants:
            CHL- which includes chlorophyll a,
            KD490 - which includes diffuse attenuation at 490nm and
            RRS which includes the remote sensing reflectance in the visible bands, aerosol optical depth at 670nm and aerosol angstrom parameter.
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains the monthly Level-3 binned (Equal-Area Sinusoidal grid) data products in three variants:
            CHL- which includes chlorophyll a,
            KD490 - which includes diffuse attenuation at 490nm and
            RRS which includes the remote sensing reflectance in the visible bands, aerosol optical depth at 670nm and aerosol angstrom parameter.
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains the rolling 32 day composite Level-3 binned (Equal-Area Sinusoidal grid) data products in three variants:
            CHL- which includes chlorophyll a,
            KD490 - which includes diffuse attenuation at 490nm and
            RRS which includes the remote sensing reflectance in the visible bands, aerosol optical depth at 670nm and aerosol angstrom parameter.
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains the rolling 32 day composite Level-3 binned (Equal-Area Sinusoidal grid) data products in three variants:
            CHL- which includes chlorophyll a,
            KD490 - which includes diffuse attenuation at 490nm and
            RRS which includes the remote sensing reflectance in the visible bands, aerosol optical depth at 670nm and aerosol angstrom parameter.
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains the rolling 32 day composite Level-3 binned (Equal-Area Sinusoidal grid) data products in three variants:
            CHL- which includes chlorophyll a,
            KD490 - which includes diffuse attenuation at 490nm and
            RRS which includes the remote sensing reflectance in the visible bands, aerosol optical depth at 670nm and aerosol angstrom parameter.
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains seasonal climatologies of Level-3 binned (Equal-Area Sinusoidal grid) data products in three variants:
            CHL- which includes chlorophyll a,
            KD490 - which includes diffuse attenuation at 490nm and
            RRS which includes the remote sensing reflectance in the visible bands, aerosol optical depth at 670nm and aerosol angstrom parameter.
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains seasonal climatologies of Level-3 binned (Equal-Area Sinusoidal grid) data products in three variants:
            CHL- which includes chlorophyll a,
            KD490 - which includes diffuse attenuation at 490nm and
            RRS which includes the remote sensing reflectance in the visible bands, aerosol optical depth at 670nm and aerosol angstrom parameter.
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains seasonal climatologies of Level-3 binned (Equal-Area Sinusoidal grid) data products in three variants:
            CHL- which includes chlorophyll a,
            KD490 - which includes diffuse attenuation at 490nm and
            RRS which includes the remote sensing reflectance in the visible bands, aerosol optical depth at 670nm and aerosol angstrom parameter.
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains the seasonal Level-3 binned (Equal-Area Sinusoidal grid) data products in three variants:
            CHL- which includes chlorophyll a,
            KD490 - which includes diffuse attenuation at 490nm and
            RRS which includes the remote sensing reflectance in the visible bands, aerosol optical depth at 670nm and aerosol angstrom parameter.
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains the seasonal Level-3 binned (Equal-Area Sinusoidal grid) data products in three variants:
            CHL- which includes chlorophyll a,
            KD490 - which includes diffuse attenuation at 490nm and
            RRS which includes the remote sensing reflectance in the visible bands, aerosol optical depth at 670nm and aerosol angstrom parameter.
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains the seasonal Level-3 binned (Equal-Area Sinusoidal grid) data products in three variants:
            CHL- which includes chlorophyll a,
            KD490 - which includes diffuse attenuation at 490nm and
            RRS which includes the remote sensing reflectance in the visible bands, aerosol optical depth at 670nm and aerosol angstrom parameter.
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contain annual Level-3 binned (Equal-Area Sinusoidal grid) data products in three variants:
            CHL- which includes chlorophyll a,
            KD490 - which includes diffuse attenuation at 490nm and
            RRS which includes the remote sensing reflectance in the visible bands, aerosol optical depth at 670nm and aerosol angstrom parameter.
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contain annual Level-3 binned (Equal-Area Sinusoidal grid) data products in three variants:
            CHL- which includes chlorophyll a,
            KD490 - which includes diffuse attenuation at 490nm and
            RRS which includes the remote sensing reflectance in the visible bands, aerosol optical depth at 670nm and aerosol angstrom parameter.
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contain annual Level-3 binned (Equal-Area Sinusoidal grid) data products in three variants:
            CHL- which includes chlorophyll a,
            KD490 - which includes diffuse attenuation at 490nm and
            RRS which includes the remote sensing reflectance in the visible bands, aerosol optical depth at 670nm and aerosol angstrom parameter.
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains eight day composite Level-3 Standard Mapped Image (Equal-Angular Cylindrical grid) data products.  Each product contains one of the following parameters at 4 or 9 km resolution:
        Remote sensing reflectance at 443, 520, 555 and 670nm
        Diffuse attenuation at 490nm
        Aerosol opticatl thickness at 670nm
        Chlorophyll a concentration
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains eight day composite Level-3 Standard Mapped Image (Equal-Angular Cylindrical grid) data products.  Each product contains one of the following parameters at 4 or 9 km resolution:
        Remote sensing reflectance at 443, 520, 555 and 670nm
        Diffuse attenuation at 490nm
        Aerosol opticatl thickness at 670nm
        Chlorophyll a concentration
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains eight day composite Level-3 Standard Mapped Image (Equal-Angular Cylindrical grid) data products.  Each product contains one of the following parameters at 4 or 9 km resolution:
        Remote sensing reflectance at 443, 520, 555 and 670nm
        Diffuse attenuation at 490nm
        Aerosol opticatl thickness at 670nm
        Chlorophyll a concentration
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains the mission long cumulative Level-3 Standard Mapped Image (Equal-Angular Cylindrical grid) data products.  Each product contains one of the following parameters at 4 or 9 km resolution:
        Remote sensing reflectance at 443, 520, 555 and 670nm
        Diffuse attenuation at 490nm
        Aerosol opticatl thickness at 670nm
        Chlorophyll a concentration
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains the mission long cumulative Level-3 Standard Mapped Image (Equal-Angular Cylindrical grid) data products.  Each product contains one of the following parameters at 4 or 9 km resolution:
        Remote sensing reflectance at 443, 520, 555 and 670nm
        Diffuse attenuation at 490nm
        Aerosol opticatl thickness at 670nm
        Chlorophyll a concentration
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains the mission long cumulative Level-3 Standard Mapped Image (Equal-Angular Cylindrical grid) data products.  Each product contains one of the following parameters at 4 or 9 km resolution:
        Remote sensing reflectance at 443, 520, 555 and 670nm
        Diffuse attenuation at 490nm
        Aerosol opticatl thickness at 670nm
        Chlorophyll a concentration
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains the daily Level-3 Standard Mapped Image (Equal-Angular Cylindrical grid) data products.  Each product contains one of the following parameters at 4 or 9 km resolution:
        Remote sensing reflectance at 443, 520, 555 and 670nm
        Diffuse attenuation at 490nm
        Aerosol opticatl thickness at 670nm
        Chlorophyll a concentration
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains the daily Level-3 Standard Mapped Image (Equal-Angular Cylindrical grid) data products.  Each product contains one of the following parameters at 4 or 9 km resolution:
        Remote sensing reflectance at 443, 520, 555 and 670nm
        Diffuse attenuation at 490nm
        Aerosol opticatl thickness at 670nm
        Chlorophyll a concentration
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains the daily Level-3 Standard Mapped Image (Equal-Angular Cylindrical grid) data products.  Each product contains one of the following parameters at 4 or 9 km resolution:
        Remote sensing reflectance at 443, 520, 555 and 670nm
        Diffuse attenuation at 490nm
        Aerosol opticatl thickness at 670nm
        Chlorophyll a concentration
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains monthly climatological Level-3 Standard Mapped Image (Equal-Angular Cylindrical grid) data products.  Each product contains one of the following parameters at 4 or 9 km resolution:
        Remote sensing reflectance at 443, 520, 555 and 670nm
        Diffuse attenuation at 490nm
        Aerosol opticatl thickness at 670nm
        Chlorophyll a concentration
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains monthly climatological Level-3 Standard Mapped Image (Equal-Angular Cylindrical grid) data products.  Each product contains one of the following parameters at 4 or 9 km resolution:
        Remote sensing reflectance at 443, 520, 555 and 670nm
        Diffuse attenuation at 490nm
        Aerosol opticatl thickness at 670nm
        Chlorophyll a concentration
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains monthly climatological Level-3 Standard Mapped Image (Equal-Angular Cylindrical grid) data products.  Each product contains one of the following parameters at 4 or 9 km resolution:
        Remote sensing reflectance at 443, 520, 555 and 670nm
        Diffuse attenuation at 490nm
        Aerosol opticatl thickness at 670nm
        Chlorophyll a concentration
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains monthly composite Level-3 Standard Mapped Image (Equal-Angular Cylindrical grid) data products.  Each product contains one of the following parameters at 4 or 9 km resolution:
        Remote sensing reflectance at 443, 520, 555 and 670nm
        Diffuse attenuation at 490nm
        Aerosol opticatl thickness at 670nm
        Chlorophyll a concentration
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains monthly composite Level-3 Standard Mapped Image (Equal-Angular Cylindrical grid) data products.  Each product contains one of the following parameters at 4 or 9 km resolution:
        Remote sensing reflectance at 443, 520, 555 and 670nm
        Diffuse attenuation at 490nm
        Aerosol opticatl thickness at 670nm
        Chlorophyll a concentration
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains monthly composite Level-3 Standard Mapped Image (Equal-Angular Cylindrical grid) data products.  Each product contains one of the following parameters at 4 or 9 km resolution:
        Remote sensing reflectance at 443, 520, 555 and 670nm
        Diffuse attenuation at 490nm
        Aerosol opticatl thickness at 670nm
        Chlorophyll a concentration
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains rolling 32-day composite Level-3 Standard Mapped Image (Equal-Angular Cylindrical grid) data products.  Each product contains one of the following parameters at 4 or 9 km resolution:
        Remote sensing reflectance at 443, 520, 555 and 670nm
        Diffuse attenuation at 490nm
        Aerosol opticatl thickness at 670nm
        Chlorophyll a concentration
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains rolling 32-day composite Level-3 Standard Mapped Image (Equal-Angular Cylindrical grid) data products.  Each product contains one of the following parameters at 4 or 9 km resolution:
        Remote sensing reflectance at 443, 520, 555 and 670nm
        Diffuse attenuation at 490nm
        Aerosol opticatl thickness at 670nm
        Chlorophyll a concentration
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains rolling 32-day composite Level-3 Standard Mapped Image (Equal-Angular Cylindrical grid) data products.  Each product contains one of the following parameters at 4 or 9 km resolution:
        Remote sensing reflectance at 443, 520, 555 and 670nm
        Diffuse attenuation at 490nm
        Aerosol opticatl thickness at 670nm
        Chlorophyll a concentration
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains seasonal climatological Level-3 Standard Mapped Image (Equal-Angular Cylindrical grid) data products.  Each product contains one of the following parameters at 4 or 9 km resolution:
        Remote sensing reflectance at 443, 520, 555 and 670nm
        Diffuse attenuation at 490nm
        Aerosol opticatl thickness at 670nm
        Chlorophyll a concentration
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains seasonal climatological Level-3 Standard Mapped Image (Equal-Angular Cylindrical grid) data products.  Each product contains one of the following parameters at 4 or 9 km resolution:
        Remote sensing reflectance at 443, 520, 555 and 670nm
        Diffuse attenuation at 490nm
        Aerosol opticatl thickness at 670nm
        Chlorophyll a concentration
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains seasonal climatological Level-3 Standard Mapped Image (Equal-Angular Cylindrical grid) data products.  Each product contains one of the following parameters at 4 or 9 km resolution:
        Remote sensing reflectance at 443, 520, 555 and 670nm
        Diffuse attenuation at 490nm
        Aerosol opticatl thickness at 670nm
        Chlorophyll a concentration
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains seasonal Level-3 Standard Mapped Image (Equal-Angular Cylindrical grid) data products.  Each product contains one of the following parameters at 4 or 9 km resolution:
        Remote sensing reflectance at 443, 520, 555 and 670nm
        Diffuse attenuation at 490nm
        Aerosol opticatl thickness at 670nm
        Chlorophyll a concentration
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains seasonal Level-3 Standard Mapped Image (Equal-Angular Cylindrical grid) data products.  Each product contains one of the following parameters at 4 or 9 km resolution:
        Remote sensing reflectance at 443, 520, 555 and 670nm
        Diffuse attenuation at 490nm
        Aerosol opticatl thickness at 670nm
        Chlorophyll a concentration
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains seasonal Level-3 Standard Mapped Image (Equal-Angular Cylindrical grid) data products.  Each product contains one of the following parameters at 4 or 9 km resolution:
        Remote sensing reflectance at 443, 520, 555 and 670nm
        Diffuse attenuation at 490nm
        Aerosol opticatl thickness at 670nm
        Chlorophyll a concentration
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains annual Level-3 Standard Mapped Image (Equal-Angular Cylindrical grid) data products.  Each product contains one of the following parameters at 4 or 9 km resolution:
        Remote sensing reflectance at 443, 520, 555 and 670nm
        Diffuse attenuation at 490nm
        Aerosol opticatl thickness at 670nm
        Chlorophyll a concentration
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains annual Level-3 Standard Mapped Image (Equal-Angular Cylindrical grid) data products.  Each product contains one of the following parameters at 4 or 9 km resolution:
        Remote sensing reflectance at 443, 520, 555 and 670nm
        Diffuse attenuation at 490nm
        Aerosol opticatl thickness at 670nm
        Chlorophyll a concentration
The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.
        
        The most important objective of the Coastal Zone Color Scanner mission was to determine if satellite remote sensing of color could be used to identify and quantify material suspended or dissolved in ocean waters. Specifically CZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.
        
        This product contains annual Level-3 Standard Mapped Image (Equal-Angular Cylindrical grid) data products.  Each product contains one of the following parameters at 4 or 9 km resolution:
        Remote sensing reflectance at 443, 520, 555 and 670nm
        Diffuse attenuation at 490nm
        Aerosol opticatl thickness at 670nm
        Chlorophyll a concentration
This archive of daily rawinsonde measurements of wind direction and
speed, atmospheric pressure, humidity, air temperature, and
geopotential height as well as surface-based observation of cloud
cover (amount, type and height) from Soviet North Pole drifting
stations was assembled under the direction of Dr. J. Kahl, with
funding from the National Oceanic and Atmospheric Administration, the National Science Foundation, and the Electric Power Research
Institute. Soundings were recorded from April 19, 1954 to July 31,
1990 at drifting stations located in the Arctic Ocean, north of
approximately 70 degrees North. Data were obtained from several
different sources. All of these data are ultimately derived from the
set of bound volumes of handwritten tables kept at the Arctic and
Antarctic Research Institute (AARI) in St. Petersburg, Russia. Data
are in 21 ASCII text format files with an average size of under 10 MB.
The data are available via ftp.
This archive of daily rawinsonde measurements of wind direction and
speed, atmospheric pressure, humidity, air temperature, and
geopotential height as well as surface-based observation of cloud
cover (amount, type and height) from Soviet North Pole drifting
stations was assembled under the direction of Dr. J. Kahl, with
funding from the National Oceanic and Atmospheric Administration, the National Science Foundation, and the Electric Power Research
Institute. Soundings were recorded from April 19, 1954 to July 31,
1990 at drifting stations located in the Arctic Ocean, north of
approximately 70 degrees North. Data were obtained from several
different sources. All of these data are ultimately derived from the
set of bound volumes of handwritten tables kept at the Arctic and
Antarctic Research Institute (AARI) in St. Petersburg, Russia. Data
are in 21 ASCII text format files with an average size of under 10 MB.
The data are available via ftp.
This archive of daily rawinsonde measurements of wind direction and
speed, atmospheric pressure, humidity, air temperature, and
geopotential height as well as surface-based observation of cloud
cover (amount, type and height) from Soviet North Pole drifting
stations was assembled under the direction of Dr. J. Kahl, with
funding from the National Oceanic and Atmospheric Administration, the National Science Foundation, and the Electric Power Research
Institute. Soundings were recorded from April 19, 1954 to July 31,
1990 at drifting stations located in the Arctic Ocean, north of
approximately 70 degrees North. Data were obtained from several
different sources. All of these data are ultimately derived from the
set of bound volumes of handwritten tables kept at the Arctic and
Antarctic Research Institute (AARI) in St. Petersburg, Russia. Data
are in 21 ASCII text format files with an average size of under 10 MB.
The data are available via ftp.
Daily rainfall data, by site & date
Daily rainfall data, by site & date
ABSTRACT: Daily rainfall data, by site & date
The Tropical Rainfall Measuring Mission (TRMM) is a joint U.S.-Japan satellite mission to monitor tropical and subtropical precipitation and to estimate its associated latent heating. TRMM was successfully launched on November 27, at 4:27 PM (EST) from the Tanegashima SpaceCenter in Japan.

The rainfall measuring instruments on the TRMM satellite include the Precipitation Radar (PR), an electronically scanning radar operating at 13.8 GHz; TRMM Microwave Image (TMI), a nine-channel passive microwave radiometer; and Visible and Infrared Scanner (VIRS), a five-channel visible/infrared radiometer.

The purpose of the 3B42 algorithm is to produce TRMM-adjusted merged-infrared (IR) precipitation and root-mean-square (RMS) precipitation-error estimates. The algorithm consists of two separate steps. The first step uses the TRMM VIRS and TMI orbit data (TRMM products 1B01 and 2A12) and the monthly TMI/TRMM Combined Instrument (TCI) calibration parameters (from TRMM product 3B31) to produce monthly IR calibration parameters. The second step uses these derived monthly IR calibration parameters to adjust the merged-IR precipitation data, which consists of GMS, GOES-E, GOES-W, Meteosat-7, Meteosat-5, and NOAA-12 data. The final gridded, adjusted merged-IR precipitation (mm/hr) and RMS precipitation-error estimates have a 3-hourly temporal resolution and a 0.25-degree by 0.25-degree spatial resolution. Spatial coverage extends from 50 degrees south to 50 degrees north latitude.

The daily accumulated (beginning at 00Z and ending at 21Z; unit: mm) rainfall product is derived from this 3-hourly product. The data are stored in flat binary. The file size is about 2.25 MB (uncompressed).
GIS layers that describe the soils of the BOREAS SSA.  Original data were submitted as vector layers that were then gridded by BOREAS staff to a 30-meter pixel size in the AEAC projection.
GIS layers that describe the soils of the BOREAS SSA.  Original data were submitted as vector layers that were then gridded by BOREAS staff to a 30-meter pixel size in the AEAC projection.
ABSTRACT: GIS layers that describe the soils of the BOREAS SSA.  Original data were submitted as vector layers that were then gridded by BOREAS staff to a 30-meter pixel size in the AEAC projection.
This data set provides Daymet output data as mosaicked gridded estimates of daily weather parameters for North America, including continuous surfaces of day length, precipitation, shortwave radiation, snow water equivalent, maximum air temperature, minimum air temperature, and water vapor pressure. The Daymet data product was derived from selected meteorological station data by interpolation and extrapolation algorithms.The data product covers the period January 1, 1980 to December 31 of the most recently processed calendar year for Canada and the United States. Data for additional calendar years will become available by June of the following year. For Mexico, data are available for the period January 1, 1980 to December 31, 2009.Data are available on a daily time step at a 1-km x 1-km spatial resolution in Lambert Conformal Conic projection with a spatial extent that covers the conterminous United States, Mexico, and Southern Canada as meteorological station density allows. Data are assembled by parameter and year with each yearly file containing a time dimension of 365 days.Data are provided in Climate and Forecast (CF) metadata convention compliant (version 1.4) netCDF file formats.
This data set provides Daymet output data as mosaicked gridded estimates of daily weather parameters for North America, including continuous surfaces of day length, precipitation, shortwave radiation, snow water equivalent, maximum air temperature, minimum air temperature, and water vapor pressure. The Daymet data product was derived from selected meteorological station data by interpolation and extrapolation algorithms.The data product covers the period January 1, 1980 to December 31 of the most recently processed calendar year for Canada and the United States. Data for additional calendar years will become available by June of the following year. For Mexico, data are available for the period January 1, 1980 to December 31, 2009.Data are available on a daily time step at a 1-km x 1-km spatial resolution in Lambert Conformal Conic projection with a spatial extent that covers the conterminous United States, Mexico, and Southern Canada as meteorological station density allows. Data are assembled by parameter and year with each yearly file containing a time dimension of 365 days.Data are provided in Climate and Forecast (CF) metadata convention compliant (version 1.4) netCDF file formats.
ABSTRACT: This data set provides Daymet output data as mosaicked gridded estimates of daily weather parameters for North America, including daily continuous surfaces of day length, precipitation amount, shortwave radiation, snow water equivalent, maximum air temperature, minimum air temperature, and water vapor pressure. The Daymet data product was derived from selected meteorological station data by interpolation and extrapolation algorithms. The current data product covers the period January 1, 1980 to December 31, 2013. Data are available on a daily time step at a 1-km x 1-km spatial resolution in Lambert Conformal Conic projection with a spatial extent that covers the conterminous United States, Mexico, and Southern Canada as meteorological station density allows. Daymet mosaic data sets are available from the ORNL DAAC via two download mechanisms:  1) Data files may be obtained through DAAC search and order tools or directly from the FTP site. 2) Data can be subset spatially and temporally prior to downloading via the THREDDS (Thematic Real-time Environmental Data Services) Data Server. Data are provided in Climate and Forecast (CF) metadata convention compliant (version 1.4) netCDF file formats. Other tools are provided on the ORNL DAAC Daymet website that allow extraction of Daymet variable daily data as a text file for a single pixel or the download of gridded Daymet data in 2 degree by 2 degree tile data sets.
NASA P-3B in-situ measurements of aerosol properties
NASA P-3B in-situ trace gas measurements
NASA P-3B: latitude, longitude, altitude, aircraft status parameters, ambient temperature, static pressure, and other meteorological data
NASA UC-12 aircraft HSRL measurements of aerosol optical property profiles at 1064 and 532 nm
NASA UC-12 aircraft ACAM measurements of column NO2, CH2O, and O3
Ground-based in-situ measurements of aerosol properties in UMBC site
Ground-based in-situ trace gas and UV measurements at Edgewood Site
Ground-based lidar measurements of extinction, backscattering profiles and AOD at UMBC site.
Ground-based PANDORA Network measurements of NO2 and O3 column density
Data from Ozonesondes launched from Beltsville and Edgewood, in June and July, 2011
The Declassified Intelligence Satellite Photographs (DISP) Yearly Satellite Photographic Mosaics of Greenland are composites of black-and-white photographs of Greenland taken from American satellites in 1962 and 1963. The mosaics provide details of ice sheet morphology, glaciers, rock outcrops, the coastline, and other features. The image mosaics are useful for comparing the extent and internal configuration of the Greenland ice sheet with current satellite data. The data set consists of one tagged image file (.TIF) for each year.

The mosaic images are available via FTP. The files are large-- the 1962 mosaic image dimensions are 17,092 by 28,484 pixels and the file size is 464.3 MB. The 1963 image dimensions are 17,792 by 27,805 pixels and the file size is 471.8 MB.
The Declassified Intelligence Satellite Photographs (DISP) Yearly Satellite Photographic Mosaics of Greenland are composites of black-and-white photographs of Greenland taken from American satellites in 1962 and 1963. The mosaics provide details of ice sheet morphology, glaciers, rock outcrops, the coastline, and other features. The image mosaics are useful for comparing the extent and internal configuration of the Greenland ice sheet with current satellite data. The data set consists of one tagged image file (.TIF) for each year.

The mosaic images are available via FTP. The files are large-- the 1962 mosaic image dimensions are 17,092 by 28,484 pixels and the file size is 464.3 MB. The 1963 image dimensions are 17,792 by 27,805 pixels and the file size is 471.8 MB.
The Declassified Intelligence Satellite Photographs (DISP) Yearly Satellite Photographic Mosaics of Greenland are composites of black-and-white photographs of Greenland taken from American satellites in 1962 and 1963. The mosaics provide details of ice sheet morphology, glaciers, rock outcrops, the coastline, and other features. The image mosaics are useful for comparing the extent and internal configuration of the Greenland ice sheet with current satellite data. The data set consists of one tagged image file (.TIF) for each year.

The mosaic images are available via FTP. The files are large-- the 1962 mosaic image dimensions are 17,092 by 28,484 pixels and the file size is 464.3 MB. The 1963 image dimensions are 17,792 by 27,805 pixels and the file size is 471.8 MB.
<!--#include virtual="/ssi/f17_availability.ssi" -->

DMSP SSM/I Daily and Monthly Polar Gridded Bootstrap Sea Ice Concentrations in polar stereographic projection currently include Defense Meteorological Satellite Program (DMSP) -F8, -F11, and -F13 daily and monthly sea ice concentrations. Using the Bootstrap algorithm, data gridded at a resolution of 25 x 25 km, begin 25 June 1987. Processing is ongoing. The Special Sensor Microwave Imager (SSM/I) derived ice concentrations are daily total and monthly averaged ice fractions for both hemispheres. The data are provided in Hierarchical Data Format (HDF), with accompanying Graphical Interchange Format (GIF) browse files and are available via FTP. Note: Users interested in sea ice concentration using the NASA Team algorithm are advised to use Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I Passive Microwave Data.
<!--#include virtual="/ssi/f17_availability.ssi" -->

DMSP SSM/I Daily and Monthly Polar Gridded Bootstrap Sea Ice Concentrations in polar stereographic projection currently include Defense Meteorological Satellite Program (DMSP) -F8, -F11, and -F13 daily and monthly sea ice concentrations. Using the Bootstrap algorithm, data gridded at a resolution of 25 x 25 km, begin 25 June 1987. Processing is ongoing. The Special Sensor Microwave Imager (SSM/I) derived ice concentrations are daily total and monthly averaged ice fractions for both hemispheres. The data are provided in Hierarchical Data Format (HDF), with accompanying Graphical Interchange Format (GIF) browse files and are available via FTP. Note: Users interested in sea ice concentration using the NASA Team algorithm are advised to use Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I Passive Microwave Data.
<!--#include virtual="/ssi/f17_availability.ssi" -->

