This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
This dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program.  Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed (at 10 meters), water vapor, cloud water, and rain rate.  The SSMIS data have been carefully intercalibrated on the brightness temperature level with the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values.  This algorithm is a product of 20 years of refinements, improvements, and verifications.  The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite.  The netCDF SSMI/SSMIS collection will be available for F8, F10, F11, F13, F14, F15, F16, F17 for each temporal aggregation: daily, 3-day, weekly and monthly.
The Geological Survey of Alabama (GSA) analyzed changes in the coastal shoreline and sedimentation using Landsat geotiff images as part of the Sediment Analysis Network for Decision Support (SANDS) project.  The daytime geotiffs images from Landsat 5 and Landsat 7 were analyzed for sediment re-distribution after a hurricane over the Gulf of Mexico coastline in Alabama and part of the Florida area having coordinates 31 to 27 North latitude and 90 to 84.25 West longitude.  These are seasonal data for storms from 2001-2008.  In addition to the analyzed files, the data files include the ESRI files for zipped bands and grids, metadata, and storm temporal information for the sediment analysis images.
The Geological Survey of Alabama (GSA) analyzed changes in the coastal shoreline and sedimentation using Landsat geotiff images as part of the Sediment Analysis Network for Decision Support (SANDS) project.  The daytime geotiffs images from Landsat 5 and Landsat 7 were analyzed for sediment re-distribution after a hurricane over the Gulf of Mexico coastline in Alabama and part of the Florida area having coordinates 31 to 27 North latitude and 90 to 84.25 West longitude.  These are seasonal data for storms from 2001-2008.  In addition to the analyzed files, the data files include the ESRI files for zipped bands and grids, metadata, and storm temporal information for the sediment analysis images.
The Sediment Analysis Network for Decision Support (SANDS) analyzes Geotiff images for sediment redistribution after a hurricane on the Gulf coast and then creates a product based on the analysis.  This dataset consists of the set of daytime geotiff images from Landsat 5 and Landsat 7 provided to Geological Survey of Alabama for their analysis.  Subsetted coordinates are 31-27N latitude and 90-84.25W longitude (Gulf of Mexico coastline in Alabama and portions of Florida).  These are seasonal data for storms.
The Sediment Analysis Network for Decision Support (SANDS) analyzes Geotiff images for sediment redistribution after a hurricane on the Gulf coast and then creates a product based on the analysis.  This dataset consists of the set of daytime geotiff images from Landsat 5 and Landsat 7 provided to Geological Survey of Alabama for their analysis.  Subsetted coordinates are 31-27N latitude and 90-84.25W longitude (Gulf of Mexico coastline in Alabama and portions of Florida).  These are seasonal data for storms.
The Sediment Analysis Network for Decision Support (SANDS) analyzes Geotiff images for sediment redistribution after a hurricane on the Gulf coast and then creates a product based on the analysis.  This dataset consists of the set of daytime geotiff images from Landsat 5 and Landsat 7 provided to Geological Survey of Alabama for their analysis.  Subsetted coordinates are 31-27N latitude and 90-84.25W longitude (Gulf of Mexico coastline in Alabama and portions of Florida).  These are seasonal data for storms.
Sediment Analysis Network for Decision Support (SANDS) MODIS Geological Survey of AL (GSA) Analysis -- The Geological Survey of Alabama (GSA) generated this dataset from geoTIFF MODIS images as part of the Sediment Analysis Network for Decision Support (SANDS) project. The geoTIFF images were analyzed for sediment redistribution after hurricanes on the Gulf coast. Daytime images for Terra and Aqua MODIS Reflectance bands 8-16 for H10V6 and H10V5 were analyzed for each storm. These are seasonal data for storms from 2001-2008. In addition to the analyzed files, the data files include the ESRI files for zipped bands and/or grids, metadata, and storm temporal information for the sediment analysis images.
Sediment Analysis Network for Decision Support (SANDS) MODIS GeoTIFF -- The Sediment Analysis Network for Decision Support (SANDS) analyzes GeoTIFF images to determine sediment redistribution after a hurricane on the Gulf coast and then creates a product based on the analysis. This dataset consists of the set of GeoTIFF images provided to the Geological Survey of Alabama for their analysis. Only daytime images for Terra and Aqua MODIS Reflectance bands 8-16 for H10V6 and H10V5 were used to create the GeoTIFF images for each storm. These are seasonal data for storms.
Sediment Analysis Network for Decision Support (SANDS) MODIS Gulf Subsetted -- The Sediment Analysis Network for Decision Support (SANDS) analyzes GeoTIFF images to determine sediment redistribution after a hurricane on the Gulf coast and then creates a product based on the analysis. This dataset consists of daytime images for Terra and Aqua MODIS Reflectance bands 8-16 for H10V6 and H10V5 for each storm, subsetted to 31-27N latitude and 90-84.25W longitude (Gulf of Mexico coastline in Alabama and portions of Florida). These are seasonal data for storms.
The Tropical Composition, Cloud and Climate Coupling (TC4) mission TC4 field experiment was completed during July and August 2007 based out of San Jose, Costa Rica.  The Advanced Microwave Precipitation Radiometer (AMPR) instrument played a key role in the experiment.  The AMPR remotely senses passive microwave signatures of geophysical parameters from an airborne platform.  The instrument is a low noise system which can provide multi-frequency microwave imagery with high spatial and temporal resolution.  AMPR data were collected at a combination of frequencies (10.7, 19.35, 37.1, and 85.5 GHz) unique to current NASA aircraft instrumentation.  These frequencies are well suited to the study of rain cloud systems, but are also useful to studies of various ocean and land surface processes.
The Tropical Composition, Cloud and Climate Coupling (TC4) mission TC4 field experiment was completed during July and August 2007 based out of San Jose, Costa Rica.  The Advanced Microwave Precipitation Radiometer (AMPR) instrument played a key role in the experiment.  The AMPR remotely senses passive microwave signatures of geophysical parameters from an airborne platform.  The instrument is a low noise system which can provide multi-frequency microwave imagery with high spatial and temporal resolution.  AMPR data were collected at a combination of frequencies (10.7, 19.35, 37.1, and 85.5 GHz) unique to current NASA aircraft instrumentation.  These frequencies are well suited to the study of rain cloud systems, but are also useful to studies of various ocean and land surface processes.
The Tropical Composition, Cloud and Climate Coupling (TC4) mission TC4 field experiment was completed during July and August 2007 based out of San Jose, Costa Rica.  The Advanced Microwave Precipitation Radiometer (AMPR) instrument played a key role in the experiment.  The AMPR remotely senses passive microwave signatures of geophysical parameters from an airborne platform.  The instrument is a low noise system which can provide multi-frequency microwave imagery with high spatial and temporal resolution.  AMPR data were collected at a combination of frequencies (10.7, 19.35, 37.1, and 85.5 GHz) unique to current NASA aircraft instrumentation.  These frequencies are well suited to the study of rain cloud systems, but are also useful to studies of various ocean and land surface processes.
The Tropical Composition, Cloud and Climate Coupling (TC4) mission TC4 field experiment was completed during July and August 2007 based out of San Jose, Costa Rica.  These lightning data were collected during the NASA TC4 field campaign and are restricted to the TC4 community.
Measurements of air temperature, pressure, and relative humidity were made on each flight using two Vaisalla RS902 sondes located under the wings of the aircraft. A Heiltronics KT11.k6 infrared pyrometer was used to measure sea surface temperatures (SST).
Measurements of air temperature, pressure, and relative humidity were made on each flight using two Vaisalla RS902 sondes located under the wings of the aircraft. A Heiltronics KT11.k6 infrared pyrometer was used to measure sea surface temperatures (SST).
The Advanced Microwave Precipitation Radiometer (AMPR) remotely senses passive microwave signatures of geophysical parameters from an airborne platform. The instrument is a low noise system which can provide multi-frequency microwave imagery with high spatial and temporal resolution. AMPR data are collected at a combination of frequencies (10.7, 19.35, 37.1, and 85.5 GHz) unique to current NASA aircraft instrumentation. These frequencies are well suited to the study of rain cloud systems, but are also useful to studies of various ocean and land surface processes.  AMPR data were collected at four microwave frequencies (10.7, 19.35, 37.1 and 85.5 GHz) for the period of 06-18-2005 through 07-28-2005.
The Advanced Microwave Precipitation Radiometer (AMPR) remotely senses passive microwave signatures of geophysical parameters from an airborne platform. The instrument is a low noise system which can provide multi-frequency microwave imagery with high spatial and temporal resolution. AMPR data are collected at a combination of frequencies (10.7, 19.35, 37.1, and 85.5 GHz) unique to current NASA aircraft instrumentation. These frequencies are well suited to the study of rain cloud systems, but are also useful to studies of various ocean and land surface processes.  AMPR data were collected at four microwave frequencies (10.7, 19.35, 37.1 and 85.5 GHz) for the period of 06-18-2005 through 07-28-2005.
The Advanced Microwave Precipitation Radiometer (AMPR) remotely senses passive microwave signatures of geophysical parameters from an airborne platform. The instrument is a low noise system which can provide multi-frequency microwave imagery with high spatial and temporal resolution. AMPR data are collected at a combination of frequencies (10.7, 19.35, 37.1, and 85.5 GHz) unique to current NASA aircraft instrumentation. These frequencies are well suited to the study of rain cloud systems, but are also useful to studies of various ocean and land surface processes.  AMPR data were collected at four microwave frequencies (10.7, 19.35, 37.1 and 85.5 GHz) for the period of 06-18-2005 through 07-28-2005.
The Cloud Radar System (CRS) provides vertically profiled reflectivity and Doppler velocity at aircraft nadir along the flight track.  The CRS is a 94 GHz (W-band; 3 mm wavelength) Doppler radar developed for autonomous operation in the NASA ER-2 high-altitude aircraft and for ground-based operation. It will provide high-resolution profiles of reflectivity and Doppler velocity in clouds and it has important applications to atmospheric remote sensing studies. The CRS was designed to fly with the Cloud Lidar System (CLS), in the tail cone of an ER-2 superpod. There are two basic modes of operation of the CRS: 1) ER-2 with reflectivity, Doppler, and linear-depolarization measurements, and 2) ground-based with full polarimetric capability. The TCSP mission used the ER-2 mode.
The Cloud Radar System (CRS) provides vertically profiled reflectivity and Doppler velocity at aircraft nadir along the flight track.  The CRS is a 94 GHz (W-band; 3 mm wavelength) Doppler radar developed for autonomous operation in the NASA ER-2 high-altitude aircraft and for ground-based operation. It will provide high-resolution profiles of reflectivity and Doppler velocity in clouds and it has important applications to atmospheric remote sensing studies. The CRS was designed to fly with the Cloud Lidar System (CLS), in the tail cone of an ER-2 superpod. There are two basic modes of operation of the CRS: 1) ER-2 with reflectivity, Doppler, and linear-depolarization measurements, and 2) ground-based with full polarimetric capability. The TCSP mission used the ER-2 mode.
The Cloud Radar System (CRS) provides vertically profiled reflectivity and Doppler velocity at aircraft nadir along the flight track.  The CRS is a 94 GHz (W-band; 3 mm wavelength) Doppler radar developed for autonomous operation in the NASA ER-2 high-altitude aircraft and for ground-based operation. It will provide high-resolution profiles of reflectivity and Doppler velocity in clouds and it has important applications to atmospheric remote sensing studies. The CRS was designed to fly with the Cloud Lidar System (CLS), in the tail cone of an ER-2 superpod. There are two basic modes of operation of the CRS: 1) ER-2 with reflectivity, Doppler, and linear-depolarization measurements, and 2) ground-based with full polarimetric capability. The TCSP mission used the ER-2 mode.
The Cloud Radar System (CRS) provides vertically profiled reflectivity and Doppler velocity at aircraft nadir along the flight track.  The CRS is a 94 GHz (W-band; 3 mm wavelength) Doppler radar developed for autonomous operation in the NASA ER-2 high-altitude aircraft and for ground-based operation. It will provide high-resolution profiles of reflectivity and Doppler velocity in clouds and it has important applications to atmospheric remote sensing studies. The CRS was designed to fly with the Cloud Lidar System (CLS), in the tail cone of an ER-2 superpod. There are two basic modes of operation of the CRS: 1) ER-2 with reflectivity, Doppler, and linear-depolarization measurements, and 2) ground-based with full polarimetric capability. The TCSP mission used the ER-2 mode.
The Cloud Radar System (CRS) provides vertically profiled reflectivity and Doppler velocity at aircraft nadir along the flight track.  The CRS is a 94 GHz (W-band; 3 mm wavelength) Doppler radar developed for autonomous operation in the NASA ER-2 high-altitude aircraft and for ground-based operation. It will provide high-resolution profiles of reflectivity and Doppler velocity in clouds and it has important applications to atmospheric remote sensing studies. The CRS was designed to fly with the Cloud Lidar System (CLS), in the tail cone of an ER-2 superpod. There are two basic modes of operation of the CRS: 1) ER-2 with reflectivity, Doppler, and linear-depolarization measurements, and 2) ground-based with full polarimetric capability. The TCSP mission used the ER-2 mode.
The Cloud Radar System (CRS) provides vertically profiled reflectivity and Doppler velocity at aircraft nadir along the flight track.  The CRS is a 94 GHz (W-band; 3 mm wavelength) Doppler radar developed for autonomous operation in the NASA ER-2 high-altitude aircraft and for ground-based operation. It will provide high-resolution profiles of reflectivity and Doppler velocity in clouds and it has important applications to atmospheric remote sensing studies. The CRS was designed to fly with the Cloud Lidar System (CLS), in the tail cone of an ER-2 superpod. There are two basic modes of operation of the CRS: 1) ER-2 with reflectivity, Doppler, and linear-depolarization measurements, and 2) ground-based with full polarimetric capability. The TCSP mission used the ER-2 mode.
The EDOP  provides vertically profiled reflectivity and Doppler velocity at aircraft nadir along the flight track.  The ER-2 Doppler radar (EDOP) is an X-band (9.6 GHz) Doppler radar mounted in the nose of the ER-2 aircraft. 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 at the surface of 1 km. Each Antenna measures the doppler velocity, doppler spectral width, and reflectivity factor. Doppler velocities provide a measure of the pulse volume-weighted hydrometer motion (hydrometer fallspeed + air motion.) Vertical air motion can be calculated from the nadir beam by removing the fallspeed contribution with an approximation.  Along-track horizontal air motion can be calculated by combining doppler wind speeds from forward and nadir beams. The linear depolarization ratio (the ratio of the cross-polar to the co-polar reflectivites) can be measured along forward beam. EDOP is a coherent Doppler radar system at 9.6 GHz. EDOP provides measurements from a forward pointing beam that is used in combination with the nadir beam for estimating the along-track winds.
The EDOP  provides vertically profiled reflectivity and Doppler velocity at aircraft nadir along the flight track.  The ER-2 Doppler radar (EDOP) is an X-band (9.6 GHz) Doppler radar mounted in the nose of the ER-2 aircraft. 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 at the surface of 1 km. Each Antenna measures the doppler velocity, doppler spectral width, and reflectivity factor. Doppler velocities provide a measure of the pulse volume-weighted hydrometer motion (hydrometer fallspeed + air motion.) Vertical air motion can be calculated from the nadir beam by removing the fallspeed contribution with an approximation.  Along-track horizontal air motion can be calculated by combining doppler wind speeds from forward and nadir beams. The linear depolarization ratio (the ratio of the cross-polar to the co-polar reflectivites) can be measured along forward beam. EDOP is a coherent Doppler radar system at 9.6 GHz. EDOP provides measurements from a forward pointing beam that is used in combination with the nadir beam for estimating the along-track winds.
The EDOP  provides vertically profiled reflectivity and Doppler velocity at aircraft nadir along the flight track.  The ER-2 Doppler radar (EDOP) is an X-band (9.6 GHz) Doppler radar mounted in the nose of the ER-2 aircraft. 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 at the surface of 1 km. Each Antenna measures the doppler velocity, doppler spectral width, and reflectivity factor. Doppler velocities provide a measure of the pulse volume-weighted hydrometer motion (hydrometer fallspeed + air motion.) Vertical air motion can be calculated from the nadir beam by removing the fallspeed contribution with an approximation.  Along-track horizontal air motion can be calculated by combining doppler wind speeds from forward and nadir beams. The linear depolarization ratio (the ratio of the cross-polar to the co-polar reflectivites) can be measured along forward beam. EDOP is a coherent Doppler radar system at 9.6 GHz. EDOP provides measurements from a forward pointing beam that is used in combination with the nadir beam for estimating the along-track winds.
The EDOP  provides vertically profiled reflectivity and Doppler velocity at aircraft nadir along the flight track.  The ER-2 Doppler radar (EDOP) is an X-band (9.6 GHz) Doppler radar mounted in the nose of the ER-2 aircraft. 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 at the surface of 1 km. Each Antenna measures the doppler velocity, doppler spectral width, and reflectivity factor. Doppler velocities provide a measure of the pulse volume-weighted hydrometer motion (hydrometer fallspeed + air motion.) Vertical air motion can be calculated from the nadir beam by removing the fallspeed contribution with an approximation.  Along-track horizontal air motion can be calculated by combining doppler wind speeds from forward and nadir beams. The linear depolarization ratio (the ratio of the cross-polar to the co-polar reflectivites) can be measured along forward beam. EDOP is a coherent Doppler radar system at 9.6 GHz. EDOP provides measurements from a forward pointing beam that is used in combination with the nadir beam for estimating the along-track winds.
The EDOP  provides vertically profiled reflectivity and Doppler velocity at aircraft nadir along the flight track.  The ER-2 Doppler radar (EDOP) is an X-band (9.6 GHz) Doppler radar mounted in the nose of the ER-2 aircraft. 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 at the surface of 1 km. Each Antenna measures the doppler velocity, doppler spectral width, and reflectivity factor. Doppler velocities provide a measure of the pulse volume-weighted hydrometer motion (hydrometer fallspeed + air motion.) Vertical air motion can be calculated from the nadir beam by removing the fallspeed contribution with an approximation.  Along-track horizontal air motion can be calculated by combining doppler wind speeds from forward and nadir beams. The linear depolarization ratio (the ratio of the cross-polar to the co-polar reflectivites) can be measured along forward beam. EDOP is a coherent Doppler radar system at 9.6 GHz. EDOP provides measurements from a forward pointing beam that is used in combination with the nadir beam for estimating the along-track winds.
The EDOP  provides vertically profiled reflectivity and Doppler velocity at aircraft nadir along the flight track.  The ER-2 Doppler radar (EDOP) is an X-band (9.6 GHz) Doppler radar mounted in the nose of the ER-2 aircraft. 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 at the surface of 1 km. Each Antenna measures the doppler velocity, doppler spectral width, and reflectivity factor. Doppler velocities provide a measure of the pulse volume-weighted hydrometer motion (hydrometer fallspeed + air motion.) Vertical air motion can be calculated from the nadir beam by removing the fallspeed contribution with an approximation.  Along-track horizontal air motion can be calculated by combining doppler wind speeds from forward and nadir beams. The linear depolarization ratio (the ratio of the cross-polar to the co-polar reflectivites) can be measured along forward beam. EDOP is a coherent Doppler radar system at 9.6 GHz. EDOP provides measurements from a forward pointing beam that is used in combination with the nadir beam for estimating the along-track winds.
The ER-2 Lightning Instrument Package (LIP) consists of 7 rotating vane type electric field sensors and a two channel conductivity probe along with a central computer to record and monitor the instruments.  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. A more detailed discription of the LIP can be found at: tcsp.nsstc.nasa.gov/camex4/instrument_documentation/lip.pdf
The ER-2 Lightning Instrument Package (LIP) consists of 7 rotating vane type electric field sensors and a two channel conductivity probe along with a central computer to record and monitor the instruments.  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. A more detailed discription of the LIP can be found at: tcsp.nsstc.nasa.gov/camex4/instrument_documentation/lip.pdf
The ER-2 Lightning Instrument Package (LIP) consists of 7 rotating vane type electric field sensors and a two channel conductivity probe along with a central computer to record and monitor the instruments.  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. A more detailed discription of the LIP can be found at: tcsp.nsstc.nasa.gov/camex4/instrument_documentation/lip.pdf
The ER2 MTP is a passive microwave radiometer which measures the thermal emission from oxygen molecules in the atmosphere for a selection of elevation angles (normally 10 between +60 and -58 degrees). The current observing frequencies are 55.5, 56.6 and 58.8 GHz. Measured 'brightness temperature' versus elevation angle is converted to air temperature versus altitude using a modified statistical retrieval procedure with a Bayesian component. An altitude temperature profile (ATP) is produced in this manner every 13 seconds or approximately 3 km along the flight path. The ATP can be used to produce a color-coded temperature curtain (CTC) of the temperature field which the ER2 has flown through, and to identify the tropopause location. ATPs can also be used to locate altitudes where the air is cold enough to condense nitric acid or water vapor to form polar stratospheric
clouds (PSCs).
The MODIS Airborne Simulator (MAS) is a multi-spectral line-scanner system that acquires image data in 50 spectral bands over wavelengths ranging from 0.46 to 14.3 microns.  Flown on the ER-2 aircraft at an operating altitude of 19.8 km (65,000 ft.) it produces nominal pixel sizes of 50 meters.  MAS includes nine spectral bands in the visible/near infrared, 16 bands in the shortwave infrared, 16 bands in the mid-wave infrared, and nine bands in the thermal infrared regions of the spectrum.  The instrument field-of-view is 86 degrees, with an IFOV of 2.5 mrad.The MODIS Airborne Simulator (MAS) collected calibrated multi-spectral imagery from the ER-2 aircraft during the TCSP experiment.  The MAS was developed by NASA primarily to validate L1B and L2 science products from the EOS satellite program.  MAS data enables (1) the mapping of sub-pixel variation within the co-incident footprints of many orbital instruments (e.g. MODIS, AIRS, HIRS, AVHRR, GOES) in the visible and thermal infrared spectral regions and (2) the estimation of surface, aerosol, and cloud properties at 50 meter spatial resolution.
Geostationary satellite-derived wind sets have traditionally been generated from image triplets with 30 or 60 minute intervals, and occasionally 15 minute intervals. During GOES special rapid-scan operations, co-located images are available at intervals of 7.5, 5, 3, and even 1 minute. The area covered is reduced as the interval decreases. In this experiment images at five minute intervals were used for the 0.65 micrometer VIS, 3.9 micrometer IR (darkness only), and 10.7 micrometer IR channels.  GOES-11 was brought out of storage and image products were centered on the TCSP study region.  Regular image processing was available beginning on 12 July.  The scan schedule was maintained through the end of July.
In support of the Tropical Cloud Systems and Processes (TCSP) mission, visible and infrared imagery from the Geostationary Operational Environmental Satellite 11 and 12 (GOES11, GOES 12) was collected and archived. Two channels were archived: channel 1-- visible (0.65 microns), and channel 2-- infrared (11 microns). Data files in McIDAS format as well as browse images were created.
HAMSR is a 25-channel microwave atmospheric sounder operating as a cross-track scanner. It is functionally similar to AMSU-A/B, but with additional channels. There are three bands: an 8-channel band near 50-GHz, used for primary temperature sounding; a 10-channel band near 118 GHz, used for secondary temperature sounding and assessment of scattering; a 7-channel band near 183 GHz, used for water vapor sounding. The instrument is continuously self-calibrating using internal calibration targets. Radiometric sensitivity at the composite sampling cells provided in the archive is typically 0.1 and ranges up to 0.25 K for the stratospheric channels. Calibration accuracy is estimated at better than 1 K for temperature sounding and better than 2 K for water vapor sounding. Temperature weighting function peaks are distributed between the surface and the flight altitude. HAMSR was mounted in a wing pod of a NASA ER-2 research aircraft.
HAMSR is a 25-channel microwave atmospheric sounder operating as a cross-track scanner. It is functionally similar to AMSU-A/B, but with additional channels. There are three bands: an 8-channel band near 50-GHz, used for primary temperature sounding; a 10-channel band near 118 GHz, used for secondary temperature sounding and assessment of scattering; a 7-channel band near 183 GHz, used for water vapor sounding. The instrument is continuously self-calibrating using internal calibration targets. Radiometric sensitivity at the composite sampling cells provided in the archive is typically 0.1 and ranges up to 0.25 K for the stratospheric channels. Calibration accuracy is estimated at better than 1 K for temperature sounding and better than 2 K for water vapor sounding. Temperature weighting function peaks are distributed between the surface and the flight altitude. HAMSR was mounted in a wing pod of a NASA ER-2 research aircraft.
The Ticosonde/Aura-TCSP 2005 radiosonde data set consists of 4 soundings per day (00, 06, 12 and 18 UT) launched from Juan Santamaria International Airport, WMO station 78762, latitude 10 degrees N and 84.2 degrees W.  The launch program began at 00 UT on 16 June 2005 and ended 00 UT 24 August 2005. With a very few exceptions, the sondes were Vaiasala model RS92-SGP and the ground station was a DigiCORA MW11 equipped for GPS wind-finding and upgraded for RS92 telemetry. A small number of ascents were made with RS90-AG and RS80-15G sondes.  Most ascents were done with 500-g latex balloons filled with hydrogen. Exceptions included 24 ascents at 06 and 18UT in July that were piggybacked on a larger payload consisting of the University of Colorado cryogenic frostpoint hygrometer (CFH) and an ECC ozonesonde. Median termination altitude for all ascents was approximately 26 km.  Data were recorded at the maximum MW11 sample rate of one every two seconds.
The Ticosonde/Aura-TCSP 2005 radiosonde data set consists of 4 soundings per day (00, 06, 12 and 18 UT) launched from Juan Santamaria International Airport, WMO station 78762, latitude 10 degrees N and 84.2 degrees W.  The launch program began at 00 UT on 16 June 2005 and ended 00 UT 24 August 2005. With a very few exceptions, the sondes were Vaiasala model RS92-SGP and the ground station was a DigiCORA MW11 equipped for GPS wind-finding and upgraded for RS92 telemetry. A small number of ascents were made with RS90-AG and RS80-15G sondes.  Most ascents were done with 500-g latex balloons filled with hydrogen. Exceptions included 24 ascents at 06 and 18UT in July that were piggybacked on a larger payload consisting of the University of Colorado cryogenic frostpoint hygrometer (CFH) and an ECC ozonesonde. Median termination altitude for all ascents was approximately 26 km.  Data were recorded at the maximum MW11 sample rate of one every two seconds.
The Ticosonde/Aura-TCSP 2005 radiosonde data set consists of 4 soundings per day (00, 06, 12 and 18 UT) launched from Juan Santamaria International Airport, WMO station 78762, latitude 10 degrees N and 84.2 degrees W.  The launch program began at 00 UT on 16 June 2005 and ended 00 UT 24 August 2005. With a very few exceptions, the sondes were Vaiasala model RS92-SGP and the ground station was a DigiCORA MW11 equipped for GPS wind-finding and upgraded for RS92 telemetry. A small number of ascents were made with RS90-AG and RS80-15G sondes.  Most ascents were done with 500-g latex balloons filled with hydrogen. Exceptions included 24 ascents at 06 and 18UT in July that were piggybacked on a larger payload consisting of the University of Colorado cryogenic frostpoint hygrometer (CFH) and an ECC ozonesonde. Median termination altitude for all ascents was approximately 26 km.  Data were recorded at the maximum MW11 sample rate of one every two seconds.
The Ticosonde/Aura-TCSP 2005 radiosonde data set consists of 4 soundings per day (00, 06, 12 and 18 UT) launched from Juan Santamaria International Airport, WMO station 78762, latitude 10 degrees N and 84.2 degrees W.  The launch program began at 00 UT on 16 June 2005 and ended 00 UT 24 August 2005. With a very few exceptions, the sondes were Vaiasala model RS92-SGP and the ground station was a DigiCORA MW11 equipped for GPS wind-finding and upgraded for RS92 telemetry. A small number of ascents were made with RS90-AG and RS80-15G sondes.  Most ascents were done with 500-g latex balloons filled with hydrogen. Exceptions included 24 ascents at 06 and 18UT in July that were piggybacked on a larger payload consisting of the University of Colorado cryogenic frostpoint hygrometer (CFH) and an ECC ozonesonde. Median termination altitude for all ascents was approximately 26 km.  Data were recorded at the maximum MW11 sample rate of one every two seconds.
The Ticosonde/Aura-TCSP 2005 radiosonde data set consists of 4 soundings per day (00, 06, 12 and 18 UT) launched from Juan Santamaria International Airport, WMO station 78762, latitude 10 degrees N and 84.2 degrees W.  The launch program began at 00 UT on 16 June 2005 and ended 00 UT 24 August 2005. With a very few exceptions, the sondes were Vaiasala model RS92-SGP and the ground station was a DigiCORA MW11 equipped for GPS wind-finding and upgraded for RS92 telemetry. A small number of ascents were made with RS90-AG and RS80-15G sondes.  Most ascents were done with 500-g latex balloons filled with hydrogen. Exceptions included 24 ascents at 06 and 18UT in July that were piggybacked on a larger payload consisting of the University of Colorado cryogenic frostpoint hygrometer (CFH) and an ECC ozonesonde. Median termination altitude for all ascents was approximately 26 km.  Data were recorded at the maximum MW11 sample rate of one every two seconds.
The Ticosonde/Aura-TCSP 2005 radiosonde data set consists of 4 soundings per day (00, 06, 12 and 18 UT) launched from Juan Santamaria International Airport, WMO station 78762, latitude 10 degrees N and 84.2 degrees W.  The launch program began at 00 UT on 16 June 2005 and ended 00 UT 24 August 2005. With a very few exceptions, the sondes were Vaiasala model RS92-SGP and the ground station was a DigiCORA MW11 equipped for GPS wind-finding and upgraded for RS92 telemetry. A small number of ascents were made with RS90-AG and RS80-15G sondes.  Most ascents were done with 500-g latex balloons filled with hydrogen. Exceptions included 24 ascents at 06 and 18UT in July that were piggybacked on a larger payload consisting of the University of Colorado cryogenic frostpoint hygrometer (CFH) and an ECC ozonesonde. Median termination altitude for all ascents was approximately 26 km.  Data were recorded at the maximum MW11 sample rate of one every two seconds.
The Ticosonde/Aura-TCSP 2005 radiosonde data set consists of 4 soundings per day (00, 06, 12 and 18 UT) launched from Juan Santamaria International Airport, WMO station 78762, latitude 10 degrees N and 84.2 degrees W.  The launch program began at 00 UT on 16 June 2005 and ended 00 UT 24 August 2005. With a very few exceptions, the sondes were Vaiasala model RS92-SGP and the ground station was a DigiCORA MW11 equipped for GPS wind-finding and upgraded for RS92 telemetry. A small number of ascents were made with RS90-AG and RS80-15G sondes.  Most ascents were done with 500-g latex balloons filled with hydrogen. Exceptions included 24 ascents at 06 and 18UT in July that were piggybacked on a larger payload consisting of the University of Colorado cryogenic frostpoint hygrometer (CFH) and an ECC ozonesonde. Median termination altitude for all ascents was approximately 26 km.  Data were recorded at the maximum MW11 sample rate of one every two seconds.
The Advanced Microwave Precipitation Radiometer (AMPR) was deployed during the Tropical Rainfall Measuring Mission - Large Scale Biosphere-Atmosphere Experiment (TRMM-LBA); the second of three TRMM ground validation missions.  AMPR data were collected at four distinct microwave frequencies (10.7, 19.35, 37.1 and 85.5 GHz) for the time period of January 23 through February 26, 1999.  The geographic domain of the TRMM-LBA region was wholly within Brazilian Amazon Basin between 16 S to 6N latitude and 76W to 49 W longitude.  The TRMM-LBA mission was to study convection over humid tropical land regions within the range of research-quality radar, lightning, radiosonde and raingage sites located in the Amazon Basin (Rondonia, Brazil).
The Advanced Microwave Precipitation Radiometer (AMPR) was deployed during the Tropical Rainfall Measuring Mission - Large Scale Biosphere-Atmosphere Experiment (TRMM-LBA); the second of three TRMM ground validation missions.  AMPR data were collected at four distinct microwave frequencies (10.7, 19.35, 37.1 and 85.5 GHz) for the time period of January 23 through February 26, 1999.  The geographic domain of the TRMM-LBA region was wholly within Brazilian Amazon Basin between 16 S to 6N latitude and 76W to 49 W longitude.  The TRMM-LBA mission was to study convection over humid tropical land regions within the range of research-quality radar, lightning, radiosonde and raingage sites located in the Amazon Basin (Rondonia, Brazil).
The Advanced Microwave Precipitation Radiometer (AMPR) was deployed during the Tropical Rainfall Measuring Mission - Large Scale Biosphere-Atmosphere Experiment (TRMM-LBA); the second of three TRMM ground validation missions.  AMPR data were collected at four distinct microwave frequencies (10.7, 19.35, 37.1 and 85.5 GHz) for the time period of January 23 through February 26, 1999.  The geographic domain of the TRMM-LBA region was wholly within Brazilian Amazon Basin between 16 S to 6N latitude and 76W to 49 W longitude.  The TRMM-LBA mission was to study convection over humid tropical land regions within the range of research-quality radar, lightning, radiosonde and raingage sites located in the Amazon Basin (Rondonia, Brazil).
The TRMM Microwave Imager (TMI) is a 5-channel, dual-polarized, passive microwave radiometer.  Microwave radiation is emitted by the Earth's surface and by water droplets within clouds.  The TMI is used to measure several important meteorological parameters over sea surfaces.  The TMI, a successor to the SSM/I, measures radiation at frequencies of 10.7, 19.4, 21.3, 37, 85.5 GHz.  It orbits at an altitude of 218 miles, much lower than the SSM/I, thus providing better resolution.
The TRMM Microwave Imager (TMI) is a 5-channel, dual-polarized, passive microwave radiometer.  Microwave radiation is emitted by the Earth's surface and by water droplets within clouds.  The TMI is used to measure several important meteorological parameters over sea surfaces.  The TMI, a successor to the SSM/I, measures radiation at frequencies of 10.7, 19.4, 21.3, 37, 85.5 GHz.  It orbits at an altitude of 218 miles, much lower than the SSM/I, thus providing better resolution.
The TRMM Microwave Imager (TMI) is a 5-channel, dual-polarized, passive microwave radiometer.  Microwave radiation is emitted by the Earth's surface and by water droplets within clouds.  The TMI is used to measure several important meteorological parameters over sea surfaces.  The TMI, a successor to the SSM/I, measures radiation at frequencies of 10.7, 19.4, 21.3, 37, 85.5 GHz.  It orbits at an altitude of 218 miles, much lower than the SSM/I, thus providing better resolution.
The Lightning Imaging Sensor (LIS) Science Computing Facility (SCF) generates a cloud-to-ground lightning product from the data collected from the U.S. National Lightning Detection Network, a commercial lightning detection network operated by Global Atmospherics, Inc. (GAI), formerly Geomet Data Services. The lightning products are made by binning the number of  flashes that occur over a 15 min period to a pixel (pixel is approximately 8 km by 8 km). The data set begins on July 19, 1994 and continues through the present.  This data is distributed by the Global Hydrology Resource Center (GHRC).
The Lightning Imaging Sensor (LIS) Science Computing Facility (SCF) generates a cloud-to-ground lightning product from the data collected from the U.S. National Lightning Detection Network, a commercial lightning detection network operated by Global Atmospherics, Inc. (GAI), formerly Geomet Data Services. The lightning products are made by binning the number of  flashes that occur over a 15 min period to a pixel (pixel is approximately 8 km by 8 km). The data set begins on July 19, 1994 and continues through the present.  This data is distributed by the Global Hydrology Resource Center (GHRC).
The Lightning Imaging Sensor (LIS) Science Computing Facility (SCF) generates a cloud-to-ground lightning product from the data collected from the U.S. National Lightning Detection Network, a commercial lightning detection network operated by Global Atmospherics, Inc. (GAI), formerly Geomet Data Services. The lightning products are made by binning the number of  flashes that occur over a 15 min period to a pixel (pixel is approximately 8 km by 8 km). The data set begins on July 19, 1994 and continues through the present.  This data is distributed by the Global Hydrology Resource Center (GHRC).
The Global Hydrology Resource Center generates a cloud-to-ground lightning product from the data collected from the U.S. National Lightning Detection Network, a commercial lightning detection network operated by Global Atmospherics, Inc. (GAI), formerly Geomet Data Services. The daily products are produced by binning the number of flashes occurring in each pixel (pixel is approximately 8 km by 8 km) during a 24 hr period (00 UTC to 00 UTC). The data set begins on July 8, 1994 and continues through the present.
The Global Hydrology Resource Center generates a cloud-to-ground lightning product from the data collected from the U.S. National Lightning Detection Network, a commercial lightning detection network operated by Global Atmospherics, Inc. (GAI), formerly Geomet Data Services. The daily products are produced by binning the number of flashes occurring in each pixel (pixel is approximately 8 km by 8 km) during a 24 hr period (00 UTC to 00 UTC). The data set begins on July 8, 1994 and continues through the present.
The U.S. National Lightning Detection Network is a commercial lightning detection network operated by Vaisala. A network of over 100 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, 1995 to present.
This data set contains sea level anomalies and alongshore ocean currents (U and V) derived from satellite altimeters and tide gauge data.  Currents and heights near the coast are calculated using tide
This data set contains sea level anomalies and alongshore ocean currents (U and V) derived from satellite altimeters and tide gauge data.  Currents and heights near the coast are calculated using tide
This data set contains sea level anomalies and alongshore ocean currents (U and V) derived from satellite altimeters and tide gauge data.  Currents and heights near the coast are calculated using tide
These data are the near real time (within 7-9 hours of measurement) sea surface height anomalies (SSHA) and orbits from the AltiKa altimeter onboard SARAL.  SARAL is a French/Indian collaborative mission to measure sea surface height using the Ka band altimeter AltiKa.  The major difference between these data and the OGDR data produced by the project is that the orbit from SARAL has been adjusted to the GPS based orbit on OSTM/Jason-2 during cross-overs of the two satellites.  This produces a more accurate orbit, and thus more accurate SSHA from AltiKa, with an RMS of 1.5 cm.  This dataset also contains wave height and improved wind speed, sea state bias, dynamic fit to the DORIS-DIODE orbit, and cross-over based correction.
These data are the near real time (within 7-9 hours of measurement) sea surface height anomalies (SSHA) and orbits from the AltiKa altimeter onboard SARAL.  SARAL is a French/Indian collaborative mission to measure sea surface height using the Ka band altimeter AltiKa.  The major difference between these data and the OGDR data produced by the project is that the orbit from SARAL has been adjusted to the GPS based orbit on OSTM/Jason-2 during cross-overs of the two satellites.  This produces a more accurate orbit, and thus more accurate SSHA from AltiKa, with an RMS of 1.5 cm.  This dataset also contains wave height and improved wind speed, sea state bias, dynamic fit to the DORIS-DIODE orbit, and cross-over based correction.
These data are the near real time (within 7-9 hours of measurement) sea surface height anomalies (SSHA) and orbits from the AltiKa altimeter onboard SARAL.  SARAL is a French/Indian collaborative mission to measure sea surface height using the Ka band altimeter AltiKa.  The major difference between these data and the OGDR data produced by the project is that the orbit from SARAL has been adjusted to the GPS based orbit on OSTM/Jason-2 during cross-overs of the two satellites.  This produces a more accurate orbit, and thus more accurate SSHA from AltiKa, with an RMS of 1.5 cm.  This dataset also contains wave height and improved wind speed, sea state bias, dynamic fit to the DORIS-DIODE orbit, and cross-over based correction.
These data are the near real time (within 7-9 hours of measurement) sea surface height anomalies (SSHA) and orbits from the AltiKa altimeter onboard SARAL.  SARAL is a French/Indian collaborative mission to measure sea surface height using the Ka band altimeter AltiKa.  The major difference between these data and the OGDR data produced by the project is that the orbit from SARAL has been adjusted to the GPS based orbit on OSTM/Jason-2 during cross-overs of the two satellites.  This produces a more accurate orbit, and thus more accurate SSHA from AltiKa, with an RMS of 1.5 cm.  This dataset also contains wave height and improved wind speed, sea state bias, dynamic fit to the DORIS-DIODE orbit, and cross-over based correction.
These data are the near real time (within 7-9 hours of measurement) sea surface height anomalies (SSHA) and orbits from the AltiKa altimeter onboard SARAL.  SARAL is a French/Indian collaborative mission to measure sea surface height using the Ka band altimeter AltiKa.  The major difference between these data and the OGDR data produced by the project is that the orbit from SARAL has been adjusted to the GPS based orbit on OSTM/Jason-2 during cross-overs of the two satellites.  This produces a more accurate orbit, and thus more accurate SSHA from AltiKa, with an RMS of 1.5 cm.  This dataset also contains wave height and improved wind speed, sea state bias, dynamic fit to the DORIS-DIODE orbit, and cross-over based correction.
This data set contains sea surface temperature (SST) data on a monthly 1 degree grid from the Advanced Microwave Scanning Radiometer (AMSR-E) aboard NASA's Aqua spacecraft. The data were produced by Remote Sensing Systems in support of the CMIP5 (Coupled Model Intercomparison Project Phase 5) under the World Climate Research Program (WCRP).   Along with this dataset, two additional ancillary data files are included in the same directory which contain the number of observations and standard error co-located on the same 1 degree grids.  AMSR-E, a passive-microwave radiometer launched on the Aqua platform on 4 May 2002, was provided by the National Space Development Agency (NASDA) of Japan 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 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.
This data set contains sea surface temperature (SST) data on a monthly 1 degree grid from the Advanced Microwave Scanning Radiometer (AMSR-E) aboard NASA's Aqua spacecraft. The data were produced by Remote Sensing Systems in support of the CMIP5 (Coupled Model Intercomparison Project Phase 5) under the World Climate Research Program (WCRP).   Along with this dataset, two additional ancillary data files are included in the same directory which contain the number of observations and standard error co-located on the same 1 degree grids.  AMSR-E, a passive-microwave radiometer launched on the Aqua platform on 4 May 2002, was provided by the National Space Development Agency (NASDA) of Japan 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 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.
This data set contains sea surface temperature (SST) data on a monthly 1 degree grid from the Advanced Microwave Scanning Radiometer (AMSR-E) aboard NASA's Aqua spacecraft. The data were produced by Remote Sensing Systems in support of the CMIP5 (Coupled Model Intercomparison Project Phase 5) under the World Climate Research Program (WCRP).   Along with this dataset, two additional ancillary data files are included in the same directory which contain the number of observations and standard error co-located on the same 1 degree grids.  AMSR-E, a passive-microwave radiometer launched on the Aqua platform on 4 May 2002, was provided by the National Space Development Agency (NASDA) of Japan 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 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.
This data set contains sea surface temperature (SST) data on a monthly 1 degree grid from the Advanced Microwave Scanning Radiometer (AMSR-E) aboard NASA's Aqua spacecraft. The data were produced by Remote Sensing Systems in support of the CMIP5 (Coupled Model Intercomparison Project Phase 5) under the World Climate Research Program (WCRP).   Along with this dataset, two additional ancillary data files are included in the same directory which contain the number of observations and standard error co-located on the same 1 degree grids.  AMSR-E, a passive-microwave radiometer launched on the Aqua platform on 4 May 2002, was provided by the National Space Development Agency (NASDA) of Japan 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 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.
This data set contains sea surface temperature (SST) data on a monthly 1 degree grid from the Advanced Microwave Scanning Radiometer (AMSR-E) aboard NASA's Aqua spacecraft. The data were produced by Remote Sensing Systems in support of the CMIP5 (Coupled Model Intercomparison Project Phase 5) under the World Climate Research Program (WCRP).   Along with this dataset, two additional ancillary data files are included in the same directory which contain the number of observations and standard error co-located on the same 1 degree grids.  AMSR-E, a passive-microwave radiometer launched on the Aqua platform on 4 May 2002, was provided by the National Space Development Agency (NASDA) of Japan 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 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.
This data set contains sea surface temperature (SST) data on a monthly 1 degree grid from the Advanced Microwave Scanning Radiometer (AMSR-E) aboard NASA's Aqua spacecraft. The data were produced by Remote Sensing Systems in support of the CMIP5 (Coupled Model Intercomparison Project Phase 5) under the World Climate Research Program (WCRP).   Along with this dataset, two additional ancillary data files are included in the same directory which contain the number of observations and standard error co-located on the same 1 degree grids.  AMSR-E, a passive-microwave radiometer launched on the Aqua platform on 4 May 2002, was provided by the National Space Development Agency (NASDA) of Japan 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 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.
This data set contains sea surface temperature (SST) data on a monthly 1 degree grid from the Advanced Microwave Scanning Radiometer (AMSR-E) aboard NASA's Aqua spacecraft. The data were produced by Remote Sensing Systems in support of the CMIP5 (Coupled Model Intercomparison Project Phase 5) under the World Climate Research Program (WCRP).   Along with this dataset, two additional ancillary data files are included in the same directory which contain the number of observations and standard error co-located on the same 1 degree grids.  AMSR-E, a passive-microwave radiometer launched on the Aqua platform on 4 May 2002, was provided by the National Space Development Agency (NASDA) of Japan 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 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 version 2.0 Aquarius CAP Level 2 product contains the first release of the operational orbital/swath data based on the Combined Active Passive (CAP) algorithm.  CAP is a P.I. produced data set developed and provided by the JPL Climate Oceans and Solid Earth group (S. Yueh).   This Level 2 data set contains sea surface salinity (SSS), wind speed and wind direction data derived from 3 different radiometers and the scatterometer aboard the AQUARIUS/SAC-D satellite.  The CAP algorithm simultaneously retrieves the salinity, wind speed and direction by minimizing the sum of squared differences between model and observations. Each data file covers one 98 minute orbit.
The version 2.0 Aquarius CAP Level 2 product contains the first release of the operational orbital/swath data based on the Combined Active Passive (CAP) algorithm.  CAP is a P.I. produced data set developed and provided by the JPL Climate Oceans and Solid Earth group (S. Yueh).   This Level 2 data set contains sea surface salinity (SSS), wind speed and wind direction data derived from 3 different radiometers and the scatterometer aboard the AQUARIUS/SAC-D satellite.  The CAP algorithm simultaneously retrieves the salinity, wind speed and direction by minimizing the sum of squared differences between model and observations. Each data file covers one 98 minute orbit.
The version 2.0 Aquarius CAP Level 2 product contains the first release of the operational orbital/swath data based on the Combined Active Passive (CAP) algorithm.  CAP is a P.I. produced data set developed and provided by the JPL Climate Oceans and Solid Earth group (S. Yueh).   This Level 2 data set contains sea surface salinity (SSS), wind speed and wind direction data derived from 3 different radiometers and the scatterometer aboard the AQUARIUS/SAC-D satellite.  The CAP algorithm simultaneously retrieves the salinity, wind speed and direction by minimizing the sum of squared differences between model and observations. Each data file covers one 98 minute orbit.
The version 2.0 Aquarius Level 2 product contains the official first release of the operational orbital/swath data from AQUARIUS/SAC-D mission.
The version 2.0 Aquarius Level 2 product contains the official first release of the operational orbital/swath data from AQUARIUS/SAC-D mission.
The version 2.0 Aquarius Level 2 product contains the official first release of the operational orbital/swath data from AQUARIUS/SAC-D mission.
The version 3.0 Aquarius Level 2 product contains the official second release of the operational orbital/swath data from AQUARIUS/SAC-D mission.
The version 3.0 Aquarius Level 2 product contains the official second release of the operational orbital/swath data from AQUARIUS/SAC-D mission.
The version 3.0 Aquarius Level 2 product contains the official second release of the operational orbital/swath data from AQUARIUS/SAC-D mission.
The version 3.0 Aquarius Level 2 product contains the official second release of the operational orbital/swath data from AQUARIUS/SAC-D mission.
The version 3.0 Aquarius Level 2 product contains the official second release of the operational orbital/swath data from AQUARIUS/SAC-D mission.
The version 3.0 Aquarius Level 2 product contains the official second release of the operational orbital/swath data from AQUARIUS/SAC-D mission.
The version 3.0 Aquarius Level 2 product contains the official second release of the operational orbital/swath data from AQUARIUS/SAC-D mission.
The version 3.0 Aquarius Level 2 product contains the official second release of the operational orbital/swath data from AQUARIUS/SAC-D mission.
The version 3.0 Aquarius Level 2 product contains the official second release of the operational orbital/swath data from AQUARIUS/SAC-D mission.