DMSP SSM/I Daily and Monthly Polar Gridded Bootstrap Sea Ice Concentrations in polar stereographic projection currently include Defense Meteorological Satellite Program (DMSP) -F8, -F11, and -F13 daily and monthly sea ice concentrations. Using the Bootstrap algorithm, data gridded at a resolution of 25 x 25 km, begin 25 June 1987. Processing is ongoing. The Special Sensor Microwave Imager (SSM/I) derived ice concentrations are daily total and monthly averaged ice fractions for both hemispheres. The data are provided in Hierarchical Data Format (HDF), with accompanying Graphical Interchange Format (GIF) browse files and are available via FTP. Note: Users interested in sea ice concentration using the NASA Team algorithm are advised to use Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I Passive Microwave Data.
ERS-1 Standard Beam Data in ASF Frame Size
ERS-1 Standard Beam Data in ASF Frame Size
ERS-1 Standard Beam Data in ASF Frame Size
ERS-1 Standard Beam Data in ASF Frame Size Processed to Level 0
ERS-1 Standard Beam Data in ASF Frame Size Processed to Level 0
ERS-1 Standard Beam Data in ASF Frame Size Processed to Level 0
ERS-1 Standard Beam Data in ASF Frame Size Processed to Level 1
ERS-1 Standard Beam Data in ASF Frame Size Processed to Level 1
ERS-1 Standard Beam Data in ASF Frame Size Processed to Level 1
ERS-1 Standard Beam Data in Full Swath Format
ERS-1 Standard Beam Data in Full Swath Format
ERS-1 Standard Beam Data in Full Swath Format
ERS-2 Standard Beam Data in ASF Frame Size
ERS-2 Standard Beam Data in ASF Frame Size
ERS-2 Standard Beam Data in ASF Frame Size
ERS-2 Standard Beam Data in ASF Frame Size Processed to Level 0
ERS-2 Standard Beam Data in ASF Frame Size Processed to Level 0
ERS-2 Standard Beam Data in ASF Frame Size Processed to Level 0
ERS-2 Standard Beam Data in ASF Frame Size Processed to Level 1
ERS-2 Standard Beam Data in ASF Frame Size Processed to Level 1
ERS-2 Standard Beam Data in ASF Frame Size Processed to Level 1
ERS-2 Standard Beam Data in Full Swath Format
ERS-2 Standard Beam Data in Full Swath Format
ERS-2 Standard Beam Data in Full Swath Format
This data set contains forest canopy scan data from the Echidna Validation Instrument (EVI) and field measurements data from three campaigns conducted in the United States: 2007 New England Campaign; 2008 Sierra National Forest Campaign; and 2009 New England Campaign. The New England field sites were located in Harvard Forest (Massachusetts), Howland Research Forest (Maine), and the Bartlett Experimental Forest (New Hampshire).The objective of the research was to evaluate the ability of the EVI ground-based, scanning near-infrared lidar to retrieve stem diameter, stem count density, stand height, leaf area index, foliage profile, foliage area volume density, and other useful forest structural parameters rapidly and accurately.The EVI scan data are Andrieu Transpose (AT) Projection images in ENVI *.img and *.hdr file pairs. There are 28 images from the 2007 New England Campaign, 30 images from the 2008 Sierra National Forest Campaign, and 54 images from the 2009 New England Campaign. There are range-weighted mean preview image files (.jpg format) for each AT Projection image.Manual measurements of tree structural properties were made during each campaign at EVI scan locations. The field measurements are provided in one file for each campaign (.csv format). Parameters include species identification, DBH, tree height, crown base, etc. organized by field plot. There is also a data file (.csv format) which compares EVI derived measurements to the field measured data (DBH, stem density, basal area, biomass, and LAI) from the 2007 New England Campaign.
This data set contains forest canopy scan data from the Echidna Validation Instrument (EVI) and field measurements data from three campaigns conducted in the United States: 2007 New England Campaign; 2008 Sierra National Forest Campaign; and 2009 New England Campaign. The New England field sites were located in Harvard Forest (Massachusetts), Howland Research Forest (Maine), and the Bartlett Experimental Forest (New Hampshire).The objective of the research was to evaluate the ability of the EVI ground-based, scanning near-infrared lidar to retrieve stem diameter, stem count density, stand height, leaf area index, foliage profile, foliage area volume density, and other useful forest structural parameters rapidly and accurately.The EVI scan data are Andrieu Transpose (AT) Projection images in ENVI *.img and *.hdr file pairs. There are 28 images from the 2007 New England Campaign, 30 images from the 2008 Sierra National Forest Campaign, and 54 images from the 2009 New England Campaign. There are range-weighted mean preview image files (.jpg format) for each AT Projection image.Manual measurements of tree structural properties were made during each campaign at EVI scan locations. The field measurements are provided in one file for each campaign (.csv format). Parameters include species identification, DBH, tree height, crown base, etc. organized by field plot. There is also a data file (.csv format) which compares EVI derived measurements to the field measured data (DBH, stem density, basal area, biomass, and LAI) from the 2007 New England Campaign.
ABSTRACT: This data set contains forest canopy scan data from the Echidna Validation Instrument (EVI) and field measurements data from three campaigns conducted in the United States: 2007 New England Campaign; 2008 Sierra National Forest Campaign; and 2009 New England Campaign. The New England field sites were located in Harvard Forest (Massachusetts), Howland Research Forest (Maine), and the Bartlett Experimental Forest (New Hampshire).The objective of the research was to evaluate the ability of the EVI ground-based, scanning near-infrared lidar to retrieve stem diameter, stem count density, stand height, leaf area index, foliage profile, foliage area volume density, and other useful forest structural parameters rapidly and accurately.The EVI scan data are Andrieu Transpose (AT) Projection images in ENVI *.img and *.hdr file pairs. There are 28 images from the 2007 New England Campaign, 30 images from the 2008 Sierra National Forest Campaign, and 54 images from the 2009 New England Campaign. There are range-weighted mean preview image files (.jpg format) for each AT Projection image.Manual measurements of tree structural properties were made during each campaign at EVI scan locations. The field measurements are provided in one file for each campaign (.csv format). Parameters include species identification, DBH, tree height, crown base, etc. organized by field plot. There is also a data file (.csv format) which compares EVI derived measurements to the field measured data (DBH, stem density, basal area, biomass, and LAI) from the 2007 New England Campaign.
This model product contains the source code for the Ecosystem Demography Model (ED version 1.0) as well as model input and output data for a portion of South America including the Brazilian Amazon. The model output data are estimates of potential average live biomass (kg C/m2), potential average soil carbon (kg C/m2), and potential above-ground net primary production (NPP) (kg C/m2/yr) at 1.0 degree resolution. To produce these estimates, ED was forced with ISLSCP I data for 1987 and 1988, averaged into a single year (Moorcroft et al., 2001). Data for the three estimates are provided in both ASCII text and in NetCDF formatted files. ED is an individual-based terrestrial ecosystem model that predicts both ecosystem structure (e.g. above and below-ground biomass, vegetation height and basal area, and soil carbon stocks) and corresponding ecosystem fluxes (e.g. NPP, NEP and evapotranspiration) from climate, soil, and land-use inputs. The model consists of integrated sub-models governing processes such as leaf-level physiology, plant allocation, allometry, phenology, dispersal, the effects of fire disturbances, and below-ground sub-models for soil carbon dynamics and hydrology. Using a new method for scaling-up it is possible to predict ED's large-scale behavior without simulating the fate of every plant individually. ED is used to examine how climate and edaphic factors, natural disturbances, and human land-use practices affect ecosystem structure and fluxes. This data set contains six zip files which each uncompress into six unique subdirectories. Each subdirectory is described in detail in the Model Product Description section of this document. Installation and execution instructions are provided in the Model Documentation and User's Guide section of this document.
This model product contains the source code for the Ecosystem Demography Model (ED version 1.0) as well as model input and output data for a portion of South America including the Brazilian Amazon. The model output data are estimates of potential average live biomass (kg C/m2), potential average soil carbon (kg C/m2), and potential above-ground net primary production (NPP) (kg C/m2/yr) at 1.0 degree resolution. To produce these estimates, ED was forced with ISLSCP I data for 1987 and 1988, averaged into a single year (Moorcroft et al., 2001). Data for the three estimates are provided in both ASCII text and in NetCDF formatted files. ED is an individual-based terrestrial ecosystem model that predicts both ecosystem structure (e.g. above and below-ground biomass, vegetation height and basal area, and soil carbon stocks) and corresponding ecosystem fluxes (e.g. NPP, NEP and evapotranspiration) from climate, soil, and land-use inputs. The model consists of integrated sub-models governing processes such as leaf-level physiology, plant allocation, allometry, phenology, dispersal, the effects of fire disturbances, and below-ground sub-models for soil carbon dynamics and hydrology. Using a new method for scaling-up it is possible to predict ED's large-scale behavior without simulating the fate of every plant individually. ED is used to examine how climate and edaphic factors, natural disturbances, and human land-use practices affect ecosystem structure and fluxes. This data set contains six zip files which each uncompress into six unique subdirectories. Each subdirectory is described in detail in the Model Product Description section of this document. Installation and execution instructions are provided in the Model Documentation and User's Guide section of this document.
ABSTRACT: This model product contains the source code for the Ecosystem Demography Model (ED version 1.0) as well as model input and output data for a portion of South America including the Brazilian Amazon. The model output data are estimates of potential average live biomass (kg C/m2), potential average soil carbon (kg C/m2), and potential above-ground net primary production (NPP) (kg C/m2/yr) at 1.0 degree resolution. To produce these estimates, ED was forced with ISLSCP I data for 1987 and 1988, averaged into a single year (Moorcroft et al., 2001). Data for the three estimates are provided in both ASCII text and in NetCDF formatted files. ED is an individual-based terrestrial ecosystem model that predicts both ecosystem structure (e.g. above and below-ground biomass, vegetation height and basal area, and soil carbon stocks) and corresponding ecosystem fluxes (e.g. NPP, NEP and evapotranspiration) from climate, soil, and land-use inputs. The model consists of integrated sub-models governing processes such as leaf-level physiology, plant allocation, allometry, phenology, dispersal, the effects of fire disturbances, and below-ground sub-models for soil carbon dynamics and hydrology. Using a new method for scaling-up it is possible to predict ED's large-scale behavior without simulating the fate of every plant individually. ED is used to examine how climate and edaphic factors, natural disturbances, and human land-use practices affect ecosystem structure and fluxes. This data set contains six zip files which each uncompress into six unique subdirectories. Each subdirectory is described in detail in the Model Product Description section of this document. Installation and execution instructions are provided in the Model Documentation and User's Guide section of this document.
This model product contains the source code for the Ecosystem Demography Model (ED version 1.0) as well as model input and output data files for the conterminous United States. The ED is a mechanistic ecosystem model built around established sub-models of leaf level physiology, organic matter decomposition, hydrology, and functional biodiversity. It was used herein to estimate ecosystem carbon stocks and fluxes in the conterminous U.S. at 1.0 degree resolution from 1700 to 1990. Output data of carbon stocks and fluxes are stored in NetCDF format. To produce the U.S. scenario, ED was run from an estimated state of ecosystems in the year 1700 to an estimated state of ecosystems in the year 1990 for each 1 degree by 1 degree grid cell through time using ISLSCP Initiative I climate and soil data and a gridded land-use history reconstruction as inputs (Hurtt et al., 2002). The land-use history was based on several sources including: spatial distribution of potential vegetation in 1700, spatial patterns of cropland from 1700 to 1990, regional estimates of land use and logging from 1700 to 1990, and U.S. Forest Inventory and Analysis (FIA) data on the current age distribution of forest stands. The Miami Land Use History Model (Miami-LU), a far simpler empirically-based ecosystem model, was used to track the history of disturbance, land use, fire, and ecosystem recovery. The effects of fire suppression were also included. Atmospheric CO2 concentrations and climatic conditions were held constant throughout the runs to focus on the consequences of land-use and fire-management changes on carbon stocks and fluxes.
This model product contains the source code for the Ecosystem Demography Model (ED version 1.0) as well as model input and output data files for the conterminous United States. The ED is a mechanistic ecosystem model built around established sub-models of leaf level physiology, organic matter decomposition, hydrology, and functional biodiversity. It was used herein to estimate ecosystem carbon stocks and fluxes in the conterminous U.S. at 1.0 degree resolution from 1700 to 1990. Output data of carbon stocks and fluxes are stored in NetCDF format. To produce the U.S. scenario, ED was run from an estimated state of ecosystems in the year 1700 to an estimated state of ecosystems in the year 1990 for each 1 degree by 1 degree grid cell through time using ISLSCP Initiative I climate and soil data and a gridded land-use history reconstruction as inputs (Hurtt et al., 2002). The land-use history was based on several sources including: spatial distribution of potential vegetation in 1700, spatial patterns of cropland from 1700 to 1990, regional estimates of land use and logging from 1700 to 1990, and U.S. Forest Inventory and Analysis (FIA) data on the current age distribution of forest stands. The Miami Land Use History Model (Miami-LU), a far simpler empirically-based ecosystem model, was used to track the history of disturbance, land use, fire, and ecosystem recovery. The effects of fire suppression were also included. Atmospheric CO2 concentrations and climatic conditions were held constant throughout the runs to focus on the consequences of land-use and fire-management changes on carbon stocks and fluxes.
ABSTRACT: This model product contains the source code for the Ecosystem Demography Model (ED version 1.0) as well as model input and output data files for the conterminous United States. The ED is a mechanistic ecosystem model built around established sub-models of leaf level physiology, organic matter decomposition, hydrology, and functional biodiversity. It was used herein to estimate ecosystem carbon stocks and fluxes in the conterminous U.S. at 1.0 degree resolution from 1700 to 1990. Output data of carbon stocks and fluxes are stored in NetCDF format. To produce the U.S. scenario, ED was run from an estimated state of ecosystems in the year 1700 to an estimated state of ecosystems in the year 1990 for each 1 degree by 1 degree grid cell through time using ISLSCP Initiative I climate and soil data and a gridded land-use history reconstruction as inputs (Hurtt et al., 2002). The land-use history was based on several sources including: spatial distribution of potential vegetation in 1700, spatial patterns of cropland from 1700 to 1990, regional estimates of land use and logging from 1700 to 1990, and U.S. Forest Inventory and Analysis (FIA) data on the current age distribution of forest stands. The Miami Land Use History Model (Miami-LU), a far simpler empirically-based ecosystem model, was used to track the history of disturbance, land use, fire, and ecosystem recovery. The effects of fire suppression were also included. Atmospheric CO2 concentrations and climatic conditions were held constant throughout the runs to focus on the consequences of land-use and fire-management changes on carbon stocks and fluxes.
Eddy flux & surface exchange processes in non-uniform areas
Eddy flux & surface exchange processes in non-uniform areas
ABSTRACT: Eddy flux & surface exchange processes in non-uniform areas
Surface flux measurements by eddy correlation technique during FIFE
Surface flux measurements by eddy correlation technique during FIFE
ABSTRACT: Surface flux measurements by eddy correlation technique during FIFE
Areal average evapotranspiration by measuring & modeling surface controls
Areal average evapotranspiration by measuring & modeling surface controls
ABSTRACT: Areal average evapotranspiration by measuring & modeling surface controls
Latent & sensible heat flux by eddy correlation & aerodynamic characterization of vegetation
Latent & sensible heat flux by eddy correlation & aerodynamic characterization of vegetation
ABSTRACT: Latent & sensible heat flux by eddy correlation & aerodynamic characterization of vegetation
FIFE observations of surface fluxes
FIFE observations of surface fluxes
ABSTRACT: FIFE observations of surface fluxes
Effects of Climate Change on Global Food Production from SRES Emissions and Socioeconomic Scenarios is an update to a major crop modeling study by 

the NASA Goddard Institute for Space Studies (GISS). The initial study was published in 1997, based on output of HadCM2 model forced with greenhouse gas concentration from the IS95 

emission scenarios in 1997. Results of the initial study are presented at SEDAC's Potential Impacts of Climate Change on World Food Supply: Data Sets from a Major Crop Modeling Study, 

released in 2001. The co-authors developed and tested a method for investigating the spatial implications of climate change on crop production. The Decision Support System for 

Agrotechnology Transfer (DSSAT) dynamic process crop growth models, are specified and validated for one hundred and twenty seven sites in the major world agricultural regions. Results 

from the crop models, calibrated and validated in the major crop-growing regions, are then used to test functional forms describing the response of yield changes in the climate and 

environmental conditions. This updated version is based on HadCM3 model output along with GHG concentrations from the Special Report on Emissions Scenarios (SRES). The crop yield 

estimates incorporate some major improvements: 1) consistent crop simulation methodology and climate change scenarios; 2) weighting of model site results by contribution to regional and 

national, and rainfed and irrigated production; 3) quantitative foundation for estimation of physiological CO2 effects on crop yields; 4) Adaptation is explicitly considered; and 5) 

results are reported by country rather than by Basic Linked System region. The data are produced by A. Iglesias and C. Rosenzweig and the maps are produced by the Columbia University 

Center for International Earth Science Information Network (CIESIN).
Effects of Climate Change on Global Food Production from SRES Emissions and Socioeconomic Scenarios is an update to a major crop modeling study by 

the NASA Goddard Institute for Space Studies (GISS). The initial study was published in 1997, based on output of HadCM2 model forced with greenhouse gas concentration from the IS95 

emission scenarios in 1997. Results of the initial study are presented at SEDAC's Potential Impacts of Climate Change on World Food Supply: Data Sets from a Major Crop Modeling Study, 

released in 2001. The co-authors developed and tested a method for investigating the spatial implications of climate change on crop production. The Decision Support System for 

Agrotechnology Transfer (DSSAT) dynamic process crop growth models, are specified and validated for one hundred and twenty seven sites in the major world agricultural regions. Results 

from the crop models, calibrated and validated in the major crop-growing regions, are then used to test functional forms describing the response of yield changes in the climate and 

environmental conditions. This updated version is based on HadCM3 model output along with GHG concentrations from the Special Report on Emissions Scenarios (SRES). The crop yield 

estimates incorporate some major improvements: 1) consistent crop simulation methodology and climate change scenarios; 2) weighting of model site results by contribution to regional and 

national, and rainfed and irrigated production; 3) quantitative foundation for estimation of physiological CO2 effects on crop yields; 4) Adaptation is explicitly considered; and 5) 

results are reported by country rather than by Basic Linked System region. The data are produced by A. Iglesias and C. Rosenzweig and the maps are produced by the Columbia University 

Center for International Earth Science Information Network (CIESIN).
Effects of Climate Change on Global Food Production from SRES Emissions and Socioeconomic Scenarios is an update to a major crop modeling study by 

the NASA Goddard Institute for Space Studies (GISS). The initial study was published in 1997, based on output of HadCM2 model forced with greenhouse gas concentration from the IS95 

emission scenarios in 1997. Results of the initial study are presented at SEDAC's Potential Impacts of Climate Change on World Food Supply: Data Sets from a Major Crop Modeling Study, 

released in 2001. The co-authors developed and tested a method for investigating the spatial implications of climate change on crop production. The Decision Support System for 

Agrotechnology Transfer (DSSAT) dynamic process crop growth models, are specified and validated for one hundred and twenty seven sites in the major world agricultural regions. Results 

from the crop models, calibrated and validated in the major crop-growing regions, are then used to test functional forms describing the response of yield changes in the climate and 

environmental conditions. This updated version is based on HadCM3 model output along with GHG concentrations from the Special Report on Emissions Scenarios (SRES). The crop yield 

estimates incorporate some major improvements: 1) consistent crop simulation methodology and climate change scenarios; 2) weighting of model site results by contribution to regional and 

national, and rainfed and irrigated production; 3) quantitative foundation for estimation of physiological CO2 effects on crop yields; 4) Adaptation is explicitly considered; and 5) 

results are reported by country rather than by Basic Linked System region. The data are produced by A. Iglesias and C. Rosenzweig and the maps are produced by the Columbia University 

Center for International Earth Science Information Network (CIESIN).
The results of published and unpublished experiments investigating the impacts of elevated carbon dioxide on the chemistry (nitrogen and lignin concentration) of leaf litter and the decomposition of plant tissues are assembled in a format appropriate for statistical meta-analysis of the effect of carbon dioxide.
The results of published and unpublished experiments investigating the impacts of elevated carbon dioxide on the chemistry (nitrogen and lignin concentration) of leaf litter and the decomposition of plant tissues are assembled in a format appropriate for statistical meta-analysis of the effect of carbon dioxide.
ABSTRACT: The results of published and unpublished experiments investigating the impacts of elevated carbon dioxide on the chemistry (nitrogen and lignin concentration) of leaf litter and the decomposition of plant tissues are assembled in a format appropriate for statistical meta-analysis of the effect of carbon dioxide.
Southern Greenland ice sheet elevation change estimates are derived from SEASAT and GEOSAT radar altimetry data from 1978 to 1988. Data are confined to 61-72 deg N, 30-50 deg W, above 1700 m elevation. The addition of GEOSAT Geodetic Mission (GM) data results in twice as many crossover points and 50% greater coverage than previous studies. Coverage above 2000 m elevation is improved to 90%, and about 75% of the area between 1700 m and 2000 m is now covered. Data are in ASCII text format, available via FTP, and consist of elevation change rate (dH/dt, cm/year) and corresponding error estimates in 50 km grid cells.
Southern Greenland ice sheet elevation change estimates are derived from SEASAT and GEOSAT radar altimetry data from 1978 to 1988. Data are confined to 61-72 deg N, 30-50 deg W, above 1700 m elevation. The addition of GEOSAT Geodetic Mission (GM) data results in twice as many crossover points and 50% greater coverage than previous studies. Coverage above 2000 m elevation is improved to 90%, and about 75% of the area between 1700 m and 2000 m is now covered. Data are in ASCII text format, available via FTP, and consist of elevation change rate (dH/dt, cm/year) and corresponding error estimates in 50 km grid cells.
Southern Greenland ice sheet elevation change estimates are derived from SEASAT and GEOSAT radar altimetry data from 1978 to 1988. Data are confined to 61-72 deg N, 30-50 deg W, above 1700 m elevation. The addition of GEOSAT Geodetic Mission (GM) data results in twice as many crossover points and 50% greater coverage than previous studies. Coverage above 2000 m elevation is improved to 90%, and about 75% of the area between 1700 m and 2000 m is now covered. Data are in ASCII text format, available via FTP, and consist of elevation change rate (dH/dt, cm/year) and corresponding error estimates in 50 km grid cells.
The Environmental Treaty Status Data Set, 2012 Release contains comprehensive information on country participation in multilateral environmental agreements through August 2012. The data were produced by the International Union for the Conservation of Nature and Natural Resources (IUCN) Environmental Law Center for use in the ECOLEX gateway to environmental law web service. The Center for International Earth Science Information Network (CIESIN) at Columbia University developed an interactive online tool that enables the users to query the data and create matrices describing multi-country participation in sets of treaties, as well as to download the results.
The Environmental Treaty Status Data Set, 2012 Release contains comprehensive information on country participation in multilateral environmental agreements through August 2012. The data were produced by the International Union for the Conservation of Nature and Natural Resources (IUCN) Environmental Law Center for use in the ECOLEX gateway to environmental law web service. The Center for International Earth Science Information Network (CIESIN) at Columbia University developed an interactive online tool that enables the users to query the data and create matrices describing multi-country participation in sets of treaties, as well as to download the results.
The Environmental Treaty Status Data Set, 2012 Release contains comprehensive information on country participation in multilateral environmental agreements through August 2012. The data were produced by the International Union for the Conservation of Nature and Natural Resources (IUCN) Environmental Law Center for use in the ECOLEX gateway to environmental law web service. The Center for International Earth Science Information Network (CIESIN) at Columbia University developed an interactive online tool that enables the users to query the data and create matrices describing multi-country participation in sets of treaties, as well as to download the results. (Suggested Usage:  To provide information on the status of country participation in international environmental agreements.)
Fast Longwave And SHortwave Fluxes (FLASHflux) Clouds and Radiative Swath (SSF) data in HDF.
Fast Longwave And SHortwave Fluxes (FLASHflux) Clouds and Radiative Swath (SSF) data in HDF.
FLASHFlux Daily Gridded Single Satellite TOA and Surfaces/Clouds data in HDF.
Biophysical parameters (DBH, NPP, biomass, bark area index, LAI, subcanopy LAI) by study site for Aspen and Spruce in the Superior National Forest, MN (SNF)
Biophysical parameters (DBH, NPP, biomass, bark area index, LAI, subcanopy LAI) by study site for Aspen and Spruce in the Superior National Forest, MN (SNF)
ABSTRACT: Biophysical parameters (DBH, NPP, biomass, bark area index, LAI, subcanopy LAI) by study site for Aspen and Spruce in the Superior National Forest, MN (SNF)
SNF study site count of the number of trees over 2 meters, broken down by species code; see also SNF Plant Species Codes
SNF study site count of the number of trees over 2 meters, broken down by species code; see also SNF Plant Species Codes
ABSTRACT: SNF study site count of the number of trees over 2 meters, broken down by species code; see also SNF Plant Species Codes
This data set provides field measurements of diameter, tree height, and crown dimensions for 1,513 trees in 30 plots at the La Selva Biological Station in Costa Rica. Fourteen of these plots were in undisturbed primary forest, six were in primary forest which had been selectively logged, seven were secondary forests, and three were abandoned pastures reverting to forest. The diameter and height data were used to calculate aboveground biomass for each of the 30 plots. The crown measurements were used to estimate a vertical profile for each plot, showing the vegetation volume in 1 meter increments from the ground to the top of the canopy.There are three comma-delimited data files and two shapefiles with this data set. The files contain the measurements and calculated biomass for the individual stems as well as the summary data at the plot level.
This data set provides field measurements of diameter, tree height, and crown dimensions for 1,513 trees in 30 plots at the La Selva Biological Station in Costa Rica. Fourteen of these plots were in undisturbed primary forest, six were in primary forest which had been selectively logged, seven were secondary forests, and three were abandoned pastures reverting to forest. The diameter and height data were used to calculate aboveground biomass for each of the 30 plots. The crown measurements were used to estimate a vertical profile for each plot, showing the vegetation volume in 1 meter increments from the ground to the top of the canopy.There are three comma-delimited data files and two shapefiles with this data set. The files contain the measurements and calculated biomass for the individual stems as well as the summary data at the plot level.
ABSTRACT: This data set provides field measurements of diameter, tree height, and crown dimensions for 1,513 trees in 30 plots at the La Selva Biological Station in Costa Rica. Fourteen of these plots were in undisturbed primary forest, six were in primary forest which had been selectively logged, seven were secondary forests, and three were abandoned pastures reverting to forest. The diameter and height data were used to calculate aboveground biomass for each of the 30 plots. The crown measurements were used to estimate a vertical profile for each plot, showing the vegetation volume in 1 meter increments from the ground to the top of the canopy.There are three comma-delimited data files and two shapefiles with this data set. The files contain the measurements and calculated biomass for the individual stems as well as the summary data at the plot level.
Steve Running's Forest-BGC Model (v.1991)
Steve Running's Forest-BGC Model (v.1991)
ABSTRACT: Steve Running's Forest-BGC Model (v.1991)
Full Resolution Full Swath Geolocated and Calibrated TOA Radiance
This data set is restricted to EOS affiliated researchers.  The U.S. National Lightning Detection Network is a commercial lightning detection network operated by Global Atmospherics, Inc., (GAI), formerly Geomet Data Services. A network of about 105 antennae are connected to a central processor that records the time, polarity, signal strength, and number of strokes of each cloud-to-ground lightning flash detected over the United States starting from Jan. 1, 1994 to present.
The US National Lightning Detection Network is a commercial network that records the time, polarity, signal strength, and number of cloud-to-ground lightning flashes over the western half of the northern hemisphere with the CONUS masked out.
Soil moistures measured by airborne gamma ray, averaged in segments or transects
Soil moistures measured by airborne gamma ray, averaged in segments or transects
ABSTRACT: Soil moistures measured by airborne gamma ray, averaged in segments or transects
Spectral reflected radiances measured with Russian GEMMA spectrometer from a helicopter
Spectral reflected radiances measured with Russian GEMMA spectrometer from a helicopter
ABSTRACT: Spectral reflected radiances measured with Russian GEMMA spectrometer from a helicopter
The GEOECOLOGY Data Base is a compilation of environmental data for research and development needs that includes information for the period 1964 to 1979. The GEOECOLOGY Data Base contains selected data on terrain and soils, water resources, forestry, vegetation, agriculture, land use, wildlife, air quality, climate, natural areas, and endangered species. Data on selected human population characteristics are also included to complement the environmental files.
The GEOECOLOGY Data Base is a compilation of environmental data for research and development needs that includes information for the period 1964 to 1979. The GEOECOLOGY Data Base contains selected data on terrain and soils, water resources, forestry, vegetation, agriculture, land use, wildlife, air quality, climate, natural areas, and endangered species. Data on selected human population characteristics are also included to complement the environmental files.
ABSTRACT: The GEOECOLOGY Data Base is a compilation of environmental data for research and development needs that includes information for the period 1964 to 1979. The GEOECOLOGY Data Base contains selected data on terrain and soils, water resources, forestry, vegetation, agriculture, land use, wildlife, air quality, climate, natural areas, and endangered species. Data on selected human population characteristics are also included to complement the environmental files.
GIS of Mexican States, Municipalities and Islands consists of attribute and boundary data for 1990. The attribute data include population, language, education, literacy, housing units and land cover classification from the 1990 Mexican population and housing census. The boundary data associated with the United States-Mexico border are consistent with the U.S. Census Bureau TIGER95 data. This dataset is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).
GIS of Mexican States, Municipalities and Islands consists of attribute and boundary data for 1990. The attribute data include population, language, education, literacy, housing units and land cover classification from the 1990 Mexican population and housing census. The boundary data associated with the United States-Mexico border are consistent with the U.S. Census Bureau TIGER95 data. This dataset is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).
GIS of Mexican States, Municipalities and Islands consists of attribute and boundary data for 1990. The attribute data include population, language, education, literacy, housing units and land cover classification from the 1990 Mexican population and housing census. The boundary data associated with the United States-Mexico border are consistent with the U.S. Census Bureau TIGER95 data. This dataset is produced by the Columbia University Center for International Earth Science Information Network (CIESIN). (Suggested Usage: To provide attribute and boundary data for Mexico for conducting assessments of human interactions with the environment)
The Population Database of Mexico contains geographically referenced population data for Mexican states, municipalities and localities from the 1990 Mexican population and housing census. The data include population by gender and age group for approximately 83.7% of the Mexican population. This dataset is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).
The Population Database of Mexico contains geographically referenced population data for Mexican states, municipalities and localities from the 1990 Mexican population and housing census. The data include population by gender and age group for approximately 83.7% of the Mexican population. This dataset is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).
The Population Database of Mexico contains geographically referenced population data for Mexican states, municipalities and localities from the 1990 Mexican population and housing census. The data include population by gender and age group for approximately 83.7% of the Mexican population. This dataset is produced by the Columbia University Center for International Earth Science Information Network (CIESIN). (Suggested Usage: To provide geographically referenced population data for Mexico for conducting assessments of human interactions with the environment.)
The Raster Based GIS Coverage of Mexican Population is a gridded coverage (1 x 1 km) of Mexican population. The data were converted from vector into raster. The population figures were derived based on available point data (the population of known localities - 30,000 in all). Cell values were derived using a weighted moving average function (Burrough, 1986), and then calculated based on known population by state. The result from this conversion is a coverage whose population data is based on square grid cells rather than a series of vectors. This dataset is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the Instituto Nacional de Estadistica Geografia e Informatica (INEGI).
The Raster Based GIS Coverage of Mexican Population is a gridded coverage (1 x 1 km) of Mexican population. The data were converted from vector into raster. The population figures were derived based on available point data (the population of known localities - 30,000 in all). Cell values were derived using a weighted moving average function (Burrough, 1986), and then calculated based on known population by state. The result from this conversion is a coverage whose population data is based on square grid cells rather than a series of vectors. This dataset is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the Instituto Nacional de Estadistica Geografia e Informatica (INEGI). (Suggested Usage: To provide raster based GIS coverage of Mexican population.)
The Raster Based GIS Coverage of Mexican Population is a gridded coverage (1 x 1 km) of Mexican population. The data were converted from vector into raster. The population figures were derived based on available point data (the population of known localities - 30,000 in all). Cell values were derived using a weighted moving average function (Burrough, 1986), and then calculated based on known population by state. The result from this conversion is a coverage whose population data is based on square grid cells rather than a series of vectors. This dataset is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the Instituto Nacional de Estadistica Geografia e Informatica (INEGI). (Suggested Usage: To provide raster based GIS coverage of Mexican population.)
The Urban Place GIS Coverage of Mexico is a vector based point Geographic Information System (GIS) coverage of 696 urban places in Mexico. Each Urban Place is geographically referenced down to one tenth of a minute. The attribute data include time-series population and selected census/geographic data items for Mexican urban places from from 1921 to 1990. The cartographic data include urban place point locations on a state boundary file of Mexico. This dataset is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the Instituto Nacional de Estadistica Geografia e Informatica (INEGI) and the Environmental Research Institute (ERI) of Michigan.
The Urban Place GIS Coverage of Mexico is a vector based point Geographic Information System (GIS) coverage of 696 urban places in Mexico. Each Urban Place is geographically referenced down to one tenth of a minute. The attribute data include time-series population and selected census/geographic data items for Mexican urban places from from 1921 to 1990. The cartographic data include urban place point locations on a state boundary file of Mexico. This dataset is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the Instituto Nacional de Estadistica Geografia e Informatica (INEGI) and the Environmental Research Institute (ERI) of Michigan.
The Urban Place GIS Coverage of Mexico is a vector based point Geographic Information System (GIS) coverage of 696 urban places in Mexico. Each Urban Place is geographically referenced down to one tenth of a minute. The attribute data include time-series population and selected census/geographic data items for Mexican urban places from from 1921 to 1990. The cartographic data include urban place point locations on a state boundary file of Mexico. This dataset is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the Instituto Nacional de Estadistica Geografia e Informatica (INEGI) and the Environmental Research Institute (ERI) of Michigan. (Suggested Usage: To provide an urban place GIS coverage of Mexico.)
The Urban Place Time-Series Population of Mexico contains population counts for more than 700 urban centers every 10 years from 1921 through 1990. The urban centers include metropolitan, conurbation, and city areas with more than 5,000 inhabitants as of 1980. This dataset is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).
The Urban Place Time-Series Population of Mexico contains population counts for more than 700 urban centers every 10 years from 1921 through 1990. The urban centers include metropolitan, conurbation, and city areas with more than 5,000 inhabitants as of 1980. This dataset is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).
The Urban Place Time-Series Population of Mexico contains population counts for more than 700 urban centers every 10 years from 1921 through 1990. The urban centers include metropolitan, conurbation, and city areas with more than 5,000 inhabitants as of 1980. This dataset is produced by the Columbia University Center for International Earth Science Information Network (CIESIN). (Suggested Usage: To provide time-series population data for Mexico for conducting assessments of human interactions with the environment.)
The Antarctic atlas consists of 28 digital elevation maps which cover
all of Antarctica north of 72.1 degrees south at a resolution of
three kilometers. Each map contains surface elevations and
coordinates for one atlas page covering 16 degrees of longitude. Data
were acquired by the Geodetic Satellite (GEOSAT) Geodetic Mission
(GM) from March 1985 through September 1986 and are available in both
Universal Transverse Mercator (UTM) coordinates, and in latitude and longitude coordinates.

Data were mapped using the UTM projection in atlas form to decrease
the distortion that usually occurs at the poles. Many features of the
Antarctic Ice Sheet are shown in more detail than in previous digital elevation models, especially along the margin of the East Antarctic
Ice Sheet. A geostatistical mapping technique (Herzfeld et al. 1993)
improved the accuracy of surface elevations compared to previous
GEOSAT elevation data sets. This atlas will facilitate the monitoring
of changes in surface elevation that could indicate mass changes in
the Antarctic Ice Sheet.
The Antarctic atlas consists of 28 digital elevation maps which cover
all of Antarctica north of 72.1 degrees south at a resolution of
three kilometers. Each map contains surface elevations and
coordinates for one atlas page covering 16 degrees of longitude. Data
were acquired by the Geodetic Satellite (GEOSAT) Geodetic Mission
(GM) from March 1985 through September 1986 and are available in both
Universal Transverse Mercator (UTM) coordinates, and in latitude and longitude coordinates.

Data were mapped using the UTM projection in atlas form to decrease
the distortion that usually occurs at the poles. Many features of the
Antarctic Ice Sheet are shown in more detail than in previous digital elevation models, especially along the margin of the East Antarctic
Ice Sheet. A geostatistical mapping technique (Herzfeld et al. 1993)
improved the accuracy of surface elevations compared to previous
GEOSAT elevation data sets. This atlas will facilitate the monitoring
of changes in surface elevation that could indicate mass changes in
the Antarctic Ice Sheet.
The Antarctic atlas consists of 28 digital elevation maps which cover
all of Antarctica north of 72.1 degrees south at a resolution of
three kilometers. Each map contains surface elevations and
coordinates for one atlas page covering 16 degrees of longitude. Data
were acquired by the Geodetic Satellite (GEOSAT) Geodetic Mission
(GM) from March 1985 through September 1986 and are available in both
Universal Transverse Mercator (UTM) coordinates, and in latitude and longitude coordinates.