Level 3, Seasonal, ancillary Reynolds SST product for version 3.0 of the Aquarius data set
Level 3, Seasonal, ancillary Reynolds SST product for version 3.0 of the Aquarius data set
Level 3, Seasonal, ancillary Reynolds SST product for version 3.0 of the Aquarius data set
Level 3, Seasonal, ancillary Reynolds SST product for version 3.0 of the Aquarius data set
Level 3, Seasonal, ancillary Reynolds SST product for version 3.0 of the Aquarius data set
Level 3, Seasonal, ancillary Reynolds SST product for version 3.0 of the Aquarius data set
Level 3, Seasonal, ancillary Reynolds SST product for version 3.0 of the Aquarius data set
Level 3, Seasonal, ancillary Reynolds SST product for version 3.0 of the Aquarius data set
Level 3, Seasonal, ancillary Reynolds SST product for version 3.0 of the Aquarius data set
Level 3, 7-day, ancillary Reynolds SST product for version 3.0 of the Aquarius data set
Level 3, 7-day, ancillary Reynolds SST product for version 3.0 of the Aquarius data set
Level 3, 7-day, ancillary Reynolds SST product for version 3.0 of the Aquarius data set
Level 3, 7-day, ancillary Reynolds SST product for version 3.0 of the Aquarius data set
Level 3, 7-day, ancillary Reynolds SST product for version 3.0 of the Aquarius data set
Level 3, 7-day, ancillary Reynolds SST product for version 3.0 of the Aquarius data set
Level 3, 7-day, ancillary Reynolds SST product for version 3.0 of the Aquarius data set
Level 3, 7-day, ancillary Reynolds SST product for version 3.0 of the Aquarius data set
Level 3, 7-day, ancillary Reynolds SST product for version 3.0 of the Aquarius data set
Level 3, Annual, ancillary Reynolds SST product for version 3.0 of the Aquarius data set
Level 3, Annual, ancillary Reynolds SST product for version 3.0 of the Aquarius data set
Level 3, Annual, ancillary Reynolds SST product for version 3.0 of the Aquarius data set
Level 3, Annual, ancillary Reynolds SST product for version 3.0 of the Aquarius data set
Level 3, Annual, ancillary Reynolds SST product for version 3.0 of the Aquarius data set
Level 3, Annual, ancillary Reynolds SST product for version 3.0 of the Aquarius data set
Level 3, Annual, ancillary Reynolds SST product for version 3.0 of the Aquarius data set
Level 3, Annual, ancillary Reynolds SST product for version 3.0 of the Aquarius data set
Level 3, Annual, ancillary Reynolds SST product for version 3.0 of the Aquarius data set
Level 3, Daily, ancillary Reynolds SST product for version 3.0 of the Aquarius data set
Level 3, Daily, ancillary Reynolds SST product for version 3.0 of the Aquarius data set
Level 3, Daily, ancillary Reynolds SST product for version 3.0 of the Aquarius data set
Level 3, Daily, ancillary Reynolds SST product for version 3.0 of the Aquarius data set
Level 3, Daily, ancillary Reynolds SST product for version 3.0 of the Aquarius data set
Level 3, Daily, ancillary Reynolds SST product for version 3.0 of the Aquarius data set
Level 3, Daily, ancillary Reynolds SST product for version 3.0 of the Aquarius data set
Level 3, Daily, ancillary Reynolds SST product for version 3.0 of the Aquarius data set
Level 3, Daily, ancillary Reynolds SST product for version 3.0 of the Aquarius data set
Level 3, Monthly, ancillary Reynolds SST product for version 3.0 of the Aquarius data set
Level 3, Monthly, ancillary Reynolds SST product for version 3.0 of the Aquarius data set
Level 3, Monthly, ancillary Reynolds SST product for version 3.0 of the Aquarius data set
Level 3, Monthly, ancillary Reynolds SST product for version 3.0 of the Aquarius data set
Level 3, Monthly, ancillary Reynolds SST product for version 3.0 of the Aquarius data set
Level 3, Monthly, ancillary Reynolds SST product for version 3.0 of the Aquarius data set
Level 3, Monthly, ancillary Reynolds SST product for version 3.0 of the Aquarius data set
Level 3, Monthly, ancillary Reynolds SST product for version 3.0 of the Aquarius data set
Level 3, Monthly, ancillary Reynolds SST product for version 3.0 of the Aquarius data set
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Ascending Seasonal Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Ascending Seasonal Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Ascending Seasonal Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Ascending Seasonal Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Ascending Seasonal Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Ascending Seasonal Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Ascending Seasonal Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Ascending Seasonal Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Ascending Seasonal Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Ascending 7-Day Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Ascending 7-Day Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Ascending 7-Day Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Ascending 7-Day Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Ascending 7-Day Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Ascending 7-Day Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Ascending 7-Day Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Ascending 7-Day Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Ascending 7-Day Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Ascending Annual Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Ascending Annual Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Ascending Annual Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Ascending Annual Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Ascending Annual Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Ascending Annual Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Ascending Annual Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Ascending Annual Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Ascending Annual Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Ascending Daily Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Ascending Daily Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Ascending Daily Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Ascending Daily Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Ascending Daily Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Ascending Daily Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Ascending Daily Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Ascending Daily Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Ascending Daily Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Ascending Monthly Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Ascending Monthly Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Ascending Monthly Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Ascending Monthly Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Ascending Monthly Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Ascending Monthly Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Ascending Monthly Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Ascending Monthly Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Ascending Monthly Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Descending Seasonal Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Descending Seasonal Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Descending Seasonal Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Descending Seasonal Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Descending Seasonal Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Descending Seasonal Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Descending Seasonal Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Descending Seasonal Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Descending Seasonal Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Descending 7-Day Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Descending 7-Day Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Descending 7-Day Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Descending 7-Day Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Descending 7-Day Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Descending 7-Day Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Descending 7-Day Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Descending 7-Day Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Descending 7-Day Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Descending Annual Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Descending Annual Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Descending Annual Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Descending Annual Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Descending Annual Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Descending Annual Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Descending Annual Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Descending Annual Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Descending Annual Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Descending Daily Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Descending Daily Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Descending Daily Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Descending Daily Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Descending Daily Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Descending Daily Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Descending Daily Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Descending Daily Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Descending Daily Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Descending Monthly Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Descending Monthly Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Descending Monthly Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Descending Monthly Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Descending Monthly Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Descending Monthly Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Descending Monthly Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Descending Monthly Data V3.0
Aquarius Official Release Level 3 Ancillary Reynolds Sea Surface Temperature Standard Mapped Image Descending Monthly Data V3.0
Level 3, monthly sea surface salinity bias adjusted smoothed product for version 3.0 of the Aquarius data set
Level 3, monthly sea surface salinity bias adjusted smoothed product for version 3.0 of the Aquarius data set
Level 3, monthly sea surface salinity bias adjusted smoothed product for version 3.0 of the Aquarius data set
Level 3, monthly sea surface salinity bias adjusted smoothed product for version 3.0 of the Aquarius data set
Level 3, monthly sea surface salinity bias adjusted smoothed product for version 3.0 of the Aquarius data set
Level 3, monthly sea surface salinity bias adjusted smoothed product for version 3.0 of the Aquarius data set
Level 3, monthly sea surface salinity bias adjusted smoothed product for version 3.0 of the Aquarius data set
Level 3, monthly sea surface salinity bias adjusted smoothed product for version 3.0 of the Aquarius data set
Level 3, monthly sea surface salinity bias adjusted smoothed product for version 3.0 of the Aquarius data set
Level 3, monthly Ascending sea surface salinity bias adjusted smoothed product for version 3.0 of the Aquarius data set
Level 3, monthly Ascending sea surface salinity bias adjusted smoothed product for version 3.0 of the Aquarius data set
Level 3, monthly Ascending sea surface salinity bias adjusted smoothed product for version 3.0 of the Aquarius data set
Level 3, monthly Ascending sea surface salinity bias adjusted smoothed product for version 3.0 of the Aquarius data set
Level 3, monthly Ascending sea surface salinity bias adjusted smoothed product for version 3.0 of the Aquarius data set
Level 3, monthly Ascending sea surface salinity bias adjusted smoothed product for version 3.0 of the Aquarius data set
Level 3, monthly Ascending sea surface salinity bias adjusted smoothed product for version 3.0 of the Aquarius data set
Level 3, monthly Ascending sea surface salinity bias adjusted smoothed product for version 3.0 of the Aquarius data set
Level 3, monthly Ascending sea surface salinity bias adjusted smoothed product for version 3.0 of the Aquarius data set
Level 3, monthly Descending sea surface salinity smoothed bias adjusted product for version 3.0 of the Aquarius data set
Level 3, monthly Descending sea surface salinity smoothed bias adjusted product for version 3.0 of the Aquarius data set
Level 3, monthly Descending sea surface salinity smoothed bias adjusted product for version 3.0 of the Aquarius data set
Level 3, monthly Descending sea surface salinity smoothed bias adjusted product for version 3.0 of the Aquarius data set
Level 3, monthly Descending sea surface salinity smoothed bias adjusted product for version 3.0 of the Aquarius data set
Level 3, monthly Descending sea surface salinity smoothed bias adjusted product for version 3.0 of the Aquarius data set
Level 3, monthly Descending sea surface salinity smoothed bias adjusted product for version 3.0 of the Aquarius data set
Level 3, monthly Descending sea surface salinity smoothed bias adjusted product for version 3.0 of the Aquarius data set
Level 3, monthly Descending sea surface salinity smoothed bias adjusted product for version 3.0 of the Aquarius data set
Level 3, seasonal sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, seasonal sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, seasonal sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, seasonal sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, seasonal sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, seasonal sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, seasonal sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, seasonal sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, seasonal sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, 7-Day sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, 7-Day sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, 7-Day sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, 7-Day sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, 7-Day sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, 7-Day sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, 7-Day sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, 7-Day sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, 7-Day sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, annual sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, annual sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, annual sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, annual sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, annual sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, annual sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, annual sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, annual sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, annual sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, Daily sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, Daily sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, Daily sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, Daily sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, Daily sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, Daily sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, Daily sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, Daily sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, Daily sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, monthly sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, monthly sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, monthly sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, monthly sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, monthly sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, monthly sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, monthly sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, monthly sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, monthly sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, seasonal ascending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, seasonal ascending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, seasonal ascending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, seasonal ascending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, seasonal ascending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, seasonal ascending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, seasonal ascending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, seasonal ascending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, seasonal ascending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
evel 3, 7-day ascending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
evel 3, 7-day ascending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
evel 3, 7-day ascending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
evel 3, 7-day ascending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
evel 3, 7-day ascending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
evel 3, 7-day ascending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
evel 3, 7-day ascending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
evel 3, 7-day ascending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
evel 3, 7-day ascending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, annual ascending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, annual ascending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, annual ascending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, annual ascending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, annual ascending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, annual ascending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, annual ascending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, annual ascending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, annual ascending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, daily ascensding sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, daily ascensding sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, daily ascensding sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, daily ascensding sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, daily ascensding sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, daily ascensding sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, daily ascensding sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, daily ascensding sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, daily ascensding sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, monthly ascending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, monthly ascending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, monthly ascending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, monthly ascending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, monthly ascending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, monthly ascending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, monthly ascending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, monthly ascending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, monthly ascending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, seasonal descending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, seasonal descending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, seasonal descending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, seasonal descending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, seasonal descending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, seasonal descending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, seasonal descending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, seasonal descending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, seasonal descending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, 7-day descending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, 7-day descending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, 7-day descending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, 7-day descending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, 7-day descending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, 7-day descending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, 7-day descending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, 7-day descending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, 7-day descending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, annual descending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, annual descending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, annual descending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, annual descending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, annual descending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, annual descending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, annual descending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, annual descending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, annual descending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, daily descending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, daily descending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, daily descending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, daily descending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, daily descending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, daily descending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, daily descending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, daily descending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, daily descending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, monthly descending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, monthly descending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, monthly descending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, monthly descending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, monthly descending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, monthly descending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, monthly descending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, monthly descending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Level 3, monthly descending sea surface salinity bias adjusted product for version 3.0 of the Aquarius data set
Version 2.0 Aquarius CAP Level 3 sea surface salinity (SSS) standard mapped 7-Day image data
Version 2.0 Aquarius CAP Level 3 sea surface salinity (SSS) standard mapped 7-Day image data
Version 2.0 Aquarius CAP Level 3 sea surface salinity (SSS) standard mapped 7-Day image data
Version 2.0 Aquarius CAP Level 3 sea surface salinity (SSS) standard mapped 7-Day image data
Version 2.0 Aquarius CAP Level 3 sea surface salinity (SSS) standard mapped 7-Day image data
Version 2.0 Aquarius CAP Level 3 sea surface salinity (SSS) standard mapped 7-Day image data
Version 2.0 Aquarius CAP Level 3 sea surface salinity (SSS) standard mapped Monthly image data
Version 2.0 Aquarius CAP Level 3 sea surface salinity (SSS) standard mapped Monthly image data
Version 2.0 Aquarius CAP Level 3 sea surface salinity (SSS) standard mapped Monthly image data
Version 2.0 Aquarius CAP Level 3 sea surface salinity (SSS) standard mapped Monthly image data
Version 2.0 Aquarius CAP Level 3 sea surface salinity (SSS) standard mapped Monthly image data
Version 2.0 Aquarius CAP Level 3 sea surface salinity (SSS) standard mapped Monthly image data
Version 2.0 Aquarius Level 3 sea surface salinity smoothed standard mapped Monthly image data
Version 2.0 Aquarius Level 3 sea surface salinity smoothed standard mapped Monthly image data
Version 2.0 Aquarius Level 3 sea surface salinity smoothed standard mapped Monthly image data
Level 3, monthly sea surface salinity smoothed product for version 3.0 of the Aquarius data set
Level 3, monthly sea surface salinity smoothed product for version 3.0 of the Aquarius data set
Level 3, monthly sea surface salinity smoothed product for version 3.0 of the Aquarius data set
Level 3, monthly sea surface salinity smoothed product for version 3.0 of the Aquarius data set
Level 3, monthly sea surface salinity smoothed product for version 3.0 of the Aquarius data set
Level 3, monthly sea surface salinity smoothed product for version 3.0 of the Aquarius data set
Level 3, monthly sea surface salinity smoothed product for version 3.0 of the Aquarius data set
Level 3, monthly sea surface salinity smoothed product for version 3.0 of the Aquarius data set
Level 3, monthly sea surface salinity smoothed product for version 3.0 of the Aquarius data set
Level 3, monthly Ascending sea surface salinity smoothed product for version 3.0 of the Aquarius data set
Level 3, monthly Ascending sea surface salinity smoothed product for version 3.0 of the Aquarius data set
Level 3, monthly Ascending sea surface salinity smoothed product for version 3.0 of the Aquarius data set
Level 3, monthly Ascending sea surface salinity smoothed product for version 3.0 of the Aquarius data set
Level 3, monthly Ascending sea surface salinity smoothed product for version 3.0 of the Aquarius data set
Level 3, monthly Ascending sea surface salinity smoothed product for version 3.0 of the Aquarius data set
Level 3, monthly Ascending sea surface salinity smoothed product for version 3.0 of the Aquarius data set
Level 3, monthly Ascending sea surface salinity smoothed product for version 3.0 of the Aquarius data set
Level 3, monthly Ascending sea surface salinity smoothed product for version 3.0 of the Aquarius data set
Level 3, monthly Descending sea surface salinity smoothed product for version 3.0 of the Aquarius data set
Level 3, monthly Descending sea surface salinity smoothed product for version 3.0 of the Aquarius data set
Level 3, monthly Descending sea surface salinity smoothed product for version 3.0 of the Aquarius data set
Level 3, monthly Descending sea surface salinity smoothed product for version 3.0 of the Aquarius data set
Level 3, monthly Descending sea surface salinity smoothed product for version 3.0 of the Aquarius data set
Level 3, monthly Descending sea surface salinity smoothed product for version 3.0 of the Aquarius data set
Level 3, monthly Descending sea surface salinity smoothed product for version 3.0 of the Aquarius data set
Level 3, monthly Descending sea surface salinity smoothed product for version 3.0 of the Aquarius data set
Level 3, monthly Descending sea surface salinity smoothed product for version 3.0 of the Aquarius data set
Version 2.0 Aquarius Level 3 sea surface salinity (SSS) standard mapped Seasonal image data
Version 2.0 Aquarius Level 3 sea surface salinity (SSS) standard mapped Seasonal image data
Version 2.0 Aquarius Level 3 sea surface salinity (SSS) standard mapped Seasonal image data
Level 3, seasonal sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, seasonal sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, seasonal sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, seasonal sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, seasonal sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, seasonal sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, seasonal sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, seasonal sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, seasonal sea surface salinity product for version 3.0 of the Aquarius data set
Version 2.0 Aquarius Level 3 sea surface salinity (SSS) standard mapped 7-Day image data
Version 2.0 Aquarius Level 3 sea surface salinity (SSS) standard mapped 7-Day image data
Version 2.0 Aquarius Level 3 sea surface salinity (SSS) standard mapped 7-Day image data
Level 3, 7-Day sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, 7-Day sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, 7-Day sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, 7-Day sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, 7-Day sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, 7-Day sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, 7-Day sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, 7-Day sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, 7-Day sea surface salinity product for version 3.0 of the Aquarius data set
Version 2.0 Aquarius Level 3 sea surface salinity standard mapped Annual image data
Version 2.0 Aquarius Level 3 sea surface salinity standard mapped Annual image data
Version 2.0 Aquarius Level 3 sea surface salinity standard mapped Annual image data
Level 3, annual sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, annual sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, annual sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, annual sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, annual sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, annual sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, annual sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, annual sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, annual sea surface salinity product for version 3.0 of the Aquarius data set
Version 2.0 Aquarius Level 3 sea surface salinity (SSS) standard mapped Daily image data
Version 2.0 Aquarius Level 3 sea surface salinity (SSS) standard mapped Daily image data
Version 2.0 Aquarius Level 3 sea surface salinity (SSS) standard mapped Daily image data
Level 3, Daily sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, Daily sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, Daily sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, Daily sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, Daily sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, Daily sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, Daily sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, Daily sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, Daily sea surface salinity product for version 3.0 of the Aquarius data set
Version 2.0 Aquarius Level 3 sea surface salinity (SSS) standard mapped Monthly image data
Version 2.0 Aquarius Level 3 sea surface salinity (SSS) standard mapped Monthly image data
Version 2.0 Aquarius Level 3 sea surface salinity (SSS) standard mapped Monthly image data
Level 3, monthly sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, monthly sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, monthly sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, monthly sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, monthly sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, monthly sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, monthly sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, monthly sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, monthly sea surface salinity product for version 3.0 of the Aquarius data set
Version 2.0 Aquarius Level 3 Sea Surface Salinity Standard Mapped Image Ascending Seasonal Data
Version 2.0 Aquarius Level 3 Sea Surface Salinity Standard Mapped Image Ascending Seasonal Data
Version 2.0 Aquarius Level 3 Sea Surface Salinity Standard Mapped Image Ascending Seasonal Data
Level 3, seasonal ascending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, seasonal ascending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, seasonal ascending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, seasonal ascending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, seasonal ascending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, seasonal ascending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, seasonal ascending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, seasonal ascending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, seasonal ascending sea surface salinity product for version 3.0 of the Aquarius data set
Version 2.0 Aquarius Level 3 Sea Surface Salinity Standard Mapped Image Ascending 7-Day Data
Version 2.0 Aquarius Level 3 Sea Surface Salinity Standard Mapped Image Ascending 7-Day Data
Version 2.0 Aquarius Level 3 Sea Surface Salinity Standard Mapped Image Ascending 7-Day Data
Level 3, 7-day ascending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, 7-day ascending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, 7-day ascending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, 7-day ascending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, 7-day ascending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, 7-day ascending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, 7-day ascending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, 7-day ascending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, 7-day ascending sea surface salinity product for version 3.0 of the Aquarius data set
Version 2.0 Aquarius Level 3 Sea Surface Salinity Standard Mapped Image Ascending Annual Data
Version 2.0 Aquarius Level 3 Sea Surface Salinity Standard Mapped Image Ascending Annual Data
Version 2.0 Aquarius Level 3 Sea Surface Salinity Standard Mapped Image Ascending Annual Data
Level 3, annual ascending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, annual ascending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, annual ascending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, annual ascending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, annual ascending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, annual ascending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, annual ascending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, annual ascending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, annual ascending sea surface salinity product for version 3.0 of the Aquarius data set
Version 2.0 Aquarius Level 3 Sea Surface Salinity Standard Mapped Image Ascending Daily Data
Version 2.0 Aquarius Level 3 Sea Surface Salinity Standard Mapped Image Ascending Daily Data
Version 2.0 Aquarius Level 3 Sea Surface Salinity Standard Mapped Image Ascending Daily Data
Level 3, daily ascensding sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, daily ascensding sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, daily ascensding sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, daily ascensding sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, daily ascensding sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, daily ascensding sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, daily ascensding sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, daily ascensding sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, daily ascensding sea surface salinity product for version 3.0 of the Aquarius data set
Version 2.0 Aquarius Level 3 Sea Surface Salinity Standard Mapped Image Ascending Monthly Data
Version 2.0 Aquarius Level 3 Sea Surface Salinity Standard Mapped Image Ascending Monthly Data
Version 2.0 Aquarius Level 3 Sea Surface Salinity Standard Mapped Image Ascending Monthly Data
Level 3, monthly ascending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, monthly ascending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, monthly ascending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, monthly ascending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, monthly ascending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, monthly ascending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, monthly ascending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, monthly ascending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, monthly ascending sea surface salinity product for version 3.0 of the Aquarius data set
Version 2.0 Aquarius Level 3 Sea Surface Salinity Standard Mapped Image Descending Seasonal Data
Version 2.0 Aquarius Level 3 Sea Surface Salinity Standard Mapped Image Descending Seasonal Data
Version 2.0 Aquarius Level 3 Sea Surface Salinity Standard Mapped Image Descending Seasonal Data
Level 3, seasonal descending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, seasonal descending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, seasonal descending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, seasonal descending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, seasonal descending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, seasonal descending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, seasonal descending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, seasonal descending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, seasonal descending sea surface salinity product for version 3.0 of the Aquarius data set
Version 2.0 Aquarius Level 3 Sea Surface Salinity Standard Mapped Image Descending 7-Day Data
Version 2.0 Aquarius Level 3 Sea Surface Salinity Standard Mapped Image Descending 7-Day Data
Version 2.0 Aquarius Level 3 Sea Surface Salinity Standard Mapped Image Descending 7-Day Data
Level 3, 7-day descending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, 7-day descending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, 7-day descending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, 7-day descending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, 7-day descending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, 7-day descending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, 7-day descending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, 7-day descending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, 7-day descending sea surface salinity product for version 3.0 of the Aquarius data set
Version 2.0 Aquarius Level 3 Sea Surface Salinity Standard Mapped Image Descending Annual Data
Version 2.0 Aquarius Level 3 Sea Surface Salinity Standard Mapped Image Descending Annual Data
Version 2.0 Aquarius Level 3 Sea Surface Salinity Standard Mapped Image Descending Annual Data
Level 3, annual descending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, annual descending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, annual descending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, annual descending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, annual descending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, annual descending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, annual descending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, annual descending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, annual descending sea surface salinity product for version 3.0 of the Aquarius data set
Version 2.0 Aquarius Level 3 Sea Surface Salinity Standard Mapped Image Descending Daily Data
Version 2.0 Aquarius Level 3 Sea Surface Salinity Standard Mapped Image Descending Daily Data
Version 2.0 Aquarius Level 3 Sea Surface Salinity Standard Mapped Image Descending Daily Data
Level 3, daily descending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, daily descending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, daily descending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, daily descending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, daily descending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, daily descending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, daily descending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, daily descending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, daily descending sea surface salinity product for version 3.0 of the Aquarius data set
Version 2.0 Aquarius Level 3 Sea Surface Salinity Standard Mapped Image Descending Monthly Data
Version 2.0 Aquarius Level 3 Sea Surface Salinity Standard Mapped Image Descending Monthly Data
Version 2.0 Aquarius Level 3 Sea Surface Salinity Standard Mapped Image Descending Monthly Data
Level 3, monthly descending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, monthly descending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, monthly descending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, monthly descending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, monthly descending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, monthly descending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, monthly descending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, monthly descending sea surface salinity product for version 3.0 of the Aquarius data set
Level 3, monthly descending sea surface salinity product for version 3.0 of the Aquarius data set
Version 2.0 Aquarius CAP Level 3 wind speed standard mapped 7-Day image data
Version 2.0 Aquarius CAP Level 3 wind speed standard mapped 7-Day image data
Version 2.0 Aquarius CAP Level 3 wind speed standard mapped 7-Day image data
Version 2.0 Aquarius CAP Level 3 wind speed standard mapped 7-Day image data
Version 2.0 Aquarius CAP Level 3 wind speed standard mapped 7-Day image data
Version 2.0 Aquarius CAP Level 3 wind speed standard mapped 7-Day image data
Version 2.0 Aquarius CAP Level 3 wind speed standard mapped Monthly image data
Version 2.0 Aquarius CAP Level 3 wind speed standard mapped Monthly image data
Version 2.0 Aquarius CAP Level 3 wind speed standard mapped Monthly image data
Version 2.0 Aquarius CAP Level 3 wind speed standard mapped Monthly image data
Version 2.0 Aquarius CAP Level 3 wind speed standard mapped Monthly image data
Version 2.0 Aquarius CAP Level 3 wind speed standard mapped Monthly image data
Version 2.0 Aquarius Level 3 ocean surface wind speed standard mapped Seasonal image data
Version 2.0 Aquarius Level 3 ocean surface wind speed standard mapped Seasonal image data
Version 2.0 Aquarius Level 3 ocean surface wind speed standard mapped Seasonal image data
Level 3, seasonal wind speed product for version 3.0 of the Aquarius data set
Level 3, seasonal wind speed product for version 3.0 of the Aquarius data set
Level 3, seasonal wind speed product for version 3.0 of the Aquarius data set
Level 3, seasonal wind speed product for version 3.0 of the Aquarius data set
Level 3, seasonal wind speed product for version 3.0 of the Aquarius data set
Level 3, seasonal wind speed product for version 3.0 of the Aquarius data set
Level 3, seasonal wind speed product for version 3.0 of the Aquarius data set
Level 3, seasonal wind speed product for version 3.0 of the Aquarius data set
Level 3, seasonal wind speed product for version 3.0 of the Aquarius data set
Version 2.0 Aquarius Level 3 ocean surface wind speed standard mapped 7-Day image data
Version 2.0 Aquarius Level 3 ocean surface wind speed standard mapped 7-Day image data
Version 2.0 Aquarius Level 3 ocean surface wind speed standard mapped 7-Day image data
Level 3, 7-Day wind speed product for version 3.0 of the Aquarius data set
Level 3, 7-Day wind speed product for version 3.0 of the Aquarius data set
Level 3, 7-Day wind speed product for version 3.0 of the Aquarius data set
Level 3, 7-Day wind speed product for version 3.0 of the Aquarius data set
Level 3, 7-Day wind speed product for version 3.0 of the Aquarius data set
Level 3, 7-Day wind speed product for version 3.0 of the Aquarius data set
Level 3, 7-Day wind speed product for version 3.0 of the Aquarius data set
Level 3, 7-Day wind speed product for version 3.0 of the Aquarius data set
Level 3, 7-Day wind speed product for version 3.0 of the Aquarius data set
Version 2.0 Aquarius Level 3 ocean surface wind speed standard mapped Annual image data
Version 2.0 Aquarius Level 3 ocean surface wind speed standard mapped Annual image data
Version 2.0 Aquarius Level 3 ocean surface wind speed standard mapped Annual image data
Level 3, annual wind speed product for version 3.0 of the Aquarius data set
Level 3, annual wind speed product for version 3.0 of the Aquarius data set
Level 3, annual wind speed product for version 3.0 of the Aquarius data set
Level 3, annual wind speed product for version 3.0 of the Aquarius data set
Level 3, annual wind speed product for version 3.0 of the Aquarius data set
Level 3, annual wind speed product for version 3.0 of the Aquarius data set
Level 3, annual wind speed product for version 3.0 of the Aquarius data set
Level 3, annual wind speed product for version 3.0 of the Aquarius data set
Level 3, annual wind speed product for version 3.0 of the Aquarius data set
Version 2.0 Aquarius Level 3 ocean surface wind speed standard mapped Daily image data
Version 2.0 Aquarius Level 3 ocean surface wind speed standard mapped Daily image data
Version 2.0 Aquarius Level 3 ocean surface wind speed standard mapped Daily image data
Level 3, Daily wind speed product for version 3.0 of the Aquarius data set
Level 3, Daily wind speed product for version 3.0 of the Aquarius data set
Level 3, Daily wind speed product for version 3.0 of the Aquarius data set
Level 3, Daily wind speed product for version 3.0 of the Aquarius data set
Level 3, Daily wind speed product for version 3.0 of the Aquarius data set
Level 3, Daily wind speed product for version 3.0 of the Aquarius data set
Level 3, Daily wind speed product for version 3.0 of the Aquarius data set
Level 3, Daily wind speed product for version 3.0 of the Aquarius data set
Level 3, Daily wind speed product for version 3.0 of the Aquarius data set
Version 2.0 Aquarius Level 3 ocean surface wind speed standard mapped Monthly image data
Version 2.0 Aquarius Level 3 ocean surface wind speed standard mapped Monthly image data
Version 2.0 Aquarius Level 3 ocean surface wind speed standard mapped Monthly image data
Level 3, monthly wind speed product for version 3.0 of the Aquarius data set
Level 3, monthly wind speed product for version 3.0 of the Aquarius data set
Level 3, monthly wind speed product for version 3.0 of the Aquarius data set
Level 3, monthly wind speed product for version 3.0 of the Aquarius data set
Level 3, monthly wind speed product for version 3.0 of the Aquarius data set
Level 3, monthly wind speed product for version 3.0 of the Aquarius data set
Level 3, monthly wind speed product for version 3.0 of the Aquarius data set
Level 3, monthly wind speed product for version 3.0 of the Aquarius data set
Level 3, monthly wind speed product for version 3.0 of the Aquarius data set
Version 2.0 Aquarius Level 3 Wind Speed Standard Mapped Image Ascending Seasonal Data
Version 2.0 Aquarius Level 3 Wind Speed Standard Mapped Image Ascending Seasonal Data
Version 2.0 Aquarius Level 3 Wind Speed Standard Mapped Image Ascending Seasonal Data
Level 3, seasonal ascending wind speed product for version 3.0 of the Aquarius data set
Level 3, seasonal ascending wind speed product for version 3.0 of the Aquarius data set
Level 3, seasonal ascending wind speed product for version 3.0 of the Aquarius data set
Level 3, seasonal ascending wind speed product for version 3.0 of the Aquarius data set
Level 3, seasonal ascending wind speed product for version 3.0 of the Aquarius data set
Level 3, seasonal ascending wind speed product for version 3.0 of the Aquarius data set
Level 3, seasonal ascending wind speed product for version 3.0 of the Aquarius data set
Level 3, seasonal ascending wind speed product for version 3.0 of the Aquarius data set
Level 3, seasonal ascending wind speed product for version 3.0 of the Aquarius data set
Version 2.0 Aquarius Level 3 Wind Speed Standard Mapped Image Ascending 7-Day Data
Version 2.0 Aquarius Level 3 Wind Speed Standard Mapped Image Ascending 7-Day Data
Version 2.0 Aquarius Level 3 Wind Speed Standard Mapped Image Ascending 7-Day Data
Level 3, 7-day ascending wind speed product for version 3.0 of the Aquarius data set
Level 3, 7-day ascending wind speed product for version 3.0 of the Aquarius data set
Level 3, 7-day ascending wind speed product for version 3.0 of the Aquarius data set
Level 3, 7-day ascending wind speed product for version 3.0 of the Aquarius data set
Level 3, 7-day ascending wind speed product for version 3.0 of the Aquarius data set
Level 3, 7-day ascending wind speed product for version 3.0 of the Aquarius data set
Level 3, 7-day ascending wind speed product for version 3.0 of the Aquarius data set
Level 3, 7-day ascending wind speed product for version 3.0 of the Aquarius data set
Level 3, 7-day ascending wind speed product for version 3.0 of the Aquarius data set
Version 2.0 Aquarius Level 3 Wind Speed Standard Mapped Image Ascending Annual Data
Version 2.0 Aquarius Level 3 Wind Speed Standard Mapped Image Ascending Annual Data
Version 2.0 Aquarius Level 3 Wind Speed Standard Mapped Image Ascending Annual Data
Level 3, annual ascending wind speed product for version 3.0 of the Aquarius data set
Level 3, annual ascending wind speed product for version 3.0 of the Aquarius data set
Level 3, annual ascending wind speed product for version 3.0 of the Aquarius data set
Level 3, annual ascending wind speed product for version 3.0 of the Aquarius data set
Level 3, annual ascending wind speed product for version 3.0 of the Aquarius data set
Level 3, annual ascending wind speed product for version 3.0 of the Aquarius data set
Level 3, annual ascending wind speed product for version 3.0 of the Aquarius data set
Level 3, annual ascending wind speed product for version 3.0 of the Aquarius data set
Level 3, annual ascending wind speed product for version 3.0 of the Aquarius data set
Version 2.0 Aquarius Level 3 Wind Speed Standard Mapped Image Ascending Daily Data
Version 2.0 Aquarius Level 3 Wind Speed Standard Mapped Image Ascending Daily Data
Version 2.0 Aquarius Level 3 Wind Speed Standard Mapped Image Ascending Daily Data
Level 3, daily ascending wind speed product for version 3.0 of the Aquarius data set
Level 3, daily ascending wind speed product for version 3.0 of the Aquarius data set
Level 3, daily ascending wind speed product for version 3.0 of the Aquarius data set
Level 3, daily ascending wind speed product for version 3.0 of the Aquarius data set
Level 3, daily ascending wind speed product for version 3.0 of the Aquarius data set
Level 3, daily ascending wind speed product for version 3.0 of the Aquarius data set
Level 3, daily ascending wind speed product for version 3.0 of the Aquarius data set
Level 3, daily ascending wind speed product for version 3.0 of the Aquarius data set
Level 3, daily ascending wind speed product for version 3.0 of the Aquarius data set
Version 2.0 Aquarius Level 3 Wind Speed Standard Mapped Image Ascending Monthly Data
Version 2.0 Aquarius Level 3 Wind Speed Standard Mapped Image Ascending Monthly Data
Version 2.0 Aquarius Level 3 Wind Speed Standard Mapped Image Ascending Monthly Data
Level 3, monthly ascending wind speed product for version 3.0 of the Aquarius data set
Level 3, monthly ascending wind speed product for version 3.0 of the Aquarius data set
Level 3, monthly ascending wind speed product for version 3.0 of the Aquarius data set
Level 3, monthly ascending wind speed product for version 3.0 of the Aquarius data set
Level 3, monthly ascending wind speed product for version 3.0 of the Aquarius data set
Level 3, monthly ascending wind speed product for version 3.0 of the Aquarius data set
Level 3, monthly ascending wind speed product for version 3.0 of the Aquarius data set
Level 3, monthly ascending wind speed product for version 3.0 of the Aquarius data set
Level 3, monthly ascending wind speed product for version 3.0 of the Aquarius data set
Version 2.0 Aquarius Level 3 Wind Speed Standard Mapped Image Descending Seasonal Data
Version 2.0 Aquarius Level 3 Wind Speed Standard Mapped Image Descending Seasonal Data
Version 2.0 Aquarius Level 3 Wind Speed Standard Mapped Image Descending Seasonal Data
Level 3, seasonal descending wind speed product for version 3.0 of the Aquarius data set
Level 3, seasonal descending wind speed product for version 3.0 of the Aquarius data set
Level 3, seasonal descending wind speed product for version 3.0 of the Aquarius data set
Level 3, seasonal descending wind speed product for version 3.0 of the Aquarius data set
Level 3, seasonal descending wind speed product for version 3.0 of the Aquarius data set
Level 3, seasonal descending wind speed product for version 3.0 of the Aquarius data set
Level 3, seasonal descending wind speed product for version 3.0 of the Aquarius data set
Level 3, seasonal descending wind speed product for version 3.0 of the Aquarius data set
Level 3, seasonal descending wind speed product for version 3.0 of the Aquarius data set
Version 2.0 Aquarius Level 3 Wind Speed Standard Mapped Image Descending 7-Day Data
Version 2.0 Aquarius Level 3 Wind Speed Standard Mapped Image Descending 7-Day Data
Version 2.0 Aquarius Level 3 Wind Speed Standard Mapped Image Descending 7-Day Data
Level 3, 7-day descending wind speed product for version 3.0 of the Aquarius data set
Level 3, 7-day descending wind speed product for version 3.0 of the Aquarius data set
Level 3, 7-day descending wind speed product for version 3.0 of the Aquarius data set
Level 3, 7-day descending wind speed product for version 3.0 of the Aquarius data set
Level 3, 7-day descending wind speed product for version 3.0 of the Aquarius data set
Level 3, 7-day descending wind speed product for version 3.0 of the Aquarius data set
Level 3, 7-day descending wind speed product for version 3.0 of the Aquarius data set
Level 3, 7-day descending wind speed product for version 3.0 of the Aquarius data set
Level 3, 7-day descending wind speed product for version 3.0 of the Aquarius data set
Version 2.0 Aquarius Level 3 Wind Speed Standard Mapped Image Descending Annual Data
Version 2.0 Aquarius Level 3 Wind Speed Standard Mapped Image Descending Annual Data
Version 2.0 Aquarius Level 3 Wind Speed Standard Mapped Image Descending Annual Data
Level 3, annual descending wind speed product for version 3.0 of the Aquarius data set
Level 3, annual descending wind speed product for version 3.0 of the Aquarius data set
Level 3, annual descending wind speed product for version 3.0 of the Aquarius data set
Level 3, annual descending wind speed product for version 3.0 of the Aquarius data set
Level 3, annual descending wind speed product for version 3.0 of the Aquarius data set
Level 3, annual descending wind speed product for version 3.0 of the Aquarius data set
Level 3, annual descending wind speed product for version 3.0 of the Aquarius data set
Level 3, annual descending wind speed product for version 3.0 of the Aquarius data set
Level 3, annual descending wind speed product for version 3.0 of the Aquarius data set
Version 2.0 Aquarius Level 3 Wind Speed Standard Mapped Image Descending Daily Data
Version 2.0 Aquarius Level 3 Wind Speed Standard Mapped Image Descending Daily Data
Version 2.0 Aquarius Level 3 Wind Speed Standard Mapped Image Descending Daily Data
Level 3, daily descending wind speed product for version 3.0 of the Aquarius data set
Level 3, daily descending wind speed product for version 3.0 of the Aquarius data set
Level 3, daily descending wind speed product for version 3.0 of the Aquarius data set
Level 3, daily descending wind speed product for version 3.0 of the Aquarius data set
Level 3, daily descending wind speed product for version 3.0 of the Aquarius data set
Level 3, daily descending wind speed product for version 3.0 of the Aquarius data set
Level 3, daily descending wind speed product for version 3.0 of the Aquarius data set
Level 3, daily descending wind speed product for version 3.0 of the Aquarius data set
Level 3, daily descending wind speed product for version 3.0 of the Aquarius data set
Version 2.0 Aquarius Level 3 Wind Speed Standard Mapped Image Descending Monthly Data
Version 2.0 Aquarius Level 3 Wind Speed Standard Mapped Image Descending Monthly Data
Version 2.0 Aquarius Level 3 Wind Speed Standard Mapped Image Descending Monthly Data
Level 3, monthly descending wind speed product for version 3.0 of the Aquarius data set
Level 3, monthly descending wind speed product for version 3.0 of the Aquarius data set
Level 3, monthly descending wind speed product for version 3.0 of the Aquarius data set
Level 3, monthly descending wind speed product for version 3.0 of the Aquarius data set
Level 3, monthly descending wind speed product for version 3.0 of the Aquarius data set
Level 3, monthly descending wind speed product for version 3.0 of the Aquarius data set
Level 3, monthly descending wind speed product for version 3.0 of the Aquarius data set
Level 3, monthly descending wind speed product for version 3.0 of the Aquarius data set
Level 3, monthly descending wind speed product for version 3.0 of the Aquarius data set
Contains wind speeds and directions derived from the Seasat-A Scatterometer (SASS), presented chronologically by swath for the period between 7 July 1978 and 10 October 1978. Robert Atlas et al. (1987) produced this product using an objective ambiguity removal scheme to dealias the wind vector data binned at 100 km cells, which were calculated by Frank Wentz (see PO.DAAC product 29). For more information, please refer to: ftp://podaac.jpl.nasa.gov/pub/ocean_wind/seasat/wind/swath_Atlas/doc/Readme_002.html
Contains wind speeds and directions derived from the Seasat-A Scatterometer (SASS), presented chronologically by swath for the period between 7 July 1978 and 10 October 1978. Robert Atlas et al. (1987) produced this product using an objective ambiguity removal scheme to dealias the wind vector data binned at 100 km cells, which were calculated by Frank Wentz (see PO.DAAC product 29). For more information, please refer to: ftp://podaac.jpl.nasa.gov/pub/ocean_wind/seasat/wind/swath_Atlas/doc/Readme_002.html
GHRSST Level 2P Global Skin Sea Surface Temperature from the Advanced Very High Resolution Radiometer (AVHRR) on the MetOp-A satellite (GDS2 version)
GHRSST Level 2P Global Skin Sea Surface Temperature from the Advanced Very High Resolution Radiometer (AVHRR) on the MetOp-A satellite (GDS2 version)
GHRSST Level 2P Global Skin Sea Surface Temperature from the Advanced Very High Resolution Radiometer (AVHRR) on the MetOp-A satellite (GDS2 version)
GHRSST Level 2P Global Skin Sea Surface Temperature from the Advanced Very High Resolution Radiometer (AVHRR) on the MetOp-A satellite (GDS2 version)
GHRSST Level 2P Global Skin Sea Surface Temperature from the Advanced Very High Resolution Radiometer (AVHRR) on the MetOp-A satellite (GDS2 version)
GHRSST Level 2P Global Skin Sea Surface Temperature from the Advanced Very High Resolution Radiometer (AVHRR) on the MetOp-A satellite (GDS2 version)
GHRSST Level 2P Global Skin Sea Surface Temperature from the Advanced Very High Resolution Radiometer (AVHRR) on the MetOp-A satellite (GDS2 version)
GHRSST Level 2P Global Skin Sea Surface Temperature from the Advanced Very High Resolution Radiometer (AVHRR) on the MetOp-A satellite (GDS2 version)
GHRSST Level 3C Global Subskin Sea Surface Temperature from the Advanced Very High Resolution Radiometer (AVHRR) on MetOP-A (GDS2 version)
GHRSST Level 3C Global Subskin Sea Surface Temperature from the Advanced Very High Resolution Radiometer (AVHRR) on MetOP-A (GDS2 version)
GHRSST Level 3C Global Subskin Sea Surface Temperature from the Advanced Very High Resolution Radiometer (AVHRR) on MetOP-A (GDS2 version)
GHRSST Level 3C Global Subskin Sea Surface Temperature from the Advanced Very High Resolution Radiometer (AVHRR) on MetOP-A (GDS2 version)
GHRSST Level 3C Global Subskin Sea Surface Temperature from the Advanced Very High Resolution Radiometer (AVHRR) on MetOP-A (GDS2 version)
GHRSST Level 3C Global Subskin Sea Surface Temperature from the Advanced Very High Resolution Radiometer (AVHRR) on MetOP-A (GDS2 version)
GHRSST Level 3C Global Subskin Sea Surface Temperature from the Advanced Very High Resolution Radiometer (AVHRR) on MetOP-A (GDS2 version)
GHRSST Level 3C Global Subskin Sea Surface Temperature from the Advanced Very High Resolution Radiometer (AVHRR) on MetOP-A (GDS2 version)
GHRSST collection
GHRSST collection
GHRSST collection
GHRSST collection
GHRSST collection
GHRSST collection
GHRSST collection
GHRSST collection
OSISAF NOAA19 NAR
OSISAF NOAA19 NAR
OSISAF NOAA19 NAR
OSISAF NOAA19 NAR
OSISAF NOAA19 NAR
OSISAF NOAA19 NAR
OSISAF NOAA19 NAR
OSISAF NOAA19 NAR
The absolute dynamic topography data (similar to sea level but with respect to the geoid) are on monthly 1 degree grids.  These data were generated to help support the CMIP5 (Coupled Model Intercomparison Project Phase 5) portion of PCMDI (Program for Climate Model Diagnosis and Intercomparison).  The dynamic topograhy are from sea surface height measured by several satellites, Envisat, TOPEX/Poseidon, Jason-1 and OSTM/Jason-2 and referenced to the geoid.  These data were provided by AVISO (French space agency data provider), which are based on a similar dynamic topography data set they already produce( http://www.aviso.oceanobs.com/index.php?id=1271 ).
The absolute dynamic topography data (similar to sea level but with respect to the geoid) are on monthly 1 degree grids.  These data were generated to help support the CMIP5 (Coupled Model Intercomparison Project Phase 5) portion of PCMDI (Program for Climate Model Diagnosis and Intercomparison).  The dynamic topograhy are from sea surface height measured by several satellites, Envisat, TOPEX/Poseidon, Jason-1 and OSTM/Jason-2 and referenced to the geoid.  These data were provided by AVISO (French space agency data provider), which are based on a similar dynamic topography data set they already produce( http://www.aviso.oceanobs.com/index.php?id=1271 ).
The absolute dynamic topography data (similar to sea level but with respect to the geoid) are on monthly 1 degree grids.  These data were generated to help support the CMIP5 (Coupled Model Intercomparison Project Phase 5) portion of PCMDI (Program for Climate Model Diagnosis and Intercomparison).  The dynamic topograhy are from sea surface height measured by several satellites, Envisat, TOPEX/Poseidon, Jason-1 and OSTM/Jason-2 and referenced to the geoid.  These data were provided by AVISO (French space agency data provider), which are based on a similar dynamic topography data set they already produce( http://www.aviso.oceanobs.com/index.php?id=1271 ).
The absolute dynamic topography data (similar to sea level but with respect to the geoid) are on monthly 1 degree grids.  These data were generated to help support the CMIP5 (Coupled Model Intercomparison Project Phase 5) portion of PCMDI (Program for Climate Model Diagnosis and Intercomparison).  The dynamic topograhy are from sea surface height measured by several satellites, Envisat, TOPEX/Poseidon, Jason-1 and OSTM/Jason-2 and referenced to the geoid.  These data were provided by AVISO (French space agency data provider), which are based on a similar dynamic topography data set they already produce( http://www.aviso.oceanobs.com/index.php?id=1271 ).
The absolute dynamic topography data (similar to sea level but with respect to the geoid) are on monthly 1 degree grids.  These data were generated to help support the CMIP5 (Coupled Model Intercomparison Project Phase 5) portion of PCMDI (Program for Climate Model Diagnosis and Intercomparison).  The dynamic topograhy are from sea surface height measured by several satellites, Envisat, TOPEX/Poseidon, Jason-1 and OSTM/Jason-2 and referenced to the geoid.  These data were provided by AVISO (French space agency data provider), which are based on a similar dynamic topography data set they already produce( http://www.aviso.oceanobs.com/index.php?id=1271 ).
The absolute dynamic topography data (similar to sea level but with respect to the geoid) are on monthly 1 degree grids.  These data were generated to help support the CMIP5 (Coupled Model Intercomparison Project Phase 5) portion of PCMDI (Program for Climate Model Diagnosis and Intercomparison).  The dynamic topograhy are from sea surface height measured by several satellites, Envisat, TOPEX/Poseidon, Jason-1 and OSTM/Jason-2 and referenced to the geoid.  These data were provided by AVISO (French space agency data provider), which are based on a similar dynamic topography data set they already produce( http://www.aviso.oceanobs.com/index.php?id=1271 ).
The absolute dynamic topography data (similar to sea level but with respect to the geoid) are on monthly 1 degree grids.  These data were generated to help support the CMIP5 (Coupled Model Intercomparison Project Phase 5) portion of PCMDI (Program for Climate Model Diagnosis and Intercomparison).  The dynamic topograhy are from sea surface height measured by several satellites, Envisat, TOPEX/Poseidon, Jason-1 and OSTM/Jason-2 and referenced to the geoid.  These data were provided by AVISO (French space agency data provider), which are based on a similar dynamic topography data set they already produce( http://www.aviso.oceanobs.com/index.php?id=1271 ).
Cross-Calibrated Multi-Platform Ocean Surface Wind Vector L2.5 First-Look Passive Microwave Analyses
Cross-Calibrated Multi-Platform Ocean Surface Wind Vector L2.5 First-Look Passive Microwave Analyses
Cross-Calibrated Multi-Platform Ocean Surface Wind Vector L2.5 First-Look Passive Microwave Analyses
Cross-Calibrated Multi-Platform Ocean Surface Wind Vector L2.5 First-Look Passive Microwave Analyses
Cross-Calibrated Multi-Platform Ocean Surface Wind Vector L2.5 First-Look SSM/I-F08 Microwave Analyses
Cross-Calibrated Multi-Platform Ocean Surface Wind Vector L2.5 First-Look SSM/I-F08 Microwave Analyses
Cross-Calibrated Multi-Platform Ocean Surface Wind Vector L2.5 First-Look SSM/I-F08 Microwave Analyses
Cross-Calibrated Multi-Platform Ocean Surface Wind Vector L2.5 First-Look SSM/I-F08 Microwave Analyses
See the description of the EUR-L4UHRfnd-MED-ODYSSEA product
See the description of the EUR-L4UHRfnd-MED-ODYSSEA product
See the description of the EUR-L4UHRfnd-MED-ODYSSEA product
See the description of the EUR-L4UHRfnd-MED-ODYSSEA product
See the description of the EUR-L4UHRfnd-MED-ODYSSEA product
See the description of the EUR-L4UHRfnd-MED-ODYSSEA product
See the description of the EUR-L4UHRfnd-MED-ODYSSEA product
See the description of the EUR-L4UHRfnd-MED-ODYSSEA product
The ODYSSEA Mediterranean 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 Mediterranean 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 Mediterranean 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 Mediterranean 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 Mediterranean 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 Mediterranean 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 Mediterranean 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 Mediterranean 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 Mediterranean 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.
GHRSST CMC CMC0.2deg L4  SST (GDS2)
GHRSST CMC CMC0.2deg L4  SST (GDS2)
GHRSST CMC CMC0.2deg L4  SST (GDS2)
GHRSST CMC CMC0.2deg L4  SST (GDS2)
GHRSST CMC CMC0.2deg L4  SST (GDS2)
GHRSST CMC CMC0.2deg L4  SST (GDS2)
GHRSST CMC CMC0.2deg L4  SST (GDS2)
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.
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.
SST calculated from the IR channels of MSG-02  full disk images at full resolution every 15 minutes. In HRIT satellite projection, 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 MSG-02  full disk images at full resolution every 15 minutes. In HRIT satellite projection, 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 MSG-02  full disk images at full resolution every 15 minutes. In HRIT satellite projection, 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 MSG-02  full disk images at full resolution every 15 minutes. In HRIT satellite projection, 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 MSG-02  full disk images at full resolution every 15 minutes. In HRIT satellite projection, 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 MSG-02  full disk images at full resolution every 15 minutes. In HRIT satellite projection, 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 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.
SST calculated from the IR channels of MTSAT-1R  Full Disk images at full resolution on an hourly basis. In HRIT satellite projection, 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 MTSAT-1R  Full Disk images at full resolution on an hourly basis. In HRIT satellite projection, 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 MTSAT-1R  Full Disk images at full resolution on an hourly basis. In HRIT satellite projection, 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 MTSAT-1R  Full Disk images at full resolution on an hourly basis. In HRIT satellite projection, 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 MTSAT-1R  Full Disk images at full resolution on an hourly basis. In HRIT satellite projection, 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 MTSAT-1R  Full Disk images at full resolution on an hourly basis. In HRIT satellite projection, 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 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.
GHRSST Bulk Sea Surface Temperature from the NOAA-19 POES Advanced Very High Resolution Radiometer, 2.2 km resolution, global coverage, beginning in the year 2009, from the Naval Oceanographic Office, Stennis Space Center, MS, USA
GHRSST Bulk Sea Surface Temperature from the NOAA-19 POES Advanced Very High Resolution Radiometer, 2.2 km resolution, global coverage, beginning in the year 2009, from the Naval Oceanographic Office, Stennis Space Center, MS, USA
GHRSST Bulk Sea Surface Temperature from the NOAA-19 POES Advanced Very High Resolution Radiometer, 2.2 km resolution, global coverage, beginning in the year 2009, from the Naval Oceanographic Office, Stennis Space Center, MS, USA
GHRSST Bulk Sea Surface Temperature from the NOAA-19 POES Advanced Very High Resolution Radiometer, 2.2 km resolution, global coverage, beginning in the year 2009, from the Naval Oceanographic Office, Stennis Space Center, MS, USA
GHRSST Bulk Sea Surface Temperature from the NOAA-19 POES Advanced Very High Resolution Radiometer, 2.2 km resolution, global coverage, beginning in the year 2009, from the Naval Oceanographic Office, Stennis Space Center, MS, USA
GHRSST Bulk Sea Surface Temperature from the NOAA-19 POES Advanced Very High Resolution Radiometer, 2.2 km resolution, global coverage, beginning in the year 2009, from the Naval Oceanographic Office, Stennis Space Center, MS, USA
GHRSST Bulk Sea Surface Temperature from the NOAA-19 POES Advanced Very High Resolution Radiometer, 2.2 km resolution, global coverage, beginning in the year 2009, from the Naval Oceanographic Office, Stennis Space Center, MS, USA
A regional Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-19 platform (launched 6 Feb 2009) produced and used operationally in  oceanographic analyses and forecasts by the US Naval Oceanographic Office (NAVO).
A regional Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-19 platform (launched 6 Feb 2009) produced and used operationally in  oceanographic analyses and forecasts by the US Naval Oceanographic Office (NAVO).
A regional Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-19 platform (launched 6 Feb 2009) produced and used operationally in  oceanographic analyses and forecasts by the US Naval Oceanographic Office (NAVO).
A regional Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-19 platform (launched 6 Feb 2009) produced and used operationally in  oceanographic analyses and forecasts by the US Naval Oceanographic Office (NAVO).
A regional Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-19 platform (launched 6 Feb 2009) produced and used operationally in  oceanographic analyses and forecasts by the US Naval Oceanographic Office (NAVO).
A regional Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-19 platform (launched 6 Feb 2009) produced and used operationally in  oceanographic analyses and forecasts by the US Naval Oceanographic Office (NAVO).
A regional Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-19 platform (launched 6 Feb 2009) produced and used operationally in  oceanographic analyses and forecasts by the US Naval Oceanographic Office (NAVO).
A regional Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-19 platform (launched 6 Feb 2009) produced and used operationally in  oceanographic analyses and forecasts by the US Naval Oceanographic Office (NAVO).
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\"
GHRSST L3P Global Subskin Sea Surface Temperature from the Advanced Very High Resolution Radiometer (AVHRR) on the EUMETSAT METOP_A satellite
GHRSST L3P Global Subskin Sea Surface Temperature from the Advanced Very High Resolution Radiometer (AVHRR) on the EUMETSAT METOP_A satellite
GHRSST L3P Global Subskin Sea Surface Temperature from the Advanced Very High Resolution Radiometer (AVHRR) on the EUMETSAT METOP_A satellite
GHRSST L3P Global Subskin Sea Surface Temperature from the Advanced Very High Resolution Radiometer (AVHRR) on the EUMETSAT METOP_A satellite
GHRSST L3P Global Subskin Sea Surface Temperature from the Advanced Very High Resolution Radiometer (AVHRR) on the EUMETSAT METOP_A satellite
GHRSST L3P Global Subskin Sea Surface Temperature from the Advanced Very High Resolution Radiometer (AVHRR) on the EUMETSAT METOP_A satellite
GHRSST L3P Global Subskin Sea Surface Temperature from the Advanced Very High Resolution Radiometer (AVHRR) on the EUMETSAT METOP_A satellite
GHRSST L3P Global Subskin Sea Surface Temperature from the Advanced Very High Resolution Radiometer (AVHRR) on the EUMETSAT METOP_A satellite
GHRSST L3P Global Subskin Sea Surface Temperature from the Advanced Very High Resolution Radiometer (AVHRR) on the EUMETSAT METOP_A satellite
GHRSST L3P NAR Subskin Sea Surface Temperature from the Advanced Very High Resolution Radiometer (AVHRR) on the EUMETSAT METOP_A satellite
GHRSST L3P NAR Subskin Sea Surface Temperature from the Advanced Very High Resolution Radiometer (AVHRR) on the EUMETSAT METOP_A satellite
GHRSST L3P NAR Subskin Sea Surface Temperature from the Advanced Very High Resolution Radiometer (AVHRR) on the EUMETSAT METOP_A satellite
GHRSST L3P NAR Subskin Sea Surface Temperature from the Advanced Very High Resolution Radiometer (AVHRR) on the EUMETSAT METOP_A satellite
GHRSST L3P NAR Subskin Sea Surface Temperature from the Advanced Very High Resolution Radiometer (AVHRR) on the EUMETSAT METOP_A satellite
GHRSST L3P NAR Subskin Sea Surface Temperature from the Advanced Very High Resolution Radiometer (AVHRR) on the EUMETSAT METOP_A satellite
GHRSST L3P NAR Subskin Sea Surface Temperature from the Advanced Very High Resolution Radiometer (AVHRR) on the EUMETSAT METOP_A satellite
GHRSST L3P NAR Subskin Sea Surface Temperature from the Advanced Very High Resolution Radiometer (AVHRR) on the EUMETSAT METOP_A satellite
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"
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.
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 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 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 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 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.
GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis
GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis
GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis
GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis
GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis
GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis
GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis
GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis
GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis
GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis
GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis
GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis
GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis
GHRSST Level 4 OSPO Global Nighttime Foundation Sea Surface Temperature Analysis (GDS version 2)
GHRSST Level 4 OSPO Global Nighttime Foundation Sea Surface Temperature Analysis (GDS version 2)
GHRSST Level 4 OSPO Global Nighttime Foundation Sea Surface Temperature Analysis (GDS version 2)
GHRSST Level 4 OSPO Global Nighttime Foundation Sea Surface Temperature Analysis (GDS version 2)
GHRSST Level 4 OSPO Global Nighttime Foundation Sea Surface Temperature Analysis (GDS version 2)
GHRSST Level 4 OSPO Global Nighttime Foundation Sea Surface Temperature Analysis (GDS version 2)
GHRSST Level 4 OSPO Global Nighttime Foundation Sea Surface Temperature Analysis (GDS version 2)
GHRSST Level 4 OSPO Global Nighttime Foundation Sea Surface Temperature Analysis (GDS version 2)
GHRSST Level 4 OSPO Global Nighttime Foundation Sea Surface Temperature Analysis (GDS version 2)
GHRSST Level 4 OSPO Global Nighttime Foundation Sea Surface Temperature Analysis (GDS version 2)
GHRSST Level 4 OSPO Global Nighttime Foundation Sea Surface Temperature Analysis (GDS version 2)
GHRSST Level 4 OSPO Global Nighttime Foundation Sea Surface Temperature Analysis (GDS version 2)
GHRSST Level 4 OSPO Global Foundation Sea Surface Temperature Analysis (GDS version 2)
GHRSST Level 4 OSPO Global Foundation Sea Surface Temperature Analysis (GDS version 2)
GHRSST Level 4 OSPO Global Foundation Sea Surface Temperature Analysis (GDS version 2)
GHRSST Level 4 OSPO Global Foundation Sea Surface Temperature Analysis (GDS version 2)
GHRSST Level 4 OSPO Global Foundation Sea Surface Temperature Analysis (GDS version 2)
GHRSST Level 4 OSPO Global Foundation Sea Surface Temperature Analysis (GDS version 2)
GHRSST Level 4 OSPO Global Foundation Sea Surface Temperature Analysis (GDS version 2)
GHRSST Level 4 OSPO Global Foundation Sea Surface Temperature Analysis (GDS version 2)
GHRSST Level 4 OSPO Global Foundation Sea Surface Temperature Analysis (GDS version 2)
GHRSST Level 4 OSPO Global Foundation Sea Surface Temperature Analysis (GDS version 2)
GHRSST Level 4 OSPO Global Foundation Sea Surface Temperature Analysis (GDS version 2)
GHRSST Level 4 OSPO Global Foundation Sea Surface Temperature Analysis (GDS version 2)
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.
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.
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.
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.
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.
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.
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.
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.
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.
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/.
GHRSST Level 3C America Regional Skin Sea Surface Temperature from the Geostationary Operational Environmental Satellites (GOES) Imager on the GOES-13 satellite
GHRSST Level 3C America Regional Skin Sea Surface Temperature from the Geostationary Operational Environmental Satellites (GOES) Imager on the GOES-13 satellite
GHRSST Level 3C America Regional Skin Sea Surface Temperature from the Geostationary Operational Environmental Satellites (GOES) Imager on the GOES-13 satellite
GHRSST Level 3C America Regional Skin Sea Surface Temperature from the Geostationary Operational Environmental Satellites (GOES) Imager on the GOES-13 satellite
GHRSST Level 3C America Regional Skin Sea Surface Temperature from the Geostationary Operational Environmental Satellites (GOES) Imager on the GOES-13 satellite
GHRSST Level 3C America Regional Skin Sea Surface Temperature from the Geostationary Operational Environmental Satellites (GOES) Imager on the GOES-13 satellite
GHRSST Level 3C America Regional Skin Sea Surface Temperature from the Geostationary Operational Environmental Satellites (GOES) Imager on the GOES-13 satellite
GHRSST Level 3C America Regional Skin Sea Surface Temperature from the Geostationary Operational Environmental Satellites (GOES) Imager on the GOES-13 satellite
GHRSST Level 3C America Regional Skin Sea Surface Temperature from the Geostationary Operational Environmental Satellites (GOES) Imager on the GOES-13 satellite
GHRSST Level 3C America Regional Skin Sea Surface Temperature from the Geostationary Operational Environmental Satellites (GOES) Imager on the GOES-13 satellite
GHRSST Level 3C America Regional Skin Sea Surface Temperature from the Geostationary Operational Environmental Satellites (GOES) Imager on the GOES-13 satellite
GHRSST Level 3C America Regional Skin Sea Surface Temperature from the Geostationary Operational Environmental Satellites (GOES) Imager on the GOES-13 satellite
GHRSST Level 2P Global Skin Sea Surface Temperature from the Infrared Atmospheric Sounding Interferometer (IASI) on the MetOp-A satellite (GDS2 version)
GHRSST Level 2P Global Skin Sea Surface Temperature from the Infrared Atmospheric Sounding Interferometer (IASI) on the MetOp-A satellite (GDS2 version)
GHRSST Level 2P Global Skin Sea Surface Temperature from the Infrared Atmospheric Sounding Interferometer (IASI) on the MetOp-A satellite (GDS2 version)
GHRSST Level 2P Global Skin Sea Surface Temperature from the Infrared Atmospheric Sounding Interferometer (IASI) on the MetOp-A satellite (GDS2 version)
GHRSST Level 2P Global Skin Sea Surface Temperature from the Infrared Atmospheric Sounding Interferometer (IASI) on the MetOp-A satellite (GDS2 version)
GHRSST Level 2P Global Skin Sea Surface Temperature from the Infrared Atmospheric Sounding Interferometer (IASI) on the MetOp-A satellite (GDS2 version)
GHRSST Level 2P Global Skin Sea Surface Temperature from the Infrared Atmospheric Sounding Interferometer (IASI) on the MetOp-A satellite (GDS2 version)
GHRSST Level 2P Global Skin Sea Surface Temperature from the Infrared Atmospheric Sounding Interferometer (IASI) on the MetOp-A satellite (GDS2 version)
GHRSST Level 2P Global Skin Sea Surface Temperature from the Infrared Atmospheric Sounding Interferometer (IASI) on the MetOp-A satellite (GDS2 version)
GHRSST Level 2P Global Skin Sea Surface Temperature from the Infrared Atmospheric Sounding Interferometer (IASI) on the MetOp-A satellite (GDS2 version)
GHRSST Level 2P Global Skin Sea Surface Temperature from the Infrared Atmospheric Sounding Interferometer (IASI) on the MetOp-A satellite (GDS2 version)
GHRSST Level 2P Global Skin Sea Surface Temperature from the Infrared Atmospheric Sounding Interferometer (IASI) on the MetOp-A satellite (GDS2 version)
GHRSST Level 2P Global Skin Sea Surface Temperature from the Infrared Atmospheric Sounding Interferometer (IASI) on the MetOp-A satellite (GDS2 version)
The L4 analysis is based upon nighttime and daytime GHRSST L2P skin (MODIS) and subskin (AMSRE) SST observations.
The L4 analysis is based upon nighttime and daytime GHRSST L2P skin (MODIS) and subskin (AMSRE) SST observations.
The L4 analysis is based upon nighttime and daytime GHRSST L2P skin (MODIS) and subskin (AMSRE) SST observations.
The L4 analysis is based upon nighttime and daytime GHRSST L2P skin (MODIS) and subskin (AMSRE) SST observations.
The L4 analysis is based upon nighttime and daytime GHRSST L2P skin (MODIS) and subskin (AMSRE) SST observations.
The L4 analysis is based upon nighttime and daytime GHRSST L2P skin (MODIS) and subskin (AMSRE) SST observations.
The L4 analysis is based upon nighttime and daytime GHRSST L2P skin (MODIS) and subskin (AMSRE) SST observations.
A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis at the JPL Physical Oceanography DAAC using weighted averages on a regional 0.011 degree grid over the oceans off North and Central America (62N- 20S, 165W - 30W). This Research to Operations (RTO) analysis is based upon a composite of either nighttime or daytime GHRSST L2P skin SST from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Aqua and Terra platforms, and subskin SST observations from the Advanced Microwave Scanning Radiometer-EOS (AMSRE).  Four unique products (composites) are created: MODIS Terra/AMSRE day and night, and MODIS Aqua/AMSRE  day and night. This particular dataset represents a MODIS Aqua and AMSRE composite using nighttime data.  The algorithm is based on a weighting scheme and compositing wherby MODIS data are used if they exist to preserve the highest resolution possible. The product is categorized as blended because no attempt is made to correct for foundation or skin temperature.
A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis at the JPL Physical Oceanography DAAC using weighted averages on a regional 0.011 degree grid over the oceans off North and Central America (62N- 20S, 165W - 30W). This Research to Operations (RTO) analysis is based upon a composite of either nighttime or daytime GHRSST L2P skin SST from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Aqua and Terra platforms, and subskin SST observations from the Advanced Microwave Scanning Radiometer-EOS (AMSRE).  Four unique products (composites) are created: MODIS Terra/AMSRE day and night, and MODIS Aqua/AMSRE  day and night. This particular dataset represents a MODIS Aqua and AMSRE composite using nighttime data.  The algorithm is based on a weighting scheme and compositing wherby MODIS data are used if they exist to preserve the highest resolution possible. The product is categorized as blended because no attempt is made to correct for foundation or skin temperature.
A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis at the JPL Physical Oceanography DAAC using weighted averages on a regional 0.011 degree grid over the oceans off North and Central America (62N- 20S, 165W - 30W). This Research to Operations (RTO) analysis is based upon a composite of either nighttime or daytime GHRSST L2P skin SST from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Aqua and Terra platforms, and subskin SST observations from the Advanced Microwave Scanning Radiometer-EOS (AMSRE).  Four unique products (composites) are created: MODIS Terra/AMSRE day and night, and MODIS Aqua/AMSRE  day and night. This particular dataset represents a MODIS Aqua and AMSRE composite using nighttime data.  The algorithm is based on a weighting scheme and compositing wherby MODIS data are used if they exist to preserve the highest resolution possible. The product is categorized as blended because no attempt is made to correct for foundation or skin temperature.
A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis at the JPL Physical Oceanography DAAC using weighted averages on a regional 0.011 degree grid over the oceans off North and Central America (62N- 20S, 165W - 30W). This Research to Operations (RTO) analysis is based upon a composite of either nighttime or daytime GHRSST L2P skin SST from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Aqua and Terra platforms, and subskin SST observations from the Advanced Microwave Scanning Radiometer-EOS (AMSRE).  Four unique products (composites) are created: MODIS Terra/AMSRE day and night, and MODIS Aqua/AMSRE  day and night. This particular dataset represents a MODIS Aqua and AMSRE composite using nighttime data.  The algorithm is based on a weighting scheme and compositing wherby MODIS data are used if they exist to preserve the highest resolution possible. The product is categorized as blended because no attempt is made to correct for foundation or skin temperature.
A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis at the JPL Physical Oceanography DAAC using weighted averages on a regional 0.011 degree grid over the oceans off North and Central America (62N- 20S, 165W - 30W). This Research to Operations (RTO) analysis is based upon a composite of either nighttime or daytime GHRSST L2P skin SST from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Aqua and Terra platforms, and subskin SST observations from the Advanced Microwave Scanning Radiometer-EOS (AMSRE).  Four unique products (composites) are created: MODIS Terra/AMSRE day and night, and MODIS Aqua/AMSRE  day and night. This particular dataset represents a MODIS Aqua and AMSRE composite using nighttime data.  The algorithm is based on a weighting scheme and compositing wherby MODIS data are used if they exist to preserve the highest resolution possible. The product is categorized as blended because no attempt is made to correct for foundation or skin temperature.
A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis at the JPL Physical Oceanography DAAC using weighted averages on a regional 0.011 degree grid over the oceans off North and Central America (62N- 20S, 165W - 30W). This Research to Operations (RTO) analysis is based upon a composite of either nighttime or daytime GHRSST L2P skin SST from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Aqua and Terra platforms, and subskin SST observations from the Advanced Microwave Scanning Radiometer-EOS (AMSRE).  Four unique products (composites) are created: MODIS Terra/AMSRE day and night, and MODIS Aqua/AMSRE  day and night. This particular dataset represents a MODIS Aqua and AMSRE composite using nighttime data.  The algorithm is based on a weighting scheme and compositing wherby MODIS data are used if they exist to preserve the highest resolution possible. The product is categorized as blended because no attempt is made to correct for foundation or skin temperature.
A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis at the JPL Physical Oceanography DAAC using weighted averages on a regional 0.011 degree grid over the oceans off North and Central America (62N- 20S, 165W - 30W). This Research to Operations (RTO) analysis is based upon a composite of either nighttime or daytime GHRSST L2P skin SST from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Aqua and Terra platforms, and subskin SST observations from the Advanced Microwave Scanning Radiometer-EOS (AMSRE).  Four unique products (composites) are created: MODIS Terra/AMSRE day and night, and MODIS Aqua/AMSRE  day and night. This particular dataset represents a MODIS Aqua and AMSRE composite using nighttime data.  The algorithm is based on a weighting scheme and compositing wherby MODIS data are used if they exist to preserve the highest resolution possible. The product is categorized as blended because no attempt is made to correct for foundation or skin temperature.
A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis at the JPL Physical Oceanography DAAC using weighted averages on a regional 0.011 degree grid over the oceans off North and Central America (62N- 20S, 165W - 30W). This Research to Operations (RTO) analysis is based upon a composite of either nighttime or daytime GHRSST L2P skin SST from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Aqua and Terra platforms, and subskin SST observations from the Advanced Microwave Scanning Radiometer-EOS (AMSRE). Four unique products (composites) are created: MODIS Terra/AMSRE day and night, and MODIS Aqua/AMSRE  day and night. This particular dataset represents a MODIS Terra and AMSRE composite using daytime data.  The algorithm is based on a weighting scheme and compositing wherby MODIS data are used if they exist to preserve the highest resolution possible. The product is categorized as blended because no attempt is made to correct for foundation or skin temperature.
A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis at the JPL Physical Oceanography DAAC using weighted averages on a regional 0.011 degree grid over the oceans off North and Central America (62N- 20S, 165W - 30W). This Research to Operations (RTO) analysis is based upon a composite of either nighttime or daytime GHRSST L2P skin SST from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Aqua and Terra platforms, and subskin SST observations from the Advanced Microwave Scanning Radiometer-EOS (AMSRE). Four unique products (composites) are created: MODIS Terra/AMSRE day and night, and MODIS Aqua/AMSRE  day and night. This particular dataset represents a MODIS Terra and AMSRE composite using daytime data.  The algorithm is based on a weighting scheme and compositing wherby MODIS data are used if they exist to preserve the highest resolution possible. The product is categorized as blended because no attempt is made to correct for foundation or skin temperature.
A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis at the JPL Physical Oceanography DAAC using weighted averages on a regional 0.011 degree grid over the oceans off North and Central America (62N- 20S, 165W - 30W). This Research to Operations (RTO) analysis is based upon a composite of either nighttime or daytime GHRSST L2P skin SST from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Aqua and Terra platforms, and subskin SST observations from the Advanced Microwave Scanning Radiometer-EOS (AMSRE). Four unique products (composites) are created: MODIS Terra/AMSRE day and night, and MODIS Aqua/AMSRE  day and night. This particular dataset represents a MODIS Terra and AMSRE composite using daytime data.  The algorithm is based on a weighting scheme and compositing wherby MODIS data are used if they exist to preserve the highest resolution possible. The product is categorized as blended because no attempt is made to correct for foundation or skin temperature.
A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis at the JPL Physical Oceanography DAAC using weighted averages on a regional 0.011 degree grid over the oceans off North and Central America (62N- 20S, 165W - 30W). This Research to Operations (RTO) analysis is based upon a composite of either nighttime or daytime GHRSST L2P skin SST from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Aqua and Terra platforms, and subskin SST observations from the Advanced Microwave Scanning Radiometer-EOS (AMSRE). Four unique products (composites) are created: MODIS Terra/AMSRE day and night, and MODIS Aqua/AMSRE  day and night. This particular dataset represents a MODIS Terra and AMSRE composite using daytime data.  The algorithm is based on a weighting scheme and compositing wherby MODIS data are used if they exist to preserve the highest resolution possible. The product is categorized as blended because no attempt is made to correct for foundation or skin temperature.
A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis at the JPL Physical Oceanography DAAC using weighted averages on a regional 0.011 degree grid over the oceans off North and Central America (62N- 20S, 165W - 30W). This Research to Operations (RTO) analysis is based upon a composite of either nighttime or daytime GHRSST L2P skin SST from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Aqua and Terra platforms, and subskin SST observations from the Advanced Microwave Scanning Radiometer-EOS (AMSRE). Four unique products (composites) are created: MODIS Terra/AMSRE day and night, and MODIS Aqua/AMSRE  day and night. This particular dataset represents a MODIS Terra and AMSRE composite using daytime data.  The algorithm is based on a weighting scheme and compositing wherby MODIS data are used if they exist to preserve the highest resolution possible. The product is categorized as blended because no attempt is made to correct for foundation or skin temperature.
A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis at the JPL Physical Oceanography DAAC using weighted averages on a regional 0.011 degree grid over the oceans off North and Central America (62N- 20S, 165W - 30W). This Research to Operations (RTO) analysis is based upon a composite of either nighttime or daytime GHRSST L2P skin SST from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Aqua and Terra platforms, and subskin SST observations from the Advanced Microwave Scanning Radiometer-EOS (AMSRE). Four unique products (composites) are created: MODIS Terra/AMSRE day and night, and MODIS Aqua/AMSRE  day and night. This particular dataset represents a MODIS Terra and AMSRE composite using daytime data.  The algorithm is based on a weighting scheme and compositing wherby MODIS data are used if they exist to preserve the highest resolution possible. The product is categorized as blended because no attempt is made to correct for foundation or skin temperature.
A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis at the JPL Physical Oceanography DAAC using weighted averages on a regional 0.011 degree grid over the oceans off North and Central America (62N- 20S, 165W - 30W). This Research to Operations (RTO) analysis is based upon a composite of either nighttime or daytime GHRSST L2P skin SST from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Aqua and Terra platforms, and subskin SST observations from the Advanced Microwave Scanning Radiometer-EOS (AMSRE). Four unique products (composites) are created: MODIS Terra/AMSRE day and night, and MODIS Aqua/AMSRE  day and night. This particular dataset represents a MODIS Terra and AMSRE composite using daytime data.  The algorithm is based on a weighting scheme and compositing wherby MODIS data are used if they exist to preserve the highest resolution possible. The product is categorized as blended because no attempt is made to correct for foundation or skin temperature.
A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis at the JPL Physical Oceanography DAAC using weighted averages on a regional 0.011 degree grid over the oceans off North and Central America (62N- 20S, 165W - 30W).  This Research to Operations (RTO) analysis is based upon a composite of either nighttime or daytime GHRSST L2P skin SST from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Aqua and Terra platforms, and subskin SST observations from the Advanced Microwave Scanning Radiometer-EOS (AMSRE). Four unique products (composites) are created: MODIS Terra/AMSRE day and night, and MODIS Aqua/AMSRE  day and night. This particular dataset represents a MODIS Terra and AMSRE composite using nighttime data.  The algorithm is based on a weighting scheme and compositing wherby MODIS data are used if they exist to preserve the highest resolution possible. The product is categorized as blended because no attempt is made to correct for foundation or skin temperature.
A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis at the JPL Physical Oceanography DAAC using weighted averages on a regional 0.011 degree grid over the oceans off North and Central America (62N- 20S, 165W - 30W).  This Research to Operations (RTO) analysis is based upon a composite of either nighttime or daytime GHRSST L2P skin SST from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Aqua and Terra platforms, and subskin SST observations from the Advanced Microwave Scanning Radiometer-EOS (AMSRE). Four unique products (composites) are created: MODIS Terra/AMSRE day and night, and MODIS Aqua/AMSRE  day and night. This particular dataset represents a MODIS Terra and AMSRE composite using nighttime data.  The algorithm is based on a weighting scheme and compositing wherby MODIS data are used if they exist to preserve the highest resolution possible. The product is categorized as blended because no attempt is made to correct for foundation or skin temperature.
A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis at the JPL Physical Oceanography DAAC using weighted averages on a regional 0.011 degree grid over the oceans off North and Central America (62N- 20S, 165W - 30W).  This Research to Operations (RTO) analysis is based upon a composite of either nighttime or daytime GHRSST L2P skin SST from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Aqua and Terra platforms, and subskin SST observations from the Advanced Microwave Scanning Radiometer-EOS (AMSRE). Four unique products (composites) are created: MODIS Terra/AMSRE day and night, and MODIS Aqua/AMSRE  day and night. This particular dataset represents a MODIS Terra and AMSRE composite using nighttime data.  The algorithm is based on a weighting scheme and compositing wherby MODIS data are used if they exist to preserve the highest resolution possible. The product is categorized as blended because no attempt is made to correct for foundation or skin temperature.
A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis at the JPL Physical Oceanography DAAC using weighted averages on a regional 0.011 degree grid over the oceans off North and Central America (62N- 20S, 165W - 30W).  This Research to Operations (RTO) analysis is based upon a composite of either nighttime or daytime GHRSST L2P skin SST from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Aqua and Terra platforms, and subskin SST observations from the Advanced Microwave Scanning Radiometer-EOS (AMSRE). Four unique products (composites) are created: MODIS Terra/AMSRE day and night, and MODIS Aqua/AMSRE  day and night. This particular dataset represents a MODIS Terra and AMSRE composite using nighttime data.  The algorithm is based on a weighting scheme and compositing wherby MODIS data are used if they exist to preserve the highest resolution possible. The product is categorized as blended because no attempt is made to correct for foundation or skin temperature.
A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis at the JPL Physical Oceanography DAAC using weighted averages on a regional 0.011 degree grid over the oceans off North and Central America (62N- 20S, 165W - 30W).  This Research to Operations (RTO) analysis is based upon a composite of either nighttime or daytime GHRSST L2P skin SST from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Aqua and Terra platforms, and subskin SST observations from the Advanced Microwave Scanning Radiometer-EOS (AMSRE). Four unique products (composites) are created: MODIS Terra/AMSRE day and night, and MODIS Aqua/AMSRE  day and night. This particular dataset represents a MODIS Terra and AMSRE composite using nighttime data.  The algorithm is based on a weighting scheme and compositing wherby MODIS data are used if they exist to preserve the highest resolution possible. The product is categorized as blended because no attempt is made to correct for foundation or skin temperature.
A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis at the JPL Physical Oceanography DAAC using weighted averages on a regional 0.011 degree grid over the oceans off North and Central America (62N- 20S, 165W - 30W).  This Research to Operations (RTO) analysis is based upon a composite of either nighttime or daytime GHRSST L2P skin SST from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Aqua and Terra platforms, and subskin SST observations from the Advanced Microwave Scanning Radiometer-EOS (AMSRE). Four unique products (composites) are created: MODIS Terra/AMSRE day and night, and MODIS Aqua/AMSRE  day and night. This particular dataset represents a MODIS Terra and AMSRE composite using nighttime data.  The algorithm is based on a weighting scheme and compositing wherby MODIS data are used if they exist to preserve the highest resolution possible. The product is categorized as blended because no attempt is made to correct for foundation or skin temperature.
A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis at the JPL Physical Oceanography DAAC using weighted averages on a regional 0.011 degree grid over the oceans off North and Central America (62N- 20S, 165W - 30W).  This Research to Operations (RTO) analysis is based upon a composite of either nighttime or daytime GHRSST L2P skin SST from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Aqua and Terra platforms, and subskin SST observations from the Advanced Microwave Scanning Radiometer-EOS (AMSRE). Four unique products (composites) are created: MODIS Terra/AMSRE day and night, and MODIS Aqua/AMSRE  day and night. This particular dataset represents a MODIS Terra and AMSRE composite using nighttime data.  The algorithm is based on a weighting scheme and compositing wherby MODIS data are used if they exist to preserve the highest resolution possible. The product is categorized as blended because no attempt is made to correct for foundation or skin temperature.
A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced as a retrospective dataset at the JPL Physical Oceanography DAAC using wavelets as basis functions in an optimal interpolation approach on a global 0.011 degree grid. The Multiscale Ultrahigh Resolution (MUR) L4 analysis is based upon nighttime GHRSST L2P skin and subskin SST observations from several instruments including the NASA Advanced Microwave Scanning Radiometer-EOS (AMSRE), and the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Aqua and Terra platforms. The ice concentration data are from the archives at the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF) High Latitude Processing Center. This data set is funded by the NASA MEaSUREs program (http://earthdata.nasa.gov/our-community/community-data-system-programs/measures-projects), and created by a team led by Dr. Toshio Chin from JPL.
A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced as a retrospective dataset at the JPL Physical Oceanography DAAC using wavelets as basis functions in an optimal interpolation approach on a global 0.011 degree grid. The Multiscale Ultrahigh Resolution (MUR) L4 analysis is based upon nighttime GHRSST L2P skin and subskin SST observations from several instruments including the NASA Advanced Microwave Scanning Radiometer-EOS (AMSRE), and the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Aqua and Terra platforms. The ice concentration data are from the archives at the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF) High Latitude Processing Center. This data set is funded by the NASA MEaSUREs program (http://earthdata.nasa.gov/our-community/community-data-system-programs/measures-projects), and created by a team led by Dr. Toshio Chin from JPL.
A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced as a retrospective dataset at the JPL Physical Oceanography DAAC using wavelets as basis functions in an optimal interpolation approach on a global 0.011 degree grid. The Multiscale Ultrahigh Resolution (MUR) L4 analysis is based upon nighttime GHRSST L2P skin and subskin SST observations from several instruments including the NASA Advanced Microwave Scanning Radiometer-EOS (AMSRE), and the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Aqua and Terra platforms. The ice concentration data are from the archives at the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF) High Latitude Processing Center. This data set is funded by the NASA MEaSUREs program (http://earthdata.nasa.gov/our-community/community-data-system-programs/measures-projects), and created by a team led by Dr. Toshio Chin from JPL.
A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced as a retrospective dataset at the JPL Physical Oceanography DAAC using wavelets as basis functions in an optimal interpolation approach on a global 0.011 degree grid. The Multiscale Ultrahigh Resolution (MUR) L4 analysis is based upon nighttime GHRSST L2P skin and subskin SST observations from several instruments including the NASA Advanced Microwave Scanning Radiometer-EOS (AMSRE), and the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Aqua and Terra platforms. The ice concentration data are from the archives at the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF) High Latitude Processing Center. This data set is funded by the NASA MEaSUREs program (http://earthdata.nasa.gov/our-community/community-data-system-programs/measures-projects), and created by a team led by Dr. Toshio Chin from JPL.