Data were mapped using the UTM projection in atlas form to decrease
the distortion that usually occurs at the poles. Many features of the
Antarctic Ice Sheet are shown in more detail than in previous digital elevation models, especially along the margin of the East Antarctic
Ice Sheet. A geostatistical mapping technique (Herzfeld et al. 1993)
improved the accuracy of surface elevations compared to previous
GEOSAT elevation data sets. This atlas will facilitate the monitoring
of changes in surface elevation that could indicate mass changes in
the Antarctic Ice Sheet.
The Advanced Along-Track Scanning Radiometer (AATSR) onboard ESA's Envisat spacecraft is designed to meet the challenging task of monitoring and detecting climate change. It builds on the success of its predecessor instruments on the ERS-1 and ERS-2 satellites, and will lead to a 15+ year record of precise and accurate global Sea- Surface Temperature (SST) measurements, thereby making a valuable contribution to the long-term climate record. The exceptionally high radiometric accuracy and stability of AATSRdata are achieved through a number of unique features. A comprehensive pre-launch calibration programme, combined with continuous in-flight calibration, ensures that the data are continually corrected for sensor drift and degradation. A "dual-view" technique offering improved atmospheric correction is used to derive SSTskin observations. The accuracies achieved with this configuration are further enhanced by using low-noise infrared detectors, cooled to their optimum operating temperature by a pair of Stirling-cycle coolers. With its high-accuracy, high-quality imagery and channels in the visible, near-infrared and thermal wavelengths, AATSR data will support many applications in addition to oceanographic and climate research, including a wide range of land-surface, cryosphere and atmospheric studies.  See Llewellyn-Jones et al (2001) ESA bulletin 105, Feb 2001 for a full description. L2P data products are then derived following the GHRSST-PP Data Processing Specification (GDS) version 1.5 avaialable from http://ghrsst-pp.metoffice.com/documents/GDS-v1.0-rev1.5.pdf.  Single Sensor Error Statistics (SSES) are provided based on the GDS-v1.5 specification.
The Advanced Along-Track Scanning Radiometer (AATSR) onboard ESA's Envisat spacecraft is designed to meet the challenging task of monitoring and detecting climate change. It builds on the success of its predecessor instruments on the ERS-1 and ERS-2 satellites, and will lead to a 15+ year record of precise and accurate global Sea- Surface Temperature (SST) measurements, thereby making a valuable contribution to the long-term climate record. The exceptionally high radiometric accuracy and stability of AATSRdata are achieved through a number of unique features. A comprehensive pre-launch calibration programme, combined with continuous in-flight calibration, ensures that the data are continually corrected for sensor drift and degradation. A "dual-view" technique offering improved atmospheric correction is used to derive SSTskin observations. The accuracies achieved with this configuration are further enhanced by using low-noise infrared detectors, cooled to their optimum operating temperature by a pair of Stirling-cycle coolers. With its high-accuracy, high-quality imagery and channels in the visible, near-infrared and thermal wavelengths, AATSR data will support many applications in addition to oceanographic and climate research, including a wide range of land-surface, cryosphere and atmospheric studies.  See Llewellyn-Jones et al (2001) ESA bulletin 105, Feb 2001 for a full description. L2P data products are then derived following the GHRSST-PP Data Processing Specification (GDS) version 1.5 avaialable from http://ghrsst-pp.metoffice.com/documents/GDS-v1.0-rev1.5.pdf.  Single Sensor Error Statistics (SSES) are provided based on the GDS-v1.5 specification.
The Advanced Along-Track Scanning Radiometer (AATSR) onboard ESA's Envisat spacecraft is designed to meet the challenging task of monitoring and detecting climate change. It builds on the success of its predecessor instruments on the ERS-1 and ERS-2 satellites, and will lead to a 15+ year record of precise and accurate global Sea- Surface Temperature (SST) measurements, thereby making a valuable contribution to the long-term climate record. The exceptionally high radiometric accuracy and stability of AATSRdata are achieved through a number of unique features. A comprehensive pre-launch calibration programme, combined with continuous in-flight calibration, ensures that the data are continually corrected for sensor drift and degradation. A "dual-view" technique offering improved atmospheric correction is used to derive SSTskin observations. The accuracies achieved with this configuration are further enhanced by using low-noise infrared detectors, cooled to their optimum operating temperature by a pair of Stirling-cycle coolers. With its high-accuracy, high-quality imagery and channels in the visible, near-infrared and thermal wavelengths, AATSR data will support many applications in addition to oceanographic and climate research, including a wide range of land-surface, cryosphere and atmospheric studies.  See Llewellyn-Jones et al (2001) ESA bulletin 105, Feb 2001 for a full description. L2P data products are then derived following the GHRSST-PP Data Processing Specification (GDS) version 1.5 avaialable from http://ghrsst-pp.metoffice.com/documents/GDS-v1.0-rev1.5.pdf.  Single Sensor Error Statistics (SSES) are provided based on the GDS-v1.5 specification.
The Advanced Along-Track Scanning Radiometer (AATSR) onboard ESA's Envisat spacecraft is designed to meet the challenging task of monitoring and detecting climate change. It builds on the success of its predecessor instruments on the ERS-1 and ERS-2 satellites, and will lead to a 15+ year record of precise and accurate global Sea- Surface Temperature (SST) measurements, thereby making a valuable contribution to the long-term climate record. The exceptionally high radiometric accuracy and stability of AATSRdata are achieved through a number of unique features. A comprehensive pre-launch calibration programme, combined with continuous in-flight calibration, ensures that the data are continually corrected for sensor drift and degradation. A "dual-view" technique offering improved atmospheric correction is used to derive SSTskin observations. The accuracies achieved with this configuration are further enhanced by using low-noise infrared detectors, cooled to their optimum operating temperature by a pair of Stirling-cycle coolers. With its high-accuracy, high-quality imagery and channels in the visible, near-infrared and thermal wavelengths, AATSR data will support many applications in addition to oceanographic and climate research, including a wide range of land-surface, cryosphere and atmospheric studies.  See Llewellyn-Jones et al (2001) ESA bulletin 105, Feb 2001 for a full description. L2P data products are then derived following the GHRSST-PP Data Processing Specification (GDS) version 1.5 avaialable from http://ghrsst-pp.metoffice.com/documents/GDS-v1.0-rev1.5.pdf.  Single Sensor Error Statistics (SSES) are provided based on the GDS-v1.5 specification.
The Advanced Along-Track Scanning Radiometer (AATSR) onboard ESA's Envisat spacecraft is designed to meet the challenging task of monitoring and detecting climate change. It builds on the success of its predecessor instruments on the ERS-1 and ERS-2 satellites, and will lead to a 15+ year record of precise and accurate global Sea- Surface Temperature (SST) measurements, thereby making a valuable contribution to the long-term climate record. The exceptionally high radiometric accuracy and stability of AATSRdata are achieved through a number of unique features. A comprehensive pre-launch calibration programme, combined with continuous in-flight calibration, ensures that the data are continually corrected for sensor drift and degradation. A "dual-view" technique offering improved atmospheric correction is used to derive SSTskin observations. The accuracies achieved with this configuration are further enhanced by using low-noise infrared detectors, cooled to their optimum operating temperature by a pair of Stirling-cycle coolers. With its high-accuracy, high-quality imagery and channels in the visible, near-infrared and thermal wavelengths, AATSR data will support many applications in addition to oceanographic and climate research, including a wide range of land-surface, cryosphere and atmospheric studies.  See Llewellyn-Jones et al (2001) ESA bulletin 105, Feb 2001 for a full description. L2P data products are then derived following the GHRSST-PP Data Processing Specification (GDS) version 1.5 avaialable from http://ghrsst-pp.metoffice.com/documents/GDS-v1.0-rev1.5.pdf.  Single Sensor Error Statistics (SSES) are provided based on the GDS-v1.5 specification.
The Advanced Along-Track Scanning Radiometer (AATSR) onboard ESA's Envisat spacecraft is designed to meet the challenging task of monitoring and detecting climate change. It builds on the success of its predecessor instruments on the ERS-1 and ERS-2 satellites, and will lead to a 15+ year record of precise and accurate global Sea- Surface Temperature (SST) measurements, thereby making a valuable contribution to the long-term climate record. The exceptionally high radiometric accuracy and stability of AATSRdata are achieved through a number of unique features. A comprehensive pre-launch calibration programme, combined with continuous in-flight calibration, ensures that the data are continually corrected for sensor drift and degradation. A "dual-view" technique offering improved atmospheric correction is used to derive SSTskin observations. The accuracies achieved with this configuration are further enhanced by using low-noise infrared detectors, cooled to their optimum operating temperature by a pair of Stirling-cycle coolers. With its high-accuracy, high-quality imagery and channels in the visible, near-infrared and thermal wavelengths, AATSR data will support many applications in addition to oceanographic and climate research, including a wide range of land-surface, cryosphere and atmospheric studies.  See Llewellyn-Jones et al (2001) ESA bulletin 105, Feb 2001 for a full description. L2P data products are then derived following the GHRSST-PP Data Processing Specification (GDS) version 1.5 avaialable from http://ghrsst-pp.metoffice.com/documents/GDS-v1.0-rev1.5.pdf.  Single Sensor Error Statistics (SSES) are provided based on the GDS-v1.5 specification.
GHRSST-PP L2P data derived using Remote Sensing Systems BMAPS format AMSR-E SSTsub-skin data.  Data are downloaded form Remote Sensing Systems every hour to capture the latest AMSRE observations.  L2P data products are then derived following the GHRSST-PP Data Processing Specification (GDS) version 1.5 avaialable from http://ghrsst-pp.metoffice.com/documents/GDS-v1.0-rev1.5.pdf.  Single Sensor Error Statistics (SSES) are provided based on the GDS-v1.5 specification.
GHRSST-PP L2P data derived using Remote Sensing Systems BMAPS format AMSR-E SSTsub-skin data.  Data are downloaded form Remote Sensing Systems every hour to capture the latest AMSRE observations.  L2P data products are then derived following the GHRSST-PP Data Processing Specification (GDS) version 1.5 avaialable from http://ghrsst-pp.metoffice.com/documents/GDS-v1.0-rev1.5.pdf.  Single Sensor Error Statistics (SSES) are provided based on the GDS-v1.5 specification.
GHRSST-PP L2P data derived using Remote Sensing Systems BMAPS format AMSR-E SSTsub-skin data.  Data are downloaded form Remote Sensing Systems every hour to capture the latest AMSRE observations.  L2P data products are then derived following the GHRSST-PP Data Processing Specification (GDS) version 1.5 avaialable from http://ghrsst-pp.metoffice.com/documents/GDS-v1.0-rev1.5.pdf.  Single Sensor Error Statistics (SSES) are provided based on the GDS-v1.5 specification.
GHRSST-PP L2P data derived using Remote Sensing Systems BMAPS format AMSR-E SSTsub-skin data.  Data are downloaded form Remote Sensing Systems every hour to capture the latest AMSRE observations.  L2P data products are then derived following the GHRSST-PP Data Processing Specification (GDS) version 1.5 avaialable from http://ghrsst-pp.metoffice.com/documents/GDS-v1.0-rev1.5.pdf.  Single Sensor Error Statistics (SSES) are provided based on the GDS-v1.5 specification.
GHRSST-PP L2P data derived using Remote Sensing Systems BMAPS format AMSR-E SSTsub-skin data.  Data are downloaded form Remote Sensing Systems every hour to capture the latest AMSRE observations.  L2P data products are then derived following the GHRSST-PP Data Processing Specification (GDS) version 1.5 avaialable from http://ghrsst-pp.metoffice.com/documents/GDS-v1.0-rev1.5.pdf.  Single Sensor Error Statistics (SSES) are provided based on the GDS-v1.5 specification.
GHRSST-PP L2P data derived using Remote Sensing Systems BMAPS format AMSR-E SSTsub-skin data.  Data are downloaded form Remote Sensing Systems every hour to capture the latest AMSRE observations.  L2P data products are then derived following the GHRSST-PP Data Processing Specification (GDS) version 1.5 avaialable from http://ghrsst-pp.metoffice.com/documents/GDS-v1.0-rev1.5.pdf.  Single Sensor Error Statistics (SSES) are provided based on the GDS-v1.5 specification.
SST calculated from the IR channels of MSG at full resolution on a hourly basis. Remapping is made by space averaging, the production of the 3-hourly data is made by averaging over time the SSTs having the best confidence level. Data from distinct satellites are not averaged together. L2P data products are then derived following the GHRSST-PP Data Processing Specification (GDS) version 1.5 avaialable from http://ghrsst-pp.metoffice.com/documents/GDS-v1.0-rev1.5.pdf.  Single Sensor Error Statistics (SSES) are provided based on the GDS-v1.5 specification.
SST calculated from the IR channels of MSG at full resolution on a hourly basis. Remapping is made by space averaging, the production of the 3-hourly data is made by averaging over time the SSTs having the best confidence level. Data from distinct satellites are not averaged together. L2P data products are then derived following the GHRSST-PP Data Processing Specification (GDS) version 1.5 avaialable from http://ghrsst-pp.metoffice.com/documents/GDS-v1.0-rev1.5.pdf.  Single Sensor Error Statistics (SSES) are provided based on the GDS-v1.5 specification.
SST calculated from the IR channels of MSG at full resolution on a hourly basis. Remapping is made by space averaging, the production of the 3-hourly data is made by averaging over time the SSTs having the best confidence level. Data from distinct satellites are not averaged together. L2P data products are then derived following the GHRSST-PP Data Processing Specification (GDS) version 1.5 avaialable from http://ghrsst-pp.metoffice.com/documents/GDS-v1.0-rev1.5.pdf.  Single Sensor Error Statistics (SSES) are provided based on the GDS-v1.5 specification.
SST calculated from the IR channels of MSG at full resolution on a hourly basis. Remapping is made by space averaging, the production of the 3-hourly data is made by averaging over time the SSTs having the best confidence level. Data from distinct satellites are not averaged together. L2P data products are then derived following the GHRSST-PP Data Processing Specification (GDS) version 1.5 avaialable from http://ghrsst-pp.metoffice.com/documents/GDS-v1.0-rev1.5.pdf.  Single Sensor Error Statistics (SSES) are provided based on the GDS-v1.5 specification.
SST calculated from the IR channels of MSG at full resolution on a hourly basis. Remapping is made by space averaging, the production of the 3-hourly data is made by averaging over time the SSTs having the best confidence level. Data from distinct satellites are not averaged together. L2P data products are then derived following the GHRSST-PP Data Processing Specification (GDS) version 1.5 avaialable from http://ghrsst-pp.metoffice.com/documents/GDS-v1.0-rev1.5.pdf.  Single Sensor Error Statistics (SSES) are provided based on the GDS-v1.5 specification.
SST calculated from the IR channels of MSG at full resolution on a hourly basis. Remapping is made by space averaging, the production of the 3-hourly data is made by averaging over time the SSTs having the best confidence level. Data from distinct satellites are not averaged together. L2P data products are then derived following the GHRSST-PP Data Processing Specification (GDS) version 1.5 avaialable from http://ghrsst-pp.metoffice.com/documents/GDS-v1.0-rev1.5.pdf.  Single Sensor Error Statistics (SSES) are provided based on the GDS-v1.5 specification.
GHRSST-PP L2P data derived using Remote Sensing Systems BMAPS format TMI SSTsub-skin data.  Data are downloaded form Remote Sensing Systems every hour to capture the latest TMI observations.  L2P data products are then derived following the GHRSST-PP Data Processing Specification (GDS) version 1.5 avaialable from http://ghrsst-pp.metoffice.com/documents/GDS-v1.0-rev1.5.pdf.  Single Sensor Error Statistics (SSES) are provided based on the GDS-v1.5 specification.
GHRSST-PP L2P data derived using Remote Sensing Systems BMAPS format TMI SSTsub-skin data.  Data are downloaded form Remote Sensing Systems every hour to capture the latest TMI observations.  L2P data products are then derived following the GHRSST-PP Data Processing Specification (GDS) version 1.5 avaialable from http://ghrsst-pp.metoffice.com/documents/GDS-v1.0-rev1.5.pdf.  Single Sensor Error Statistics (SSES) are provided based on the GDS-v1.5 specification.
GHRSST-PP L2P data derived using Remote Sensing Systems BMAPS format TMI SSTsub-skin data.  Data are downloaded form Remote Sensing Systems every hour to capture the latest TMI observations.  L2P data products are then derived following the GHRSST-PP Data Processing Specification (GDS) version 1.5 avaialable from http://ghrsst-pp.metoffice.com/documents/GDS-v1.0-rev1.5.pdf.  Single Sensor Error Statistics (SSES) are provided based on the GDS-v1.5 specification.
GHRSST-PP L2P data derived using Remote Sensing Systems BMAPS format TMI SSTsub-skin data.  Data are downloaded form Remote Sensing Systems every hour to capture the latest TMI observations.  L2P data products are then derived following the GHRSST-PP Data Processing Specification (GDS) version 1.5 avaialable from http://ghrsst-pp.metoffice.com/documents/GDS-v1.0-rev1.5.pdf.  Single Sensor Error Statistics (SSES) are provided based on the GDS-v1.5 specification.
GHRSST-PP L2P data derived using Remote Sensing Systems BMAPS format TMI SSTsub-skin data.  Data are downloaded form Remote Sensing Systems every hour to capture the latest TMI observations.  L2P data products are then derived following the GHRSST-PP Data Processing Specification (GDS) version 1.5 avaialable from http://ghrsst-pp.metoffice.com/documents/GDS-v1.0-rev1.5.pdf.  Single Sensor Error Statistics (SSES) are provided based on the GDS-v1.5 specification.
GHRSST-PP L2P data derived using Remote Sensing Systems BMAPS format TMI SSTsub-skin data.  Data are downloaded form Remote Sensing Systems every hour to capture the latest TMI observations.  L2P data products are then derived following the GHRSST-PP Data Processing Specification (GDS) version 1.5 avaialable from http://ghrsst-pp.metoffice.com/documents/GDS-v1.0-rev1.5.pdf.  Single Sensor Error Statistics (SSES) are provided based on the GDS-v1.5 specification.
The production of these AVHRR L2P data is a NEODAAS activity in collaboration with the UK National Centre for Ocean Forecasting (NCOF). NEODAAS-Dundee acquires approx. 15 AVHRR HRPT passes per day over NW Europe and Arctic, each approx. 15 minutes duration. These are immediately transferred to NEODAAS-Plymouth where they are processed into SST products and converted to L2P.
The production of these AVHRR L2P data is a NEODAAS activity in collaboration with the UK National Centre for Ocean Forecasting (NCOF). NEODAAS-Dundee acquires approx. 15 AVHRR HRPT passes per day over NW Europe and Arctic, each approx. 15 minutes duration. These are immediately transferred to NEODAAS-Plymouth where they are processed into SST products and converted to L2P.
The production of these AVHRR L2P data is a NEODAAS activity in collaboration with the UK National Centre for Ocean Forecasting (NCOF). NEODAAS-Dundee acquires approx. 15 AVHRR HRPT passes per day over NW Europe and Arctic, each approx. 15 minutes duration. These are immediately transferred to NEODAAS-Plymouth where they are processed into SST products and converted to L2P.
The production of these AVHRR L2P data is a NEODAAS activity in collaboration with the UK National Centre for Ocean Forecasting (NCOF). NEODAAS-Dundee acquires approx. 15 AVHRR HRPT passes per day over NW Europe and Arctic, each approx. 15 minutes duration. These are immediately transferred to NEODAAS-Plymouth where they are processed into SST products and converted to L2P.
The production of these AVHRR L2P data is a NEODAAS activity in collaboration with the UK National Centre for Ocean Forecasting (NCOF). NEODAAS-Dundee acquires approx. 15 AVHRR HRPT passes per day over NW Europe and Arctic, each approx. 15 minutes duration. These are immediately transferred to NEODAAS-Plymouth where they are processed into SST products and converted to L2P.
The production of these AVHRR L2P data is a NEODAAS activity in collaboration with the UK National Centre for Ocean Forecasting (NCOF). NEODAAS-Dundee acquires approx. 15 AVHRR HRPT passes per day over NW Europe and Arctic, each approx. 15 minutes duration. These are immediately transferred to NEODAAS-Plymouth where they are processed into SST products and converted to L2P.
The production of these AVHRR L2P data is a NEODAAS activity in collaboration with the UK National Centre for Ocean Forecasting (NCOF). NEODAAS-Dundee acquires approx. 15 AVHRR HRPT passes per day over NW Europe and Arctic, each approx. 15 minutes duration. These are immediately transferred to NEODAAS-Plymouth where they are processed into SST products and converted to L2P.
The production of these AVHRR L2P data is a NEODAAS activity in collaboration with the UK National Centre for Ocean Forecasting (NCOF). NEODAAS-Dundee acquires approx. 15 AVHRR HRPT passes per day over NW Europe and Arctic, each approx. 15 minutes duration. These are immediately transferred to NEODAAS-Plymouth where they are processed into SST products and converted to L2P.
The production of these AVHRR L2P data is a NEODAAS activity in collaboration with the UK National Centre for Ocean Forecasting (NCOF). NEODAAS-Dundee acquires approx. 15 AVHRR HRPT passes per day over NW Europe and Arctic, each approx. 15 minutes duration. These are immediately transferred to NEODAAS-Plymouth where they are processed into SST products and converted to L2P.
The production of these AVHRR L2P data is a NEODAAS activity in collaboration with the UK National Centre for Ocean Forecasting (NCOF). NEODAAS-Dundee acquires approx. 15 AVHRR HRPT passes per day over NW Europe and Arctic, each approx. 15 minutes duration. These are immediately transferred to NEODAAS-Plymouth where they are processed into SST products and converted to L2P.
The production of these AVHRR L2P data is a NEODAAS activity in collaboration with the UK National Centre for Ocean Forecasting (NCOF). NEODAAS-Dundee acquires approx. 15 AVHRR HRPT passes per day over NW Europe and Arctic, each approx. 15 minutes duration. These are immediately transferred to NEODAAS-Plymouth where they are processed into SST products and converted to L2P.
The production of these AVHRR L2P data is a NEODAAS activity in collaboration with the UK National Centre for Ocean Forecasting (NCOF). NEODAAS-Dundee acquires approx. 15 AVHRR HRPT passes per day over NW Europe and Arctic, each approx. 15 minutes duration. These are immediately transferred to NEODAAS-Plymouth where they are processed into SST products and converted to L2P.
The production of the MODIS L2P data is a joint collaboration between JPL, OBPG and RSMAS. RSMAS is responsible for sea surface temperature algorithm development, error statistics and quality flagging, while the OBPG is NASA ground data system for production of daily MODIS ocean products in HDF file format. JPL acquires MODIS ocean granules from the OBPG and reformats them to the GHRSST L2P specification with complete metadata. 
Data consists of global coverage at 1 km resolution with approximately 288 five minute observation granules per day. For nighttime data both SST from the 11 and 4 um bands are available. Ocean chlorophyll and K490 diffuse attenuation coefficient are also available for daytime periods as non standard L2P core fields.
The production of the MODIS L2P data is a joint collaboration between JPL, OBPG and RSMAS. RSMAS is responsible for sea surface temperature algorithm development, error statistics and quality flagging, while the OBPG is NASA ground data system for production of daily MODIS ocean products in HDF file format. JPL acquires MODIS ocean granules from the OBPG and reformats them to the GHRSST L2P specification with complete metadata. 
Data consists of global coverage at 1 km resolution with approximately 288 five minute observation granules per day. For nighttime data both SST from the 11 and 4 um bands are available. Ocean chlorophyll and K490 diffuse attenuation coefficient are also available for daytime periods as non standard L2P core fields.
The production of the MODIS L2P data is a joint collaboration between JPL, OBPG and RSMAS. RSMAS is responsible for sea surface temperature algorithm development, error statistics and quality flagging, while the OBPG is NASA ground data system for production of daily MODIS ocean products in HDF file format. JPL acquires MODIS ocean granules from the OBPG and reformats them to the GHRSST L2P specification with complete metadata. 
Data consists of global coverage at 1 km resolution with approximately 288 five minute observation granules per day. For nighttime data both SST from the 11 and 4 um bands are available. Ocean chlorophyll and K490 diffuse attenuation coefficient are also available for daytime periods as non standard L2P core fields.
The production of the MODIS L2P data is a joint collaboration between JPL, OBPG and RSMAS. RSMAS is responsible for sea surface temperature algorithm development, error statistics and quality flagging, while the OBPG is NASA ground data system for production of daily MODIS ocean products in HDF file format. JPL acquires MODIS ocean granules from the OBPG and reformats them to the GHRSST L2P specification with complete metadata. 
Data consists of global coverage at 1 km resolution with approximately 288 five minute observation granules per day. For nighttime data both SST from the 11 and 4 um bands are available. Ocean chlorophyll and K490 diffuse attenuation coefficient are also available for daytime periods as non standard L2P core fields.
The production of the MODIS L2P data is a joint collaboration between JPL, OBPG and RSMAS. RSMAS is responsible for sea surface temperature algorithm development, error statistics and quality flagging, while the OBPG is NASA ground data system for production of daily MODIS ocean products in HDF file format. JPL acquires MODIS ocean granules from the OBPG and reformats them to the GHRSST L2P specification with complete metadata. 
Data consists of global coverage at 1 km resolution with approximately 288 five minute observation granules per day. For nighttime data both SST from the 11 and 4 um bands are available. Ocean chlorophyll and K490 diffuse attenuation coefficient are also available for daytime periods as non standard L2P core fields.
The production of the MODIS L2P data is a joint collaboration between JPL, OBPG and RSMAS. RSMAS is responsible for sea surface temperature algorithm development, error statistics and quality flagging, while the OBPG is NASA ground data system for production of daily MODIS ocean products in HDF file format. JPL acquires MODIS ocean granules from the OBPG and reformats them to the GHRSST L2P specification with complete metadata. 
Data consists of global coverage at 1 km resolution with approximately 288 five minute observation granules per day. For nighttime data both SST from the 11 and 4 um bands are available. Ocean chlorophyll and K490 diffuse attenuation coefficient are also available for daytime periods as non standard L2P core fields.
The production of the MODIS L2P data is a joint collaboration between JPL, OBPG and RSMAS. RSMAS is responsible for sea surface temperature algorithm development, error statistics and quality flagging, while the OBPG is NASA ground data system for production of daily MODIS ocean products in HDF file format. JPL acquires MODIS ocean granules from the OBPG and reformats them to the GHRSST L2P specification with complete metadata. 
Data consists of global coverage at 1 km resolution with approximately 288 five minute observation granules per day. For nighttime data both SST from the 11 and 4 um bands are available. Ocean chlorophyll and K490 diffuse attenuation coefficient are also available for daytime periods as non standard L2P core fields.
The production of the MODIS L2P data is a joint collaboration between JPL, OBPG and RSMAS. RSMAS is responsible for sea surface temperature algorithm development, error statistics and quality flagging, while the OBPG is NASA ground data system for production of daily MODIS ocean products in HDF file format. JPL acquires MODIS ocean granules from the OBPG and reformats them to the GHRSST L2P specification with complete metadata. 
Data consists of global coverage at 1 km resolution with approximately 288 five minute observation granules per day. For nighttime data both SST from the 11 and 4 um bands are available. Ocean chlorophyll and K490 diffuse attenuation coefficient are also available for daytime periods as non standard L2P core fields.
The production of the MODIS L2P data is a joint collaboration between JPL, OBPG and RSMAS. RSMAS is responsible for sea surface temperature algorithm development, error statistics and quality flagging, while the OBPG is NASA ground data system for production of daily MODIS ocean products in HDF file format. JPL acquires MODIS ocean granules from the OBPG and reformats them to the GHRSST L2P specification with complete metadata. 
Data consists of global coverage at 1 km resolution with approximately 288 five minute observation granules per day. For nighttime data both SST from the 11 and 4 um bands are available. Ocean chlorophyll and K490 diffuse attenuation coefficient are also available for daytime periods as non standard L2P core fields.
The production of the MODIS L2P data is a joint collaboration between JPL, OBPG and RSMAS. RSMAS is responsible for sea surface temperature algorithm development, error statistics and quality flagging, while the OBPG is NASA ground data system for production of daily MODIS ocean products in HDF file format. JPL acquires MODIS ocean granules from the OBPG and reformats them to the GHRSST L2P specification with complete metadata. 
Data consists of global coverage at 1 km resolution with approximately 288 five minute observation granules per day. For nighttime data both SST from the 11 and 4 um bands are available. Ocean chlorophyll and K490 diffuse attenuation coefficient are also available for daytime periods as non standard L2P core fields.
The production of the MODIS L2P data is a joint collaboration between JPL, OBPG and RSMAS. RSMAS is responsible for sea surface temperature algorithm development, error statistics and quality flagging, while the OBPG is NASA ground data system for production of daily MODIS ocean products in HDF file format. JPL acquires MODIS ocean granules from the OBPG and reformats them to the GHRSST L2P specification with complete metadata. 
Data consists of global coverage at 1 km resolution with approximately 288 five minute observation granules per day. For nighttime data both SST from the 11 and 4 um bands are available. Ocean chlorophyll and K490 diffuse attenuation coefficient are also available for daytime periods as non standard L2P core fields.
The production of the MODIS L2P data is a joint collaboration between JPL, OBPG and RSMAS. RSMAS is responsible for sea surface temperature algorithm development, error statistics and quality flagging, while the OBPG is NASA ground data system for production of daily MODIS ocean products in HDF file format. JPL acquires MODIS ocean granules from the OBPG and reformats them to the GHRSST L2P specification with complete metadata. 
Data consists of global coverage at 1 km resolution with approximately 288 five minute observation granules per day. For nighttime data both SST from the 11 and 4 um bands are available. Ocean chlorophyll and K490 diffuse attenuation coefficient are also available for daytime periods as non standard L2P core fields.
Multi-Channel SST retreivals generated in real-time from METOP-A AVHRR Global Area Coverage data.  Used operationally in Navy oceanographic analyses and forecasts.
Multi-Channel SST retreivals generated in real-time from METOP-A AVHRR Global Area Coverage data.  Used operationally in Navy oceanographic analyses and forecasts.
Multi-Channel SST retreivals generated in real-time from METOP-A AVHRR Global Area Coverage data.  Used operationally in Navy oceanographic analyses and forecasts.
Multi-Channel SST retreivals generated in real-time from METOP-A AVHRR Global Area Coverage data.  Used operationally in Navy oceanographic analyses and forecasts.
Multi-Channel SST retreivals generated in real-time from METOP-A AVHRR Global Area Coverage data.  Used operationally in Navy oceanographic analyses and forecasts.
Multi-Channel SST retreivals generated in real-time from METOP-A AVHRR Global Area Coverage data.  Used operationally in Navy oceanographic analyses and forecasts.
Multi-Channel SST retreivals generated in real-time from NOAA-17 AVHRR Global Area Coverage data.  Used operationally in Navy oceanographic analyses and forecasts.
Multi-Channel SST retreivals generated in real-time from NOAA-17 AVHRR Global Area Coverage data.  Used operationally in Navy oceanographic analyses and forecasts.
Multi-Channel SST retreivals generated in real-time from NOAA-17 AVHRR Global Area Coverage data.  Used operationally in Navy oceanographic analyses and forecasts.
Multi-Channel SST retreivals generated in real-time from NOAA-17 AVHRR Global Area Coverage data.  Used operationally in Navy oceanographic analyses and forecasts.
Multi-Channel SST retreivals generated in real-time from NOAA-17 AVHRR Global Area Coverage data.  Used operationally in Navy oceanographic analyses and forecasts.
Multi-Channel SST retreivals generated in real-time from NOAA-17 AVHRR Global Area Coverage data.  Used operationally in Navy oceanographic analyses and forecasts.
Multi-Channel SST retreivals generated in real-time from NOAA-17 AVHRR Local Area Coverage data.  Used operationally in Navy oceanographic analyses and forecasts.
Multi-Channel SST retreivals generated in real-time from NOAA-17 AVHRR Local Area Coverage data.  Used operationally in Navy oceanographic analyses and forecasts.
Multi-Channel SST retreivals generated in real-time from NOAA-17 AVHRR Local Area Coverage data.  Used operationally in Navy oceanographic analyses and forecasts.
Multi-Channel SST retreivals generated in real-time from NOAA-17 AVHRR Local Area Coverage data.  Used operationally in Navy oceanographic analyses and forecasts.
Multi-Channel SST retreivals generated in real-time from NOAA-18 AVHRR Global Area Coverage data.  Used operationally in Navy oceanographic analyses and forecasts.
Multi-Channel SST retreivals generated in real-time from NOAA-18 AVHRR Global Area Coverage data.  Used operationally in Navy oceanographic analyses and forecasts.
Multi-Channel SST retreivals generated in real-time from NOAA-18 AVHRR Global Area Coverage data.  Used operationally in Navy oceanographic analyses and forecasts.
Multi-Channel SST retreivals generated in real-time from NOAA-18 AVHRR Global Area Coverage data.  Used operationally in Navy oceanographic analyses and forecasts.
Multi-Channel SST retreivals generated in real-time from NOAA-18 AVHRR Global Area Coverage data.  Used operationally in Navy oceanographic analyses and forecasts.
Multi-Channel SST retreivals generated in real-time from NOAA-18 AVHRR Global Area Coverage data.  Used operationally in Navy oceanographic analyses and forecasts.
Multi-Channel SST retreivals generated in real-time from NOAA-18 AVHRR Local Area Coverage data.  Used operationally in Navy oceanographic analyses and forecasts.
Multi-Channel SST retreivals generated in real-time from NOAA-18 AVHRR Local Area Coverage data.  Used operationally in Navy oceanographic analyses and forecasts.
Multi-Channel SST retreivals generated in real-time from NOAA-18 AVHRR Local Area Coverage data.  Used operationally in Navy oceanographic analyses and forecasts.
Multi-Channel SST retreivals generated in real-time from NOAA-18 AVHRR Local Area Coverage data.  Used operationally in Navy oceanographic analyses and forecasts.
Multi-Channel SST retreivals generated in real-time from NOAA-18 AVHRR Local Area Coverage data.  Used operationally in Navy oceanographic analyses and forecasts.
Multi-Channel SST retreivals generated in real-time from NOAA-18 AVHRR Local Area Coverage data.  Used operationally in Navy oceanographic analyses and forecasts.
SST calculated from the IR channels of GOES-11 at full resolution on a half hourly basis. In raw satellite projection, vertically adjacent pixels averaged and read out at every pixel. L2P data product
SST calculated from the IR channels of GOES-11 at full resolution on a half hourly basis. In raw satellite projection, vertically adjacent pixels averaged and read out at every pixel. L2P data product
SST calculated from the IR channels of GOES-11 at full resolution on a half hourly basis. In raw satellite projection, vertically adjacent pixels averaged and read out at every pixel. L2P data product
SST calculated from the IR channels of GOES-11 at full resolution on a half hourly basis. In raw satellite projection, vertically adjacent pixels averaged and read out at every pixel. L2P data product
SST calculated from the IR channels of GOES-11 at full resolution on a half hourly basis. In raw satellite projection, vertically adjacent pixels averaged and read out at every pixel. L2P data product
SST calculated from the IR channels of GOES-11 at full resolution on a half hourly basis. In raw satellite projection, vertically adjacent pixels averaged and read out at every pixel. L2P data product
SST calculated from the IR channels of GOES-12 at full resolution on a half hourly basis. In raw satellite projection, vertically adjacent pixels averaged and read out at every pixel. L2P data products are then derived following the GHRSST-PP Data Processing Specification (GDS) version 1.5 available from http://ghrsst-pp.metoffice.com/documents/GDS-v1.0-rev1.5.pdf.  Single Sensor Error Statistics (SSES) are provided based on the GDS-v1.5 specification.
SST calculated from the IR channels of GOES-12 at full resolution on a half hourly basis. In raw satellite projection, vertically adjacent pixels averaged and read out at every pixel. L2P data products are then derived following the GHRSST-PP Data Processing Specification (GDS) version 1.5 available from http://ghrsst-pp.metoffice.com/documents/GDS-v1.0-rev1.5.pdf.  Single Sensor Error Statistics (SSES) are provided based on the GDS-v1.5 specification.
SST calculated from the IR channels of GOES-12 at full resolution on a half hourly basis. In raw satellite projection, vertically adjacent pixels averaged and read out at every pixel. L2P data products are then derived following the GHRSST-PP Data Processing Specification (GDS) version 1.5 available from http://ghrsst-pp.metoffice.com/documents/GDS-v1.0-rev1.5.pdf.  Single Sensor Error Statistics (SSES) are provided based on the GDS-v1.5 specification.
SST calculated from the IR channels of GOES-12 at full resolution on a half hourly basis. In raw satellite projection, vertically adjacent pixels averaged and read out at every pixel. L2P data products are then derived following the GHRSST-PP Data Processing Specification (GDS) version 1.5 available from http://ghrsst-pp.metoffice.com/documents/GDS-v1.0-rev1.5.pdf.  Single Sensor Error Statistics (SSES) are provided based on the GDS-v1.5 specification.
SST calculated from the IR channels of GOES-12 at full resolution on a half hourly basis. In raw satellite projection, vertically adjacent pixels averaged and read out at every pixel. L2P data products are then derived following the GHRSST-PP Data Processing Specification (GDS) version 1.5 available from http://ghrsst-pp.metoffice.com/documents/GDS-v1.0-rev1.5.pdf.  Single Sensor Error Statistics (SSES) are provided based on the GDS-v1.5 specification.
SST calculated from the IR channels of GOES-12 at full resolution on a half hourly basis. In raw satellite projection, vertically adjacent pixels averaged and read out at every pixel. L2P data products are then derived following the GHRSST-PP Data Processing Specification (GDS) version 1.5 available from http://ghrsst-pp.metoffice.com/documents/GDS-v1.0-rev1.5.pdf.  Single Sensor Error Statistics (SSES) are provided based on the GDS-v1.5 specification.
The Advanced Microwave Scanning Radiometer (AMSR-E) was launched on May 4, 2002, aboard NASA\'s Aqua spacecraft. The National Space Development Agency of Japan (NASDA) provided AMSR-E to NASA as an indispensable part of Aqua\'s global hydrology mission. Over the oceans, AMSR-E is  measuring a number of important geophysical parameters, including sea-surface temperature (SST), wind speed, atmospheric water vapor, cloud water, and rain rate. A key feature of AMSR-E is its capability to see through clouds, thereby providing an uninterrupted view of global SST and surface wind fields.
The Advanced Microwave Scanning Radiometer (AMSR-E) was launched on May 4, 2002, aboard NASA\'s Aqua spacecraft. The National Space Development Agency of Japan (NASDA) provided AMSR-E to NASA as an indispensable part of Aqua\'s global hydrology mission. Over the oceans, AMSR-E is  measuring a number of important geophysical parameters, including sea-surface temperature (SST), wind speed, atmospheric water vapor, cloud water, and rain rate. A key feature of AMSR-E is its capability to see through clouds, thereby providing an uninterrupted view of global SST and surface wind fields.
The Advanced Microwave Scanning Radiometer (AMSR-E) was launched on May 4, 2002, aboard NASA\'s Aqua spacecraft. The National Space Development Agency of Japan (NASDA) provided AMSR-E to NASA as an indispensable part of Aqua\'s global hydrology mission. Over the oceans, AMSR-E is  measuring a number of important geophysical parameters, including sea-surface temperature (SST), wind speed, atmospheric water vapor, cloud water, and rain rate. A key feature of AMSR-E is its capability to see through clouds, thereby providing an uninterrupted view of global SST and surface wind fields.
The Advanced Microwave Scanning Radiometer (AMSR-E) was launched on May 4, 2002, aboard NASA\'s Aqua spacecraft. The National Space Development Agency of Japan (NASDA) provided AMSR-E to NASA as an indispensable part of Aqua\'s global hydrology mission. Over the oceans, AMSR-E is  measuring a number of important geophysical parameters, including sea-surface temperature (SST), wind speed, atmospheric water vapor, cloud water, and rain rate. A key feature of AMSR-E is its capability to see through clouds, thereby providing an uninterrupted view of global SST and surface wind fields.
The Advanced Microwave Scanning Radiometer (AMSR-E) was launched on May 4, 2002, aboard NASA\'s Aqua spacecraft. The National Space Development Agency of Japan (NASDA) provided AMSR-E to NASA as an indispensable part of Aqua\'s global hydrology mission. Over the oceans, AMSR-E is  measuring a number of important geophysical parameters, including sea-surface temperature (SST), wind speed, atmospheric water vapor, cloud water, and rain rate. A key feature of AMSR-E is its capability to see through clouds, thereby providing an uninterrupted view of global SST and surface wind fields.
The Advanced Microwave Scanning Radiometer (AMSR-E) was launched on May 4, 2002, aboard NASA\'s Aqua spacecraft. The National Space Development Agency of Japan (NASDA) provided AMSR-E to NASA as an indispensable part of Aqua\'s global hydrology mission. Over the oceans, AMSR-E is  measuring a number of important geophysical parameters, including sea-surface temperature (SST), wind speed, atmospheric water vapor, cloud water, and rain rate. A key feature of AMSR-E is its capability to see through clouds, thereby providing an uninterrupted view of global SST and surface wind fields.
Version-4 TMI Ocean Products, in November 1997, the TMI radiometer with a 10.7 GHz channel was launched aboard the TRMM satellite.The important feature of microwave retrievals is that SST can be measured through clouds, which are nearly transparent at 10.7 GHz. This is a distinct advantage over the traditional infrared SST observations that require a cloud-free field of view. Ocean areas with persistent cloud coverage can now be viewed on a daily basis. Furthermore, microwave retrievals are not affected by aerosols and are insensitive to atmospheric water vapor. However, the microwave retrievals are sensitive to sea-surface roughness, while the infrared retrievals are not. A primary function of the TRMM SST retrieval algorithm is the removal of surface roughness effects. The microwave and infrared SST retrievals are very complementary and can be combined to obtain a reliable global data set. The algorithm for retrieving SSTs from radiometer data is described in \"AMSR Ocean Algorithm\"
Version-4 TMI Ocean Products, in November 1997, the TMI radiometer with a 10.7 GHz channel was launched aboard the TRMM satellite.The important feature of microwave retrievals is that SST can be measured through clouds, which are nearly transparent at 10.7 GHz. This is a distinct advantage over the traditional infrared SST observations that require a cloud-free field of view. Ocean areas with persistent cloud coverage can now be viewed on a daily basis. Furthermore, microwave retrievals are not affected by aerosols and are insensitive to atmospheric water vapor. However, the microwave retrievals are sensitive to sea-surface roughness, while the infrared retrievals are not. A primary function of the TRMM SST retrieval algorithm is the removal of surface roughness effects. The microwave and infrared SST retrievals are very complementary and can be combined to obtain a reliable global data set. The algorithm for retrieving SSTs from radiometer data is described in \"AMSR Ocean Algorithm\"
Version-4 TMI Ocean Products, in November 1997, the TMI radiometer with a 10.7 GHz channel was launched aboard the TRMM satellite.The important feature of microwave retrievals is that SST can be measured through clouds, which are nearly transparent at 10.7 GHz. This is a distinct advantage over the traditional infrared SST observations that require a cloud-free field of view. Ocean areas with persistent cloud coverage can now be viewed on a daily basis. Furthermore, microwave retrievals are not affected by aerosols and are insensitive to atmospheric water vapor. However, the microwave retrievals are sensitive to sea-surface roughness, while the infrared retrievals are not. A primary function of the TRMM SST retrieval algorithm is the removal of surface roughness effects. The microwave and infrared SST retrievals are very complementary and can be combined to obtain a reliable global data set. The algorithm for retrieving SSTs from radiometer data is described in \"AMSR Ocean Algorithm\"
Version-4 TMI Ocean Products, in November 1997, the TMI radiometer with a 10.7 GHz channel was launched aboard the TRMM satellite.The important feature of microwave retrievals is that SST can be measured through clouds, which are nearly transparent at 10.7 GHz. This is a distinct advantage over the traditional infrared SST observations that require a cloud-free field of view. Ocean areas with persistent cloud coverage can now be viewed on a daily basis. Furthermore, microwave retrievals are not affected by aerosols and are insensitive to atmospheric water vapor. However, the microwave retrievals are sensitive to sea-surface roughness, while the infrared retrievals are not. A primary function of the TRMM SST retrieval algorithm is the removal of surface roughness effects. The microwave and infrared SST retrievals are very complementary and can be combined to obtain a reliable global data set. The algorithm for retrieving SSTs from radiometer data is described in \"AMSR Ocean Algorithm\"
Version-4 TMI Ocean Products, in November 1997, the TMI radiometer with a 10.7 GHz channel was launched aboard the TRMM satellite.The important feature of microwave retrievals is that SST can be measured through clouds, which are nearly transparent at 10.7 GHz. This is a distinct advantage over the traditional infrared SST observations that require a cloud-free field of view. Ocean areas with persistent cloud coverage can now be viewed on a daily basis. Furthermore, microwave retrievals are not affected by aerosols and are insensitive to atmospheric water vapor. However, the microwave retrievals are sensitive to sea-surface roughness, while the infrared retrievals are not. A primary function of the TRMM SST retrieval algorithm is the removal of surface roughness effects. The microwave and infrared SST retrievals are very complementary and can be combined to obtain a reliable global data set. The algorithm for retrieving SSTs from radiometer data is described in \"AMSR Ocean Algorithm\"