A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced as a retrospective dataset at the JPL Physical Oceanography DAAC using wavelets as basis functions in an optimal interpolation approach on a global 0.011 degree grid. The Multiscale Ultrahigh Resolution (MUR) L4 analysis is based upon nighttime GHRSST L2P skin and subskin SST observations from several instruments including the NASA Advanced Microwave Scanning Radiometer-EOS (AMSRE), and the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Aqua and Terra platforms. The ice concentration data are from the archives at the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF) High Latitude Processing Center. This data set is funded by the NASA MEaSUREs program (http://earthdata.nasa.gov/our-community/community-data-system-programs/measures-projects), and created by a team led by Dr. Toshio Chin from JPL.
A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced as a retrospective dataset at the JPL Physical Oceanography DAAC using wavelets as basis functions in an optimal interpolation approach on a global 0.011 degree grid. The Multiscale Ultrahigh Resolution (MUR) L4 analysis is based upon nighttime GHRSST L2P skin and subskin SST observations from several instruments including the NASA Advanced Microwave Scanning Radiometer-EOS (AMSRE), and the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Aqua and Terra platforms. The ice concentration data are from the archives at the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF) High Latitude Processing Center. This data set is funded by the NASA MEaSUREs program (http://earthdata.nasa.gov/our-community/community-data-system-programs/measures-projects), and created by a team led by Dr. Toshio Chin from JPL.
A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced as a retrospective dataset at the JPL Physical Oceanography DAAC using wavelets as basis functions in an optimal interpolation approach on a global 0.011 degree grid. The Multiscale Ultrahigh Resolution (MUR) L4 analysis is based upon nighttime GHRSST L2P skin and subskin SST observations from several instruments including the NASA Advanced Microwave Scanning Radiometer-EOS (AMSRE), and the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Aqua and Terra platforms. The ice concentration data are from the archives at the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF) High Latitude Processing Center. This data set is funded by the NASA MEaSUREs program (http://earthdata.nasa.gov/our-community/community-data-system-programs/measures-projects), and created by a team led by Dr. Toshio Chin from JPL.
A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced as a retrospective dataset at the JPL Physical Oceanography DAAC using wavelets as basis functions in an optimal interpolation approach on a global 0.011 degree grid. The Multiscale Ultrahigh Resolution (MUR) L4 analysis is based upon nighttime GHRSST L2P skin and subskin SST observations from several instruments including the NASA Advanced Microwave Scanning Radiometer-EOS (AMSRE), and the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Aqua and Terra platforms. The ice concentration data are from the archives at the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF) High Latitude Processing Center. This data set is funded by the NASA MEaSUREs program (http://earthdata.nasa.gov/our-community/community-data-system-programs/measures-projects), and created by a team led by Dr. Toshio Chin from JPL.
A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced as a retrospective dataset at the JPL Physical Oceanography DAAC using wavelets as basis functions in an optimal interpolation approach on a global 0.011 degree grid. The Multiscale Ultrahigh Resolution (MUR) L4 analysis is based upon nighttime GHRSST L2P skin and subskin SST observations from several instruments including the NASA Advanced Microwave Scanning Radiometer-EOS (AMSRE), and the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Aqua and Terra platforms. The ice concentration data are from the archives at the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF) High Latitude Processing Center. This data set is funded by the NASA MEaSUREs program (http://earthdata.nasa.gov/our-community/community-data-system-programs/measures-projects), and created by a team led by Dr. Toshio Chin from JPL.
A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced as a retrospective dataset at the JPL Physical Oceanography DAAC using wavelets as basis functions in an optimal interpolation approach on a global 0.011 degree grid. The Multiscale Ultrahigh Resolution (MUR) L4 analysis is based upon nighttime GHRSST L2P skin and subskin SST observations from several instruments including the NASA Advanced Microwave Scanning Radiometer-EOS (AMSRE), and the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Aqua and Terra platforms. The ice concentration data are from the archives at the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF) High Latitude Processing Center. This data set is funded by the NASA MEaSUREs program (http://earthdata.nasa.gov/our-community/community-data-system-programs/measures-projects), and created by a team led by Dr. Toshio Chin from JPL.
A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced as a retrospective dataset at the JPL Physical Oceanography DAAC using wavelets as basis functions in an optimal interpolation approach on a global 0.011 degree grid. The Multiscale Ultrahigh Resolution (MUR) L4 analysis is based upon nighttime GHRSST L2P skin and subskin SST observations from several instruments including the NASA Advanced Microwave Scanning Radiometer-EOS (AMSRE), and the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Aqua and Terra platforms. The ice concentration data are from the archives at the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF) High Latitude Processing Center. This data set is funded by the NASA MEaSUREs program (http://earthdata.nasa.gov/our-community/community-data-system-programs/measures-projects), and created by a team led by Dr. Toshio Chin from JPL.
This product contains along track gridded Sea Surface Height Anomalies (SSHA) from TOPEX/Poseidon, Jason-1 and OSTM/Jason-2 (depending on time period), with biases and cross-calibrations applied.
This product contains along track gridded Sea Surface Height Anomalies (SSHA) from TOPEX/Poseidon, Jason-1 and OSTM/Jason-2 (depending on time period), with biases and cross-calibrations applied.
This dataset contains along track geo-referenced  Sea Surface Height Anomalies (SSHA) from TOPEX/Poseidon, Jason-1 and OSTM/Jason-2 (depending on time period) merged onto a single mean reference orbit.  All biases and cross-calibrations have been applied to the data so SSHA are consistent between satellites to form a single climate data record.  Altimeter data from the multi-mission Geophysical Data Records (GDRs) are interpolated to a common reference orbit facilitating direct time series analysis of the geo-referenced SSH.  The data are in netCDF format in an array based on 3-dimensions (rev#, index, cycle) that permits direct access of individual locations at specific times (i.e., temporal and spatial sub-sampling). The data start at September 1992 and is presently updated to June 2012.  The newest data are appended to the file annually.
This dataset contains along track geo-referenced  Sea Surface Height Anomalies (SSHA) from TOPEX/Poseidon, Jason-1 and OSTM/Jason-2 (depending on time period) merged onto a single mean reference orbit.  All biases and cross-calibrations have been applied to the data so SSHA are consistent between satellites to form a single climate data record.  Altimeter data from the multi-mission Geophysical Data Records (GDRs) are interpolated to a common reference orbit facilitating direct time series analysis of the geo-referenced SSH.  The data are in netCDF format in an array based on 3-dimensions (rev#, index, cycle) that permits direct access of individual locations at specific times (i.e., temporal and spatial sub-sampling). The data start at September 1992 and is presently updated to June 2012.  The newest data are appended to the file annually.
This dataset contains along track geo-referenced  Sea Surface Height Anomalies (SSHA) from TOPEX/Poseidon, Jason-1 and OSTM/Jason-2 (depending on time period) merged onto a single mean reference orbit.  All biases and cross-calibrations have been applied to the data so SSHA are consistent between satellites to form a single climate data record.  Altimeter data from the multi-mission Geophysical Data Records (GDRs) are interpolated to a common reference orbit facilitating direct time series analysis of the geo-referenced SSH.  The data are in netCDF format in an array based on 3-dimensions (rev#, index, cycle) that permits direct access of individual locations at specific times (i.e., temporal and spatial sub-sampling). The data start at September 1992 and is presently updated to June 2012.  The newest data are appended to the file annually.
This dataset contains along track geo-referenced  Sea Surface Height Anomalies (SSHA) from TOPEX/Poseidon, Jason-1 and OSTM/Jason-2 (depending on time period) merged onto a single mean reference orbit.  All biases and cross-calibrations have been applied to the data so SSHA are consistent between satellites to form a single climate data record.  Altimeter data from the multi-mission Geophysical Data Records (GDRs) are interpolated to a common reference orbit facilitating direct time series analysis of the geo-referenced SSH.  The data are in netCDF format in an array based on 3-dimensions (rev#, index, cycle) that permits direct access of individual locations at specific times (i.e., temporal and spatial sub-sampling). The data start at September 1992 and is presently updated to June 2012.  The newest data are appended to the file annually.
This dataset contains along track geo-referenced  Sea Surface Height Anomalies (SSHA) from TOPEX/Poseidon, Jason-1 and OSTM/Jason-2 (depending on time period) merged onto a single mean reference orbit.  All biases and cross-calibrations have been applied to the data so SSHA are consistent between satellites to form a single climate data record.  Altimeter data from the multi-mission Geophysical Data Records (GDRs) are interpolated to a common reference orbit facilitating direct time series analysis of the geo-referenced SSH.  The data are in netCDF format in an array based on 3-dimensions (rev#, index, cycle) that permits direct access of individual locations at specific times (i.e., temporal and spatial sub-sampling). The data start at September 1992 and is presently updated to June 2012.  The newest data are appended to the file annually.
This dataset contains along track geo-referenced  Sea Surface Height Anomalies (SSHA) from TOPEX/Poseidon, Jason-1 and OSTM/Jason-2 (depending on time period) merged onto a single mean reference orbit.  All biases and cross-calibrations have been applied to the data so SSHA are consistent between satellites to form a single climate data record.  Altimeter data from the multi-mission Geophysical Data Records (GDRs) are interpolated to a common reference orbit facilitating direct time series analysis of the geo-referenced SSH.  The data are in netCDF format in an array based on 3-dimensions (rev#, index, cycle) that permits direct access of individual locations at specific times (i.e., temporal and spatial sub-sampling). The data start at September 1992 and is presently updated to June 2012.  The newest data are appended to the file annually.
This dataset contains along track geo-referenced  Sea Surface Height Anomalies (SSHA) from TOPEX/Poseidon, Jason-1 and OSTM/Jason-2 (depending on time period) merged onto a single mean reference orbit.  All biases and cross-calibrations have been applied to the data so SSHA are consistent between satellites to form a single climate data record.  Altimeter data from the multi-mission Geophysical Data Records (GDRs) are interpolated to a common reference orbit facilitating direct time series analysis of the geo-referenced SSH.  The data are in netCDF format in an array based on 3-dimensions (rev#, index, cycle) that permits direct access of individual locations at specific times (i.e., temporal and spatial sub-sampling). The data start at September 1992 and is presently updated to June 2012.  The newest data are appended to the file annually.
This dataset contains along track geo-referenced  Sea Surface Height Anomalies (SSHA) from TOPEX/Poseidon, Jason-1 and OSTM/Jason-2 (depending on time period) merged onto a single mean reference orbit.  All biases and cross-calibrations have been applied to the data so SSHA are consistent between satellites to form a single climate data record.  Altimeter data from the multi-mission Geophysical Data Records (GDRs) are interpolated to a common reference orbit facilitating direct time series analysis of the geo-referenced SSH.  The data are in netCDF format in an array based on 3-dimensions (rev#, index, cycle) that permits direct access of individual locations at specific times (i.e., temporal and spatial sub-sampling). The data start at September 1992 and is presently updated to June 2012.  The newest data are appended to the file annually.
This dataset contains along track geo-referenced  Sea Surface Height Anomalies (SSHA) from TOPEX/Poseidon, Jason-1 and OSTM/Jason-2 (depending on time period) merged onto a single mean reference orbit.  All biases and cross-calibrations have been applied to the data so SSHA are consistent between satellites to form a single climate data record.  Altimeter data from the multi-mission Geophysical Data Records (GDRs) are interpolated to a common reference orbit facilitating direct time series analysis of the geo-referenced SSH.  The data are in netCDF format in an array based on 3-dimensions (rev#, index, cycle) that permits direct access of individual locations at specific times (i.e., temporal and spatial sub-sampling). The data start at September 1992 and is presently updated to June 2012.  The newest data are appended to the file annually.
This dataset contains along track geo-referenced  Sea Surface Height Anomalies (SSHA) from TOPEX/Poseidon, Jason-1 and OSTM/Jason-2 (depending on time period) merged onto a single mean reference orbit.  All biases and cross-calibrations have been applied to the data so SSHA are consistent between satellites to form a single climate data record.  Altimeter data from the multi-mission Geophysical Data Records (GDRs) are interpolated to a common reference orbit facilitating direct time series analysis of the geo-referenced SSH.  The data are in netCDF format in an array based on 3-dimensions (rev#, index, cycle) that permits direct access of individual locations at specific times (i.e., temporal and spatial sub-sampling). The data start at September 1992 and is presently updated to June 2012.  The newest data are appended to the file annually.
This dataset contains along track geo-referenced  Sea Surface Height Anomalies (SSHA) from TOPEX/Poseidon, Jason-1 and OSTM/Jason-2 (depending on time period) merged onto a single mean reference orbit.  All biases and cross-calibrations have been applied to the data so SSHA are consistent between satellites to form a single climate data record.  Altimeter data from the multi-mission Geophysical Data Records (GDRs) are interpolated to a common reference orbit facilitating direct time series analysis of the geo-referenced SSH.  The data are in netCDF format in an array based on 3-dimensions (rev#, index, cycle) that permits direct access of individual locations at specific times (i.e., temporal and spatial sub-sampling). The data start at September 1992 and is presently updated to June 2012.  The newest data are appended to the file annually.
This dataset contains along track geo-referenced  Sea Surface Height Anomalies (SSHA) from TOPEX/Poseidon, Jason-1 and OSTM/Jason-2 (depending on time period) merged onto a single mean reference orbit.  All biases and cross-calibrations have been applied to the data so SSHA are consistent between satellites to form a single climate data record.  Altimeter data from the multi-mission Geophysical Data Records (GDRs) are interpolated to a common reference orbit facilitating direct time series analysis of the geo-referenced SSH.  The data are in netCDF format in an array based on 3-dimensions (rev#, index, cycle) that permits direct access of individual locations at specific times (i.e., temporal and spatial sub-sampling). The data start at September 1992 and is presently updated to June 2012.  The newest data are appended to the file annually.
This dataset contains along track geo-referenced  Sea Surface Height Anomalies (SSHA) from TOPEX/Poseidon, Jason-1 and OSTM/Jason-2 (depending on time period) merged onto a single mean reference orbit.  All biases and cross-calibrations have been applied to the data so SSHA are consistent between satellites to form a single climate data record.  Altimeter data from the multi-mission Geophysical Data Records (GDRs) are interpolated to a common reference orbit facilitating direct time series analysis of the geo-referenced SSH.  The data are in netCDF format in an array based on 3-dimensions (rev#, index, cycle) that permits direct access of individual locations at specific times (i.e., temporal and spatial sub-sampling). The data start at September 1992 and is presently updated to June 2012.  The newest data are appended to the file annually.
This product contains along track gridded Sea Surface Height Anomalies (SSHA) from TOPEX/Poseidon, Jason-1 and OSTM/Jason-2 (depending on time period).
This product contains along track gridded Sea Surface Height Anomalies (SSHA) from TOPEX/Poseidon, Jason-1 and OSTM/Jason-2 (depending on time period).
This dataset contains along track geo-referenced Sea Surface Height Anomalies (SSHA) from TOPEX/Poseidon, Jason-1 and OSTM/Jason-2 (depending on time period) merged into a single mean reference orbit.  All biases and cross-calibrations have been applied to the data so SSHA are consistent between satellites to form a single climate data record.  Altimeter data from the multi-mission Geophysical Data Records (GDRs) are interpolated to a common reference orbit facilitating direct time series analysis of the geo-referenced SSH.  The data are in netCDF format in an array based on 3-dimensions (rev#, index, cycle) that permits direct access of individual locations at specific times (i.e., temporal and spatial sub-sampling). Each file consists of one cycle of data, which is approximately 10 days long.  Cycle 001 coincides with cycle 001 of TOPEX/Poseidon (September 25-October 3, 1992) and is presently updated to cycle 660, which coincides with OSTM/Jason-2 cycle 078 (August 14-24, 2010).
This dataset contains along track geo-referenced Sea Surface Height Anomalies (SSHA) from TOPEX/Poseidon, Jason-1 and OSTM/Jason-2 (depending on time period) merged into a single mean reference orbit.  All biases and cross-calibrations have been applied to the data so SSHA are consistent between satellites to form a single climate data record.  Altimeter data from the multi-mission Geophysical Data Records (GDRs) are interpolated to a common reference orbit facilitating direct time series analysis of the geo-referenced SSH.  The data are in netCDF format in an array based on 3-dimensions (rev#, index, cycle) that permits direct access of individual locations at specific times (i.e., temporal and spatial sub-sampling). Each file consists of one cycle of data, which is approximately 10 days long.  Cycle 001 coincides with cycle 001 of TOPEX/Poseidon (September 25-October 3, 1992) and is presently updated to cycle 660, which coincides with OSTM/Jason-2 cycle 078 (August 14-24, 2010).
This dataset contains along track geo-referenced Sea Surface Height Anomalies (SSHA) from TOPEX/Poseidon, Jason-1 and OSTM/Jason-2 (depending on time period) merged into a single mean reference orbit.  All biases and cross-calibrations have been applied to the data so SSHA are consistent between satellites to form a single climate data record.  Altimeter data from the multi-mission Geophysical Data Records (GDRs) are interpolated to a common reference orbit facilitating direct time series analysis of the geo-referenced SSH.  The data are in netCDF format in an array based on 3-dimensions (rev#, index, cycle) that permits direct access of individual locations at specific times (i.e., temporal and spatial sub-sampling). Each file consists of one cycle of data, which is approximately 10 days long.  Cycle 001 coincides with cycle 001 of TOPEX/Poseidon (September 25-October 3, 1992) and is presently updated to cycle 660, which coincides with OSTM/Jason-2 cycle 078 (August 14-24, 2010).
This dataset contains along track geo-referenced Sea Surface Height Anomalies (SSHA) from TOPEX/Poseidon, Jason-1 and OSTM/Jason-2 (depending on time period) merged into a single mean reference orbit.  All biases and cross-calibrations have been applied to the data so SSHA are consistent between satellites to form a single climate data record.  Altimeter data from the multi-mission Geophysical Data Records (GDRs) are interpolated to a common reference orbit facilitating direct time series analysis of the geo-referenced SSH.  The data are in netCDF format in an array based on 3-dimensions (rev#, index, cycle) that permits direct access of individual locations at specific times (i.e., temporal and spatial sub-sampling). Each file consists of one cycle of data, which is approximately 10 days long.  Cycle 001 coincides with cycle 001 of TOPEX/Poseidon (September 25-October 3, 1992) and is presently updated to cycle 660, which coincides with OSTM/Jason-2 cycle 078 (August 14-24, 2010).
This dataset contains along track geo-referenced Sea Surface Height Anomalies (SSHA) from TOPEX/Poseidon, Jason-1 and OSTM/Jason-2 (depending on time period) merged into a single mean reference orbit.  All biases and cross-calibrations have been applied to the data so SSHA are consistent between satellites to form a single climate data record.  Altimeter data from the multi-mission Geophysical Data Records (GDRs) are interpolated to a common reference orbit facilitating direct time series analysis of the geo-referenced SSH.  The data are in netCDF format in an array based on 3-dimensions (rev#, index, cycle) that permits direct access of individual locations at specific times (i.e., temporal and spatial sub-sampling). Each file consists of one cycle of data, which is approximately 10 days long.  Cycle 001 coincides with cycle 001 of TOPEX/Poseidon (September 25-October 3, 1992) and is presently updated to cycle 660, which coincides with OSTM/Jason-2 cycle 078 (August 14-24, 2010).
This dataset contains along track geo-referenced Sea Surface Height Anomalies (SSHA) from TOPEX/Poseidon, Jason-1 and OSTM/Jason-2 (depending on time period) merged into a single mean reference orbit.  All biases and cross-calibrations have been applied to the data so SSHA are consistent between satellites to form a single climate data record.  Altimeter data from the multi-mission Geophysical Data Records (GDRs) are interpolated to a common reference orbit facilitating direct time series analysis of the geo-referenced SSH.  The data are in netCDF format in an array based on 3-dimensions (rev#, index, cycle) that permits direct access of individual locations at specific times (i.e., temporal and spatial sub-sampling). Each file consists of one cycle of data, which is approximately 10 days long.  Cycle 001 coincides with cycle 001 of TOPEX/Poseidon (September 25-October 3, 1992) and is presently updated to cycle 660, which coincides with OSTM/Jason-2 cycle 078 (August 14-24, 2010).
This dataset contains along track geo-referenced Sea Surface Height Anomalies (SSHA) from TOPEX/Poseidon, Jason-1 and OSTM/Jason-2 (depending on time period) merged into a single mean reference orbit.  All biases and cross-calibrations have been applied to the data so SSHA are consistent between satellites to form a single climate data record.  Altimeter data from the multi-mission Geophysical Data Records (GDRs) are interpolated to a common reference orbit facilitating direct time series analysis of the geo-referenced SSH.  The data are in netCDF format in an array based on 3-dimensions (rev#, index, cycle) that permits direct access of individual locations at specific times (i.e., temporal and spatial sub-sampling). Each file consists of one cycle of data, which is approximately 10 days long.  Cycle 001 coincides with cycle 001 of TOPEX/Poseidon (September 25-October 3, 1992) and is presently updated to cycle 660, which coincides with OSTM/Jason-2 cycle 078 (August 14-24, 2010).
This dataset contains along track geo-referenced Sea Surface Height Anomalies (SSHA) from TOPEX/Poseidon, Jason-1 and OSTM/Jason-2 (depending on time period) merged into a single mean reference orbit.  All biases and cross-calibrations have been applied to the data so SSHA are consistent between satellites to form a single climate data record.  Altimeter data from the multi-mission Geophysical Data Records (GDRs) are interpolated to a common reference orbit facilitating direct time series analysis of the geo-referenced SSH.  The data are in netCDF format in an array based on 3-dimensions (rev#, index, cycle) that permits direct access of individual locations at specific times (i.e., temporal and spatial sub-sampling). Each file consists of one cycle of data, which is approximately 10 days long.  Cycle 001 coincides with cycle 001 of TOPEX/Poseidon (September 25-October 3, 1992) and is presently updated to cycle 660, which coincides with OSTM/Jason-2 cycle 078 (August 14-24, 2010).
This dataset contains along track geo-referenced Sea Surface Height Anomalies (SSHA) from TOPEX/Poseidon, Jason-1 and OSTM/Jason-2 (depending on time period) merged into a single mean reference orbit.  All biases and cross-calibrations have been applied to the data so SSHA are consistent between satellites to form a single climate data record.  Altimeter data from the multi-mission Geophysical Data Records (GDRs) are interpolated to a common reference orbit facilitating direct time series analysis of the geo-referenced SSH.  The data are in netCDF format in an array based on 3-dimensions (rev#, index, cycle) that permits direct access of individual locations at specific times (i.e., temporal and spatial sub-sampling). Each file consists of one cycle of data, which is approximately 10 days long.  Cycle 001 coincides with cycle 001 of TOPEX/Poseidon (September 25-October 3, 1992) and is presently updated to cycle 660, which coincides with OSTM/Jason-2 cycle 078 (August 14-24, 2010).
This dataset contains along track geo-referenced Sea Surface Height Anomalies (SSHA) from TOPEX/Poseidon, Jason-1 and OSTM/Jason-2 (depending on time period) merged into a single mean reference orbit.  All biases and cross-calibrations have been applied to the data so SSHA are consistent between satellites to form a single climate data record.  Altimeter data from the multi-mission Geophysical Data Records (GDRs) are interpolated to a common reference orbit facilitating direct time series analysis of the geo-referenced SSH.  The data are in netCDF format in an array based on 3-dimensions (rev#, index, cycle) that permits direct access of individual locations at specific times (i.e., temporal and spatial sub-sampling). Each file consists of one cycle of data, which is approximately 10 days long.  Cycle 001 coincides with cycle 001 of TOPEX/Poseidon (September 25-October 3, 1992) and is presently updated to cycle 660, which coincides with OSTM/Jason-2 cycle 078 (August 14-24, 2010).
This dataset contains along track geo-referenced Sea Surface Height Anomalies (SSHA) from TOPEX/Poseidon, Jason-1 and OSTM/Jason-2 (depending on time period) merged into a single mean reference orbit.  All biases and cross-calibrations have been applied to the data so SSHA are consistent between satellites to form a single climate data record.  Altimeter data from the multi-mission Geophysical Data Records (GDRs) are interpolated to a common reference orbit facilitating direct time series analysis of the geo-referenced SSH.  The data are in netCDF format in an array based on 3-dimensions (rev#, index, cycle) that permits direct access of individual locations at specific times (i.e., temporal and spatial sub-sampling). Each file consists of one cycle of data, which is approximately 10 days long.  Cycle 001 coincides with cycle 001 of TOPEX/Poseidon (September 25-October 3, 1992) and is presently updated to cycle 660, which coincides with OSTM/Jason-2 cycle 078 (August 14-24, 2010).
This dataset contains along track geo-referenced Sea Surface Height Anomalies (SSHA) from TOPEX/Poseidon, Jason-1 and OSTM/Jason-2 (depending on time period) merged into a single mean reference orbit.  All biases and cross-calibrations have been applied to the data so SSHA are consistent between satellites to form a single climate data record.  Altimeter data from the multi-mission Geophysical Data Records (GDRs) are interpolated to a common reference orbit facilitating direct time series analysis of the geo-referenced SSH.  The data are in netCDF format in an array based on 3-dimensions (rev#, index, cycle) that permits direct access of individual locations at specific times (i.e., temporal and spatial sub-sampling). Each file consists of one cycle of data, which is approximately 10 days long.  Cycle 001 coincides with cycle 001 of TOPEX/Poseidon (September 25-October 3, 1992) and is presently updated to cycle 660, which coincides with OSTM/Jason-2 cycle 078 (August 14-24, 2010).
This dataset contains along track geo-referenced Sea Surface Height Anomalies (SSHA) from TOPEX/Poseidon, Jason-1 and OSTM/Jason-2 (depending on time period) merged into a single mean reference orbit.  All biases and cross-calibrations have been applied to the data so SSHA are consistent between satellites to form a single climate data record.  Altimeter data from the multi-mission Geophysical Data Records (GDRs) are interpolated to a common reference orbit facilitating direct time series analysis of the geo-referenced SSH.  The data are in netCDF format in an array based on 3-dimensions (rev#, index, cycle) that permits direct access of individual locations at specific times (i.e., temporal and spatial sub-sampling). Each file consists of one cycle of data, which is approximately 10 days long.  Cycle 001 coincides with cycle 001 of TOPEX/Poseidon (September 25-October 3, 1992) and is presently updated to cycle 660, which coincides with OSTM/Jason-2 cycle 078 (August 14-24, 2010).
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.  The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.  The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.  The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.  The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.  The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
SST is derived from the MODIS IR channels using two channels in either the thermal IR (11-12 um) or channels in the mid-IR region (3.8-4.1 um). The approach is similar to the multi-channel sea surface temperature (MCSST) or the non-linear SST (NLSST) methods used to generate AVHRR-based SST.

The MODIS data are available in a variety of spatial resolutions and temporal periods. The Level 3 mapped products are global gridded data sets with all points filled even over land.
This product contains near-surface current estimates, derived using quasi-linear and steady flow momentum equations. The horizontal velocity is directly estimated from sea surface height, surface vector wind and sea surface temperature, these data were collected from the various satellites and in site instruments (see processing methods). The model formulation combines geostrophic, Ekman and Stommel shear dynamics, and a complementary term from the surface buoyancy gradient
This product contains near-surface current estimates, derived using quasi-linear and steady flow momentum equations. The horizontal velocity is directly estimated from sea surface height, surface vector wind and sea surface temperature, these data were collected from the various satellites and in site instruments (see processing methods). The model formulation combines geostrophic, Ekman and Stommel shear dynamics, and a complementary term from the surface buoyancy gradient
This product contains near-surface current estimates, derived using quasi-linear and steady flow momentum equations. The horizontal velocity is directly estimated from sea surface height, surface vector wind and sea surface temperature, these data were collected from the various satellites and in site instruments (see processing methods). The model formulation combines geostrophic, Ekman and Stommel shear dynamics, and a complementary term from the surface buoyancy gradient
This product contains near-surface current estimates, derived using quasi-linear and steady flow momentum equations. The horizontal velocity is directly estimated from sea surface height, surface vector wind and sea surface temperature, these data were collected from the various satellites and in site instruments (see processing methods). The model formulation combines geostrophic, Ekman and Stommel shear dynamics, and a complementary term from the surface buoyancy gradient
This product contains near-surface current estimates, derived using quasi-linear and steady flow momentum equations. The horizontal velocity is directly estimated from sea surface height, surface vector wind and sea surface temperature, these data were collected from the various satellites and in site instruments (see processing methods). The model formulation combines geostrophic, Ekman and Stommel shear dynamics, and a complementary term from the surface buoyancy gradient
This product contains near-surface current estimates, derived using quasi-linear and steady flow momentum equations. The horizontal velocity is directly estimated from sea surface height, surface vector wind and sea surface temperature, these data were collected from the various satellites and in site instruments (see processing methods). The model formulation combines geostrophic, Ekman and Stommel shear dynamics, and a complementary term from the surface buoyancy gradient
This product contains near-surface current estimates, derived using quasi-linear and steady flow momentum equations. The horizontal velocity is directly estimated from sea surface height, surface vector wind and sea surface temperature, these data were collected from the various satellites and in site instruments (see processing methods). The model formulation combines geostrophic, Ekman and Stommel shear dynamics, and a complementary term from the surface buoyancy gradient
This product contains near-surface current estimates, derived using quasi-linear and steady flow momentum equations. The horizontal velocity is directly estimated from sea surface height, surface vector wind and sea surface temperature, these data were collected from the various satellites and in site instruments (see processing methods). The model formulation combines geostrophic, Ekman and Stommel shear dynamics, and a complementary term from the surface buoyancy gradient
SST calculated from the IR channels of GOES-13 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-13 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-13 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-13 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-13 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-13 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
Multi-functional Transport Satellites (MTSAT) are a series of geostationary weather satellites operated by the Japan Meteorological Agency (JMA). MTSAT carries an aeronautical mission to assist air navigation, plus a meteorological mission to provide imagery over the Asia-Pacific region for the hemisphere centered on 140 East. The meteorological mission includes an imager giving nominal hourly full Earth disk images in five spectral bands (one visible, four infrared). MTSAT are spin stabilized satellites. With this system images are built up by scanning with a mirror that is tilted in small successive steps from the north pole to south pole at a rate such that on each rotation of the satellite an adjacent strip of the Earth is scanned. It takes about 25 minutes to scan the full Earth's disk. This builds a picture 10,000 pixels for the visible images (1.25 km resolution) and 2,500 pixels (4 km resolution) for the infrared images. The MTSAT-2 (also known as Himawari 7) and it's radiometer (MTSAT-2 Imager) was successfully launched on 18 February 2006. For this Group for High Resolution Sea Surface Temperature (GHRSST) dataset, skin sea surface temperature (SST) measurements are calculated from the IR channels of the MTSAT-2 Imager full resolution data in satellite projection on a hourly basis. L2P datasets including Single Sensor Error Statistics (SSES) are then derived following the GHRSST Data Processing Specification (GDS) version 1.5.
Multi-functional Transport Satellites (MTSAT) are a series of geostationary weather satellites operated by the Japan Meteorological Agency (JMA). MTSAT carries an aeronautical mission to assist air navigation, plus a meteorological mission to provide imagery over the Asia-Pacific region for the hemisphere centered on 140 East. The meteorological mission includes an imager giving nominal hourly full Earth disk images in five spectral bands (one visible, four infrared). MTSAT are spin stabilized satellites. With this system images are built up by scanning with a mirror that is tilted in small successive steps from the north pole to south pole at a rate such that on each rotation of the satellite an adjacent strip of the Earth is scanned. It takes about 25 minutes to scan the full Earth's disk. This builds a picture 10,000 pixels for the visible images (1.25 km resolution) and 2,500 pixels (4 km resolution) for the infrared images. The MTSAT-2 (also known as Himawari 7) and it's radiometer (MTSAT-2 Imager) was successfully launched on 18 February 2006. For this Group for High Resolution Sea Surface Temperature (GHRSST) dataset, skin sea surface temperature (SST) measurements are calculated from the IR channels of the MTSAT-2 Imager full resolution data in satellite projection on a hourly basis. L2P datasets including Single Sensor Error Statistics (SSES) are then derived following the GHRSST Data Processing Specification (GDS) version 1.5.
Multi-functional Transport Satellites (MTSAT) are a series of geostationary weather satellites operated by the Japan Meteorological Agency (JMA). MTSAT carries an aeronautical mission to assist air navigation, plus a meteorological mission to provide imagery over the Asia-Pacific region for the hemisphere centered on 140 East. The meteorological mission includes an imager giving nominal hourly full Earth disk images in five spectral bands (one visible, four infrared). MTSAT are spin stabilized satellites. With this system images are built up by scanning with a mirror that is tilted in small successive steps from the north pole to south pole at a rate such that on each rotation of the satellite an adjacent strip of the Earth is scanned. It takes about 25 minutes to scan the full Earth's disk. This builds a picture 10,000 pixels for the visible images (1.25 km resolution) and 2,500 pixels (4 km resolution) for the infrared images. The MTSAT-2 (also known as Himawari 7) and it's radiometer (MTSAT-2 Imager) was successfully launched on 18 February 2006. For this Group for High Resolution Sea Surface Temperature (GHRSST) dataset, skin sea surface temperature (SST) measurements are calculated from the IR channels of the MTSAT-2 Imager full resolution data in satellite projection on a hourly basis. L2P datasets including Single Sensor Error Statistics (SSES) are then derived following the GHRSST Data Processing Specification (GDS) version 1.5.
Multi-functional Transport Satellites (MTSAT) are a series of geostationary weather satellites operated by the Japan Meteorological Agency (JMA). MTSAT carries an aeronautical mission to assist air navigation, plus a meteorological mission to provide imagery over the Asia-Pacific region for the hemisphere centered on 140 East. The meteorological mission includes an imager giving nominal hourly full Earth disk images in five spectral bands (one visible, four infrared). MTSAT are spin stabilized satellites. With this system images are built up by scanning with a mirror that is tilted in small successive steps from the north pole to south pole at a rate such that on each rotation of the satellite an adjacent strip of the Earth is scanned. It takes about 25 minutes to scan the full Earth's disk. This builds a picture 10,000 pixels for the visible images (1.25 km resolution) and 2,500 pixels (4 km resolution) for the infrared images. The MTSAT-2 (also known as Himawari 7) and it's radiometer (MTSAT-2 Imager) was successfully launched on 18 February 2006. For this Group for High Resolution Sea Surface Temperature (GHRSST) dataset, skin sea surface temperature (SST) measurements are calculated from the IR channels of the MTSAT-2 Imager full resolution data in satellite projection on a hourly basis. L2P datasets including Single Sensor Error Statistics (SSES) are then derived following the GHRSST Data Processing Specification (GDS) version 1.5.
Multi-functional Transport Satellites (MTSAT) are a series of geostationary weather satellites operated by the Japan Meteorological Agency (JMA). MTSAT carries an aeronautical mission to assist air navigation, plus a meteorological mission to provide imagery over the Asia-Pacific region for the hemisphere centered on 140 East. The meteorological mission includes an imager giving nominal hourly full Earth disk images in five spectral bands (one visible, four infrared). MTSAT are spin stabilized satellites. With this system images are built up by scanning with a mirror that is tilted in small successive steps from the north pole to south pole at a rate such that on each rotation of the satellite an adjacent strip of the Earth is scanned. It takes about 25 minutes to scan the full Earth's disk. This builds a picture 10,000 pixels for the visible images (1.25 km resolution) and 2,500 pixels (4 km resolution) for the infrared images. The MTSAT-2 (also known as Himawari 7) and it's radiometer (MTSAT-2 Imager) was successfully launched on 18 February 2006. For this Group for High Resolution Sea Surface Temperature (GHRSST) dataset, skin sea surface temperature (SST) measurements are calculated from the IR channels of the MTSAT-2 Imager full resolution data in satellite projection on a hourly basis. L2P datasets including Single Sensor Error Statistics (SSES) are then derived following the GHRSST Data Processing Specification (GDS) version 1.5.
Multi-functional Transport Satellites (MTSAT) are a series of geostationary weather satellites operated by the Japan Meteorological Agency (JMA). MTSAT carries an aeronautical mission to assist air navigation, plus a meteorological mission to provide imagery over the Asia-Pacific region for the hemisphere centered on 140 East. The meteorological mission includes an imager giving nominal hourly full Earth disk images in five spectral bands (one visible, four infrared). MTSAT are spin stabilized satellites. With this system images are built up by scanning with a mirror that is tilted in small successive steps from the north pole to south pole at a rate such that on each rotation of the satellite an adjacent strip of the Earth is scanned. It takes about 25 minutes to scan the full Earth's disk. This builds a picture 10,000 pixels for the visible images (1.25 km resolution) and 2,500 pixels (4 km resolution) for the infrared images. The MTSAT-2 (also known as Himawari 7) and it's radiometer (MTSAT-2 Imager) was successfully launched on 18 February 2006. For this Group for High Resolution Sea Surface Temperature (GHRSST) dataset, skin sea surface temperature (SST) measurements are calculated from the IR channels of the MTSAT-2 Imager full resolution data in satellite projection on a hourly basis. L2P datasets including Single Sensor Error Statistics (SSES) are then derived following the GHRSST Data Processing Specification (GDS) version 1.5.
Similar to the OSTM/Jason-2 OGDR, but also includes a GPS based orbit and SSHA calculated from that orbit, as supposed to the DORIS based orbit that is already available in the OGDR.  It has a 5 hour time lag due to the time needed to recalculate the orbit and SSH.
Similar to the OSTM/Jason-2 OGDR, but also includes a GPS based orbit and SSHA calculated from that orbit, as supposed to the DORIS based orbit that is already available in the OGDR.  It has a 5 hour time lag due to the time needed to recalculate the orbit and SSH.
This SeaWinds-on-QuikSCAT scatterometer-derived Arctic sea ice classification data set is provided as a service to the ocean and sea ice research communities on behalf of Dr. David Long at Brigham Young University (BYU) and the Scatterometer Climate Record Pathfinder (SCP). This data set provides nominal 4.45 km (pixel resolution) gridded fields of the classification of sea ice including First Year Ice, and Multi-Year Ice from SeaWinds on QuikSCAT scatterometer observations on a daily basis from 20 June 2002 through 31 December 2008. It is unique from other sea ice classification datasets primarily due to its utilization of passive microwave AMSR-E data to provide the initial ocean/ice masking edge and the use of a seasonal threshold for First-Year and Multi-Year sea ice classification from only SeaWinds data. The dataset is derived from daily gridded QuikSCAT vertically polarized (V-pol) egg backscatter data from the SCP, which is made available at the PO.DAAC: http://podaac.jpl.nasa.gov/dataset/QUIKSCAT_BYU_L3_OW_SIGMA0_ENHANCED. A seasonally varying threshold was derived to discriminate between FY and MY sea ice based on QuikSCAT V-pol egg backscatter. The sea ice mask was derived by applying threshold constraints to the SCP AMSR-E 6 GHz images, which are made available by the BYU SCP: http://www.scp.byu.edu/data/AMSRE/SIR/AMSRE_sir.html. The data is provided in netCDF version 3 format. Further details on the algorithms and validation are described in further detail by Swan and Long (2012). For more information on the QuikSCAT platform and mission, please visit http://podaac.jpl.nasa.gov/OceanWind/QuikSCAT.
This SeaWinds-on-QuikSCAT scatterometer-derived Arctic sea ice classification data set is provided as a service to the ocean and sea ice research communities on behalf of Dr. David Long at Brigham Young University (BYU) and the Scatterometer Climate Record Pathfinder (SCP). This data set provides nominal 4.45 km (pixel resolution) gridded fields of the classification of sea ice including First Year Ice, and Multi-Year Ice from SeaWinds on QuikSCAT scatterometer observations on a daily basis from 20 June 2002 through 31 December 2008. It is unique from other sea ice classification datasets primarily due to its utilization of passive microwave AMSR-E data to provide the initial ocean/ice masking edge and the use of a seasonal threshold for First-Year and Multi-Year sea ice classification from only SeaWinds data. The dataset is derived from daily gridded QuikSCAT vertically polarized (V-pol) egg backscatter data from the SCP, which is made available at the PO.DAAC: http://podaac.jpl.nasa.gov/dataset/QUIKSCAT_BYU_L3_OW_SIGMA0_ENHANCED. A seasonally varying threshold was derived to discriminate between FY and MY sea ice based on QuikSCAT V-pol egg backscatter. The sea ice mask was derived by applying threshold constraints to the SCP AMSR-E 6 GHz images, which are made available by the BYU SCP: http://www.scp.byu.edu/data/AMSRE/SIR/AMSRE_sir.html. The data is provided in netCDF version 3 format. Further details on the algorithms and validation are described in further detail by Swan and Long (2012). For more information on the QuikSCAT platform and mission, please visit http://podaac.jpl.nasa.gov/OceanWind/QuikSCAT.
This SeaWinds-on-QuikSCAT scatterometer-derived Arctic sea ice classification data set is provided as a service to the ocean and sea ice research communities on behalf of Dr. David Long at Brigham Young University (BYU) and the Scatterometer Climate Record Pathfinder (SCP). This data set provides nominal 4.45 km (pixel resolution) gridded fields of the classification of sea ice including First Year Ice, and Multi-Year Ice from SeaWinds on QuikSCAT scatterometer observations on a daily basis from 20 June 2002 through 31 December 2008. It is unique from other sea ice classification datasets primarily due to its utilization of passive microwave AMSR-E data to provide the initial ocean/ice masking edge and the use of a seasonal threshold for First-Year and Multi-Year sea ice classification from only SeaWinds data. The dataset is derived from daily gridded QuikSCAT vertically polarized (V-pol) egg backscatter data from the SCP, which is made available at the PO.DAAC: http://podaac.jpl.nasa.gov/dataset/QUIKSCAT_BYU_L3_OW_SIGMA0_ENHANCED. A seasonally varying threshold was derived to discriminate between FY and MY sea ice based on QuikSCAT V-pol egg backscatter. The sea ice mask was derived by applying threshold constraints to the SCP AMSR-E 6 GHz images, which are made available by the BYU SCP: http://www.scp.byu.edu/data/AMSRE/SIR/AMSRE_sir.html. The data is provided in netCDF version 3 format. Further details on the algorithms and validation are described in further detail by Swan and Long (2012). For more information on the QuikSCAT platform and mission, please visit http://podaac.jpl.nasa.gov/OceanWind/QuikSCAT.
This SeaWinds-on-QuikSCAT scatterometer-derived Arctic sea ice classification data set is provided as a service to the ocean and sea ice research communities on behalf of Dr. David Long at Brigham Young University (BYU) and the Scatterometer Climate Record Pathfinder (SCP). This data set provides nominal 4.45 km (pixel resolution) gridded fields of the classification of sea ice including First Year Ice, and Multi-Year Ice from SeaWinds on QuikSCAT scatterometer observations on a daily basis from 20 June 2002 through 31 December 2008. It is unique from other sea ice classification datasets primarily due to its utilization of passive microwave AMSR-E data to provide the initial ocean/ice masking edge and the use of a seasonal threshold for First-Year and Multi-Year sea ice classification from only SeaWinds data. The dataset is derived from daily gridded QuikSCAT vertically polarized (V-pol) egg backscatter data from the SCP, which is made available at the PO.DAAC: http://podaac.jpl.nasa.gov/dataset/QUIKSCAT_BYU_L3_OW_SIGMA0_ENHANCED. A seasonally varying threshold was derived to discriminate between FY and MY sea ice based on QuikSCAT V-pol egg backscatter. The sea ice mask was derived by applying threshold constraints to the SCP AMSR-E 6 GHz images, which are made available by the BYU SCP: http://www.scp.byu.edu/data/AMSRE/SIR/AMSRE_sir.html. The data is provided in netCDF version 3 format. Further details on the algorithms and validation are described in further detail by Swan and Long (2012). For more information on the QuikSCAT platform and mission, please visit http://podaac.jpl.nasa.gov/OceanWind/QuikSCAT.
This dataset contains ocean surface Eastward wind from SeaWinds on QuikSCAT binned and averaged monthly on 1 degree grids. The coverage is from August 1999 through October 2009.
This dataset contains ocean surface Eastward wind from SeaWinds on QuikSCAT binned and averaged monthly on 1 degree grids. The coverage is from August 1999 through October 2009.
This dataset contains ocean surface Eastward wind from SeaWinds on QuikSCAT binned and averaged monthly on 1 degree grids. The coverage is from August 1999 through October 2009.
This dataset contains ocean surface Eastward wind from SeaWinds on QuikSCAT binned and averaged monthly on 1 degree grids. The coverage is from August 1999 through October 2009.
This dataset contains ocean surface Eastward wind from SeaWinds on QuikSCAT binned and averaged monthly on 1 degree grids. The coverage is from August 1999 through October 2009.
This dataset contains ocean surface Eastward wind from SeaWinds on QuikSCAT binned and averaged monthly on 1 degree grids. The coverage is from August 1999 through October 2009.
This dataset contains ocean surface Northward wind from SeaWinds on QuikSCAT binned and averaged monthly on 1 degree grids. The coverage is from August 1999 through October 2009.
This dataset contains ocean surface Northward wind from SeaWinds on QuikSCAT binned and averaged monthly on 1 degree grids. The coverage is from August 1999 through October 2009.
This dataset contains ocean surface Northward wind from SeaWinds on QuikSCAT binned and averaged monthly on 1 degree grids. The coverage is from August 1999 through October 2009.
This dataset contains ocean surface Northward wind from SeaWinds on QuikSCAT binned and averaged monthly on 1 degree grids. The coverage is from August 1999 through October 2009.
This dataset contains ocean surface Northward wind from SeaWinds on QuikSCAT binned and averaged monthly on 1 degree grids. The coverage is from August 1999 through October 2009.
This dataset contains ocean surface Northward wind from SeaWinds on QuikSCAT binned and averaged monthly on 1 degree grids. The coverage is from August 1999 through October 2009.
This dataset contains ocean surface wind speed from SeaWinds on QuikSCAT binned and averaged monthly on 1 degree grids. The coverage is from August 1999 through October 2009.
This dataset contains ocean surface wind speed from SeaWinds on QuikSCAT binned and averaged monthly on 1 degree grids. The coverage is from August 1999 through October 2009.
This dataset contains ocean surface wind speed from SeaWinds on QuikSCAT binned and averaged monthly on 1 degree grids. The coverage is from August 1999 through October 2009.
This dataset contains ocean surface wind speed from SeaWinds on QuikSCAT binned and averaged monthly on 1 degree grids. The coverage is from August 1999 through October 2009.
This dataset contains ocean surface wind speed from SeaWinds on QuikSCAT binned and averaged monthly on 1 degree grids. The coverage is from August 1999 through October 2009.
This dataset contains ocean surface wind speed from SeaWinds on QuikSCAT binned and averaged monthly on 1 degree grids. The coverage is from August 1999 through October 2009.
This dataset contains the latest reprocessed version 3 of the Level 2B science-quality ocean surface wind vector retrievals from the QuikSCAT scatterometer. The retrievals are provided on a non-uniform grid within the swath at 12.5 km pixel resolution. Higher resolution is achieved through a slice composite technique in which high resolution slice measurements from L1B data are composited into a 12.5 km wind vector cell. Version 3 processing begins with the same L1B (time-ordered backscatter) data as used in the previous processing. Version 3 has several improvements over the previous JPL processing of the QuikSCAT L2B winds: 1) changes to measurement binning, which was done in order to decrease noise and reduce gaps in the 12.5 km L2B wind retrievals, 2) an improved geophysical model function (GMF) to model the effect of wind on backscatter, 3) a neural network approach to correct rain contaminated winds speeds, 4) cross-track dependent wind speed biases were estimated and removed from the wind retrievals. The 12.5 km binning resolution enables users to obtain wind vector retrievals 10 km closer to shore when compared to the 25 km L2B dataset (only available in versions 1 and 2). More details to the processing changes and improvements are noted by Fore et al. (2013): ftp://podaac.jpl.nasa.gov/allData/quikscat/L2B12/docs/fore_et_al_ieee_2013.pdf . Each L2B file corresponds to a specific orbital revolution (rev) number, which begins at the southernmost point of the ascending orbit. This is the official dataset produced by the QuikSCAT Project through the SeaWinds Processing and Analysis Center (SeaPAC). The Version 3 User Guide document is accessible here: ftp://podaac.jpl.nasa.gov/allData/quikscat/L2B12/docs/qscat_l2b_v3_ug_v1_0.pdf . Read software is made available in MATLAB, Python, R, and IDL and is accessible here: ftp://podaac.jpl.nasa.gov/allData/quikscat/L2B12/sw/
This dataset contains the latest reprocessed version 3 of the Level 2B science-quality ocean surface wind vector retrievals from the QuikSCAT scatterometer. The retrievals are provided on a non-uniform grid within the swath at 12.5 km pixel resolution. Higher resolution is achieved through a slice composite technique in which high resolution slice measurements from L1B data are composited into a 12.5 km wind vector cell. Version 3 processing begins with the same L1B (time-ordered backscatter) data as used in the previous processing. Version 3 has several improvements over the previous JPL processing of the QuikSCAT L2B winds: 1) changes to measurement binning, which was done in order to decrease noise and reduce gaps in the 12.5 km L2B wind retrievals, 2) an improved geophysical model function (GMF) to model the effect of wind on backscatter, 3) a neural network approach to correct rain contaminated winds speeds, 4) cross-track dependent wind speed biases were estimated and removed from the wind retrievals. The 12.5 km binning resolution enables users to obtain wind vector retrievals 10 km closer to shore when compared to the 25 km L2B dataset (only available in versions 1 and 2). More details to the processing changes and improvements are noted by Fore et al. (2013): ftp://podaac.jpl.nasa.gov/allData/quikscat/L2B12/docs/fore_et_al_ieee_2013.pdf . Each L2B file corresponds to a specific orbital revolution (rev) number, which begins at the southernmost point of the ascending orbit. This is the official dataset produced by the QuikSCAT Project through the SeaWinds Processing and Analysis Center (SeaPAC). The Version 3 User Guide document is accessible here: ftp://podaac.jpl.nasa.gov/allData/quikscat/L2B12/docs/qscat_l2b_v3_ug_v1_0.pdf . Read software is made available in MATLAB, Python, R, and IDL and is accessible here: ftp://podaac.jpl.nasa.gov/allData/quikscat/L2B12/sw/
This dataset contains the latest reprocessed version 3 of the Level 2B science-quality ocean surface wind vector retrievals from the QuikSCAT scatterometer. The retrievals are provided on a non-uniform grid within the swath at 12.5 km pixel resolution. Higher resolution is achieved through a slice composite technique in which high resolution slice measurements from L1B data are composited into a 12.5 km wind vector cell. Version 3 processing begins with the same L1B (time-ordered backscatter) data as used in the previous processing. Version 3 has several improvements over the previous JPL processing of the QuikSCAT L2B winds: 1) changes to measurement binning, which was done in order to decrease noise and reduce gaps in the 12.5 km L2B wind retrievals, 2) an improved geophysical model function (GMF) to model the effect of wind on backscatter, 3) a neural network approach to correct rain contaminated winds speeds, 4) cross-track dependent wind speed biases were estimated and removed from the wind retrievals. The 12.5 km binning resolution enables users to obtain wind vector retrievals 10 km closer to shore when compared to the 25 km L2B dataset (only available in versions 1 and 2). More details to the processing changes and improvements are noted by Fore et al. (2013): ftp://podaac.jpl.nasa.gov/allData/quikscat/L2B12/docs/fore_et_al_ieee_2013.pdf . Each L2B file corresponds to a specific orbital revolution (rev) number, which begins at the southernmost point of the ascending orbit. This is the official dataset produced by the QuikSCAT Project through the SeaWinds Processing and Analysis Center (SeaPAC). The Version 3 User Guide document is accessible here: ftp://podaac.jpl.nasa.gov/allData/quikscat/L2B12/docs/qscat_l2b_v3_ug_v1_0.pdf . Read software is made available in MATLAB, Python, R, and IDL and is accessible here: ftp://podaac.jpl.nasa.gov/allData/quikscat/L2B12/sw/
This dataset contains the latest reprocessed version 3 of the Level 2B science-quality ocean surface wind vector retrievals from the QuikSCAT scatterometer. The retrievals are provided on a non-uniform grid within the swath at 12.5 km pixel resolution. Higher resolution is achieved through a slice composite technique in which high resolution slice measurements from L1B data are composited into a 12.5 km wind vector cell. Version 3 processing begins with the same L1B (time-ordered backscatter) data as used in the previous processing. Version 3 has several improvements over the previous JPL processing of the QuikSCAT L2B winds: 1) changes to measurement binning, which was done in order to decrease noise and reduce gaps in the 12.5 km L2B wind retrievals, 2) an improved geophysical model function (GMF) to model the effect of wind on backscatter, 3) a neural network approach to correct rain contaminated winds speeds, 4) cross-track dependent wind speed biases were estimated and removed from the wind retrievals. The 12.5 km binning resolution enables users to obtain wind vector retrievals 10 km closer to shore when compared to the 25 km L2B dataset (only available in versions 1 and 2). More details to the processing changes and improvements are noted by Fore et al. (2013): ftp://podaac.jpl.nasa.gov/allData/quikscat/L2B12/docs/fore_et_al_ieee_2013.pdf . Each L2B file corresponds to a specific orbital revolution (rev) number, which begins at the southernmost point of the ascending orbit. This is the official dataset produced by the QuikSCAT Project through the SeaWinds Processing and Analysis Center (SeaPAC). The Version 3 User Guide document is accessible here: ftp://podaac.jpl.nasa.gov/allData/quikscat/L2B12/docs/qscat_l2b_v3_ug_v1_0.pdf . Read software is made available in MATLAB, Python, R, and IDL and is accessible here: ftp://podaac.jpl.nasa.gov/allData/quikscat/L2B12/sw/
These are sea level anomalies from 1950 to 2009.  The sea level anomalies are generated by calculating a CycloStationary Empirical Orthogonal Function (CSEOF) using the gridded sea level anomalies derived from satellite altimeters available from AVISO.  Once the CSEOF is tuned to the satellite data, it is applied to historical tide gauge data to generate sea level before the advent of satellite altimetry (1992).