Version-4 TMI Ocean Products, in November 1997, the TMI radiometer with a 10.7 GHz channel was launched aboard the TRMM satellite.The important feature of microwave retrievals is that SST can be measured through clouds, which are nearly transparent at 10.7 GHz. This is a distinct advantage over the traditional infrared SST observations that require a cloud-free field of view. Ocean areas with persistent cloud coverage can now be viewed on a daily basis. Furthermore, microwave retrievals are not affected by aerosols and are insensitive to atmospheric water vapor. However, the microwave retrievals are sensitive to sea-surface roughness, while the infrared retrievals are not. A primary function of the TRMM SST retrieval algorithm is the removal of surface roughness effects. The microwave and infrared SST retrievals are very complementary and can be combined to obtain a reliable global data set. The algorithm for retrieving SSTs from radiometer data is described in \"AMSR Ocean Algorithm\"
The Advanced Microwave Scanning Radiometer (AMSR-E) was launched on May 4, 2002, aboard NASA's Aqua spacecraft. The National Space Development Agency of Japan (NASDA) provided AMSR-E to NASA as an indispensable part of Aqua's global hydrology mission. Over the oceans, AMSR-E is measuring a number of important geophysical parameters, including sea-surface temperature (SST), wind speed, atmospheric water vapor, cloud water, and rain rate. A key feature of AMSR-E is its capability to see through clouds, thereby providing an uninterrupted view of global SST and surface wind fields.
The Advanced Microwave Scanning Radiometer (AMSR-E) was launched on May 4, 2002, aboard NASA's Aqua spacecraft. The National Space Development Agency of Japan (NASDA) provided AMSR-E to NASA as an indispensable part of Aqua's global hydrology mission. Over the oceans, AMSR-E is measuring a number of important geophysical parameters, including sea-surface temperature (SST), wind speed, atmospheric water vapor, cloud water, and rain rate. A key feature of AMSR-E is its capability to see through clouds, thereby providing an uninterrupted view of global SST and surface wind fields.
The Advanced Microwave Scanning Radiometer (AMSR-E) was launched on May 4, 2002, aboard NASA's Aqua spacecraft. The National Space Development Agency of Japan (NASDA) provided AMSR-E to NASA as an indispensable part of Aqua's global hydrology mission. Over the oceans, AMSR-E is measuring a number of important geophysical parameters, including sea-surface temperature (SST), wind speed, atmospheric water vapor, cloud water, and rain rate. A key feature of AMSR-E is its capability to see through clouds, thereby providing an uninterrupted view of global SST and surface wind fields.
The Advanced Microwave Scanning Radiometer (AMSR-E) was launched on May 4, 2002, aboard NASA's Aqua spacecraft. The National Space Development Agency of Japan (NASDA) provided AMSR-E to NASA as an indispensable part of Aqua's global hydrology mission. Over the oceans, AMSR-E is measuring a number of important geophysical parameters, including sea-surface temperature (SST), wind speed, atmospheric water vapor, cloud water, and rain rate. A key feature of AMSR-E is its capability to see through clouds, thereby providing an uninterrupted view of global SST and surface wind fields.
The Advanced Microwave Scanning Radiometer (AMSR-E) was launched on May 4, 2002, aboard NASA's Aqua spacecraft. The National Space Development Agency of Japan (NASDA) provided AMSR-E to NASA as an indispensable part of Aqua's global hydrology mission. Over the oceans, AMSR-E is measuring a number of important geophysical parameters, including sea-surface temperature (SST), wind speed, atmospheric water vapor, cloud water, and rain rate. A key feature of AMSR-E is its capability to see through clouds, thereby providing an uninterrupted view of global SST and surface wind fields.
The Advanced Microwave Scanning Radiometer (AMSR-E) was launched on May 4, 2002, aboard NASA's Aqua spacecraft. The National Space Development Agency of Japan (NASDA) provided AMSR-E to NASA as an indispensable part of Aqua's global hydrology mission. Over the oceans, AMSR-E is measuring a number of important geophysical parameters, including sea-surface temperature (SST), wind speed, atmospheric water vapor, cloud water, and rain rate. A key feature of AMSR-E is its capability to see through clouds, thereby providing an uninterrupted view of global SST and surface wind fields.
Version-3b TMI Ocean Products, in November 1997, the TMI radiometer with a 10.7 GHz channel was launched aboard the TRMM satellite.The important feature of microwave retrievals is that SST can be measured through clouds, which are nearly transparent at 10.7 GHz. This is a distinct advantage over the traditional infrared SST observations that require a cloud-free field of view. Ocean areas with persistent cloud coverage can now be viewed on a daily basis. Furthermore, microwave retrievals are not affected by aerosols and are insensitive to atmospheric water vapor. However, the microwave retrievals are sensitive to sea-surface roughness, while the infrared retrievals are not. A primary function of the TRMM SST retrieval algorithm is the removal of surface roughness effects. The microwave and infrared SST retrievals are very complementary and can be combined to obtain a reliable global data set. The algorithm for retrieving SSTs from radiometer data is described in "AMSR Ocean Algorithm"
Version-3b TMI Ocean Products, in November 1997, the TMI radiometer with a 10.7 GHz channel was launched aboard the TRMM satellite.The important feature of microwave retrievals is that SST can be measured through clouds, which are nearly transparent at 10.7 GHz. This is a distinct advantage over the traditional infrared SST observations that require a cloud-free field of view. Ocean areas with persistent cloud coverage can now be viewed on a daily basis. Furthermore, microwave retrievals are not affected by aerosols and are insensitive to atmospheric water vapor. However, the microwave retrievals are sensitive to sea-surface roughness, while the infrared retrievals are not. A primary function of the TRMM SST retrieval algorithm is the removal of surface roughness effects. The microwave and infrared SST retrievals are very complementary and can be combined to obtain a reliable global data set. The algorithm for retrieving SSTs from radiometer data is described in "AMSR Ocean Algorithm"
Version-3b TMI Ocean Products, in November 1997, the TMI radiometer with a 10.7 GHz channel was launched aboard the TRMM satellite.The important feature of microwave retrievals is that SST can be measured through clouds, which are nearly transparent at 10.7 GHz. This is a distinct advantage over the traditional infrared SST observations that require a cloud-free field of view. Ocean areas with persistent cloud coverage can now be viewed on a daily basis. Furthermore, microwave retrievals are not affected by aerosols and are insensitive to atmospheric water vapor. However, the microwave retrievals are sensitive to sea-surface roughness, while the infrared retrievals are not. A primary function of the TRMM SST retrieval algorithm is the removal of surface roughness effects. The microwave and infrared SST retrievals are very complementary and can be combined to obtain a reliable global data set. The algorithm for retrieving SSTs from radiometer data is described in "AMSR Ocean Algorithm"
Version-3b TMI Ocean Products, in November 1997, the TMI radiometer with a 10.7 GHz channel was launched aboard the TRMM satellite.The important feature of microwave retrievals is that SST can be measured through clouds, which are nearly transparent at 10.7 GHz. This is a distinct advantage over the traditional infrared SST observations that require a cloud-free field of view. Ocean areas with persistent cloud coverage can now be viewed on a daily basis. Furthermore, microwave retrievals are not affected by aerosols and are insensitive to atmospheric water vapor. However, the microwave retrievals are sensitive to sea-surface roughness, while the infrared retrievals are not. A primary function of the TRMM SST retrieval algorithm is the removal of surface roughness effects. The microwave and infrared SST retrievals are very complementary and can be combined to obtain a reliable global data set. The algorithm for retrieving SSTs from radiometer data is described in "AMSR Ocean Algorithm"
Version-3b TMI Ocean Products, in November 1997, the TMI radiometer with a 10.7 GHz channel was launched aboard the TRMM satellite.The important feature of microwave retrievals is that SST can be measured through clouds, which are nearly transparent at 10.7 GHz. This is a distinct advantage over the traditional infrared SST observations that require a cloud-free field of view. Ocean areas with persistent cloud coverage can now be viewed on a daily basis. Furthermore, microwave retrievals are not affected by aerosols and are insensitive to atmospheric water vapor. However, the microwave retrievals are sensitive to sea-surface roughness, while the infrared retrievals are not. A primary function of the TRMM SST retrieval algorithm is the removal of surface roughness effects. The microwave and infrared SST retrievals are very complementary and can be combined to obtain a reliable global data set. The algorithm for retrieving SSTs from radiometer data is described in "AMSR Ocean Algorithm"
Version-3b TMI Ocean Products, in November 1997, the TMI radiometer with a 10.7 GHz channel was launched aboard the TRMM satellite.The important feature of microwave retrievals is that SST can be measured through clouds, which are nearly transparent at 10.7 GHz. This is a distinct advantage over the traditional infrared SST observations that require a cloud-free field of view. Ocean areas with persistent cloud coverage can now be viewed on a daily basis. Furthermore, microwave retrievals are not affected by aerosols and are insensitive to atmospheric water vapor. However, the microwave retrievals are sensitive to sea-surface roughness, while the infrared retrievals are not. A primary function of the TRMM SST retrieval algorithm is the removal of surface roughness effects. The microwave and infrared SST retrievals are very complementary and can be combined to obtain a reliable global data set. The algorithm for retrieving SSTs from radiometer data is described in "AMSR Ocean Algorithm"
The GAMSSA v1.0 system blends NAVOCEANO's GAC 9.9 km x 4.4 km resolution AVHRR L2P SST1m data (NOAA-17, NOAA-18 and METOP-A), European Space Agency's 0.17  AATSR skin SST Meteo Product (EnviSat), Remote Sensing System's 25 km resolution AMSR-E L2P sub-skin SSTs (Aqua) and in situ bulk SSTs from the GTS.  In order to produce a foundation SST estimate, the AATSR skin SST data stream is converted to foundation SST using the Donlon et al. (2002) skin to foundation temperature conversion algorithms.  These empirically-derived algorithms apply a small correction for the cool-skin effect depending on surface wind speed, and filter out SST values suspected to be affected by diurnal warming by excluding cases which have experienced recent surface wind speeds of below 6 ms-1 during the day and less than 2 ms-1 during the night.  Wind data used are the 0.75  horizontal resolution, 3-hourly, instantaneous 10 m winds derived from the BoM 60 level, Global Area Prediction System (GASP) NWP forecasts.  The remaining satellite and in situ sub-skin and bulk SST data streams are similarly filtered to remove suspected diurnal warming events using calculated times for sunrise and sunset at the measurement location, GASP forecast winds and the same wind speed thresholds as applied to the AATSR data.
The GAMSSA v1.0 system blends NAVOCEANO's GAC 9.9 km x 4.4 km resolution AVHRR L2P SST1m data (NOAA-17, NOAA-18 and METOP-A), European Space Agency's 0.17  AATSR skin SST Meteo Product (EnviSat), Remote Sensing System's 25 km resolution AMSR-E L2P sub-skin SSTs (Aqua) and in situ bulk SSTs from the GTS.  In order to produce a foundation SST estimate, the AATSR skin SST data stream is converted to foundation SST using the Donlon et al. (2002) skin to foundation temperature conversion algorithms.  These empirically-derived algorithms apply a small correction for the cool-skin effect depending on surface wind speed, and filter out SST values suspected to be affected by diurnal warming by excluding cases which have experienced recent surface wind speeds of below 6 ms-1 during the day and less than 2 ms-1 during the night.  Wind data used are the 0.75  horizontal resolution, 3-hourly, instantaneous 10 m winds derived from the BoM 60 level, Global Area Prediction System (GASP) NWP forecasts.  The remaining satellite and in situ sub-skin and bulk SST data streams are similarly filtered to remove suspected diurnal warming events using calculated times for sunrise and sunset at the measurement location, GASP forecast winds and the same wind speed thresholds as applied to the AATSR data.
The GAMSSA v1.0 system blends NAVOCEANO's GAC 9.9 km x 4.4 km resolution AVHRR L2P SST1m data (NOAA-17, NOAA-18 and METOP-A), European Space Agency's 0.17  AATSR skin SST Meteo Product (EnviSat), Remote Sensing System's 25 km resolution AMSR-E L2P sub-skin SSTs (Aqua) and in situ bulk SSTs from the GTS.  In order to produce a foundation SST estimate, the AATSR skin SST data stream is converted to foundation SST using the Donlon et al. (2002) skin to foundation temperature conversion algorithms.  These empirically-derived algorithms apply a small correction for the cool-skin effect depending on surface wind speed, and filter out SST values suspected to be affected by diurnal warming by excluding cases which have experienced recent surface wind speeds of below 6 ms-1 during the day and less than 2 ms-1 during the night.  Wind data used are the 0.75  horizontal resolution, 3-hourly, instantaneous 10 m winds derived from the BoM 60 level, Global Area Prediction System (GASP) NWP forecasts.  The remaining satellite and in situ sub-skin and bulk SST data streams are similarly filtered to remove suspected diurnal warming events using calculated times for sunrise and sunset at the measurement location, GASP forecast winds and the same wind speed thresholds as applied to the AATSR data.
The GAMSSA v1.0 system blends NAVOCEANO's GAC 9.9 km x 4.4 km resolution AVHRR L2P SST1m data (NOAA-17, NOAA-18 and METOP-A), European Space Agency's 0.17  AATSR skin SST Meteo Product (EnviSat), Remote Sensing System's 25 km resolution AMSR-E L2P sub-skin SSTs (Aqua) and in situ bulk SSTs from the GTS.  In order to produce a foundation SST estimate, the AATSR skin SST data stream is converted to foundation SST using the Donlon et al. (2002) skin to foundation temperature conversion algorithms.  These empirically-derived algorithms apply a small correction for the cool-skin effect depending on surface wind speed, and filter out SST values suspected to be affected by diurnal warming by excluding cases which have experienced recent surface wind speeds of below 6 ms-1 during the day and less than 2 ms-1 during the night.  Wind data used are the 0.75  horizontal resolution, 3-hourly, instantaneous 10 m winds derived from the BoM 60 level, Global Area Prediction System (GASP) NWP forecasts.  The remaining satellite and in situ sub-skin and bulk SST data streams are similarly filtered to remove suspected diurnal warming events using calculated times for sunrise and sunset at the measurement location, GASP forecast winds and the same wind speed thresholds as applied to the AATSR data.
As part of the BLUElink> Ocean Forecasting Australia project (http://www.bom.gov.au/oceanography/projects/BLUElink/), the Australian Bureau of Meteorology has modified its pre-existing operational regional 1/4 degree resolution optimal interpolation SST analysis system to produce 1/12 degree resolution, daily SST analyses over the Australian region (20N - 70S, 60E - 170W).  The new Regional Australian Multi-Sensor SST Analysis (RAMSSA) v1.0 system combines SST data from infrared (AVHRR and AATSR) and microwave (AMSR-E) sensors on polar-orbiting satellites with in situ measurements from the GTS to produce daily foundation SST estimates, largely free of nocturnal cooling and diurnal warming effects.
As part of the BLUElink> Ocean Forecasting Australia project (http://www.bom.gov.au/oceanography/projects/BLUElink/), the Australian Bureau of Meteorology has modified its pre-existing operational regional 1/4 degree resolution optimal interpolation SST analysis system to produce 1/12 degree resolution, daily SST analyses over the Australian region (20N - 70S, 60E - 170W).  The new Regional Australian Multi-Sensor SST Analysis (RAMSSA) v1.0 system combines SST data from infrared (AVHRR and AATSR) and microwave (AMSR-E) sensors on polar-orbiting satellites with in situ measurements from the GTS to produce daily foundation SST estimates, largely free of nocturnal cooling and diurnal warming effects.
As part of the BLUElink> Ocean Forecasting Australia project (http://www.bom.gov.au/oceanography/projects/BLUElink/), the Australian Bureau of Meteorology has modified its pre-existing operational regional 1/4 degree resolution optimal interpolation SST analysis system to produce 1/12 degree resolution, daily SST analyses over the Australian region (20N - 70S, 60E - 170W).  The new Regional Australian Multi-Sensor SST Analysis (RAMSSA) v1.0 system combines SST data from infrared (AVHRR and AATSR) and microwave (AMSR-E) sensors on polar-orbiting satellites with in situ measurements from the GTS to produce daily foundation SST estimates, largely free of nocturnal cooling and diurnal warming effects.
As part of the BLUElink> Ocean Forecasting Australia project (http://www.bom.gov.au/oceanography/projects/BLUElink/), the Australian Bureau of Meteorology has modified its pre-existing operational regional 1/4 degree resolution optimal interpolation SST analysis system to produce 1/12 degree resolution, daily SST analyses over the Australian region (20N - 70S, 60E - 170W).  The new Regional Australian Multi-Sensor SST Analysis (RAMSSA) v1.0 system combines SST data from infrared (AVHRR and AATSR) and microwave (AMSR-E) sensors on polar-orbiting satellites with in situ measurements from the GTS to produce daily foundation SST estimates, largely free of nocturnal cooling and diurnal warming effects.
The L4 analysis is based upon nighttime GHRSST L2P subskin SST observations from several satellites and instruments such as: AMSRE, ATS_NR_2P, AVHRR18_G, AVHRR17_NAR, AVHRR18_NAR and SEVIRI. The ice field from the Swedish and Finnish ice services is used to mask out areas with ice. Besides SST, the data contain an estimated error standard deviation of the analysed SSTs and a sea/land/lake/ice field composite mask.
The L4 analysis is based upon nighttime GHRSST L2P subskin SST observations from several satellites and instruments such as: AMSRE, ATS_NR_2P, AVHRR18_G, AVHRR17_NAR, AVHRR18_NAR and SEVIRI. The ice field from the Swedish and Finnish ice services is used to mask out areas with ice. Besides SST, the data contain an estimated error standard deviation of the analysed SSTs and a sea/land/lake/ice field composite mask.
The L4 analysis is based upon nighttime GHRSST L2P subskin SST observations from several satellites and instruments such as: AMSRE, ATS_NR_2P, AVHRR18_G, AVHRR17_NAR, AVHRR18_NAR and SEVIRI. The ice field from the Swedish and Finnish ice services is used to mask out areas with ice. Besides SST, the data contain an estimated error standard deviation of the analysed SSTs and a sea/land/lake/ice field composite mask.
The L4 analysis is based upon nighttime GHRSST L2P subskin SST observations from several satellites and instruments such as: AMSRE, ATS_NR_2P, AVHRR18_G, AVHRR17_NAR, AVHRR18_NAR and SEVIRI. The ice field from the Swedish and Finnish ice services is used to mask out areas with ice. Besides SST, the data contain an estimated error standard deviation of the analysed SSTs and a sea/land/lake/ice field composite mask.
The ODYSSEA Galapagos and Pacific Central America Sea Surface Temperature is produced at Ifremer/CERSAT. It provides a daily cloud-free field of foundation sea surface temperature at 2 km resolution (0.02 degree). It is generated by merging all available satellite sensors for sea surface temperature (micro-wave and infra-red), after careful intercalibration using AATSR sensor as reference (previously re-calibrated using all available in situ data). The development of this real-time sea surface temperature at Ifremer/CERSAT is supported by ESA initially in the frame of Medspiration project.
The ODYSSEA Galapagos and Pacific Central America Sea Surface Temperature is produced at Ifremer/CERSAT. It provides a daily cloud-free field of foundation sea surface temperature at 2 km resolution (0.02 degree). It is generated by merging all available satellite sensors for sea surface temperature (micro-wave and infra-red), after careful intercalibration using AATSR sensor as reference (previously re-calibrated using all available in situ data). The development of this real-time sea surface temperature at Ifremer/CERSAT is supported by ESA initially in the frame of Medspiration project.
The ODYSSEA Galapagos and Pacific Central America Sea Surface Temperature is produced at Ifremer/CERSAT. It provides a daily cloud-free field of foundation sea surface temperature at 2 km resolution (0.02 degree). It is generated by merging all available satellite sensors for sea surface temperature (micro-wave and infra-red), after careful intercalibration using AATSR sensor as reference (previously re-calibrated using all available in situ data). The development of this real-time sea surface temperature at Ifremer/CERSAT is supported by ESA initially in the frame of Medspiration project.
The ODYSSEA Galapagos and Pacific Central America Sea Surface Temperature is produced at Ifremer/CERSAT. It provides a daily cloud-free field of foundation sea surface temperature at 2 km resolution (0.02 degree). It is generated by merging all available satellite sensors for sea surface temperature (micro-wave and infra-red), after careful intercalibration using AATSR sensor as reference (previously re-calibrated using all available in situ data). The development of this real-time sea surface temperature at Ifremer/CERSAT is supported by ESA initially in the frame of Medspiration project.
The ODYSSEA Global Sea Surface Temperature is a GHRSST Level 4 product produced at Ifremer/CERSAT (France). It provides a daily cloud-free global field of foundation sea surface temperature at 10km resolution (0.1 degree) over the full globe. It is generated by merging all available satellite sensors for sea surface temperature (micro-wave and infra-red), after careful intercalibration using AATSR sensor as reference (previously re-calibrated using all available in situ data). The development of the global real-time sea surface temperature at Ifremer/CERSAT is supported by European Commission (GMES framework) initially in the frame of MERSEA project. PO.DAAC, through the GHRSST Global Data Assembly Center (http://ghrsst.jpl.nasa.gov) acts as a global distribution node for all GHRSST L4 products in conjunction with the NOAA Longterm Stewardship and Reanalysis Facility (LTSRF; http://ghrsst.nodc.noaa.gov). More information on 
GHRSST can be found here: http://www.ghrsst-pp.org/.
The ODYSSEA Global Sea Surface Temperature is a GHRSST Level 4 product produced at Ifremer/CERSAT (France). It provides a daily cloud-free global field of foundation sea surface temperature at 10km resolution (0.1 degree) over the full globe. It is generated by merging all available satellite sensors for sea surface temperature (micro-wave and infra-red), after careful intercalibration using AATSR sensor as reference (previously re-calibrated using all available in situ data). The development of the global real-time sea surface temperature at Ifremer/CERSAT is supported by European Commission (GMES framework) initially in the frame of MERSEA project. PO.DAAC, through the GHRSST Global Data Assembly Center (http://ghrsst.jpl.nasa.gov) acts as a global distribution node for all GHRSST L4 products in conjunction with the NOAA Longterm Stewardship and Reanalysis Facility (LTSRF; http://ghrsst.nodc.noaa.gov). More information on 
GHRSST can be found here: http://www.ghrsst-pp.org/.
The ODYSSEA Global Sea Surface Temperature is a GHRSST Level 4 product produced at Ifremer/CERSAT (France). It provides a daily cloud-free global field of foundation sea surface temperature at 10km resolution (0.1 degree) over the full globe. It is generated by merging all available satellite sensors for sea surface temperature (micro-wave and infra-red), after careful intercalibration using AATSR sensor as reference (previously re-calibrated using all available in situ data). The development of the global real-time sea surface temperature at Ifremer/CERSAT is supported by European Commission (GMES framework) initially in the frame of MERSEA project. PO.DAAC, through the GHRSST Global Data Assembly Center (http://ghrsst.jpl.nasa.gov) acts as a global distribution node for all GHRSST L4 products in conjunction with the NOAA Longterm Stewardship and Reanalysis Facility (LTSRF; http://ghrsst.nodc.noaa.gov). More information on 
GHRSST can be found here: http://www.ghrsst-pp.org/.
The ODYSSEA Global Sea Surface Temperature is a GHRSST Level 4 product produced at Ifremer/CERSAT (France). It provides a daily cloud-free global field of foundation sea surface temperature at 10km resolution (0.1 degree) over the full globe. It is generated by merging all available satellite sensors for sea surface temperature (micro-wave and infra-red), after careful intercalibration using AATSR sensor as reference (previously re-calibrated using all available in situ data). The development of the global real-time sea surface temperature at Ifremer/CERSAT is supported by European Commission (GMES framework) initially in the frame of MERSEA project. PO.DAAC, through the GHRSST Global Data Assembly Center (http://ghrsst.jpl.nasa.gov) acts as a global distribution node for all GHRSST L4 products in conjunction with the NOAA Longterm Stewardship and Reanalysis Facility (LTSRF; http://ghrsst.nodc.noaa.gov). More information on 
GHRSST can be found here: http://www.ghrsst-pp.org/.
The ODYSSEA North-West Europe Shelves Sea Surface Temperature is produced at Ifremer/CERSAT. It provides a daily cloud-free field of foundation sea surface temperature at 2 km resolution (0.02 degree). It is generated by merging all available satellite sensors for sea surface temperature (micro-wave and infra-red), after careful intercalibration using AATSR sensor as reference (previously re-calibrated using all available in situ data). The development of this real-time sea surface temperature at Ifremer/CERSAT is supported by ESA initially in the frame of Medspiration project.
The ODYSSEA North-West Europe Shelves Sea Surface Temperature is produced at Ifremer/CERSAT. It provides a daily cloud-free field of foundation sea surface temperature at 2 km resolution (0.02 degree). It is generated by merging all available satellite sensors for sea surface temperature (micro-wave and infra-red), after careful intercalibration using AATSR sensor as reference (previously re-calibrated using all available in situ data). The development of this real-time sea surface temperature at Ifremer/CERSAT is supported by ESA initially in the frame of Medspiration project.
The ODYSSEA North-West Europe Shelves Sea Surface Temperature is produced at Ifremer/CERSAT. It provides a daily cloud-free field of foundation sea surface temperature at 2 km resolution (0.02 degree). It is generated by merging all available satellite sensors for sea surface temperature (micro-wave and infra-red), after careful intercalibration using AATSR sensor as reference (previously re-calibrated using all available in situ data). The development of this real-time sea surface temperature at Ifremer/CERSAT is supported by ESA initially in the frame of Medspiration project.
The ODYSSEA North-West Europe Shelves Sea Surface Temperature is produced at Ifremer/CERSAT. It provides a daily cloud-free field of foundation sea surface temperature at 2 km resolution (0.02 degree). It is generated by merging all available satellite sensors for sea surface temperature (micro-wave and infra-red), after careful intercalibration using AATSR sensor as reference (previously re-calibrated using all available in situ data). The development of this real-time sea surface temperature at Ifremer/CERSAT is supported by ESA initially in the frame of Medspiration project.
The ODYSSEA North-West Europe Shelves Sea Surface Temperature is produced at Ifremer/CERSAT. It provides a daily cloud-free field of foundation sea surface temperature at 2 km resolution (0.02 degree). It is generated by merging all available satellite sensors for sea surface temperature (micro-wave and infra-red), after careful intercalibration using AATSR sensor as reference (previously re-calibrated using all available in situ data). The development of this real-time sea surface temperature at Ifremer/CERSAT is supported by ESA initially in the frame of Medspiration project.
The ODYSSEA North-West Europe Shelves Sea Surface Temperature is produced at Ifremer/CERSAT. It provides a daily cloud-free field of foundation sea surface temperature at 2 km resolution (0.02 degree). It is generated by merging all available satellite sensors for sea surface temperature (micro-wave and infra-red), after careful intercalibration using AATSR sensor as reference (previously re-calibrated using all available in situ data). The development of this real-time sea surface temperature at Ifremer/CERSAT is supported by ESA initially in the frame of Medspiration project.
K10 L4 sea surface temperature analysis produced daily on an operational basis at the Naval Oceanographic Office using a weighted average of AVHRR, GVAR, and AMSRE SST retrievals.  Pathfinder 9km climatology is used when no new satellite SST retrievals are available after 34 days.  The age, reliability, and resolution of the data are used in the weighted average.  AMSRE L2P data are provided by Remote Sensing Systems, USA.
K10 L4 sea surface temperature analysis produced daily on an operational basis at the Naval Oceanographic Office using a weighted average of AVHRR, GVAR, and AMSRE SST retrievals.  Pathfinder 9km climatology is used when no new satellite SST retrievals are available after 34 days.  The age, reliability, and resolution of the data are used in the weighted average.  AMSRE L2P data are provided by Remote Sensing Systems, USA.
K10 L4 sea surface temperature analysis produced daily on an operational basis at the Naval Oceanographic Office using a weighted average of AVHRR, GVAR, and AMSRE SST retrievals.  Pathfinder 9km climatology is used when no new satellite SST retrievals are available after 34 days.  The age, reliability, and resolution of the data are used in the weighted average.  AMSRE L2P data are provided by Remote Sensing Systems, USA.
K10 L4 sea surface temperature analysis produced daily on an operational basis at the Naval Oceanographic Office using a weighted average of AVHRR, GVAR, and AMSRE SST retrievals.  Pathfinder 9km climatology is used when no new satellite SST retrievals are available after 34 days.  The age, reliability, and resolution of the data are used in the weighted average.  AMSRE L2P data are provided by Remote Sensing Systems, USA.
The through-cloud capabilities of microwave radiometers provide a valuable picture of global sea surface temperature (SST) while infrared radiometers (MODIS) have a higher spatial resolution.  To utilize these SSTs, scientists at Remote Sensing Systems have calculated a daily, Optimally Interpolated (OI) SST product at ~9 kilometer resolution. This product is ideal for research activities in which a complete, daily SST map is more desirable than one with missing data due to orbital gaps or environmental conditions precluding SST retrieval.
The through-cloud capabilities of microwave radiometers provide a valuable picture of global sea surface temperature (SST) while infrared radiometers (MODIS) have a higher spatial resolution.  To utilize these SSTs, scientists at Remote Sensing Systems have calculated a daily, Optimally Interpolated (OI) SST product at ~9 kilometer resolution. This product is ideal for research activities in which a complete, daily SST map is more desirable than one with missing data due to orbital gaps or environmental conditions precluding SST retrieval.
The through-cloud capabilities of microwave radiometers provide a valuable picture of global sea surface temperature (SST) while infrared radiometers (MODIS) have a higher spatial resolution.  To utilize these SSTs, scientists at Remote Sensing Systems have calculated a daily, Optimally Interpolated (OI) SST product at ~9 kilometer resolution. This product is ideal for research activities in which a complete, daily SST map is more desirable than one with missing data due to orbital gaps or environmental conditions precluding SST retrieval.
The through-cloud capabilities of microwave radiometers provide a valuable picture of global sea surface temperature (SST) while infrared radiometers (MODIS) have a higher spatial resolution.  To utilize these SSTs, scientists at Remote Sensing Systems have calculated a daily, Optimally Interpolated (OI) SST product at ~9 kilometer resolution. This product is ideal for research activities in which a complete, daily SST map is more desirable than one with missing data due to orbital gaps or environmental conditions precluding SST retrieval.
The through-cloud capabilities of microwave radiometers provide a valuable picture of global sea surface temperature (SST) while infrared radiometers (MODIS) have a higher spatial resolution.  To utilize these SSTs, scientists at Remote Sensing Systems have calculated a daily, Optimally Interpolated (OI) SST product at ~9 kilometer resolution. This product is ideal for research activities in which a complete, daily SST map is more desirable than one with missing data due to orbital gaps or environmental conditions precluding SST retrieval.
The through-cloud capabilities of microwave radiometers provide a valuable picture of global sea surface temperature (SST) while infrared radiometers (MODIS) have a higher spatial resolution.  To utilize these SSTs, scientists at Remote Sensing Systems have calculated a daily, Optimally Interpolated (OI) SST product at ~9 kilometer resolution. This product is ideal for research activities in which a complete, daily SST map is more desirable than one with missing data due to orbital gaps or environmental conditions precluding SST retrieval.
A GHRSST L4 sea surface temperature analysis produced daily at the NOAA National Climatic Data Center using optimal interpolation from AVHRR Pathfinder Version 5 data (http://pathfinder.nodc.noaa.gov) (when available, otherwise operational AVHRR data are used) and in situ ship and buoy observations. A second
 similar product is available that also includes AMSR-E data from June 2002 onward. To avoid a jump in the analysis and to provide the longest, most consistent time series, this product uses only the AVHRR Pathfinder and in situ data. The OI analysis is a daily average SST that is bias adjusted using a spatially smoothed 7-day in situ SST average. Both day and night satellite fields are independently adjusted. More information is available at http://www.ncdc.noaa.gov/oa/climate/research/sst/oi-daily.php.PO.DAAC, through the GHRSST Global Data Assembly Center (http://ghrsst.jpl.nasa.gov) acts as a global distribution node for all GHRSST L4 products in conjunction with the NOAA Longterm Stewardship and Reanalysis Facility (LTSRF; http://ghrsst.nodc.noaa.gov). More information on GHRSST can be found here: http://www.ghrsst-pp.org/.
A GHRSST L4 sea surface temperature analysis produced daily at the NOAA National Climatic Data Center using optimal interpolation from AVHRR Pathfinder Version 5 data (http://pathfinder.nodc.noaa.gov) (when available, otherwise operational AVHRR data are used) and in situ ship and buoy observations. A second
 similar product is available that also includes AMSR-E data from June 2002 onward. To avoid a jump in the analysis and to provide the longest, most consistent time series, this product uses only the AVHRR Pathfinder and in situ data. The OI analysis is a daily average SST that is bias adjusted using a spatially smoothed 7-day in situ SST average. Both day and night satellite fields are independently adjusted. More information is available at http://www.ncdc.noaa.gov/oa/climate/research/sst/oi-daily.php.PO.DAAC, through the GHRSST Global Data Assembly Center (http://ghrsst.jpl.nasa.gov) acts as a global distribution node for all GHRSST L4 products in conjunction with the NOAA Longterm Stewardship and Reanalysis Facility (LTSRF; http://ghrsst.nodc.noaa.gov). More information on GHRSST can be found here: http://www.ghrsst-pp.org/.
A GHRSST L4 sea surface temperature analysis produced daily at the NOAA National Climatic Data Center using optimal interpolation from AVHRR Pathfinder Version 5 data (http://pathfinder.nodc.noaa.gov) (when available, otherwise operational AVHRR data are used) and in situ ship and buoy observations. A second
 similar product is available that also includes AMSR-E data from June 2002 onward. To avoid a jump in the analysis and to provide the longest, most consistent time series, this product uses only the AVHRR Pathfinder and in situ data. The OI analysis is a daily average SST that is bias adjusted using a spatially smoothed 7-day in situ SST average. Both day and night satellite fields are independently adjusted. More information is available at http://www.ncdc.noaa.gov/oa/climate/research/sst/oi-daily.php.PO.DAAC, through the GHRSST Global Data Assembly Center (http://ghrsst.jpl.nasa.gov) acts as a global distribution node for all GHRSST L4 products in conjunction with the NOAA Longterm Stewardship and Reanalysis Facility (LTSRF; http://ghrsst.nodc.noaa.gov). More information on GHRSST can be found here: http://www.ghrsst-pp.org/.
A GHRSST L4 sea surface temperature analysis produced daily at the NOAA National Climatic Data Center using optimal interpolation from AVHRR Pathfinder Version 5 data (http://pathfinder.nodc.noaa.gov) (when available, otherwise operational AVHRR data are used) and in situ ship and buoy observations. A second
 similar product is available that also includes AMSR-E data from June 2002 onward. To avoid a jump in the analysis and to provide the longest, most consistent time series, this product uses only the AVHRR Pathfinder and in situ data. The OI analysis is a daily average SST that is bias adjusted using a spatially smoothed 7-day in situ SST average. Both day and night satellite fields are independently adjusted. More information is available at http://www.ncdc.noaa.gov/oa/climate/research/sst/oi-daily.php.PO.DAAC, through the GHRSST Global Data Assembly Center (http://ghrsst.jpl.nasa.gov) acts as a global distribution node for all GHRSST L4 products in conjunction with the NOAA Longterm Stewardship and Reanalysis Facility (LTSRF; http://ghrsst.nodc.noaa.gov). More information on GHRSST can be found here: http://www.ghrsst-pp.org/.
A GHRSST L4 sea surface temperature analysis produced daily at the NOAA National Climatic Data Center using optimal interpolation from AVHRR Pathfinder Version 5 data (http://pathfinder.nodc.noaa.gov) (when available, otherwise operational AVHRR data are used) and in situ ship and buoy observations. A second similar product is available that also includes AMSR-E data from June 2002 onward. To avoid a jump in the analysis and to provide the longest, most consistent time series, this product uses only the AVHRR Pathfinder and in situ data. The OI analysis is a daily average SST that is bias adjusted using a spatially smoothed 7-day in situ SST average. Both day and night satellite fields are independently adjusted. More information is available at http://www.ncdc.noaa.
gov/oa/climate/research/sst/oi-daily.php. PO.DAAC, through the GHRSST Global Data Assembly Center (http://ghrsst.jpl.nasa.gov) acts as a global distribution node for all GHRSST L4 products in conjunction with the NOAA Longterm Stewardship and Reanalysis Facility (LTSRF; http://ghrsst.nodc.noaa.gov).More information on GHRSST can be found here: http://www.ghrsst-pp.org/.
A GHRSST L4 sea surface temperature analysis produced daily at the NOAA National Climatic Data Center using optimal interpolation from AVHRR Pathfinder Version 5 data (http://pathfinder.nodc.noaa.gov) (when available, otherwise operational AVHRR data are used) and in situ ship and buoy observations. A second similar product is available that also includes AMSR-E data from June 2002 onward. To avoid a jump in the analysis and to provide the longest, most consistent time series, this product uses only the AVHRR Pathfinder and in situ data. The OI analysis is a daily average SST that is bias adjusted using a spatially smoothed 7-day in situ SST average. Both day and night satellite fields are independently adjusted. More information is available at http://www.ncdc.noaa.
gov/oa/climate/research/sst/oi-daily.php. PO.DAAC, through the GHRSST Global Data Assembly Center (http://ghrsst.jpl.nasa.gov) acts as a global distribution node for all GHRSST L4 products in conjunction with the NOAA Longterm Stewardship and Reanalysis Facility (LTSRF; http://ghrsst.nodc.noaa.gov).More information on GHRSST can be found here: http://www.ghrsst-pp.org/.
A GHRSST L4 sea surface temperature analysis produced daily at the NOAA National Climatic Data Center using optimal interpolation from AVHRR Pathfinder Version 5 data (http://pathfinder.nodc.noaa.gov) (when available, otherwise operational AVHRR data are used) and in situ ship and buoy observations. A second similar product is available that also includes AMSR-E data from June 2002 onward. To avoid a jump in the analysis and to provide the longest, most consistent time series, this product uses only the AVHRR Pathfinder and in situ data. The OI analysis is a daily average SST that is bias adjusted using a spatially smoothed 7-day in situ SST average. Both day and night satellite fields are independently adjusted. More information is available at http://www.ncdc.noaa.
gov/oa/climate/research/sst/oi-daily.php. PO.DAAC, through the GHRSST Global Data Assembly Center (http://ghrsst.jpl.nasa.gov) acts as a global distribution node for all GHRSST L4 products in conjunction with the NOAA Longterm Stewardship and Reanalysis Facility (LTSRF; http://ghrsst.nodc.noaa.gov).More information on GHRSST can be found here: http://www.ghrsst-pp.org/.
A GHRSST L4 sea surface temperature analysis produced daily at the NOAA National Climatic Data Center using optimal interpolation from AVHRR Pathfinder Version 5 data (http://pathfinder.nodc.noaa.gov) (when available, otherwise operational AVHRR data are used) and in situ ship and buoy observations. A second similar product is available that also includes AMSR-E data from June 2002 onward. To avoid a jump in the analysis and to provide the longest, most consistent time series, this product uses only the AVHRR Pathfinder and in situ data. The OI analysis is a daily average SST that is bias adjusted using a spatially smoothed 7-day in situ SST average. Both day and night satellite fields are independently adjusted. More information is available at http://www.ncdc.noaa.
gov/oa/climate/research/sst/oi-daily.php. PO.DAAC, through the GHRSST Global Data Assembly Center (http://ghrsst.jpl.nasa.gov) acts as a global distribution node for all GHRSST L4 products in conjunction with the NOAA Longterm Stewardship and Reanalysis Facility (LTSRF; http://ghrsst.nodc.noaa.gov).More information on GHRSST can be found here: http://www.ghrsst-pp.org/.
A GHRSST L4 sea surface temperature analysis produced daily at the NOAA National Climatic Data Center using optimal interpolation from AVHRR Pathfinder Version 5 data (http://pathfinder.nodc.noaa.gov) (when available, otherwise operational AVHRR data are used) and in situ ship and buoy observations. A second similar product is available that also includes AMSR-E data from June 2002 onward. To avoid a jump in the analysis and to provide the longest, most consistent time series, this product uses only the AVHRR Pathfinder and in situ data. The OI analysis is a daily average SST that is bias adjusted using a spatially smoothed 7-day in situ SST average. Both day and night satellite fields are independently adjusted. More information is available at http://www.ncdc.noaa.
gov/oa/climate/research/sst/oi-daily.php. PO.DAAC, through the GHRSST Global Data Assembly Center (http://ghrsst.jpl.nasa.gov) acts as a global distribution node for all GHRSST L4 products in conjunction with the NOAA Longterm Stewardship and Reanalysis Facility (LTSRF; http://ghrsst.nodc.noaa.gov).More information on GHRSST can be found here: http://www.ghrsst-pp.org/.
A GHRSST L4 sea surface temperature analysis produced daily at the NOAA National Climatic Data Center using optimal interpolation from AVHRR Pathfinder Version 5 data (http://pathfinder.nodc.noaa.gov) (when available, otherwise operational AVHRR data are used) and in situ ship and buoy observations. A second similar product is available that also includes AMSR-E data from June 2002 onward. To avoid a jump in the analysis and to provide the longest, most consistent time series, this product uses only the AVHRR Pathfinder and in situ data. The OI analysis is a daily average SST that is bias adjusted using a spatially smoothed 7-day in situ SST average. Both day and night satellite fields are independently adjusted. More information is available at http://www.ncdc.noaa.
gov/oa/climate/research/sst/oi-daily.php. PO.DAAC, through the GHRSST Global Data Assembly Center (http://ghrsst.jpl.nasa.gov) acts as a global distribution node for all GHRSST L4 products in conjunction with the NOAA Longterm Stewardship and Reanalysis Facility (LTSRF; http://ghrsst.nodc.noaa.gov).More information on GHRSST can be found here: http://www.ghrsst-pp.org/.
A GHRSST Level 4 sea surface temperature analysis produced daily on an operational basis at the UK Met Office using optimal interpolation from AVHRR, AATSR, SEVIRI, AMSRE, TMI and in situ sensors. PO.DAAC, through the GHRSST Global Data Assembly Center (http://ghrsst.jpl.nasa.gov) acts as a global distribution node for all GHRSST L4 products in conjunction with the NOAA Longterm Stewardship and Reanalysis Facility (LTSRF; http://ghrsst.nodc.noaa.gov). More information on GHRSST can be found here: http://www.ghrsst-pp.org/.
A GHRSST Level 4 sea surface temperature analysis produced daily on an operational basis at the UK Met Office using optimal interpolation from AVHRR, AATSR, SEVIRI, AMSRE, TMI and in situ sensors. PO.DAAC, through the GHRSST Global Data Assembly Center (http://ghrsst.jpl.nasa.gov) acts as a global distribution node for all GHRSST L4 products in conjunction with the NOAA Longterm Stewardship and Reanalysis Facility (LTSRF; http://ghrsst.nodc.noaa.gov). More information on GHRSST can be found here: http://www.ghrsst-pp.org/.
A GHRSST Level 4 sea surface temperature analysis produced daily on an operational basis at the UK Met Office using optimal interpolation from AVHRR, AATSR, SEVIRI, AMSRE, TMI and in situ sensors. PO.DAAC, through the GHRSST Global Data Assembly Center (http://ghrsst.jpl.nasa.gov) acts as a global distribution node for all GHRSST L4 products in conjunction with the NOAA Longterm Stewardship and Reanalysis Facility (LTSRF; http://ghrsst.nodc.noaa.gov). More information on GHRSST can be found here: http://www.ghrsst-pp.org/.
A GHRSST Level 4 sea surface temperature analysis produced daily on an operational basis at the UK Met Office using optimal interpolation from AVHRR, AATSR, SEVIRI, AMSRE, TMI and in situ sensors. PO.DAAC, through the GHRSST Global Data Assembly Center (http://ghrsst.jpl.nasa.gov) acts as a global distribution node for all GHRSST L4 products in conjunction with the NOAA Longterm Stewardship and Reanalysis Facility (LTSRF; http://ghrsst.nodc.noaa.gov). More information on GHRSST can be found here: http://www.ghrsst-pp.org/.
A GHRSST Level 4 sea surface temperature analysis produced daily on an operational basis at the UK Met Office using optimal interpolation from AVHRR, AATSR, SEVIRI, AMSRE, TMI and in situ sensors. PO.DAAC, through the GHRSST Global Data Assembly Center (http://ghrsst.jpl.nasa.gov) acts as a global distribution node for all GHRSST L4 products in conjunction with the NOAA Longterm Stewardship and Reanalysis Facility (LTSRF; http://ghrsst.nodc.noaa.gov). More information on GHRSST can be found here: http://www.ghrsst-pp.org/.
A GHRSST Level 4 sea surface temperature analysis produced daily on an operational basis at the UK Met Office using optimal interpolation from AVHRR, AATSR, SEVIRI, AMSRE, TMI and in situ sensors. PO.DAAC, through the GHRSST Global Data Assembly Center (http://ghrsst.jpl.nasa.gov) acts as a global distribution node for all GHRSST L4 products in conjunction with the NOAA Longterm Stewardship and Reanalysis Facility (LTSRF; http://ghrsst.nodc.noaa.gov). More information on GHRSST can be found here: http://www.ghrsst-pp.org/.
The Geoscience Laser Altimeter System (GLAS) instrument on the Ice, Cloud, and land Elevation Satellite (ICESat) provides global measurements of elevation, and repeats measurements along nearly-identical tracks; its primary mission is to measure changes in ice volume (mass balance) over time. This digital elevation model (DEM) of Greenland is derived from GLAS/ICESat laser altimetry profile data and provides new surface elevation grids of the ice sheets and coastal areas, with greater latitudinal extent and fewer slope-related effects than radar altimetry.

This DEM is generated from the first seven operational periods (from February 2003 through June 2005) of the GLAS instrument. It is provided on polar stereographic grids at 1 km grid spacing. The grid covers all of Greenland south of 83� N. Elevations are reported as centimeters above the datums, relative to both the WGS 84 ellipsoid and the EGM96 geoid, in two separate elevation data files. A data quality map of the interpolation distance is distributed in addition to the elevation data. ENVI header files are also provided.

The data are in 4-byte (long) signed integer binary files (big endian byte order) and are available via FTP.
The Geoscience Laser Altimeter System (GLAS) instrument on the Ice, Cloud, and land Elevation Satellite (ICESat) provides global measurements of elevation, and repeats measurements along nearly-identical tracks; its primary mission is to measure changes in ice volume (mass balance) over time. This digital elevation model (DEM) of Greenland is derived from GLAS/ICESat laser altimetry profile data and provides new surface elevation grids of the ice sheets and coastal areas, with greater latitudinal extent and fewer slope-related effects than radar altimetry.

This DEM is generated from the first seven operational periods (from February 2003 through June 2005) of the GLAS instrument. It is provided on polar stereographic grids at 1 km grid spacing. The grid covers all of Greenland south of 83� N. Elevations are reported as centimeters above the datums, relative to both the WGS 84 ellipsoid and the EGM96 geoid, in two separate elevation data files. A data quality map of the interpolation distance is distributed in addition to the elevation data. ENVI header files are also provided.

The data are in 4-byte (long) signed integer binary files (big endian byte order) and are available via FTP.
The Geoscience Laser Altimeter System (GLAS) instrument on the Ice, Cloud, and land Elevation Satellite (ICESat) provides global measurements of elevation, and repeats measurements along nearly-identical tracks; its primary mission is to measure changes in ice volume (mass balance) over time. This digital elevation model (DEM) of Greenland is derived from GLAS/ICESat laser altimetry profile data and provides new surface elevation grids of the ice sheets and coastal areas, with greater latitudinal extent and fewer slope-related effects than radar altimetry.

This DEM is generated from the first seven operational periods (from February 2003 through June 2005) of the GLAS instrument. It is provided on polar stereographic grids at 1 km grid spacing. The grid covers all of Greenland south of 83� N. Elevations are reported as centimeters above the datums, relative to both the WGS 84 ellipsoid and the EGM96 geoid, in two separate elevation data files. A data quality map of the interpolation distance is distributed in addition to the elevation data. ENVI header files are also provided.

The data are in 4-byte (long) signed integer binary files (big endian byte order) and are available via FTP.
Data granules contain approximately 23 minutes (1/4 orbit) of data and will include pulse travel times, the transmitted and received waveform, transmitted and received energy, echo time offsets, and instrument filter and threshold values.
Data granules contain approximately 23 minutes (1/4 orbit) of data and will include pulse travel times, the transmitted and received waveform, transmitted and received energy, echo time offsets, and instrument filter and threshold values.
Data granules contain approximately 190 minutes (2 orbits) of data and will include normalized backscatter, photon counter, cloud digitizer, and timing data that serve as input to higher level atmospheric products.
Data granules contain approximately 190 minutes (2 orbits) of data and will include normalized backscatter, photon counter, cloud digitizer, and timing data that serve as input to higher level atmospheric products.
The level 1A global engineering data (GLAH03) data granules contain approximately 190 minutes (2 orbits) of GLAS instrument housekeeping data including temperatures, voltages, and currents.
The level 1A global engineering data (GLA03) data granules contain approximately 190 minutes (2 orbits) of GLAS instrument housekeeping data including temperatures, voltages, and currents.
Data granules contain approximately 190 minutes (2 orbits) of data and will include data from the laser reference system, the instrument star tracker, the gyro, the spacecraft star trackers, and the attitude control system.
Data granules contain approximately 190 minutes (2 orbits) of data and will include data from the laser reference system, the instrument star tracker, the gyro, the spacecraft star trackers, and the attitude control system.
The product includes full 532 nm and 1064 nm calibrated attenuated backscatter profiles at 5 times per second, and from 10 to -1 km, at 40 times per second. Also the calibration coefficient values and molecular backscatter profiles at once per second.
The product includes full 532 nm and 1064 nm calibrated attenuated backscatter profiles at 5 times per second, and from 10 to -1 km, at 40 times per second. Also the calibration coefficient values and molecular backscatter profiles at once per second.
Data granules contain approximately 23 minutes (1/4 orbit) and include the surface elevation, surface roughness assuming no slope, surface slope assuming no roughness and geodetic and atmospheric corrections for the range at the full 40/sec resolution. (Suggested Usage: Scientists investigating surface elevation, slope, reflection, and other surface characteristics can use the GLAH06 data. The data values were used in the creation of the surface-specific altimeter products (GLAH12-15). Each GLAH06 file was created from an equivalent GLA06 binary formatted file. Users that want to access the data used to create the GLAH06 values can use the equivalent GLAHxx files for the GLAxx files as shown in the provenance metadata for the history that created the GLA06.)
Data granules contain approximately 23 minutes (1/4 orbit) and include the surface elevation, surface roughness assuming no slope, surface slope assuming no roughness and geodetic and atmospheric corrections for the range at the full 40/sec resolution. (Suggested Usage: This is a global file that can be used by scientists investigating surface elevation, slope, reflection, and other surface characteristics. This product will be used with GLA05 to create the level 2 surface-specific altimeter products (GLA12-15).)
The level 1B waveform parameterization data will contain waveform-based range corrections and surface characteristics at the full 40 per second resolution. Data granules will contain approximately 23 minutes (1/4 orbit) of data. (Suggested Usage: GLAH05 contains scientific investigations of surface features defined by the waveform parameterization. File contains range corrections and the parameterizations of the waveform that were used to calculate surface elevation, and other surface characteristics. The data values were used as input data values to compute parameters on GLAH06 and GLAH12-15. Each GLAH05 file was created from an equivalent GLA05 binary formatted file. The provenance metadata shows the history that created the GLA05.)
The level 1B waveform parameterization data will contain waveform-based range corrections and surface characteristics at the full 40 per second resolution. Data granules will contain approximately 23 minutes (1/4 orbit) of data. (Suggested Usage: The purpose of this file is to store range corrections and all computations made from analysis of the waveform data and the parameterization of the waveform. These parameters are required to calculate surface elevation, slope and relief characteristics. These parameters also can be used by scientists investigating surface features defined by the parameterization. This is used as input for GLA06, and GLA12-15.)
The level 2 aerosol vertical structure data contains cloud and aerosol backscatter and extinction cross section profiles provided at a minimum of once per 4 seconds. Data granules will contain approximately 23 hours (14 orbits) of data.
The level 2 aerosol vertical structure data contains cloud and aerosol backscatter and extinction cross section profiles provided at a minimum of once per 4 seconds. Data granules will contain approximately 23 hours (14 orbits) of data.
GLAH12 contains the ice sheet elevation and elevation distribution corrected for geodetic and atmospheric affects calculated from algorithms fine-tuned for ice sheet returns. Data granules contain 14 orbits of data within the ice sheet mask. (Suggested Usage: GLAH12 contains ice sheet elevation and elevation distribution calculated from algorithms fine-tuned for ice sheet returns for use by researchers. Parameters are at the full 40Hz resolution that fall within the ICESat ice sheet mask. Each GLAH12 file was created from an equivalent GLA12 binary file. The data used to create the GLAH12 values are contained in the equivalent GLAHxx files for the GLAxx files. See the provenance metadata for the creation of the GLA12.)
GLA12 contains the ice sheet elevation and elevation distribution corrected for geodetic and atmospheric affects calculated from algorithms fine-tuned for ice sheet returns. Data granules contain 14 orbits of data within the ice sheet mask. (Suggested Usage: The purpose of this file is to provide ice sheet elevation and elevation distribution calculated from algorithms fine-tuned for ice sheet returns to the research user community. It will also include the laser footprint geolocation, reflectance, and the geodetic, instrument, and atmospheric corrections used to correct the range measurements. Parameters will be at the full 40 per second resolution, but only for locations that fall within the ICESat ice sheet mask.)
The level 2 cloud layer height data with top and bottom heights, and ground heights are provided at a minimum of once per 4 seconds. Data granules will contain approximately 23 hours (14 orbits) of data.
The level 2 cloud layer height data with top and bottom heights, and ground heights are provided at a minimum of once per 4 seconds. Data granules will contain approximately 23 hours (14 orbits) of data.
GLAH14 contains the land elevation and elevation distribution corrected for geodetic and atmospheric affects calculated from algorithms fine-tuned for over land returns. Data granules contain 14 orbits of data within the land mask. (Suggested Usage: GLAH14 contains GLAS land elevation data that includes geolocation and reflectance plus the geodetic, instrument, and atmospheric corrections used. Parameters are at the full 40Hz resolution for locations that fall within the ICESat land mask. Each GLAH14 file was created from an equivalent GLA14 binary file. The data used to create the GLAH14 values are contained in the equivalent GLAHxx files for the GLAxx files. See the provenance metadata for the creation of the GLA14.)
GLA14 contains the land elevation and elevation distribution corrected for geodetic and atmospheric affects calculated from algorithms fine-tuned for over land returns. Data granules contain 14 orbits of data within the land mask. (Suggested Usage: The purpose of this file is to provide land elevation data to the research user community. It will also include the laser footprint geolocation, reflectance, and the geodetic, instrument, and atmospheric corrections used to correct the range measurements. Parameters will be at the full 40 per second resolution, but only for locations that fall within the ICESat land mask.)
The level 2 planetary boundary layer and elevated aerosol layer height data will be provided at a minimum of once per 4 seconds. Data granules will contain approximately 23 hours (14 orbits) of data.
The level 2 planetary boundary layer and elevated aerosol layer height data will be provided at a minimum of once per 4 seconds. Data granules will contain approximately 23 hours (14 orbits) of data.
The level 2 thin cloud/aerosol data contains optical depths for clouds for up to 10 layers, the planetary boundary layer, and aerosols for up to 8 layers. Data granules will contain approximately 23 hours (14 orbits) of data.
The level 2 thin cloud/aerosol data contains optical depths for clouds for up to 10 layers, the planetary boundary layer, and aerosols for up to 8 layers. Data granules will contain approximately 23 hours (14 orbits) of data.
GLAH15 contains the ocean elevation and small-scale roughness corrected for geodetic and atmospheric affects, calculated from algorithms fine-tuned for ocean returns. Data granules will contain 14 orbits of data over the oceans. (Suggested Usage: GLAH15 contains ocean elevation and small-scale roughness data for researchers. Includes the geolocation, reflectance, and the geodetic, instrument, and atmospheric corrections. Parameters are at the full 40Hz resolution within the ICESat ocean mask. Each GLAH15 file was created from an equivalent GLA15 binary file. The data used to create the GLAH15 values are contained in the equivalent GLAHxx files for the GLAxx files. See the provenance metadata for the creation of the GLA15.)
GLA15 contains the ocean elevation and small-scale roughness corrected for geodetic and atmospheric affects, calculated from algorithms fine-tuned for ocean returns. Data granules will contain 14 orbits of data over the oceans. (Suggested Usage: The purpose of this file is to provide ocean elevation and small-scale roughness data to the research user community. It will also include the laser footprint geolocation, reflectance, and the geodetic, instrument, and atmospheric corrections used to correct the range measurements. Parameters will be at the full 40 per second resolution, but only for locations that fall within the ICESat ocean mask.)
GLAH13 contains sea ice and open ocean elevations corrected for geodetic and atmospheric affects, calculated from algorithms fine-tuned for sea ice returns. Granules contain 14 orbits of data within a sea ice mask. (Suggested Usage: GLAH13 contains sea ice, open ocean, and iceberg elevations corrected for geodetic and atmospheric affects, calculated from algorithms fine-tuned for sea ice returns. Granules contain 14 orbits of data within the sea ice mask. Each GLAH13 file was created from an equivalent GLA13 binary file. The data used to create the GLAH13 values are contained in the equivalent GLAHxx files for the GLAxx files. See the provenance metadata for the creation of the GLA13.)
GLA13 contains sea ice and open ocean elevations corrected for geodetic and atmospheric affects, calculated from algorithms fine-tuned for sea ice returns. Granules contain 14 orbits of data within a sea ice mask. (Suggested Usage: GLA13 contains sea ice and open ocean elevations corrected for geodetic and atmospheric affects, calculated from algorithms fine-tuned for sea ice returns. Granules contain 14 orbits of data within a sea ice mask.)
A data set of decade-mean monthly surface climate over global land areas, excluding Antarctica.  Interpolated from station data to 0.5 degree lat/lon for a range of variables:  precipitation, wet-day frequency, mean temperature and diurnal temperature range (from which maximum temperature and and minimum temperature can be determined), vapour pressure, cloud cover, ground-frost frequency.
A data set of decade-mean monthly surface climate over global land areas, excluding Antarctica.  Interpolated from station data to 0.5 degree lat/lon for a range of variables:  precipitation, wet-day frequency, mean temperature and diurnal temperature range (from which maximum temperature and and minimum temperature can be determined), vapour pressure, cloud cover, ground-frost frequency.
ABSTRACT: A data set of decade-mean monthly surface climate over global land areas, excluding Antarctica.  Interpolated from station data to 0.5 degree lat/lon for a range of variables:  precipitation, wet-day frequency, mean temperature and diurnal temperature range (from which maximum temperature and and minimum temperature can be determined), vapour pressure, cloud cover, ground-frost frequency.
The Global 15x15 Minute Grids of the Downscaled GDP Based on the Special Report on Emissions Scenarios (SRES) B2 Scenario, 1990 and 2025, are geospatial distributions of Gross Domestic Product (GDP) per unit area (GDP densities). These global grids were generated using the Country-level GDP and Downscaled Projections Based on the SRES B2 Scenario, 1990-2100 dataset, and CIESIN's Gridded Population of World, Version 2 (GPWv2) dataset as the base map. First, the GDP per capita was developed at a country-level for 1990 and 2025. Then the gridded GDP was developed within each country by applying the GDP per capita to each grid cell of the GPW, under the assumption that the GDP per capita was uniform within a country. This dataset is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
The Global 15x15 Minute Grids of the Downscaled GDP Based on the Special Report on Emissions Scenarios (SRES) B2 Scenario, 1990 and 2025, are geospatial distributions of Gross Domestic Product (GDP) per unit area (GDP densities). These global grids were generated using the Country-level GDP and Downscaled Projections Based on the SRES B2 Scenario, 1990-2100 dataset, and CIESIN's Gridded Population of World, Version 2 (GPWv2) dataset as the base map. First, the GDP per capita was developed at a country-level for 1990 and 2025. Then the gridded GDP was developed within each country by applying the GDP per capita to each grid cell of the GPW, under the assumption that the GDP per capita was uniform within a country. This dataset is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
The Global 15x15 Minute Grids of the Downscaled GDP Based on the Special Report on Emissions Scenarios (SRES) B2 Scenario, 1990 and 2025, are geospatial distributions of Gross Domestic Product (GDP) per unit area (GDP densities). These global grids were generated using the Country-level GDP and Downscaled Projections Based on the SRES B2 Scenario, 1990-2100 dataset, and CIESIN's Gridded Population of World, Version 2 (GPWv2) dataset as the base map. First, the GDP per capita was developed at a country-level for 1990 and 2025. Then the gridded GDP was developed within each country by applying the GDP per capita to each grid cell of the GPW, under the assumption that the GDP per capita was uniform within a country. This dataset is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
The Global 15x15 Minute Grids of the Downscaled Population Based on the Special Report on Emissions Scenarios (SRES) B2 Scenario, 1990 and 2025, are geospatial distributions of the downscaled population per unit area (population densities). These global grids were generated using the Country-level Population and Downscaled Projections Based on the SRES B2 Scenario, 1990-2100 dataset, and CIESIN's Gridded Population of World, Version 2 (GPWv2) dataset as the base map. The 1990 GPW was used as the base distribution and the country-level downscaled projections were used to replace population estimates of 1990 in GPW and 2025. The fractional distribution of the population at each grid cell is the same as the 1990 GPW, sub-nationally. This dataset is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
The Global 15x15 Minute Grids of the Downscaled Population Based on the Special Report on Emissions Scenarios (SRES) B2 Scenario, 1990 and 2025, are geospatial distributions of the downscaled population per unit area (population densities). These global grids were generated using the Country-level Population and Downscaled Projections Based on the SRES B2 Scenario, 1990-2100 dataset, and CIESIN's Gridded Population of World, Version 2 (GPWv2) dataset as the base map. The 1990 GPW was used as the base distribution and the country-level downscaled projections were used to replace population estimates of 1990 in GPW and 2025. The fractional distribution of the population at each grid cell is the same as the 1990 GPW, sub-nationally. This dataset is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
The Global 15x15 Minute Grids of the Downscaled Population Based on the Special Report on Emissions Scenarios (SRES) B2 Scenario, 1990 and 2025, are geospatial distributions of the downscaled population per unit area (population densities). These global grids were generated using the Country-level Population and Downscaled Projections Based on the SRES B2 Scenario, 1990-2100 dataset, and CIESIN's Gridded Population of World, Version 2 (GPWv2) dataset as the base map. The 1990 GPW was used as the base distribution and the country-level downscaled projections were used to replace population estimates of 1990 in GPW and 2025. The fractional distribution of the population at each grid cell is the same as the 1990 GPW, sub-nationally. This dataset is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
A data set of 30-year mean monthly surface climate over global land areas, excluding Antarctica.  Interpolated from station data to 0.5 degree lat/lon for a range of variables:  precipitation, wet-day frequency, mean temperature and diurnal temperature range (from which maximum temperature and and minimum temperature can be determined), vapour pressure, cloud cover, ground-frost frequency.
A data set of 30-year mean monthly surface climate over global land areas, excluding Antarctica.  Interpolated from station data to 0.5 degree lat/lon for a range of variables:  precipitation, wet-day frequency, mean temperature and diurnal temperature range (from which maximum temperature and and minimum temperature can be determined), vapour pressure, cloud cover, ground-frost frequency.
ABSTRACT: A data set of 30-year mean monthly surface climate over global land areas, excluding Antarctica.  Interpolated from station data to 0.5 degree lat/lon for a range of variables:  precipitation, wet-day frequency, mean temperature and diurnal temperature range (from which maximum temperature and and minimum temperature can be determined), vapour pressure, cloud cover, ground-frost frequency.
This database is a major update of the Leemans & Cramer database.  It currently contains monthly averages of mean temperature, temperature range, precipitation, rain days and sunshine hours for the terrestrial surface of the globe, gridded at 0.5 degree longitude/latitude resolution.  It was generated from a large data base, using the partial thin-plate splining algorithm.
This database is a major update of the Leemans & Cramer database.  It currently contains monthly averages of mean temperature, temperature range, precipitation, rain days and sunshine hours for the terrestrial surface of the globe, gridded at 0.5 degree longitude/latitude resolution.  It was generated from a large data base, using the partial thin-plate splining algorithm.
ABSTRACT: This database is a major update of the Leemans & Cramer database.  It currently contains monthly averages of mean temperature, temperature range, precipitation, rain days and sunshine hours for the terrestrial surface of the globe, gridded at 0.5 degree longitude/latitude resolution.  It was generated from a large data base, using the partial thin-plate splining algorithm.
A data set of mean monthly surface climate over global land areas, excluding Antarctica. Interpolated from station data to 0.5 degrees lat/lon for a range of variables: precipitation and wet-day frequency, mean temperature and diurnal temperature range (from which maximum temperature and minimum temperature can be determined), vapour pressure, sunshine, cloud cover, ground-frost frequency and windspeed.
A data set of mean monthly surface climate over global land areas, excluding Antarctica. Interpolated from station data to 0.5 degrees lat/lon for a range of variables: precipitation and wet-day frequency, mean temperature and diurnal temperature range (from which maximum temperature and minimum temperature can be determined), vapour pressure, sunshine, cloud cover, ground-frost frequency and windspeed.
ABSTRACT: A data set of mean monthly surface climate over global land areas, excluding Antarctica. Interpolated from station data to 0.5 degrees lat/lon for a range of variables: precipitation and wet-day frequency, mean temperature and diurnal temperature range (from which maximum temperature and minimum temperature can be determined), vapour pressure, sunshine, cloud cover, ground-frost frequency and windspeed.
The Global Croplands data set represents the proportion of land areas used as cropland  (land used for the cultivation of food) in the year 2000. Satellite data from Modetate Resolution Imaging Spectroradiometer (MODIS) and Satellite Pour l'Observation de la Terre (SPOT) Image Vegetation sensor were combined with agricultural inventory data to create a global data set. The visual presentation of this data demonstrates the extent to which human land use for agriculture has changed the Earth and in which areas this change is most intense. The data was compiled by Navin Ramankutty , et. al. (2008) and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
The Global Croplands data set represents the proportion of land areas used as cropland  (land used for the cultivation of food) in the year 2000. Satellite data from Modetate Resolution Imaging Spectroradiometer (MODIS) and Satellite Pour l'Observation de la Terre (SPOT) Image Vegetation sensor were combined with agricultural inventory data to create a global data set. The visual presentation of this data demonstrates the extent to which human land use for agriculture has changed the Earth and in which areas this change is most intense. The data was compiled by Navin Ramankutty , et. al. (2008) and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
The Global Croplands data set represents the proportion of land areas used as cropland  (land used for the cultivation of food) in the year 2000. Satellite data from Modetate Resolution Imaging Spectroradiometer (MODIS) and Satellite Pour l'Observation de la Terre (SPOT) Image Vegetation sensor were combined with agricultural inventory data to create a global data set. The visual presentation of this data demonstrates the extent to which human land use for agriculture has changed the Earth and in which areas this change is most intense. The data was compiled by Navin Ramankutty , et. al. (2008) and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
The Global Pastures dataset represents the proportion of land areas used as pasture land (land used to support grazing animals) in the year 2000. Satellite data from Modetate Resolution Imaging Spectroradiometer (MODIS) and Satellite Pour l'Observation de la Terre (SPOT) Image Vegetation sensor were combined with agricultural inventory data to create a global data set. The visual presentation of this data demonstrates the extent to which human land use for agriculture has changed the Earth and in which areas this change is most intense. The data was compiled by Navin Ramankutty, et. al. (2008) and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
The Global Pastures dataset represents the proportion of land areas used as pasture land (land used to support grazing animals) in the year 2000. Satellite data from Modetate Resolution Imaging Spectroradiometer (MODIS) and Satellite Pour l'Observation de la Terre (SPOT) Image Vegetation sensor were combined with agricultural inventory data to create a global data set. The visual presentation of this data demonstrates the extent to which human land use for agriculture has changed the Earth and in which areas this change is most intense. The data was compiled by Navin Ramankutty, et. al. (2008) and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
The Global Pastures dataset represents the proportion of land areas used as pasture land (land used to support grazing animals) in the year 2000. Satellite data from Modetate Resolution Imaging Spectroradiometer (MODIS) and Satellite Pour l'Observation de la Terre (SPOT) Image Vegetation sensor were combined with agricultural inventory data to create a global data set. The visual presentation of this data demonstrates the extent to which human land use for agriculture has changed the Earth and in which areas this change is most intense. The data was compiled by Navin Ramankutty, et. al. (2008) and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
Global Annual PM2.5 Grids from MODIS and MISR Aerosol Optical Depth (AOD) data sets represent a series of annual average grids (2001-2010) of fine particulate matter (solid particles and liquid droplets) that were derived from MODIS and MISR AOD satellite data. Together the grids provide a continuous surface of concentrations in micrograms per cubic meter of particulate matter of 2.5 micrometers or smaller (PM2.5) for health and environmental research. The satellite AOD retrievals were converted to ground-level concentrations based on a conversion factor developed by researchers at Dalhousie University that accounts for spatial and temporal variations in aerosol properties and vertical structure as derived from a global 3-D chemical transport model (GEOS-Chem). The raster grids have a grid cell resolution of 30 arc-minutes (0.5 degree or approximately 50 sq. km at the equator) and cover the world from 70°N to 60°S latitude. The grids were produced by researchers at Battelle Memorial Institute in collaboration with the Center for International Earth Science Information Network/Columbia University under a NASA-ROSES project entitled "Using Satellite Data to Develop Environmental Indicators: An Application of NASA Data Products to Support High Level Decisions for National and International Environmental Protection". Exposure to fine particles is associated with premature death as well as increased morbidity from respiratory and cardiovascular disease, especially in the elderly, young children, and those already suffering from these illnesses. The World Health Organization guideline for PM2.5 average annual exposure is less than or equal to 10.0 micrograms per cubic meter, whereas the US Environmental Protection Agency (EPA) primary standard is less than or equal to 12.0 micrograms per cubic meter. The EPA primary standards are designed to protect public health with an adequate margin of safety.
Global Annual PM2.5 Grids from MODIS and MISR Aerosol Optical Depth (AOD) data sets represent a series of annual average grids (2001-2010) of fine particulate matter (solid particles and liquid droplets) that were derived from MODIS and MISR AOD satellite data. Together the grids provide a continuous surface of concentrations in micrograms per cubic meter of particulate matter of 2.5 micrometers or smaller (PM2.5) for health and environmental research. The satellite AOD retrievals were converted to ground-level concentrations based on a conversion factor developed by researchers at Dalhousie University that accounts for spatial and temporal variations in aerosol properties and vertical structure as derived from a global 3-D chemical transport model (GEOS-Chem). The raster grids have a grid cell resolution of 30 arc-minutes (0.5 degree or approximately 50 sq. km at the equator) and cover the world from 70°N to 60°S latitude. The grids were produced by researchers at Battelle Memorial Institute in collaboration with the Center for International Earth Science Information Network/Columbia University under a NASA-ROSES project entitled "Using Satellite Data to Develop Environmental Indicators: An Application of NASA Data Products to Support High Level Decisions for National and International Environmental Protection". Exposure to fine particles is associated with premature death as well as increased morbidity from respiratory and cardiovascular disease, especially in the elderly, young children, and those already suffering from these illnesses. The World Health Organization guideline for PM2.5 average annual exposure is less than or equal to 10.0 micrograms per cubic meter, whereas the US Environmental Protection Agency (EPA) primary standard is less than or equal to 12.0 micrograms per cubic meter. The EPA primary standards are designed to protect public health with an adequate margin of safety.
Global Annual PM2.5 Grids from MODIS and MISR Aerosol Optical Depth (AOD) data sets represent a series of annual average grids (2001-2010) of fine particulate matter (solid particles and liquid droplets) that were derived from MODIS and MISR AOD satellite data. Together the grids provide a continuous surface of concentrations in micrograms per cubic meter of particulate matter of 2.5 micrometers or smaller (PM2.5) for health and environmental research. The satellite AOD retrievals were converted to ground-level concentrations based on a conversion factor developed by researchers at Dalhousie University that accounts for spatial and temporal variations in aerosol properties and vertical structure as derived from a global 3-D chemical transport model (GEOS-Chem). The raster grids have a grid cell resolution of 30 arc-minutes (0.5 degree or approximately 50 sq. km at the equator) and cover the world from 70°N to 60°S latitude. The grids were produced by researchers at Battelle Memorial Institute in collaboration with the Center for International Earth Science Information Network/Columbia University under a NASA-ROSES project entitled "Using Satellite Data to Develop Environmental Indicators: An Application of NASA Data Products to Support High Level Decisions for National and International Environmental Protection". Exposure to fine particles is associated with premature death as well as increased morbidity from respiratory and cardiovascular disease, especially in the elderly, young children, and those already suffering from these illnesses. The World Health Organization guideline for PM2.5 average annual exposure is less than or equal to 10.0 micrograms per cubic meter, whereas the US Environmental Protection Agency (EPA) primary standard is less than or equal to 12.0 micrograms per cubic meter. The EPA primary standards are designed to protect public health with an adequate margin of safety.
The total annual freezing and thawing indices are defined as the
cumulative number of degree-days when air temperatures are below and
above zero degrees Celsius. The total annual freezing index has been
widely used to predict permafrost distribution; estimate the
maximum thickness of sea, lake, and river ice, and the maximum depth of
ground-frost penetration; and classify snow types. The annual total
thawing index has been used to predict permafrost distribution and to
estimate the maximum depth of thaw in frozen ground. Both total
freezing and thawing indices are important parameters for engineering
design in cold regions.

Data coverage is global. The data set contains the total annual freezing and thawing indices with a spatial resolution of 0.5 degrees
latitude by 0.5 degrees longitude. Two data files are available, for
the freeze and thaw indices respectively, in flat binary format. Each
file is approximately 1 MB in size. The total annual freezing and
thawing indexes were calculated based upon the monthly mean air
temperature by Legates and Willmott (1990).
The total annual freezing and thawing indices are defined as the
cumulative number of degree-days when air temperatures are below and
above zero degrees Celsius. The total annual freezing index has been
widely used to predict permafrost distribution; estimate the
maximum thickness of sea, lake, and river ice, and the maximum depth of
ground-frost penetration; and classify snow types. The annual total
thawing index has been used to predict permafrost distribution and to
estimate the maximum depth of thaw in frozen ground. Both total
freezing and thawing indices are important parameters for engineering
design in cold regions.

Data coverage is global. The data set contains the total annual freezing and thawing indices with a spatial resolution of 0.5 degrees
latitude by 0.5 degrees longitude. Two data files are available, for
the freeze and thaw indices respectively, in flat binary format. Each
file is approximately 1 MB in size. The total annual freezing and
thawing indexes were calculated based upon the monthly mean air
temperature by Legates and Willmott (1990).
The total annual freezing and thawing indices are defined as the
cumulative number of degree-days when air temperatures are below and
above zero degrees Celsius. The total annual freezing index has been
widely used to predict permafrost distribution; estimate the
maximum thickness of sea, lake, and river ice, and the maximum depth of
ground-frost penetration; and classify snow types. The annual total
thawing index has been used to predict permafrost distribution and to
estimate the maximum depth of thaw in frozen ground. Both total
freezing and thawing indices are important parameters for engineering
design in cold regions.

Data coverage is global. The data set contains the total annual freezing and thawing indices with a spatial resolution of 0.5 degrees
latitude by 0.5 degrees longitude. Two data files are available, for
the freeze and thaw indices respectively, in flat binary format. Each
file is approximately 1 MB in size. The total annual freezing and
thawing indexes were calculated based upon the monthly mean air
temperature by Legates and Willmott (1990).
This data set is a compilation of soil respiration rates (g C m-2 yr-1) from terrestrial and wetland ecosystems reported in the literature prior to 1992. These rates were measured in a variety of ecosystems to examine rates of microbial activity, nutrient turnover, carbon cycling, root dynamics, and a variety of other soil processes. Also included in the data set are biome type, vegetation type, locality, and geographic coordinates.
This data set is a compilation of soil respiration rates (g C m-2 yr-1) from terrestrial and wetland ecosystems reported in the literature prior to 1992. These rates were measured in a variety of ecosystems to examine rates of microbial activity, nutrient turnover, carbon cycling, root dynamics, and a variety of other soil processes. Also included in the data set are biome type, vegetation type, locality, and geographic coordinates.
ABSTRACT: This data set is a compilation of soil respiration rates (g C m-2 yr-1) from terrestrial and wetland ecosystems reported in the literature prior to 1992. These rates were measured in a variety of ecosystems to examine rates of microbial activity, nutrient turnover, carbon cycling, root dynamics, and a variety of other soil processes. Also included in the data set are biome type, vegetation type, locality, and geographic coordinates.
Global Cyclone Hazard Frequency and Distribution is a 2.5 by 2.5 minute grid based on more than 1,600 storm tracks for the period 1 January 1980 through 31 December 2000 for the Atlantic, Pacific, and Indian Oceans that were assembled and modeled at UNEP/GRID-Geneva PreView. Windspeeds around storm tracks were modeled using Holland's model (1997) to assess the grid cells likely to have been exposed to high wind levels. Post-modeling, the cells were divided into deciles, 10 classes consisting of approximately equal number of grid cells. The higher the value of the grid cell, the higher the decile ranking and the greater the frequency of the hazard relative to other cells. The dataset is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, United Nations Environment Programme Global Resource Information Database Geneva (UNEP/GRID-Geneva), and Columbia University Center for International Earth Science Information Network (CIESIN).
Global Cyclone Hazard Frequency and Distribution is a 2.5 by 2.5 minute grid based on more than 1,600 storm tracks for the period 1 January 1980 through 31 December 2000 for the Atlantic, Pacific, and Indian Oceans that were assembled and modeled at UNEP/GRID-Geneva PreView. Windspeeds around storm tracks were modeled using Holland's model (1997) to assess the grid cells likely to have been exposed to high wind levels. Post-modeling, the cells were divided into deciles, 10 classes consisting of approximately equal number of grid cells. The higher the value of the grid cell, the higher the decile ranking and the greater the frequency of the hazard relative to other cells. The dataset is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, United Nations Environment Programme Global Resource Information Database Geneva (UNEP/GRID-Geneva), and Columbia University Center for International Earth Science Information Network (CIESIN).
Global Cyclone Hazard Frequency and Distribution is a 2.5 by 2.5 minute grid based on more than 1,600 storm tracks for the period 1 January 1980 through 31 December 2000 for the Atlantic, Pacific, and Indian Oceans that were assembled and modeled at UNEP/GRID-Geneva PreView. Windspeeds around storm tracks were modeled using Holland's model (1997) to assess the grid cells likely to have been exposed to high wind levels. Post-modeling, the cells were divided into deciles, 10 classes consisting of approximately equal number of grid cells. The higher the value of the grid cell, the higher the decile ranking and the greater the frequency of the hazard relative to other cells. The dataset is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, United Nations Environment Programme Global Resource Information Database Geneva (UNEP/GRID-Geneva), and Columbia University Center for International Earth Science Information Network (CIESIN).
Global Cyclone Mortality Risks and Distribution is a 2.5 by 2.5 minute grid of global cyclone mortality risks. Gridded Population of the World (GPW) 

Version 3 (beta) data provide a baseline estimation of population per grid cell from which to estimate potential mortality loss. Mortality loss estimates per hazard event are calculated 

using regional, hazard-specific mortality records of the Emergency Events Database (EM-DAT) that span the 20 years between 1981 and 2000. Data regarding the frequency and distribution of 

cyclone hazard are obtained from the Global Cyclone Hazard Frequency and Distribution dataset. In order to more accurately reflect the confidence associated with the data and procedures, 

the potential mortality estimate range is classified into deciles, 10 classes of an approximately equal number of grid cells, providing a relative estimate of cyclone-based mortality risks. 

The dataset is a result of the collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, 

and Columbia University Center for International Earth Science Information Network (CIESIN).
Global Cyclone Mortality Risks and Distribution is a 2.5 by 2.5 minute grid of global cyclone mortality risks. Gridded Population of the World (GPW) 

Version 3 (beta) data provide a baseline estimation of population per grid cell from which to estimate potential mortality loss. Mortality loss estimates per hazard event are calculated 

using regional, hazard-specific mortality records of the Emergency Events Database (EM-DAT) that span the 20 years between 1981 and 2000. Data regarding the frequency and distribution of 

cyclone hazard are obtained from the Global Cyclone Hazard Frequency and Distribution dataset. In order to more accurately reflect the confidence associated with the data and procedures, 

the potential mortality estimate range is classified into deciles, 10 classes of an approximately equal number of grid cells, providing a relative estimate of cyclone-based mortality risks. 

The dataset is a result of the collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, 

and Columbia University Center for International Earth Science Information Network (CIESIN).
Global Cyclone Mortality Risks and Distribution is a 2.5 by 2.5 minute grid of global cyclone mortality risks. Gridded Population of the World (GPW) 

Version 3 (beta) data provide a baseline estimation of population per grid cell from which to estimate potential mortality loss. Mortality loss estimates per hazard event are calculated 

using regional, hazard-specific mortality records of the Emergency Events Database (EM-DAT) that span the 20 years between 1981 and 2000. Data regarding the frequency and distribution of 

cyclone hazard are obtained from the Global Cyclone Hazard Frequency and Distribution dataset. In order to more accurately reflect the confidence associated with the data and procedures, 

the potential mortality estimate range is classified into deciles, 10 classes of an approximately equal number of grid cells, providing a relative estimate of cyclone-based mortality risks. 

The dataset is a result of the collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, 

and Columbia University Center for International Earth Science Information Network (CIESIN).
Global Cyclone Proportional Economic Loss Risk Deciles is a 2.5 by 2.5 minute grid of cyclone hazard economic loss as proportions of gross domestic 

product (GDP) per analytical unit. Estimates of GDP at risk are based on regional economic loss rates derived from historical records of the Emergency Events Database (EM-DAT). Loss rates 

are weighted by the hazard's frequency and distribution. The methodology of Sachs et al. (2003) is followed to determine baseline estimates of GDP per grid cell. To better reflect the 

confidence surrounding the data and procedures, the range of proportionalities is classified into deciles, 10 class of an approximately equal number of grid cells of increasing risk. The 

dataset is a result of the collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and 

Columbia University Center for International Earth Science Information Network (CIESIN).
Global Cyclone Proportional Economic Loss Risk Deciles is a 2.5 by 2.5 minute grid of cyclone hazard economic loss as proportions of gross domestic 

product (GDP) per analytical unit. Estimates of GDP at risk are based on regional economic loss rates derived from historical records of the Emergency Events Database (EM-DAT). Loss rates 

are weighted by the hazard's frequency and distribution. The methodology of Sachs et al. (2003) is followed to determine baseline estimates of GDP per grid cell. To better reflect the 

confidence surrounding the data and procedures, the range of proportionalities is classified into deciles, 10 class of an approximately equal number of grid cells of increasing risk. The 

dataset is a result of the collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and 

Columbia University Center for International Earth Science Information Network (CIESIN).
Global Cyclone Proportional Economic Loss Risk Deciles is a 2.5 by 2.5 minute grid of cyclone hazard economic loss as proportions of gross domestic 

product (GDP) per analytical unit. Estimates of GDP at risk are based on regional economic loss rates derived from historical records of the Emergency Events Database (EM-DAT). Loss rates 

are weighted by the hazard's frequency and distribution. The methodology of Sachs et al. (2003) is followed to determine baseline estimates of GDP per grid cell. To better reflect the 

confidence surrounding the data and procedures, the range of proportionalities is classified into deciles, 10 class of an approximately equal number of grid cells of increasing risk. The 

dataset is a result of the collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and 

Columbia University Center for International Earth Science Information Network (CIESIN).
Global Drought Hazard Frequency and Distribution is a 2.5 minute grid based upon the International Research Institute for Climate Prediction's (IRI) Weighted Anomaly of Standardized Precipitation (WASP). Utilizing average monthly precipitation data from 1980 through 2000 at a resolution of 2.5 degrees, WASP assesses the precipitation deficit or surplus over a three month temporal window that is weighted by the magnitude of the seasonal cyclic variation in precipitation. The three months' averages are derived from the precipitation data and the median rainfall for the 21 year period is calculated for each grid cell. Grid cells where the three month running average of precipitation is less than 1 mm per day ae excluded. Drought events are identified when the magnitude of a monthly precipitation deficit is less than or equal to 50 percent of its longterm median value for three or more consecutive months. Grid cells are then divided into 10 classes having an approximately equal number of grid cells. Higher grid cell values denote higher frequencies of drought occurrences. This dataset is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), Columbia University International Research Institute for Climate Prediction (IRI), and Columbia University Center for International Earth Science Information Network (CIESIN).
Global Drought Hazard Frequency and Distribution is a 2.5 minute grid based upon the International Research Institute for Climate Prediction's (IRI) Weighted Anomaly of Standardized Precipitation (WASP). Utilizing average monthly precipitation data from 1980 through 2000 at a resolution of 2.5 degrees, WASP assesses the precipitation deficit or surplus over a three month temporal window that is weighted by the magnitude of the seasonal cyclic variation in precipitation. The three months' averages are derived from the precipitation data and the median rainfall for the 21 year period is calculated for each grid cell. Grid cells where the three month running average of precipitation is less than 1 mm per day ae excluded. Drought events are identified when the magnitude of a monthly precipitation deficit is less than or equal to 50 percent of its longterm median value for three or more consecutive months. Grid cells are then divided into 10 classes having an approximately equal number of grid cells. Higher grid cell values denote higher frequencies of drought occurrences. This dataset is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), Columbia University International Research Institute for Climate Prediction (IRI), and Columbia University Center for International Earth Science Information Network (CIESIN).
Global Drought Hazard Frequency and Distribution is a 2.5 minute grid based upon the International Research Institute for Climate Prediction's (IRI) Weighted Anomaly of Standardized Precipitation (WASP). Utilizing average monthly precipitation data from 1980 through 2000 at a resolution of 2.5 degrees, WASP assesses the precipitation deficit or surplus over a three month temporal window that is weighted by the magnitude of the seasonal cyclic variation in precipitation. The three months' averages are derived from the precipitation data and the median rainfall for the 21 year period is calculated for each grid cell. Grid cells where the three month running average of precipitation is less than 1 mm per day ae excluded. Drought events are identified when the magnitude of a monthly precipitation deficit is less than or equal to 50 percent of its longterm median value for three or more consecutive months. Grid cells are then divided into 10 classes having an approximately equal number of grid cells. Higher grid cell values denote higher frequencies of drought occurrences. This dataset is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), Columbia University International Research Institute for Climate Prediction (IRI), and Columbia University Center for International Earth Science Information Network (CIESIN).
The World Inventory of Soil Emission Potentials (WISE) database was used to generate a series of uniform data sets of derived soil properties for each of the 106 soil units considered in the Soil Map of the World. These data sets were then used to generate GIS raster image files for the following variables: total available water capacity (mm water per 1 m soil depth); soil organic carbon density (kg C/m**2 for 0-30cm depth range); soil organic carbon density (kg C/m**2 for 0-100cm depth range); soil carbonate carbon density (kg C/m**2 for 0-100cm depth range); soil pH (0-30 cm depth range); and soil pH (30-100 cm depth range).
The World Inventory of Soil Emission Potentials (WISE) database was used to generate a series of uniform data sets of derived soil properties for each of the 106 soil units considered in the Soil Map of the World. These data sets were then used to generate GIS raster image files for the following variables: total available water capacity (mm water per 1 m soil depth); soil organic carbon density (kg C/m**2 for 0-30cm depth range); soil organic carbon density (kg C/m**2 for 0-100cm depth range); soil carbonate carbon density (kg C/m**2 for 0-100cm depth range); soil pH (0-30 cm depth range); and soil pH (30-100 cm depth range).
ABSTRACT: The World Inventory of Soil Emission Potentials (WISE) database was used to generate a series of uniform data sets of derived soil properties for each of the 106 soil units considered in the Soil Map of the World. These data sets were then used to generate GIS raster image files for the following variables: total available water capacity (mm water per 1 m soil depth); soil organic carbon density (kg C/m**2 for 0-30cm depth range); soil organic carbon density (kg C/m**2 for 0-100cm depth range); soil carbonate carbon density (kg C/m**2 for 0-100cm depth range); soil pH (0-30 cm depth range); and soil pH (30-100 cm depth range).
A global data set of root biomass, rooting profiles, and concentrations nutrients in roots was compiled from the primary literature and used to study distributions of root properties. This data set consists of estimates of fine root biomass and specific area, site characteristics. This data set provides analysis of rooting patterns for terrestrial biomes and compare distributions for various plant functional groups.
A global data set of root biomass, rooting profiles, and concentrations nutrients in roots was compiled from the primary literature and used to study distributions of root properties. This data set consists of estimates of fine root biomass and specific area, site characteristics. This data set provides analysis of rooting patterns for terrestrial biomes and compare distributions for various plant functional groups.
ABSTRACT: A global data set of root biomass, rooting profiles, and concentrations nutrients in roots was compiled from the primary literature and used to study distributions of root properties. This data set consists of estimates of fine root biomass and specific area, site characteristics. This data set provides analysis of rooting patterns for terrestrial biomes and compare distributions for various plant functional groups.
Plant-extractable water capacity of soil is the amount of water that can be extracted from the soil to fulfill evapotranspiration demands. This data set provides an estimate of the global distribution of plant-extractable water capacity of soil.
Plant-extractable water capacity of soil is the amount of water that can be extracted from the soil to fulfill evapotranspiration demands. This data set provides an estimate of the global distribution of plant-extractable water capacity of soil.
ABSTRACT: Plant-extractable water capacity of soil is the amount of water that can be extracted from the soil to fulfill evapotranspiration demands. This data set provides an estimate of the global distribution of plant-extractable water capacity of soil.
Nutrient measurements for fine roots were compiled from 56 published studies providing information on 372 different combinations of species, root diameter, rooting depths, and soils at a variety of locations. The compilation was used to examine dynamics of 14 nutrients, including translocation properties of roots of varying size and status.
Nutrient measurements for fine roots were compiled from 56 published studies providing information on 372 different combinations of species, root diameter, rooting depths, and soils at a variety of locations. The compilation was used to examine dynamics of 14 nutrients, including translocation properties of roots of varying size and status.
ABSTRACT: Nutrient measurements for fine roots were compiled from 56 published studies providing information on 372 different combinations of species, root diameter, rooting depths, and soils at a variety of locations. The compilation was used to examine dynamics of 14 nutrients, including translocation properties of roots of varying size and status.
Rooting depths were estimated from a global database of root profiles assembled from the primary literature to study relationships of abiotic and biotic factors associated with belowground vegetation structure. For each root profile, information recorded includes latitude and longitude, elevation, soil texture, depth of organic horizons, type of roots measured (e.g., fine or total, live or dead), sampling methods, units of measurements (root mass, length, number, surface area), and sampling depth.
Rooting depths were estimated from a global database of root profiles assembled from the primary literature to study relationships of abiotic and biotic factors associated with belowground vegetation structure. For each root profile, information recorded includes latitude and longitude, elevation, soil texture, depth of organic horizons, type of roots measured (e.g., fine or total, live or dead), sampling methods, units of measurements (root mass, length, number, surface area), and sampling depth.
ABSTRACT: Rooting depths were estimated from a global database of root profiles assembled from the primary literature to study relationships of abiotic and biotic factors associated with belowground vegetation structure. For each root profile, information recorded includes latitude and longitude, elevation, soil texture, depth of organic horizons, type of roots measured (e.g., fine or total, live or dead), sampling methods, units of measurements (root mass, length, number, surface area), and sampling depth.
Estimates of root turnover rates were calculated from measurements of live root standing crop and belowground net primary production (BNPP) compiled from the primary literature.  Vegetation characteristics, soil properties, and climate conditions were associated with turnover rates to examine patterns and controls for biomes worldwide.
Estimates of root turnover rates were calculated from measurements of live root standing crop and belowground net primary production (BNPP) compiled from the primary literature.  Vegetation characteristics, soil properties, and climate conditions were associated with turnover rates to examine patterns and controls for biomes worldwide.
ABSTRACT: Estimates of root turnover rates were calculated from measurements of live root standing crop and belowground net primary production (BNPP) compiled from the primary literature.  Vegetation characteristics, soil properties, and climate conditions were associated with turnover rates to examine patterns and controls for biomes worldwide.
Global Drought Hazard Frequency and Distribution is a 2.5 minute grid based upon the International Research Institute for Climate Prediction's (IRI) 

Weighted Anomaly of Standardized Precipitation (WASP). Utilizing average monthly precipitation data from 1980 through 2000 at a resolution of 2.5 degrees, WASP assesses the precipitation 

deficit or surplus over a three month temporal window that is weighted by the magnitude of the seasonal cyclic variation in precipitation. The three months' averages are derived from the 

precipitation data and the median rainfall for the 21 year period is calculated for each grid cell. Grid cells where the three month running average of precipitation is less than 1 mm per 

day ae excluded. Drought events are identified when the magnitude of a monthly precipitation deficit is less than or equal to 50 percent of its longterm median value for three or more 

consecutive months. Grid cells are then divided into 10 classes having an approximately equal number of grid cells. Higher grid cell values denote higher frequencies of drought occurrences. 

This dataset is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), Columbia University International Research Institute for Climate 

Prediction (IRI), and Columbia University Center for International Earth Science Information Network (CIESIN).
Global Drought Hazard Frequency and Distribution is a 2.5 minute grid based upon the International Research Institute for Climate Prediction's (IRI) 

Weighted Anomaly of Standardized Precipitation (WASP). Utilizing average monthly precipitation data from 1980 through 2000 at a resolution of 2.5 degrees, WASP assesses the precipitation 

deficit or surplus over a three month temporal window that is weighted by the magnitude of the seasonal cyclic variation in precipitation. The three months' averages are derived from the 

precipitation data and the median rainfall for the 21 year period is calculated for each grid cell. Grid cells where the three month running average of precipitation is less than 1 mm per 

day ae excluded. Drought events are identified when the magnitude of a monthly precipitation deficit is less than or equal to 50 percent of its longterm median value for three or more 

consecutive months. Grid cells are then divided into 10 classes having an approximately equal number of grid cells. Higher grid cell values denote higher frequencies of drought occurrences. 

This dataset is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), Columbia University International Research Institute for Climate 

Prediction (IRI), and Columbia University Center for International Earth Science Information Network (CIESIN).
Global Drought Hazard Frequency and Distribution is a 2.5 minute grid based upon the International Research Institute for Climate Prediction's (IRI) 

Weighted Anomaly of Standardized Precipitation (WASP). Utilizing average monthly precipitation data from 1980 through 2000 at a resolution of 2.5 degrees, WASP assesses the precipitation 

deficit or surplus over a three month temporal window that is weighted by the magnitude of the seasonal cyclic variation in precipitation. The three months' averages are derived from the 

precipitation data and the median rainfall for the 21 year period is calculated for each grid cell. Grid cells where the three month running average of precipitation is less than 1 mm per 

day ae excluded. Drought events are identified when the magnitude of a monthly precipitation deficit is less than or equal to 50 percent of its longterm median value for three or more 

consecutive months. Grid cells are then divided into 10 classes having an approximately equal number of grid cells. Higher grid cell values denote higher frequencies of drought occurrences. 

This dataset is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), Columbia University International Research Institute for Climate 

Prediction (IRI), and Columbia University Center for International Earth Science Information Network (CIESIN).
Global Drought Mortality Risks and Distribution is a 2.5 minute grid of global drought mortality risks. Gridded Population of the World, Version 3 

(GPWv3) data provide a baseline estimation of population per grid cell from which to estimate potential mortality risks due to drought hazard. Mortality loss estimates per hazard event are 

calculated using regional, hazard-specific mortality records of the Emergency Events Database (EM-DAT) that span the 20 years between 1981 and 2000. Data regarding the frequency and 

distribution of drought hazard are obtained from the Global Drought Hazard Frequency and Distribution dataset. In order to more accurately reflect the confidence associated with the data 

and procedures, the potential mortality estimate range is classified into deciles, 10 classes of increasing risk with an approximately equal number of grid cells per class, producing a 

relative estimate of drought-based mortality risks. This dataset is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank 

for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).
Global Drought Mortality Risks and Distribution is a 2.5 minute grid of global drought mortality risks. Gridded Population of the World, Version 3 

(GPWv3) data provide a baseline estimation of population per grid cell from which to estimate potential mortality risks due to drought hazard. Mortality loss estimates per hazard event are 

calculated using regional, hazard-specific mortality records of the Emergency Events Database (EM-DAT) that span the 20 years between 1981 and 2000. Data regarding the frequency and 

distribution of drought hazard are obtained from the Global Drought Hazard Frequency and Distribution dataset. In order to more accurately reflect the confidence associated with the data 

and procedures, the potential mortality estimate range is classified into deciles, 10 classes of increasing risk with an approximately equal number of grid cells per class, producing a 

relative estimate of drought-based mortality risks. This dataset is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank 

for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).
Global Drought Mortality Risks and Distribution is a 2.5 minute grid of global drought mortality risks. Gridded Population of the World, Version 3 

(GPWv3) data provide a baseline estimation of population per grid cell from which to estimate potential mortality risks due to drought hazard. Mortality loss estimates per hazard event are 

calculated using regional, hazard-specific mortality records of the Emergency Events Database (EM-DAT) that span the 20 years between 1981 and 2000. Data regarding the frequency and 

distribution of drought hazard are obtained from the Global Drought Hazard Frequency and Distribution dataset. In order to more accurately reflect the confidence associated with the data 

and procedures, the potential mortality estimate range is classified into deciles, 10 classes of increasing risk with an approximately equal number of grid cells per class, producing a 

relative estimate of drought-based mortality risks. This dataset is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank 

for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).
Global Drought Proportional Economic Loss Risk Deciles is a 2.5 minute grid of drought hazard economic loss as proportions of Gross Domestic Product 

(GDP) per analytical unit. Estimates of GDP at risk are based on regional economic loss rates derived from historical records of the Emergency Events Database (EM-DAT). Loss rates are 

weighted by the hazard's frequency and distribution. The methodology of Sachs et al. (2003) is followed to determine baseline estimates of GDP per grid cell. To better reflect the 

confidence surrounding the data and procedures, the range of proportionalities is classified into deciles, 10 class of an approximately equal number of grid cells of increasing risk. This 

dataset is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and 

Columbia University Center for International Earth Science Information Network (CIESIN).
Global Drought Proportional Economic Loss Risk Deciles is a 2.5 minute grid of drought hazard economic loss as proportions of Gross Domestic Product 

(GDP) per analytical unit. Estimates of GDP at risk are based on regional economic loss rates derived from historical records of the Emergency Events Database (EM-DAT). Loss rates are 

weighted by the hazard's frequency and distribution. The methodology of Sachs et al. (2003) is followed to determine baseline estimates of GDP per grid cell. To better reflect the 

confidence surrounding the data and procedures, the range of proportionalities is classified into deciles, 10 class of an approximately equal number of grid cells of increasing risk. This 

dataset is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and 

Columbia University Center for International Earth Science Information Network (CIESIN).
Global Drought Proportional Economic Loss Risk Deciles is a 2.5 minute grid of drought hazard economic loss as proportions of Gross Domestic Product 

(GDP) per analytical unit. Estimates of GDP at risk are based on regional economic loss rates derived from historical records of the Emergency Events Database (EM-DAT). Loss rates are 

weighted by the hazard's frequency and distribution. The methodology of Sachs et al. (2003) is followed to determine baseline estimates of GDP per grid cell. To better reflect the 

confidence surrounding the data and procedures, the range of proportionalities is classified into deciles, 10 class of an approximately equal number of grid cells of increasing risk. This 

dataset is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and 

Columbia University Center for International Earth Science Information Network (CIESIN).
Global Drought Total Economic Loss Risk Deciles is a 2.5 by 2.5 minute grid of global drought total economic loss risks. A process of spatially 

allocating Gross Domestic Product (GDP) based upon the Sachs et al. (2003) methodology is utilized. First the proportional contributions of subnational units to their respective national 

GDP are determined using sources of various origin. The contribution rates are then applied to published World Bank Development Indicators to determine a GDP value for the subnational unit. 

Once the national GDP is spatially stratified into the smallest adminstrative units available, GDP values for grid cells are derived using Gridded Population of the World, Version 3 (GPWv3) 

data of population distributions. A per capita contribution value is determined within each subnational unit, and this value is multiplied by the population per grid cell. Once a GDP value 

has been determined on a per grid cell basis, then the regionally variable loss rate as derived from the historical records of EM-DAT is used to determine the total economic loss risks 

posed to a grid cell by drought hazards. The final surface does not present absolute values of total economic loss, but rather a relative decile (1-10 with increasing risk) ranking of grid 

cells based upon the calculated economic loss risks. This dataset is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International 

Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).
Global Drought Total Economic Loss Risk Deciles is a 2.5 by 2.5 minute grid of global drought total economic loss risks. A process of spatially 

allocating Gross Domestic Product (GDP) based upon the Sachs et al. (2003) methodology is utilized. First the proportional contributions of subnational units to their respective national 

GDP are determined using sources of various origin. The contribution rates are then applied to published World Bank Development Indicators to determine a GDP value for the subnational unit. 

Once the national GDP is spatially stratified into the smallest adminstrative units available, GDP values for grid cells are derived using Gridded Population of the World, Version 3 (GPWv3) 

data of population distributions. A per capita contribution value is determined within each subnational unit, and this value is multiplied by the population per grid cell. Once a GDP value 

has been determined on a per grid cell basis, then the regionally variable loss rate as derived from the historical records of EM-DAT is used to determine the total economic loss risks 

posed to a grid cell by drought hazards. The final surface does not present absolute values of total economic loss, but rather a relative decile (1-10 with increasing risk) ranking of grid 

cells based upon the calculated economic loss risks. This dataset is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International 

Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).
Global Drought Total Economic Loss Risk Deciles is a 2.5 by 2.5 minute grid of global drought total economic loss risks. A process of spatially 

allocating Gross Domestic Product (GDP) based upon the Sachs et al. (2003) methodology is utilized. First the proportional contributions of subnational units to their respective national 

GDP are determined using sources of various origin. The contribution rates are then applied to published World Bank Development Indicators to determine a GDP value for the subnational unit. 

Once the national GDP is spatially stratified into the smallest adminstrative units available, GDP values for grid cells are derived using Gridded Population of the World, Version 3 (GPWv3) 

data of population distributions. A per capita contribution value is determined within each subnational unit, and this value is multiplied by the population per grid cell. Once a GDP value 

has been determined on a per grid cell basis, then the regionally variable loss rate as derived from the historical records of EM-DAT is used to determine the total economic loss risks 

posed to a grid cell by drought hazards. The final surface does not present absolute values of total economic loss, but rather a relative decile (1-10 with increasing risk) ranking of grid 

cells based upon the calculated economic loss risks. This dataset is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International 

Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).
Global Earthquake Hazard Distribution-Peak Ground Acceleration is a 2.5 by 2.5 minute grid of global earthquake hazards developed using Global Seismic 

Hazard Program (GSHAP) data that incorporate expert opinion in predicting localities where there exists a 10 percent chance of exceeding a peak ground acceleration (pga) of 2 meters per 

second per second (approximately one-fifth of surface gravitational acceleration) in a 50 year time span. For the purpose of identifying hazard hotspots, values of 2 meters per second per 

second and less were excluded from analysis. The resulting range of pga values were classified into deciles, 10 classes of approximately an equal number of grid cells. This dataset is the 

result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR) and Columbia University Center for International Earth Science Information Network 

(CIESIN).
Global Earthquake Hazard Distribution-Peak Ground Acceleration is a 2.5 by 2.5 minute grid of global earthquake hazards developed using Global Seismic 

Hazard Program (GSHAP) data that incorporate expert opinion in predicting localities where there exists a 10 percent chance of exceeding a peak ground acceleration (pga) of 2 meters per 

second per second (approximately one-fifth of surface gravitational acceleration) in a 50 year time span. For the purpose of identifying hazard hotspots, values of 2 meters per second per 

second and less were excluded from analysis. The resulting range of pga values were classified into deciles, 10 classes of approximately an equal number of grid cells. This dataset is the 

result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR) and Columbia University Center for International Earth Science Information Network 

(CIESIN).
Global Earthquake Hazard Distribution-Peak Ground Acceleration is a 2.5 by 2.5 minute grid of global earthquake hazards developed using Global Seismic 

Hazard Program (GSHAP) data that incorporate expert opinion in predicting localities where there exists a 10 percent chance of exceeding a peak ground acceleration (pga) of 2 meters per 

second per second (approximately one-fifth of surface gravitational acceleration) in a 50 year time span. For the purpose of identifying hazard hotspots, values of 2 meters per second per 

second and less were excluded from analysis. The resulting range of pga values were classified into deciles, 10 classes of approximately an equal number of grid cells. This dataset is the 

result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR) and Columbia University Center for International Earth Science Information Network 

(CIESIN).
Global Earthquake Hazard Frequency and Distribution is a 2.5 by 2.5 minute global utilizing Advanced National Seismic System (ANSS) Earthquake Catalog 

data of actual earthquake events exceeding 4.5 on the Richter scale during the time period 1976 through 2002. To produce the final output, the frequency of an earthquake hazard is 

calculated for each grid cell, and the resulting grid cells are then classified into deciles (10 classes consisting of an approxiamately equal number of grid cells). The greater the grid 

cell value in the final output, the higher the relative frequency of hazard posed by earthquakes. This dataset is the result of collaboration among the Columbia University Center for 

Hazards and Risk Research (CHRR) and Columbia University Center for International Earth Science Information Network (CIESIN).
Global Earthquake Hazard Frequency and Distribution is a 2.5 by 2.5 minute global utilizing Advanced National Seismic System (ANSS) Earthquake Catalog 

data of actual earthquake events exceeding 4.5 on the Richter scale during the time period 1976 through 2002. To produce the final output, the frequency of an earthquake hazard is 

calculated for each grid cell, and the resulting grid cells are then classified into deciles (10 classes consisting of an approxiamately equal number of grid cells). The greater the grid 

cell value in the final output, the higher the relative frequency of hazard posed by earthquakes. This dataset is the result of collaboration among the Columbia University Center for 

Hazards and Risk Research (CHRR) and Columbia University Center for International Earth Science Information Network (CIESIN).
Global Earthquake Hazard Frequency and Distribution is a 2.5 by 2.5 minute global utilizing Advanced National Seismic System (ANSS) Earthquake Catalog 

data of actual earthquake events exceeding 4.5 on the Richter scale during the time period 1976 through 2002. To produce the final output, the frequency of an earthquake hazard is 

calculated for each grid cell, and the resulting grid cells are then classified into deciles (10 classes consisting of an approxiamately equal number of grid cells). The greater the grid 

cell value in the final output, the higher the relative frequency of hazard posed by earthquakes. This dataset is the result of collaboration among the Columbia University Center for 

Hazards and Risk Research (CHRR) and Columbia University Center for International Earth Science Information Network (CIESIN).
Global Earthquake Hazard Frequency and Distribution is a 2.5 minute grid utilizing Advanced National Seismic System (ANSS) Earthquake Catalog data of 

actual earthquake events exceeding 4.5 on the Richter scale during the time period 1976 through 2002. To produce the final output, the frequency of an earthquake hazard is calculated for 

each grid cell, and the resulting grid cells are then classified into deciles (10 classes consisting of an approxiamately equal number of grid cells). The greater the grid cell value in the 

final output, the higher the relative frequency of hazard posed by earthquakes. This dataset is the result of collaboration among the Columbia University Center for Hazards and Risk 

Research (CHRR) and Columbia University Center for International Earth Science Information Network (CIESIN).
Global Earthquake Hazard Frequency and Distribution is a 2.5 minute grid utilizing Advanced National Seismic System (ANSS) Earthquake Catalog data of 

actual earthquake events exceeding 4.5 on the Richter scale during the time period 1976 through 2002. To produce the final output, the frequency of an earthquake hazard is calculated for 

each grid cell, and the resulting grid cells are then classified into deciles (10 classes consisting of an approxiamately equal number of grid cells). The greater the grid cell value in the 

final output, the higher the relative frequency of hazard posed by earthquakes. This dataset is the result of collaboration among the Columbia University Center for Hazards and Risk 

Research (CHRR) and Columbia University Center for International Earth Science Information Network (CIESIN).
Global Earthquake Hazard Frequency and Distribution is a 2.5 minute grid utilizing Advanced National Seismic System (ANSS) Earthquake Catalog data of 

actual earthquake events exceeding 4.5 on the Richter scale during the time period 1976 through 2002. To produce the final output, the frequency of an earthquake hazard is calculated for 

each grid cell, and the resulting grid cells are then classified into deciles (10 classes consisting of an approxiamately equal number of grid cells). The greater the grid cell value in the 

final output, the higher the relative frequency of hazard posed by earthquakes. This dataset is the result of collaboration among the Columbia University Center for Hazards and Risk 

Research (CHRR) and Columbia University Center for International Earth Science Information Network (CIESIN).
Global Earthquake Proportional Economic Loss Risk Deciles is a 2.5 minute grid of earthquake hazard economic loss as proportions of Gross Domestic 

Product (GDP) per analytical unit. Estimates of GDP at risk are based on regional economic loss rates derived from historical records of the Emergency Events Database (EM-DAT). Loss rates 

are weighted by the hazard's frequency and distribution. The methodology of Sachs et al. (2003) is followed to determine baseline estimates of GDP per grid cell. To better reflect the 

confidence surrounding the data and procedures, the range of proportionalities is classified into deciles, 10 class of an approximately equal number of grid cells of increasing risk. This 

dataset is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and 

Columbia University Center for International Earth Science Information Network (CIESIN).
Global Earthquake Proportional Economic Loss Risk Deciles is a 2.5 minute grid of earthquake hazard economic loss as proportions of Gross Domestic 

Product (GDP) per analytical unit. Estimates of GDP at risk are based on regional economic loss rates derived from historical records of the Emergency Events Database (EM-DAT). Loss rates 

are weighted by the hazard's frequency and distribution. The methodology of Sachs et al. (2003) is followed to determine baseline estimates of GDP per grid cell. To better reflect the 

confidence surrounding the data and procedures, the range of proportionalities is classified into deciles, 10 class of an approximately equal number of grid cells of increasing risk. This 

dataset is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and 

Columbia University Center for International Earth Science Information Network (CIESIN).
Global Earthquake Proportional Economic Loss Risk Deciles is a 2.5 minute grid of earthquake hazard economic loss as proportions of Gross Domestic 

Product (GDP) per analytical unit. Estimates of GDP at risk are based on regional economic loss rates derived from historical records of the Emergency Events Database (EM-DAT). Loss rates 

are weighted by the hazard's frequency and distribution. The methodology of Sachs et al. (2003) is followed to determine baseline estimates of GDP per grid cell. To better reflect the 

confidence surrounding the data and procedures, the range of proportionalities is classified into deciles, 10 class of an approximately equal number of grid cells of increasing risk. This 

dataset is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and 

Columbia University Center for International Earth Science Information Network (CIESIN).
Global Earthquake Total Economic Loss Risk Deciles is a 2.5 minute grid of global earthquake total economic loss risks. A process of spatially 

allocating Gross Domestic Product (GDP) based upon the Sachs et al. (2003) methodology is utilized. First the proportional contributions of subnational units to their respective national 

GDP are determined using sources of various origin. The contribution rates are then applied to published World Bank Development Indicators to determine a GDP value for the subnational unit. 

Once the national GDP has been spatially stratified into the smallest administrative units available, GDP values for grid cells are derived using Gridded Population of the World, Version 3 

(GPWv3) data population distributions. A per capita contribution value is determined within each subnational unit, and then this value is multiplied by the population per grid cell. Once a 

GDP value has been determined on a per grid cell basis, then the regionally variable loss rate as derived from the historical records of EM-DAT is used to determine the total economic loss 

risks posed to a grid cell by earthquake hazards. The final surface does not present absolute values of total economic loss, but rather a relative decile (1-10 with increasing risk) ranking 

of grid cells based upon the calculated economic loss risks. This dataset is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), 

International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).
Global Earthquake Total Economic Loss Risk Deciles is a 2.5 minute grid of global earthquake total economic loss risks. A process of spatially 

allocating Gross Domestic Product (GDP) based upon the Sachs et al. (2003) methodology is utilized. First the proportional contributions of subnational units to their respective national 

GDP are determined using sources of various origin. The contribution rates are then applied to published World Bank Development Indicators to determine a GDP value for the subnational unit. 

Once the national GDP has been spatially stratified into the smallest administrative units available, GDP values for grid cells are derived using Gridded Population of the World, Version 3 

(GPWv3) data population distributions. A per capita contribution value is determined within each subnational unit, and then this value is multiplied by the population per grid cell. Once a 

GDP value has been determined on a per grid cell basis, then the regionally variable loss rate as derived from the historical records of EM-DAT is used to determine the total economic loss 

risks posed to a grid cell by earthquake hazards. The final surface does not present absolute values of total economic loss, but rather a relative decile (1-10 with increasing risk) ranking 

of grid cells based upon the calculated economic loss risks. This dataset is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), 

International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).
Global Earthquake Total Economic Loss Risk Deciles is a 2.5 minute grid of global earthquake total economic loss risks. A process of spatially 

allocating Gross Domestic Product (GDP) based upon the Sachs et al. (2003) methodology is utilized. First the proportional contributions of subnational units to their respective national 

GDP are determined using sources of various origin. The contribution rates are then applied to published World Bank Development Indicators to determine a GDP value for the subnational unit. 

Once the national GDP has been spatially stratified into the smallest administrative units available, GDP values for grid cells are derived using Gridded Population of the World, Version 3 

(GPWv3) data population distributions. A per capita contribution value is determined within each subnational unit, and then this value is multiplied by the population per grid cell. Once a 

GDP value has been determined on a per grid cell basis, then the regionally variable loss rate as derived from the historical records of EM-DAT is used to determine the total economic loss 

risks posed to a grid cell by earthquake hazards. The final surface does not present absolute values of total economic loss, but rather a relative decile (1-10 with increasing risk) ranking 

of grid cells based upon the calculated economic loss risks. This dataset is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), 

International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).
The Nitrogen Fertilizer Application dataset of the Global Fertilizer and Manure, Version 1 Data Collection represents the amount of nitrogen fertilizer nutrients applied to 

croplands. The national-level nitrogen fertilizer application rates for crops are from the International Fertilizer Industry Association (IFA) "Fertilizer Use by Crop 2002" statistics 

database that is available by request from the Food and Agriculture Organization (FAO). The number of crop-specific fertilizer application rates reported for each country ranged from 2 

crops (Guinea) to over 50 crops (United States), and the years for which the data are reported range from 1994 to 2001. Spatially explicit fertilizer inputs of Nitrogen (N) were computed 

by fusing national-level statistics on fertilizer use with global maps of harvested area for 175 crops. The data were compiled by Potter et al. (2010) and are distributed by the Columbia 

University Center for International Earth Science Information Network (CIESIN).
The Nitrogen Fertilizer Application dataset of the Global Fertilizer and Manure, Version 1 Data Collection represents the amount of nitrogen fertilizer nutrients applied to 

croplands. The national-level nitrogen fertilizer application rates for crops are from the International Fertilizer Industry Association (IFA) "Fertilizer Use by Crop 2002" statistics 

database that is available by request from the Food and Agriculture Organization (FAO). The number of crop-specific fertilizer application rates reported for each country ranged from 2 

crops (Guinea) to over 50 crops (United States), and the years for which the data are reported range from 1994 to 2001. Spatially explicit fertilizer inputs of Nitrogen (N) were computed 

by fusing national-level statistics on fertilizer use with global maps of harvested area for 175 crops. The data were compiled by Potter et al. (2010) and are distributed by the Columbia 

University Center for International Earth Science Information Network (CIESIN).
The Nitrogen Fertilizer Application dataset of the Global Fertilizer and Manure, Version 1 Data Collection represents the amount of nitrogen fertilizer nutrients applied to 

croplands. The national-level nitrogen fertilizer application rates for crops are from the International Fertilizer Industry Association (IFA) "Fertilizer Use by Crop 2002" statistics 

database that is available by request from the Food and Agriculture Organization (FAO). The number of crop-specific fertilizer application rates reported for each country ranged from 2 

crops (Guinea) to over 50 crops (United States), and the years for which the data are reported range from 1994 to 2001. Spatially explicit fertilizer inputs of Nitrogen (N) were computed 

by fusing national-level statistics on fertilizer use with global maps of harvested area for 175 crops. The data were compiled by Potter et al. (2010) and are distributed by the Columbia 

University Center for International Earth Science Information Network (CIESIN).
The Nitrogen in Manure Production dataset of the Global Fertilizer and Manure, Version 1 Data Collection represents the amount of nitrogen manure produced and present on the 

landscape. The manure production at grid cell level was computed based on livestock population and nutrient excretion rates. The livestock population per grid cell was computed by 

multiplying the density values from FAO Gridded Livestock of the World by the area of grid cell. Spatially explicit manure produced and present on landscape is derived by combining the 

number of livestock heads and the nutrient excretion rate. The data were compiled by Potter et al. (2010) and are distributed by the Columbia University Center for International Earth 

Science Information Network (CIESIN).
The Nitrogen in Manure Production dataset of the Global Fertilizer and Manure, Version 1 Data Collection represents the amount of nitrogen manure produced and present on the 

landscape. The manure production at grid cell level was computed based on livestock population and nutrient excretion rates. The livestock population per grid cell was computed by 

multiplying the density values from FAO Gridded Livestock of the World by the area of grid cell. Spatially explicit manure produced and present on landscape is derived by combining the 

number of livestock heads and the nutrient excretion rate. The data were compiled by Potter et al. (2010) and are distributed by the Columbia University Center for International Earth 

Science Information Network (CIESIN).
The Nitrogen in Manure Production dataset of the Global Fertilizer and Manure, Version 1 Data Collection represents the amount of nitrogen manure produced and present on the 

landscape. The manure production at grid cell level was computed based on livestock population and nutrient excretion rates. The livestock population per grid cell was computed by 

multiplying the density values from FAO Gridded Livestock of the World by the area of grid cell. Spatially explicit manure produced and present on landscape is derived by combining the 

number of livestock heads and the nutrient excretion rate. The data were compiled by Potter et al. (2010) and are distributed by the Columbia University Center for International Earth 

Science Information Network (CIESIN).
The Phosphorus Fertilizer Application dataset of the Global Fertilizer and Manure, Version 1 Data Collection represents the amount of phosphorus fertilizer nutrients applied 

to croplands. The national-level phosphorus fertilizer application rates for crops are from the International Fertilizer Industry Association (IFA) "Fertilizer Use by Crop 2002" statistics 

database that is available by request from the Food and Agriculture Organization (FAO). The number of crop-specific fertilizer application rates reported for each country ranged from 2 

crops (Guinea) to over 50 crops (United States), and the years for which the data are reported range from 1994 to 2001. Spatially explicit fertilizer inputs of phosphorus (N) were computed 

by fusing national-level statistics on fertilizer use with global maps of harvested area for 175 crops. The data were compiled by Potter et al. (2010) and are distributed by the Columbia 

University Center for International Earth Science Information Network (CIESIN).
The Phosphorus Fertilizer Application dataset of the Global Fertilizer and Manure, Version 1 Data Collection represents the amount of phosphorus fertilizer nutrients applied 

to croplands. The national-level phosphorus fertilizer application rates for crops are from the International Fertilizer Industry Association (IFA) "Fertilizer Use by Crop 2002" statistics 

database that is available by request from the Food and Agriculture Organization (FAO). The number of crop-specific fertilizer application rates reported for each country ranged from 2 

crops (Guinea) to over 50 crops (United States), and the years for which the data are reported range from 1994 to 2001. Spatially explicit fertilizer inputs of phosphorus (N) were computed 

by fusing national-level statistics on fertilizer use with global maps of harvested area for 175 crops. The data were compiled by Potter et al. (2010) and are distributed by the Columbia 

University Center for International Earth Science Information Network (CIESIN).
The Phosphorus Fertilizer Application dataset of the Global Fertilizer and Manure, Version 1 Data Collection represents the amount of phosphorus fertilizer nutrients applied 

to croplands. The national-level phosphorus fertilizer application rates for crops are from the International Fertilizer Industry Association (IFA) "Fertilizer Use by Crop 2002" statistics 

database that is available by request from the Food and Agriculture Organization (FAO). The number of crop-specific fertilizer application rates reported for each country ranged from 2 

crops (Guinea) to over 50 crops (United States), and the years for which the data are reported range from 1994 to 2001. Spatially explicit fertilizer inputs of phosphorus (N) were computed 

by fusing national-level statistics on fertilizer use with global maps of harvested area for 175 crops. The data were compiled by Potter et al. (2010) and are distributed by the Columbia 

University Center for International Earth Science Information Network (CIESIN).
The Phosphorus in Manure Production dataset of the Global Fertilizer and Manure, Version 1 Data Collection represents the amount of phosphorus manure produced and present on the landscape. The manure production at grid cell level was computed based on livestock population and nutrient excretion rates. The livestock population per grid cell was computed by multiplying the density values from FAO Gridded Livestock of the World by the area of grid cell. Spatially explicit manure produced and present on landscape is derived by combining the number of livestock heads and the nutrient excretion rate. The data were compiled by Potter et al. (2010) and are distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
The Phosphorus in Manure Production dataset of the Global Fertilizer and Manure, Version 1 Data Collection represents the amount of phosphorus manure produced and present on the landscape. The manure production at grid cell level was computed based on livestock population and nutrient excretion rates. The livestock population per grid cell was computed by multiplying the density values from FAO Gridded Livestock of the World by the area of grid cell. Spatially explicit manure produced and present on landscape is derived by combining the number of livestock heads and the nutrient excretion rate. The data were compiled by Potter et al. (2010) and are distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
The Phosphorus in Manure Production dataset of the Global Fertilizer and Manure, Version 1 Data Collection represents the amount of phosphorus manure produced and present on the landscape. The manure production at grid cell level was computed based on livestock population and nutrient excretion rates. The livestock population per grid cell was computed by multiplying the density values from FAO Gridded Livestock of the World by the area of grid cell. Spatially explicit manure produced and present on landscape is derived by combining the number of livestock heads and the nutrient excretion rate. The data were compiled by Potter et al. (2010) and are distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
Global Flood Hazard Frequency and Distribution is a 2.5 minute grid derived from a global listing of extreme flood events between 1985 and 2003 (poor or 

missing data in the early/mid 1990s) compiled by Dartmouth Flood Observatory and georeferenced to the nearest degree. The resultant flood frequency grid was then classified into 10 classes 

of approximately equal number of grid cells. The greater the grid cell value in the final data set, the higher the relative frequency of flood occurrence. This dataset is the result of 

collaboration among the Columbia University Center for Hazards and Risk Research (CHRR) and Columbia University Center for International Earth Science Information Network 

(CIESIN).
Global Flood Hazard Frequency and Distribution is a 2.5 minute grid derived from a global listing of extreme flood events between 1985 and 2003 (poor or 

missing data in the early/mid 1990s) compiled by Dartmouth Flood Observatory and georeferenced to the nearest degree. The resultant flood frequency grid was then classified into 10 classes 

of approximately equal number of grid cells. The greater the grid cell value in the final data set, the higher the relative frequency of flood occurrence. This dataset is the result of 

collaboration among the Columbia University Center for Hazards and Risk Research (CHRR) and Columbia University Center for International Earth Science Information Network 

(CIESIN).
Global Flood Hazard Frequency and Distribution is a 2.5 minute grid derived from a global listing of extreme flood events between 1985 and 2003 (poor or 

missing data in the early/mid 1990s) compiled by Dartmouth Flood Observatory and georeferenced to the nearest degree. The resultant flood frequency grid was then classified into 10 classes 

of approximately equal number of grid cells. The greater the grid cell value in the final data set, the higher the relative frequency of flood occurrence. This dataset is the result of 

collaboration among the Columbia University Center for Hazards and Risk Research (CHRR) and Columbia University Center for International Earth Science Information Network 

(CIESIN).
Global Flood Mortality Risks and Distribution is a 2.5 minute grid of global flood mortality risks. Gridded Population of the World, Version 3 (GPWv3) data provided a baseline population per grid cell from which to estimate potential mortality risks due to flood hazard. Mortality loss estimates per flood event are calculated using regional, hazard-specific mortality records of the Emergency Events Database (EM-DAT) that span the 20 years between 1981 and 2000. Data regarding the frequency and distribution of flood hazard are obtained from the Global Flood Hazard Frequency and Distribution dataset. In order to more accurately reflect the confidence associated with the data and the procedures, the potential mortality estimate range is classified into deciles, 10 classes of increasing hazard with an approximately equal number of grid cells per class, producing a relative estimate of flood-based mortality risks. This dataset is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).
Global Flood Mortality Risks and Distribution is a 2.5 minute grid of global flood mortality risks. Gridded Population of the World, Version 3 (GPWv3) data provided a baseline population per grid cell from which to estimate potential mortality risks due to flood hazard. Mortality loss estimates per flood event are calculated using regional, hazard-specific mortality records of the Emergency Events Database (EM-DAT) that span the 20 years between 1981 and 2000. Data regarding the frequency and distribution of flood hazard are obtained from the Global Flood Hazard Frequency and Distribution dataset. In order to more accurately reflect the confidence associated with the data and the procedures, the potential mortality estimate range is classified into deciles, 10 classes of increasing hazard with an approximately equal number of grid cells per class, producing a relative estimate of flood-based mortality risks. This dataset is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).
Global Flood Mortality Risks and Distribution is a 2.5 minute grid of global flood mortality risks. Gridded Population of the World, Version 3 (GPWv3) data provided a baseline population per grid cell from which to estimate potential mortality risks due to flood hazard. Mortality loss estimates per flood event are calculated using regional, hazard-specific mortality records of the Emergency Events Database (EM-DAT) that span the 20 years between 1981 and 2000. Data regarding the frequency and distribution of flood hazard are obtained from the Global Flood Hazard Frequency and Distribution dataset. In order to more accurately reflect the confidence associated with the data and the procedures, the potential mortality estimate range is classified into deciles, 10 classes of increasing hazard with an approximately equal number of grid cells per class, producing a relative estimate of flood-based mortality risks. This dataset is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).
Global Flood Proportional Economic Loss Risk Deciles is a 2.5 minute grid of flood hazard economic loss as proportions of Gross Domestic Product (GDP) 

per analytical unit. Estimates of GDP at risk are based on regional economic loss rates derived from historical records of the Emergency Events Database (EM-DAT). Loss rates are weighted by 

the hazard's frequency and distribution. The methodology of Sachs et al. (2003) is followed to determine baseline estimates of GDP per grid cell. To better reflect the confidence 

surrounding the data and procedures, the range of proportionalities is classified into deciles, 10 class of an approximately equal number of grid cells of increasing risk. This dataset is 

the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia 

University Center for International Earth Science Information Network (CIESIN).
Global Flood Proportional Economic Loss Risk Deciles is a 2.5 minute grid of flood hazard economic loss as proportions of Gross Domestic Product (GDP) 

per analytical unit. Estimates of GDP at risk are based on regional economic loss rates derived from historical records of the Emergency Events Database (EM-DAT). Loss rates are weighted by 

the hazard's frequency and distribution. The methodology of Sachs et al. (2003) is followed to determine baseline estimates of GDP per grid cell. To better reflect the confidence 

surrounding the data and procedures, the range of proportionalities is classified into deciles, 10 class of an approximately equal number of grid cells of increasing risk. This dataset is 

the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia 

University Center for International Earth Science Information Network (CIESIN).
Global Flood Proportional Economic Loss Risk Deciles is a 2.5 minute grid of flood hazard economic loss as proportions of Gross Domestic Product (GDP) 

per analytical unit. Estimates of GDP at risk are based on regional economic loss rates derived from historical records of the Emergency Events Database (EM-DAT). Loss rates are weighted by 

the hazard's frequency and distribution. The methodology of Sachs et al. (2003) is followed to determine baseline estimates of GDP per grid cell. To better reflect the confidence 

surrounding the data and procedures, the range of proportionalities is classified into deciles, 10 class of an approximately equal number of grid cells of increasing risk. This dataset is 

the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia 

University Center for International Earth Science Information Network (CIESIN).
Global Flood Total Economic Loss Risk Deciles is a 2.5 minute grid of global flood total economic loss risks. A process of spatially allocating Gross 

Domestic Product (GDP) based upon the Sachs et al. (2003) methodology is utilized. First the proportional contributions of subnational units to their respective national GDP are determined 

using sources of various origins. The contribution rates are then applied to published World Bank Development Indicators to determine a GDP value for the subnational unit. Once the national 

GDP has been spatially stratified into the smallest administrative units available, GDP values for grid cells are derived using Gridded Population of the World, Version 3 (GPWv3) data of 

population distributions. A per capita contribution value is determined within each subnational unit, and this value is multiplied by the population per grid cell. Once a GDP value has been 

determined on a per grid cell basis, then the regionally variable loss rate as derived from the historical records of EM-DAT is used to determine the total economic loss risks posed to a 

grid cell by flood hazards. The final surface does not present absolute values of total economic loss, but rather a relative decile (1-10 with increasing risk) ranking of grid cells based 

upon the calculated economic loss risks. This dataset is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for 

Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).
Global Flood Total Economic Loss Risk Deciles is a 2.5 minute grid of global flood total economic loss risks. A process of spatially allocating Gross 

Domestic Product (GDP) based upon the Sachs et al. (2003) methodology is utilized. First the proportional contributions of subnational units to their respective national GDP are determined 

using sources of various origins. The contribution rates are then applied to published World Bank Development Indicators to determine a GDP value for the subnational unit. Once the national 

GDP has been spatially stratified into the smallest administrative units available, GDP values for grid cells are derived using Gridded Population of the World, Version 3 (GPWv3) data of 

population distributions. A per capita contribution value is determined within each subnational unit, and this value is multiplied by the population per grid cell. Once a GDP value has been 

determined on a per grid cell basis, then the regionally variable loss rate as derived from the historical records of EM-DAT is used to determine the total economic loss risks posed to a 

grid cell by flood hazards. The final surface does not present absolute values of total economic loss, but rather a relative decile (1-10 with increasing risk) ranking of grid cells based 

upon the calculated economic loss risks. This dataset is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for 

Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).
Global Flood Total Economic Loss Risk Deciles is a 2.5 minute grid of global flood total economic loss risks. A process of spatially allocating Gross 

Domestic Product (GDP) based upon the Sachs et al. (2003) methodology is utilized. First the proportional contributions of subnational units to their respective national GDP are determined 

using sources of various origins. The contribution rates are then applied to published World Bank Development Indicators to determine a GDP value for the subnational unit. Once the national 

GDP has been spatially stratified into the smallest administrative units available, GDP values for grid cells are derived using Gridded Population of the World, Version 3 (GPWv3) data of 

population distributions. A per capita contribution value is determined within each subnational unit, and this value is multiplied by the population per grid cell. Once a GDP value has been 

determined on a per grid cell basis, then the regionally variable loss rate as derived from the historical records of EM-DAT is used to determine the total economic loss risks posed to a 

grid cell by flood hazards. The final surface does not present absolute values of total economic loss, but rather a relative decile (1-10 with increasing risk) ranking of grid cells based 

upon the calculated economic loss risks. This dataset is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for 

Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).
A comprehensive global database has been assembled to quantify CO2 fluxes and pathways across different levels of integration (from photosynthesis up to net ecosystem production) in forest ecosystems. The database fills an important gap for model calibration, model validation, and hypothesis testing at global and regional scales. The database archive includes: a Microsoft Office Access Database; data files for all tables in the database; query outputs from the database; and SQL script file for re-creating the database from the tables. The database is structured by site (i.e., a forest or stand of known geographical location, biome, species composition, and management regime). It contains carbon budget variables (fluxes and stocks), ecosystem traits (standing biomass, leaf area index, age), and ancillary information (management regime, climate, soil characteristics) for 529 sites from eight forest biomes. Data entries originated from peer-reviewed literature and personal communications with researchers involved in Fluxnet. Flux estimates were included in the database when they were based on direct measurements (e.g., tower-based eddy covariance system measurements), derived from single or multiple direct measurements, or modeled. Stand description was based on observed values, and climatic description was based on the CRU data set and ORCHIDEE model output. Uncertainty for each carbon balance component in the database was estimated in a uniformed way by expert judgment. Robustness of CO2 balances was tested, and closure terms were introduced as a numerical way to approach data quality and flux uncertainty at the biome level.
A comprehensive global database has been assembled to quantify CO2 fluxes and pathways across different levels of integration (from photosynthesis up to net ecosystem production) in forest ecosystems. The database fills an important gap for model calibration, model validation, and hypothesis testing at global and regional scales. The database archive includes: a Microsoft Office Access Database; data files for all tables in the database; query outputs from the database; and SQL script file for re-creating the database from the tables. The database is structured by site (i.e., a forest or stand of known geographical location, biome, species composition, and management regime). It contains carbon budget variables (fluxes and stocks), ecosystem traits (standing biomass, leaf area index, age), and ancillary information (management regime, climate, soil characteristics) for 529 sites from eight forest biomes. Data entries originated from peer-reviewed literature and personal communications with researchers involved in Fluxnet. Flux estimates were included in the database when they were based on direct measurements (e.g., tower-based eddy covariance system measurements), derived from single or multiple direct measurements, or modeled. Stand description was based on observed values, and climatic description was based on the CRU data set and ORCHIDEE model output. Uncertainty for each carbon balance component in the database was estimated in a uniformed way by expert judgment. Robustness of CO2 balances was tested, and closure terms were introduced as a numerical way to approach data quality and flux uncertainty at the biome level.
ABSTRACT: A comprehensive global database has been assembled to quantify CO2 fluxes and pathways across different levels of integration (from photosynthesis up to net ecosystem production) in forest ecosystems. The database fills an important gap for model calibration, model validation, and hypothesis testing at global and regional scales. The database archive includes: a Microsoft Office Access Database; data files for all tables in the database; query outputs from the database; and SQL script file for re-creating the database from the tables. The database is structured by site (i.e., a forest or stand of known geographical location, biome, species composition, and management regime). It contains carbon budget variables (fluxes and stocks), ecosystem traits (standing biomass, leaf area index, age), and ancillary information (management regime, climate, soil characteristics) for 529 sites from eight forest biomes. Data entries originated from peer-reviewed literature and personal communications with researchers involved in Fluxnet. Flux estimates were included in the database when they were based on direct measurements (e.g., tower-based eddy covariance system measurements), derived from single or multiple direct measurements, or modeled. Stand description was based on observed values, and climatic description was based on the CRU data set and ORCHIDEE model output. Uncertainty for each carbon balance component in the database was estimated in a uniformed way by expert judgment. Robustness of CO2 balances was tested, and closure terms were introduced as a numerical way to approach data quality and flux uncertainty at the biome level.
This data set provides estimates of different forms of naturally occurring soil phosphorus (P) including labile inorganic P, organic P, occluded P, secondary mineral P, apatite P, and total P on a global scale at 0.5-degree resolution. The data were assembled from chronosequence information and global spatial databases to develop a map of total soil P and the distribution among mineral bound, labile, organic, occluded, and secondary P forms in soils. Uncertainty was calculated for the different forms. The data set has no explicit temporal component -- data were nominally for the pre-industrial period ca. 1850.The estimated global spatial variation and distribution of different soil P forms presented in this study will be useful for global biogeochemistry models that include P as a limiting element in biological production by providing initial estimates of the available soil P for plant uptake and microbial utilization (Yang et al., 2013).There is one netCDF data file (.nc) with this data set.
This data set provides estimates of different forms of naturally occurring soil phosphorus (P) including labile inorganic P, organic P, occluded P, secondary mineral P, apatite P, and total P on a global scale at 0.5-degree resolution. The data were assembled from chronosequence information and global spatial databases to develop a map of total soil P and the distribution among mineral bound, labile, organic, occluded, and secondary P forms in soils. Uncertainty was calculated for the different forms. The data set has no explicit temporal component -- data were nominally for the pre-industrial period ca. 1850.The estimated global spatial variation and distribution of different soil P forms presented in this study will be useful for global biogeochemistry models that include P as a limiting element in biological production by providing initial estimates of the available soil P for plant uptake and microbial utilization (Yang et al., 2013).There is one netCDF data file (.nc) with this data set.
ABSTRACT: This data set provides estimates of different forms of naturally occurring soil phosphorus (P) including labile inorganic P, organic P, occluded P, secondary mineral P, apatite P, and total P on a global scale at 0.5-degree resolution. The data were assembled from chronosequence information and global spatial databases to develop a map of total soil P and the distribution among mineral bound, labile, organic, occluded, and secondary P forms in soils. Uncertainty was calculated for the different forms. The data set has no explicit temporal component -- data were nominally for the pre-industrial period ca. 1850.The estimated global spatial variation and distribution of different soil P forms presented in this study will be useful for global biogeochemistry models that include P as a limiting element in biological production by providing initial estimates of the available soil P for plant uptake and microbial utilization (Yang et al., 2013).There is one netCDF data file (.nc) with this data set.
Global data-surfaces pre-generated by  SoilData, at a resolution of 5x5 arc-minutes, in ASCII GRID format for ARC INFO, and for the soil depth interval 0-100 cm.
Global data-surfaces pre-generated by  SoilData, at a resolution of 5x5 arc-minutes, in ASCII GRID format for ARC INFO, and for the soil depth interval 0-100 cm.
ABSTRACT: Global data-surfaces pre-generated by  SoilData, at a resolution of 5x5 arc-minutes, in ASCII GRID format for ARC INFO, and for the soil depth interval 0-100 cm.
Global Historical Climatology Network: Long-Term Monthly Temperature, Precipitation, Sea Level Pressure, and Station Pressure Data
Global Historical Climatology Network: Long-Term Monthly Temperature, Precipitation, Sea Level Pressure, and Station Pressure Data
ABSTRACT: Global Historical Climatology Network: Long-Term Monthly Temperature, Precipitation, Sea Level Pressure, and Station Pressure Data
Global Landslide Hazard Distribution is a 2.5 minute grid of global landslide and snow avalanche hazards based upon work of the Norwegian Geotechnical Institute (NGI). The hazards mapping of NGI incorporates a range of data including slope, soil, soil moisture conditions, precipitation, seismicity, and temperature. Shuttle Radar Topography Mission (SRTM) elevation data at 30 seconds resolution are also incorporated. Hazards values 4 and below are considered negligible and only values 5 through 9 are utilized in further analyses. To ensure compatibility with other datasets, 1 is added to each of the values to provide a hazard ranking ranging 6 through 10 in increasing hazard. This dataset is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), Norwegian Geotechnical Institute (NGI), and Columbia University Center for International Earth Science and Information Network (CIESIN).
Global Landslide Hazard Distribution is a 2.5 minute grid of global landslide and snow avalanche hazards based upon work of the Norwegian Geotechnical Institute (NGI). The hazards mapping of NGI incorporates a range of data including slope, soil, soil moisture conditions, precipitation, seismicity, and temperature. Shuttle Radar Topography Mission (SRTM) elevation data at 30 seconds resolution are also incorporated. Hazards values 4 and below are considered negligible and only values 5 through 9 are utilized in further analyses. To ensure compatibility with other datasets, 1 is added to each of the values to provide a hazard ranking ranging 6 through 10 in increasing hazard. This dataset is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), Norwegian Geotechnical Institute (NGI), and Columbia University Center for International Earth Science and Information Network (CIESIN).
Global Landslide Hazard Distribution is a 2.5 minute grid of global landslide and snow avalanche hazards based upon work of the Norwegian Geotechnical Institute (NGI). The hazards mapping of NGI incorporates a range of data including slope, soil, soil moisture conditions, precipitation, seismicity, and temperature. Shuttle Radar Topography Mission (SRTM) elevation data at 30 seconds resolution are also incorporated. Hazards values 4 and below are considered negligible and only values 5 through 9 are utilized in further analyses. To ensure compatibility with other datasets, 1 is added to each of the values to provide a hazard ranking ranging 6 through 10 in increasing hazard. This dataset is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), Norwegian Geotechnical Institute (NGI), and Columbia University Center for International Earth Science and Information Network (CIESIN).
Global Landslide Mortality Risks and Distribution is a 2.5 minute grid of global landslide 

mortality risks. Gridded Population of the World, Version 3 (GPWv3) data provide a baseline estimation of population per grid cell 

from which to estimate potential mortality risks due to landslide hazard. Mortality loss estimates per hazard event are caculated 

using regional, hazard-specific mortality records of the Emergency Events Database (EM-DAT) that span the 20 years between 1981 and 

2000. Data regarding the frequency and distribution of landslide hazard are obtained from the Global Landslide Hazard Distribution 

dataset. In order to more accurately reflect the confidence associated with the data and procedures, the potential mortality estimate 

range is classified into deciles, 10 classes of increasing risk with an approximately equal number of grid cells per class, producing 

a relative estimate of landslide-based mortality risks. This dataset is the result of collaboration among the Columbia University 

Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia 

University Center for International Earth Science Information Network (CIESIN).
Global Landslide Mortality Risks and Distribution is a 2.5 minute grid of global landslide 

mortality risks. Gridded Population of the World, Version 3 (GPWv3) data provide a baseline estimation of population per grid cell 

from which to estimate potential mortality risks due to landslide hazard. Mortality loss estimates per hazard event are caculated 

using regional, hazard-specific mortality records of the Emergency Events Database (EM-DAT) that span the 20 years between 1981 and 

2000. Data regarding the frequency and distribution of landslide hazard are obtained from the Global Landslide Hazard Distribution 

dataset. In order to more accurately reflect the confidence associated with the data and procedures, the potential mortality estimate 

range is classified into deciles, 10 classes of increasing risk with an approximately equal number of grid cells per class, producing 

a relative estimate of landslide-based mortality risks. This dataset is the result of collaboration among the Columbia University 

Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia 

University Center for International Earth Science Information Network (CIESIN).
Global Landslide Mortality Risks and Distribution is a 2.5 minute grid of global landslide 

mortality risks. Gridded Population of the World, Version 3 (GPWv3) data provide a baseline estimation of population per grid cell 

from which to estimate potential mortality risks due to landslide hazard. Mortality loss estimates per hazard event are caculated 

using regional, hazard-specific mortality records of the Emergency Events Database (EM-DAT) that span the 20 years between 1981 and 

2000. Data regarding the frequency and distribution of landslide hazard are obtained from the Global Landslide Hazard Distribution 

dataset. In order to more accurately reflect the confidence associated with the data and procedures, the potential mortality estimate 

range is classified into deciles, 10 classes of increasing risk with an approximately equal number of grid cells per class, producing 

a relative estimate of landslide-based mortality risks. This dataset is the result of collaboration among the Columbia University 

Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia 

University Center for International Earth Science Information Network (CIESIN).
Global Landslide Proportional Economic Loss Risk Deciles is a 2.5 minute grid of landslide 

hazard economic loss as proportions of Gross Domestic Product (GDP) per analytical unit. Estimates of GDP at risk are based on 

regional economic loss rates derived from historical records of the Emergency Events Database (EM-DAT). Loss rates are weighted by 

the hazard's frequency and distribution. The methodology of Sachs et al. (2003) is followed to determine baseline estimates of GDP 

per grid cell. To better reflect the confidence surrounding the data and procedures, the range of proportionalities is classified 

into deciles, 10 class of an approximately equal number of grid cells of increasing risk. This dataset is the result of collaboration 

among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The 

World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).
Global Landslide Proportional Economic Loss Risk Deciles is a 2.5 minute grid of landslide 

hazard economic loss as proportions of Gross Domestic Product (GDP) per analytical unit. Estimates of GDP at risk are based on 

regional economic loss rates derived from historical records of the Emergency Events Database (EM-DAT). Loss rates are weighted by 

the hazard's frequency and distribution. The methodology of Sachs et al. (2003) is followed to determine baseline estimates of GDP 

per grid cell. To better reflect the confidence surrounding the data and procedures, the range of proportionalities is classified 

into deciles, 10 class of an approximately equal number of grid cells of increasing risk. This dataset is the result of collaboration 

among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The 

World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).
Global Landslide Proportional Economic Loss Risk Deciles is a 2.5 minute grid of landslide 

hazard economic loss as proportions of Gross Domestic Product (GDP) per analytical unit. Estimates of GDP at risk are based on 

regional economic loss rates derived from historical records of the Emergency Events Database (EM-DAT). Loss rates are weighted by 

the hazard's frequency and distribution. The methodology of Sachs et al. (2003) is followed to determine baseline estimates of GDP 

per grid cell. To better reflect the confidence surrounding the data and procedures, the range of proportionalities is classified 

into deciles, 10 class of an approximately equal number of grid cells of increasing risk. This dataset is the result of collaboration 

among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The 

World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).
Global Landslide Total Economic Loss Risk Deciles is a 2.5 minute grid of global landslide total 

economic loss risks. A process of spatially allocating Gross Domestic Product (GDP) based upon the Sachs et al. (2003) methodology is 

utilized. First the proportional contributions of subnational units to their respective national GDP are determined using sources of 

various origins. The contribution rates are then applied to published World Bank Development Indicators to determine a GDP value for 

the subnational unit. Once the national GDP has been spatially stratified into the smallest administrative units available, GDP 

values for grid cells are derived using Gridded Population of the World, Version 3 (GPWv3) data of population distributions. A per 

capita contribution value is determined within each subnational unit, and this value is multiplied by the population per grid cell. 

Once a GDP value has been determined on a per grid cell basis, then the regionally variable loss rate as derived from the historical 

records of EM-DAT is used to determine the total economic loss risks posed to a grid cell by landslide hazards. The final surface 

does not present absolute values of total economic loss, but rather a relative decile (1-10 with increasing risk) ranking of grid 

cells based upon the calculated economic loss risks. This dataset is the result of collaboration among the Columbia University Center 

for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University 

Center for International Earth Science Information Network (CIESIN).
Global Landslide Total Economic Loss Risk Deciles is a 2.5 minute grid of global landslide total 

economic loss risks. A process of spatially allocating Gross Domestic Product (GDP) based upon the Sachs et al. (2003) methodology is 

utilized. First the proportional contributions of subnational units to their respective national GDP are determined using sources of 

various origins. The contribution rates are then applied to published World Bank Development Indicators to determine a GDP value for 

the subnational unit. Once the national GDP has been spatially stratified into the smallest administrative units available, GDP 

values for grid cells are derived using Gridded Population of the World, Version 3 (GPWv3) data of population distributions. A per 

capita contribution value is determined within each subnational unit, and this value is multiplied by the population per grid cell. 

Once a GDP value has been determined on a per grid cell basis, then the regionally variable loss rate as derived from the historical 

records of EM-DAT is used to determine the total economic loss risks posed to a grid cell by landslide hazards. The final surface 

does not present absolute values of total economic loss, but rather a relative decile (1-10 with increasing risk) ranking of grid 

cells based upon the calculated economic loss risks. This dataset is the result of collaboration among the Columbia University Center 

for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University 

Center for International Earth Science Information Network (CIESIN).
Global Landslide Total Economic Loss Risk Deciles is a 2.5 minute grid of global landslide total 

economic loss risks. A process of spatially allocating Gross Domestic Product (GDP) based upon the Sachs et al. (2003) methodology is 

utilized. First the proportional contributions of subnational units to their respective national GDP are determined using sources of 

various origins. The contribution rates are then applied to published World Bank Development Indicators to determine a GDP value for 

the subnational unit. Once the national GDP has been spatially stratified into the smallest administrative units available, GDP 

values for grid cells are derived using Gridded Population of the World, Version 3 (GPWv3) data of population distributions. A per 

capita contribution value is determined within each subnational unit, and this value is multiplied by the population per grid cell. 

Once a GDP value has been determined on a per grid cell basis, then the regionally variable loss rate as derived from the historical 

records of EM-DAT is used to determine the total economic loss risks posed to a grid cell by landslide hazards. The final surface 

does not present absolute values of total economic loss, but rather a relative decile (1-10 with increasing risk) ranking of grid 

cells based upon the calculated economic loss risks. This dataset is the result of collaboration among the Columbia University Center 

for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University 

Center for International Earth Science Information Network (CIESIN).
Leaf Area Index (LAI) data from the scientific literature, 1932-2000, have been compiled at the ORNL DAAC to support model development and EOS MODIS product validation. Like net primary productivity (NPP), leaf area index (LAI) is a key parameter for global and regional models of biosphere/atmosphere exchange.
Leaf Area Index (LAI) data from the scientific literature, 1932-2000, have been compiled at the ORNL DAAC to support model development and EOS MODIS product validation. Like net primary productivity (NPP), leaf area index (LAI) is a key parameter for global and regional models of biosphere/atmosphere exchange.
ABSTRACT: Leaf Area Index (LAI) data from the scientific literature, 1932-2000, have been compiled at the ORNL DAAC to support model development and EOS MODIS product validation. Like net primary productivity (NPP), leaf area index (LAI) is a key parameter for global and regional models of biosphere/atmosphere exchange.
This data set provides global gridded estimates of atmospheric deposition of total inorganic nitrogen (N), NHx (NH3 and NH4+), and NOy (all oxidized forms of nitrogen other than N2O), in mg N/m2/year, for the years 1860 and 1993 and projections for the year 2050. The data set was generated using a global three-dimensional chemistry-transport model (TM3) with a spatial resolution of 5 degrees longitude by 3.75 degrees latitude (Jeuken et al., 2001; Lelieveld and Dentener, 2000). Nitrogen emissions estimates (Van Aardenne et al., 2001) and projection scenario data (IPCC, 1996; 2000) were used as input to the model.
This data set provides global gridded estimates of atmospheric deposition of total inorganic nitrogen (N), NHx (NH3 and NH4+), and NOy (all oxidized forms of nitrogen other than N2O), in mg N/m2/year, for the years 1860 and 1993 and projections for the year 2050. The data set was generated using a global three-dimensional chemistry-transport model (TM3) with a spatial resolution of 5 degrees longitude by 3.75 degrees latitude (Jeuken et al., 2001; Lelieveld and Dentener, 2000). Nitrogen emissions estimates (Van Aardenne et al., 2001) and projection scenario data (IPCC, 1996; 2000) were used as input to the model.
ABSTRACT: This data set provides global gridded estimates of atmospheric deposition of total inorganic nitrogen (N), NHx (NH3 and NH4+), and NOy (all oxidized forms of nitrogen other than N2O), in mg N/m2/year, for the years 1860 and 1993 and projections for the year 2050. The data set was generated using a global three-dimensional chemistry-transport model (TM3) with a spatial resolution of 5 degrees longitude by 3.75 degrees latitude (Jeuken et al., 2001; Lelieveld and Dentener, 2000). Nitrogen emissions estimates (Van Aardenne et al., 2001) and projection scenario data (IPCC, 1996; 2000) were used as input to the model.
An historical monthly precipitation data set for global land areas from 1900 to January 1, 1999, gridded at two different resolutions (2.5 degrees latitude by 3.75 degrees longitude and 5 degrees latitude/longitude).
An historical monthly precipitation data set for global land areas from 1900 to January 1, 1999, gridded at two different resolutions (2.5 degrees latitude by 3.75 degrees longitude and 5 degrees latitude/longitude).
ABSTRACT: An historical monthly precipitation data set for global land areas from 1900 to January 1, 1999, gridded at two different resolutions (2.5 degrees latitude by 3.75 degrees longitude and 5 degrees latitude/longitude).
Global Multihazard Frequency and Distribution is a 2.5 minute grid presenting a simple 

multihazard index based solely on summated single-hazard decile values. The hazards of interest include cyclones, droughts, 

earthquakes, floods, landslides, and volcanoes. This data set is further enriched by the inclusion of data pertaining to population, 

Gross Domestic Product (GDP), and transportation infrastructure. This dataset is the result of collaboration among the Columbia 

University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and 

Columbia University Center for International Earth Science Information Network (CIESIN).
Global Multihazard Frequency and Distribution is a 2.5 minute grid presenting a simple 

multihazard index based solely on summated single-hazard decile values. The hazards of interest include cyclones, droughts, 

earthquakes, floods, landslides, and volcanoes. This data set is further enriched by the inclusion of data pertaining to population, 

Gross Domestic Product (GDP), and transportation infrastructure. This dataset is the result of collaboration among the Columbia 

University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and 

Columbia University Center for International Earth Science Information Network (CIESIN).
Global Multihazard Frequency and Distribution is a 2.5 minute grid presenting a simple 

multihazard index based solely on summated single-hazard decile values. The hazards of interest include cyclones, droughts, 

earthquakes, floods, landslides, and volcanoes. This data set is further enriched by the inclusion of data pertaining to population, 

Gross Domestic Product (GDP), and transportation infrastructure. This dataset is the result of collaboration among the Columbia 

University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and 

Columbia University Center for International Earth Science Information Network (CIESIN).
Global Multihazard Mortality Risks and Distribution is a 2.5 minute grid identifying and 

characterizing the nature of multihazard risk at the global scale. For this study, multihazard considers the hazards posed by 

cyclones, droughts, earthquakes, floods, landslides and volcanoes. The specific hazards are grouped into the following hazard 

categories: drought (drought), seismic (earthquakes and volcanoes), and hydro (cyclones, floods, and landslides). Each grid cell is 

assessed for each hazard category; and is considered at high risk or not at high risk. Treated as a binary value, the at-risk values 

of the hazards categories function as a 3-digit index of multihazard. For each of the hazard category combinations, aggregate 

analyses determine the total population, area, and length of major transportation features, as well as, the value of Gross Domestic 

Product (GDP) and agricultural GDP. This dataset is the result of collaboration among the Columbia University Center for Hazards and 

Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for 

International Earth Science Information Network (CIESIN).