These are sea level anomalies from 1950 to 2009.  The sea level anomalies are generated by calculating a CycloStationary Empirical Orthogonal Function (CSEOF) using the gridded sea level anomalies derived from satellite altimeters available from AVISO.  Once the CSEOF is tuned to the satellite data, it is applied to historical tide gauge data to generate sea level before the advent of satellite altimetry (1992).
These are sea level anomalies from 1950 to 2009.  The sea level anomalies are generated by calculating a CycloStationary Empirical Orthogonal Function (CSEOF) using the gridded sea level anomalies derived from satellite altimeters available from AVISO.  Once the CSEOF is tuned to the satellite data, it is applied to historical tide gauge data to generate sea level before the advent of satellite altimetry (1992).
These are sea level anomalies from 1950 to 2009.  The sea level anomalies are generated by calculating a CycloStationary Empirical Orthogonal Function (CSEOF) using the gridded sea level anomalies derived from satellite altimeters available from AVISO.  Once the CSEOF is tuned to the satellite data, it is applied to historical tide gauge data to generate sea level before the advent of satellite altimetry (1992).
These are sea level anomalies from 1950 to 2009.  The sea level anomalies are generated by calculating a CycloStationary Empirical Orthogonal Function (CSEOF) using the gridded sea level anomalies derived from satellite altimeters available from AVISO.  Once the CSEOF is tuned to the satellite data, it is applied to historical tide gauge data to generate sea level before the advent of satellite altimetry (1992).
These are sea level anomalies from 1950 to 2009.  The sea level anomalies are generated by calculating a CycloStationary Empirical Orthogonal Function (CSEOF) using the gridded sea level anomalies derived from satellite altimeters available from AVISO.  Once the CSEOF is tuned to the satellite data, it is applied to historical tide gauge data to generate sea level before the advent of satellite altimetry (1992).
GHRSST L4 dataset for the South Atlantic region produced by REMO
GHRSST L4 dataset for the South Atlantic region produced by REMO
GHRSST L4 dataset for the South Atlantic region produced by REMO
GHRSST L4 dataset for the South Atlantic region produced by REMO
GHRSST L4 dataset for the South Atlantic region produced by REMO
GHRSST L4 dataset for the South Atlantic region produced by REMO
GHRSST L4 dataset for the South Atlantic region produced by REMO
GHRSST L4 dataset for the South Atlantic region produced by REMO
GHRSST L4 dataset for the South Atlantic region produced by REMO
GHRSST L4 dataset for the South Atlantic region produced by REMO
GHRSST L4 dataset for the South Atlantic region produced by REMO
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.
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.
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.
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.
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.
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.
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.
Reynolds Historical Reconstructed SST is derived from the Reynolds Optimally Interpolated SST and in-situ observations.
Reynolds Historical Reconstructed SST is derived from the Reynolds Optimally Interpolated SST and in-situ observations.
Reynolds Historical Reconstructed SST is derived from the Reynolds Optimally Interpolated SST and in-situ observations.
Reynolds Historical Reconstructed SST is derived from the Reynolds Optimally Interpolated SST and in-situ observations.
Reynolds Historical Reconstructed SST is derived from the Reynolds Optimally Interpolated SST and in-situ observations.
Reynolds Historical Reconstructed SST is derived from the Reynolds Optimally Interpolated SST and in-situ observations.
Reynolds Historical Reconstructed SST is derived from the Reynolds Optimally Interpolated SST and in-situ observations.
Multichannel sea-surface temperatures (MCSST) have been computed from AVHRR radiances operational since 1981. In-situ data is derived from both ship and buoys. Both these data sets are optimally interpolated [Reynolds and Smith, 1994] onto a 1 degree by 1 degree grid. The optimally interpolated data is available on both weekly and monthly grids.
Multichannel sea-surface temperatures (MCSST) have been computed from AVHRR radiances operational since 1981. In-situ data is derived from both ship and buoys. Both these data sets are optimally interpolated [Reynolds and Smith, 1994] onto a 1 degree by 1 degree grid. The optimally interpolated data is available on both weekly and monthly grids.
Multichannel sea-surface temperatures (MCSST) have been computed from AVHRR radiances operational since 1981. In-situ data is derived from both ship and buoys. Both these data sets are optimally interpolated [Reynolds and Smith, 1994] onto a 1 degree by 1 degree grid. The optimally interpolated data is available on both weekly and monthly grids.
Multichannel sea-surface temperatures (MCSST) have been computed from AVHRR radiances operational since 1981. In-situ data is derived from both ship and buoys. Both these data sets are optimally interpolated [Reynolds and Smith, 1994] onto a 1 degree by 1 degree grid. The optimally interpolated data is available on both weekly and monthly grids.
Multichannel sea-surface temperatures (MCSST) have been computed from AVHRR radiances operational since 1981. In-situ data is derived from both ship and buoys. Both these data sets are optimally interpolated [Reynolds and Smith, 1994] onto a 1 degree by 1 degree grid. The optimally interpolated data is available on both weekly and monthly grids.
Multichannel sea-surface temperatures (MCSST) have been computed from AVHRR radiances operational since 1981. In-situ data is derived from both ship and buoys. Both these data sets are optimally interpolated [Reynolds and Smith, 1994] onto a 1 degree by 1 degree grid. The optimally interpolated data is available on both weekly and monthly grids.
GHRSST Level 3C Atlantic Subskin Sea Surface Temperature from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on  MetOp-A satellite (GDS2 version)
GHRSST Level 3C Atlantic Subskin Sea Surface Temperature from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on  MetOp-A satellite (GDS2 version)
GHRSST Level 3C Atlantic Subskin Sea Surface Temperature from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on  MetOp-A satellite (GDS2 version)
GHRSST Level 3C Atlantic Subskin Sea Surface Temperature from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on  MetOp-A satellite (GDS2 version)
GHRSST Level 3C Atlantic Subskin Sea Surface Temperature from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on  MetOp-A satellite (GDS2 version)
GHRSST Level 3C Atlantic Subskin Sea Surface Temperature from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on  MetOp-A satellite (GDS2 version)
GHRSST Level 3C Atlantic Subskin Sea Surface Temperature from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on  MetOp-A satellite (GDS2 version)
GHRSST Level 3C Atlantic Subskin Sea Surface Temperature from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on  MetOp-A satellite (GDS2 version)
GHRSST Level 3C Atlantic Subskin Sea Surface Temperature from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on  MetOp-A satellite (GDS2 version)
GHRSST Level 3C Atlantic Subskin Sea Surface Temperature from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on  MetOp-A satellite (GDS2 version)
GHRSST Level 3C Atlantic Subskin Sea Surface Temperature from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on  MetOp-A satellite (GDS2 version)
GHRSST Level 3C Atlantic Subskin Sea Surface Temperature from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on  MetOp-A satellite (GDS2 version)
ECHO collection for the shipboard ADCP dataset for SPURS-I field campaign cruises
ECHO collection for the shipboard ADCP dataset for SPURS-I field campaign cruises
ECHO collection for the shipboard ADCP dataset for SPURS-I field campaign cruises
ECHO collection for the shipboard ADCP dataset for SPURS-I field campaign cruises
ECHO collection for the shipboard ADCP dataset for SPURS-I field campaign cruises
ECHO collection for the shipboard ADCP dataset for SPURS-I field campaign cruises
ECHO collection for the shipboard ADCP dataset for SPURS-I field campaign cruises
ECHO collection for the shipboard ADCP dataset for SPURS-I field campaign cruises
ECHO collection for the shipboard ADCP dataset for SPURS-I field campaign cruises
ECHO collection for SPURS-I field campaign ARGO profiler float CTD dataset
ECHO collection for SPURS-I field campaign ARGO profiler float CTD dataset
ECHO collection for SPURS-I field campaign ARGO profiler float CTD dataset
ECHO collection for SPURS-I field campaign ARGO profiler float CTD dataset
ECHO collection for SPURS-I field campaign ARGO profiler float CTD dataset
ECHO collection for SPURS-I field campaign ARGO profiler float CTD dataset
ECHO collection for SPURS-I field campaign ARGO profiler float CTD dataset
ECHO collection for SPURS-I field campaign ARGO profiler float CTD dataset
ECHO collection for the Vessel-based CTD profile dataset for SPURS-I field campaign cruises (V1.0)
ECHO collection for the Vessel-based CTD profile dataset for SPURS-I field campaign cruises (V1.0)
ECHO collection for the Vessel-based CTD profile dataset for SPURS-I field campaign cruises (V1.0)
ECHO collection for the Vessel-based CTD profile dataset for SPURS-I field campaign cruises (V1.0)
ECHO collection for the Vessel-based CTD profile dataset for SPURS-I field campaign cruises (V1.0)
ECHO collection for the Vessel-based CTD profile dataset for SPURS-I field campaign cruises (V1.0)
ECHO collection for the Vessel-based CTD profile dataset for SPURS-I field campaign cruises (V1.0)
ECHO collection for the Vessel-based CTD profile dataset for SPURS-I field campaign cruises (V1.0)
ECHO collection for the SPURS-I field campaign Drifter dataset
ECHO collection for the SPURS-I field campaign Drifter dataset
ECHO collection for the SPURS-I field campaign Drifter dataset
ECHO collection for the SPURS-I field campaign Drifter dataset
ECHO collection for the SPURS-I field campaign Drifter dataset
ECHO collection for the SPURS-I field campaign Drifter dataset
ECHO collection for the SPURS-I field campaign Drifter dataset
ECHO collection for the SPURS-I field campaign Drifter dataset
ECHO collection for the SPURS-I field campaign Drifter dataset
ECHO collection for the SPURS-I field campaign Knorr Ecomapper data
ECHO collection for the SPURS-I field campaign Knorr Ecomapper data
ECHO collection for the SPURS-I field campaign Knorr Ecomapper data
ECHO collection for the SPURS-I field campaign Knorr Ecomapper data
ECHO collection for the SPURS-I field campaign Knorr Ecomapper data
ECHO collection for the SPURS-I field campaign Knorr Ecomapper data
ECHO collection for the SPURS-I field campaign Knorr Ecomapper data
ECHO collection for the SPURS-I field campaign Knorr Ecomapper data
ECHO collection for the SPURS-I field campaign Knorr Ecomapper data
ECHO collection for the SPURS-I field campaign Neutrally Buoyant Float CTD dataset
ECHO collection for the SPURS-I field campaign Neutrally Buoyant Float CTD dataset
ECHO collection for the SPURS-I field campaign Neutrally Buoyant Float CTD dataset
ECHO collection for the SPURS-I field campaign Neutrally Buoyant Float CTD dataset
ECHO collection for the SPURS-I field campaign Neutrally Buoyant Float CTD dataset
ECHO collection for the SPURS-I field campaign Neutrally Buoyant Float CTD dataset
ECHO collection for the SPURS-I field campaign Neutrally Buoyant Float CTD dataset
ECHO collection for the SPURS-I field campaign Neutrally Buoyant Float CTD dataset
ECHO collection for the SPURS-I field campaign Neutrally Buoyant Float CTD dataset
ECHO collection for the Vessel-based Meteorological dataset for SPURS-I field campaign cruises
ECHO collection for the Vessel-based Meteorological dataset for SPURS-I field campaign cruises
ECHO collection for the Vessel-based Meteorological dataset for SPURS-I field campaign cruises
ECHO collection for the Vessel-based Meteorological dataset for SPURS-I field campaign cruises
ECHO collection for the Vessel-based Meteorological dataset for SPURS-I field campaign cruises
ECHO collection for the Vessel-based Meteorological dataset for SPURS-I field campaign cruises
ECHO collection for the Vessel-based Meteorological dataset for SPURS-I field campaign cruises
ECHO collection for the Vessel-based Meteorological dataset for SPURS-I field campaign cruises
ECHO collection for the Vessel-based Meteorological dataset for SPURS-I field campaign cruises
ECHO collection for the SPURS-I field campaign PICO Mooring CTD dataset
ECHO collection for the SPURS-I field campaign PICO Mooring CTD dataset
ECHO collection for the SPURS-I field campaign PICO Mooring CTD dataset
ECHO collection for the SPURS-I field campaign PICO Mooring CTD dataset
ECHO collection for the SPURS-I field campaign PICO Mooring CTD dataset
ECHO collection for the SPURS-I field campaign PICO Mooring CTD dataset
ECHO collection for the SPURS-I field campaign PICO Mooring CTD dataset
ECHO collection for the SPURS-I field campaign PICO Mooring CTD dataset
ECHO collection for the SPURS-I field campaign PICO Mooring CTD dataset
ECHO collection for the WHOI Mooring CTD, meteorological and surface flux dataset for the SPURS-I field
ECHO collection for the WHOI Mooring CTD, meteorological and surface flux dataset for the SPURS-I field
ECHO collection for the WHOI Mooring CTD, meteorological and surface flux dataset for the SPURS-I field
ECHO collection for the WHOI Mooring CTD, meteorological and surface flux dataset for the SPURS-I field
ECHO collection for the WHOI Mooring CTD, meteorological and surface flux dataset for the SPURS-I field
ECHO collection for the WHOI Mooring CTD, meteorological and surface flux dataset for the SPURS-I field
ECHO collection for the WHOI Mooring CTD, meteorological and surface flux dataset for the SPURS-I field
ECHO collection for the WHOI Mooring CTD, meteorological and surface flux dataset for the SPURS-I field
ECHO collection for the WHOI Mooring CTD, meteorological and surface flux dataset for the SPURS-I field
ECHO collection for the SPURS-I field campaign Seaglider CTD dataset
ECHO collection for the SPURS-I field campaign Seaglider CTD dataset
ECHO collection for the SPURS-I field campaign Seaglider CTD dataset
ECHO collection for the SPURS-I field campaign Seaglider CTD dataset
ECHO collection for the SPURS-I field campaign Seaglider CTD dataset
ECHO collection for the SPURS-I field campaign Seaglider CTD dataset
ECHO collection for the SPURS-I field campaign Seaglider CTD dataset
ECHO collection for the SPURS-I field campaign Seaglider CTD dataset
ECHO collection for the SPURS-I field campaign Seaglider CTD dataset
ECHO collection for the SPURS-I field campaign Sarmiento SeaSoar CTD dataset
ECHO collection for the SPURS-I field campaign Sarmiento SeaSoar CTD dataset
ECHO collection for the SPURS-I field campaign Sarmiento SeaSoar CTD dataset
ECHO collection for the SPURS-I field campaign Sarmiento SeaSoar CTD dataset
ECHO collection for the SPURS-I field campaign Sarmiento SeaSoar CTD dataset
ECHO collection for the SPURS-I field campaign Sarmiento SeaSoar CTD dataset
ECHO collection for the SPURS-I field campaign Sarmiento SeaSoar CTD dataset
ECHO collection for the SPURS-I field campaign Sarmiento SeaSoar CTD dataset
ECHO collection for the SPURS-I field campaign Sarmiento SeaSoar CTD dataset
ECHO collection for the SPURS-I field campaign Tenuse Glider CTD data
ECHO collection for the SPURS-I field campaign Tenuse Glider CTD data
ECHO collection for the SPURS-I field campaign Tenuse Glider CTD data
ECHO collection for the SPURS-I field campaign Tenuse Glider CTD data
ECHO collection for the SPURS-I field campaign Tenuse Glider CTD data
ECHO collection for the SPURS-I field campaign Tenuse Glider CTD data
ECHO collection for the SPURS-I field campaign Tenuse Glider CTD data
ECHO collection for the SPURS-I field campaign Tenuse Glider CTD data
ECHO collection for the SPURS-I field campaign Tenuse Glider CTD data
ECHO collection for the ship Thermosalinograph (TSG) dataset for SPURS-I field campaign
ECHO collection for the ship Thermosalinograph (TSG) dataset for SPURS-I field campaign
ECHO collection for the ship Thermosalinograph (TSG) dataset for SPURS-I field campaign
ECHO collection for the ship Thermosalinograph (TSG) dataset for SPURS-I field campaign
ECHO collection for the ship Thermosalinograph (TSG) dataset for SPURS-I field campaign
ECHO collection for the ship Thermosalinograph (TSG) dataset for SPURS-I field campaign
ECHO collection for the ship Thermosalinograph (TSG) dataset for SPURS-I field campaign
ECHO collection for the ship Thermosalinograph (TSG) dataset for SPURS-I field campaign
ECHO collection for the ship Thermosalinograph (TSG) dataset for SPURS-I field campaign
ECHO collection for the Vessel-based Underway-CTD profile dataset for SPURS-I field campaign cruises
ECHO collection for the Vessel-based Underway-CTD profile dataset for SPURS-I field campaign cruises
ECHO collection for the Vessel-based Underway-CTD profile dataset for SPURS-I field campaign cruises
ECHO collection for the Vessel-based Underway-CTD profile dataset for SPURS-I field campaign cruises
ECHO collection for the Vessel-based Underway-CTD profile dataset for SPURS-I field campaign cruises
ECHO collection for the Vessel-based Underway-CTD profile dataset for SPURS-I field campaign cruises
ECHO collection for the Vessel-based Underway-CTD profile dataset for SPURS-I field campaign cruises
ECHO collection for the Vessel-based Underway-CTD profile dataset for SPURS-I field campaign cruises
ECHO collection for the Vessel-based Underway-CTD profile dataset for SPURS-I field campaign cruises
ECHO collection for the SPURS-I field campaign Waveglider dataset
ECHO collection for the SPURS-I field campaign Waveglider dataset
ECHO collection for the SPURS-I field campaign Waveglider dataset
ECHO collection for the SPURS-I field campaign Waveglider dataset
ECHO collection for the SPURS-I field campaign Waveglider dataset
ECHO collection for the SPURS-I field campaign Waveglider dataset
ECHO collection for the SPURS-I field campaign Waveglider dataset
ECHO collection for the SPURS-I field campaign Waveglider dataset
ECHO collection for the SPURS-I field campaign Waveglider dataset
Contains the spherical harmonic coefficients C(1,0), C(1,1), S(1,1) vs time.
This dataset provides weekly values of the spherical harmonic coefficients of the gravity field complete to degree and order 5, derived from satellite laser ranging up to five geodetic satellites, computed by Minkang Cheng, John Ries and Byron Tapley (2011).
Dynamic ocean topography is the sea surface height referenced from the Earth's geoid, rather than a smooth ellipse. The data contains a time mean dynamic topography.
Dynamic ocean topography is the sea surface height referenced from the Earth's geoid, rather than a smooth ellipse. The images are of dynamic topography and geostrophic currents.
Dynamic ocean topography is the sea surface height referenced from the Earth's geoid, rather than a smooth ellipse. The images are of dynamic topography and geostrophic currents.
The Tellus ECCO Ocean Bottom Pressure (OBP) data contain monthly averaged OBP generated from the ECCO model (http://www.ecco-group.org), where OBP is defined as the atmospheric and oceanic masses above the sea floor.  The KF080 version supersedes the previous version because a long period tide error was found, which impacted the altimeter data used in ECCO.  This error has been corrected and the KF080 has the correct tides used in the altimeter data for the assimilation.  This version also uses a Kalman filter 080.
The land water content data used to create these images were generated from the Global Land Data Assimilation System (GLDAS).
The land water content data used to create these images were generated from The Global Land Data Assimilation System (GLDAS).
The land water content contained in this dataset were generated from The Global Land Data Assimilation System (GLDAS). GLDAS outputs land water content by using numerous land surface models and data assimilation. For more information on GLDAS please visit http://gcmd.nasa.gov/records/GCMD_GES_DISC_GLDAS_NOAH10SUBP_3H_V001.html. The data are available on monthly 1 degree grids in NetCDF format.    This total water content is directly comparable to what GRACE measures over land. The Greenland values should NOT be used, the model is not valid there.
The land water content contained in this dataset were generated from The Global Land Data Assimilation System (GLDAS). GLDAS outputs land water content by using numerous land surface models and data assimilation. For more information on GLDAS please visit http://gcmd.nasa.gov/records/GCMD_GES_DISC_GLDAS_NOAH10SUBP_3H_V001.html. The data are available on monthly 1 degree grids in NetCDF format.    This total water content is directly comparable to what GRACE measures over land. The Greenland values should NOT be used, the model is not valid there.
The land water content contained in this dataset were generated from The Global Land Data Assimilation System (GLDAS). GLDAS outputs land water content by using numerous land surface models and data assimilation. For more information on GLDAS please visit http://gcmd.nasa.gov/records/GCMD_GES_DISC_GLDAS_NOAH10SUBP_3H_V001.html. The data are available on monthly 1 degree grids in ASCII format.    This total water content is directly comparable to what GRACE measures over land. The Greenland values should NOT be used, the model is not valid there.
The animation was generated from monthly land mass grids containing terrestrial water masses (aquifers, river basins, etc.) given as equivalent water thickness derived from GRACE gravity data, in GIF format.  Equivalent water thickness is used to represent changes in water mass as height changes since the area of a grid pixel remains only the height can change as the amount of water changes.  The time coverage for the monthly grids are determined by GRACE months.  For the list of GRACE month dates visit http://grace.jpl.nasa.gov/data/GraceMonths/.   For information please visit http://grace.jpl.nasa.gov/data/gracemonthlymassgridsland/
The images were generated from monthly land mass grids containing terrestrial water masses (aquifers, river basins, etc.) given as equivalent water thickness derived from JPL (Jet Propulsion Laboratory) GRACE gravity data, in GIF format.   Equivalent water thickness is used to represent changes in water mass as height changes since the area of a grid pixel remains only the height can change as the amount of water changes.  The time coverage for the monthly grids are determined by GRACE months.  For the list of GRACE month dates visit http://grace.jpl.nasa.gov/data/GraceMonths/.   For information please visit http://grace.jpl.nasa.gov/data/gracemonthlymassgridsland/
The images were generated from monthly land mass grids containing terrestrial water masses (aquifers, river basins, etc.) given as equivalent water thickness derived from GRACE gravity data, in GIF format.  Equivalent water thickness is used to represent changes in water mass as height changes since the area of a grid pixel remains only the height can change as the amount of water changes.  The time coverage for the monthly grids are determined by GRACE months.  For the list of GRACE month dates visit http://grace.jpl.nasa.gov/data/GraceMonths/.   For information please visit http://grace.jpl.nasa.gov/data/gracemonthlymassgridsland/
ftp://podaac-ftp.jpl.nasa.gov/allData/tellus/L3/land_mass/RL05/ascii
The twin satellites of the Gravity Recovery and Climate Experiment (GRACE), launched in March of 2002, are making detailed monthly measurements of Earth's gravity field changes. These observations can detect regional mass changes of Earth's water reservoirs over land, ice and oceans. GRACE measures gravity variations by relating it to the distance variations between the two satellites, which fly in the same orbit, separated by about 240 km at an altitude of ~450 km. The monthly land mass grids contain terrestrial water storage anomalies (in aquifers, river basins, etc.) from GRACE time-variable gravity data relative to a time-mean. The storage anomalies are given in 'equivalent water thickness' (in NetCDF format). The time coverage for the monthly grids are determined by GRACE months. For the list of GRACE month dates visit http://grace.jpl.nasa.gov/data/GraceMonths/ . For information please visit http://grace.jpl.nasa.gov/data/gracemonthlymassgridsland/ .
The twin satellites of the Gravity Recovery and Climate Experiment (GRACE), launched in March of 2002, are making detailed monthly measurements of Earth's gravity field changes. These observations can detect regional mass changes of Earth's water reservoirs over land, ice and oceans. GRACE measures gravity variations by relating it to the distance variations between the two satellites, which fly in the same orbit, separated by about 240 km at an altitude of ~450 km. The monthly land mass grids contain terrestrial water storage anomalies (in aquifers, river basins, etc.) from GRACE time-variable gravity data relative to a time-mean. The storage anomalies are given in 'equivalent water thickness' (in NetCDF format). The time coverage for the monthly grids are determined by GRACE months. For the list of GRACE month dates visit http://grace.jpl.nasa.gov/data/GraceMonths/ . For information please visit http://grace.jpl.nasa.gov/data/gracemonthlymassgridsland/ .
The twin satellites of the Gravity Recovery and Climate Experiment (GRACE), launched in March of 2002, are making detailed monthly measurements of Earth's gravity field changes. These observations can detect regional mass changes of Earth's water reservoirs over land, ice and oceans. GRACE measures gravity variations by relating it to the distance variations between the two satellites, which fly in the same orbit, separated by about 240 km at an altitude of ~450 km. The monthly land mass grids contain terrestrial water storage anomalies (in aquifers, river basins, etc.) from GRACE time-variable gravity data relative to a time-mean. The storage anomalies are given in 'equivalent water thickness' (in ASCII format). The time coverage for the monthly grids are determined by GRACE months. For the list of GRACE month dates visit http://grace.jpl.nasa.gov/data/GraceMonths/ . For information please visit http://grace.jpl.nasa.gov/data/gracemonthlymassgridsland/ .
The images were generated from the monthly ocean mass grids, which contain ocean water mass given as equivalent water thickness derived from the Center of Space Research (CSR) GRACE ocean bottom pressure.  Equivalent water thickness is used to represent the water mass so it can be comparable to sea level anomalies due to water mass changes.  There are several different types of Gaussian smoothing (300-750 km radius) and image resolutions.  The time coverage for the monthly grids are determined by GRACE months.  For the list of GRACE month dates visit http://grace.jpl.nasa.gov/data/GraceMonths/.  For more information please visit http://grace.jpl.nasa.gov/data/GRACEMONTHLYMASSGRIDSOCEAN/
The images were generated from the monthly ocean mass grids, which contain ocean water mass given as equivalent water thickness derived from the GFZ GRACE ocean bottom pressure.  Equivalent water thickness is used to represent the water mass so it can be comparable to sea level anomalies due to water mass changes.  There are several different types of Gaussian smoothing (300-750 km radius) and image resolutions.  The time coverage for the monthly grids are determined by GRACE months.  For the list of GRACE month dates visit http://grace.jpl.nasa.gov/data/GraceMonths/.  For more information please visit http://grace.jpl.nasa.gov/data/GRACEMONTHLYMASSGRIDSOCEAN/
The images were generated from the monthly ocean mass grids, which contain ocean water mass given as equivalent water thickness derived from the Jet Propulsion Laboratory (JPL) GRACE ocean bottom pressure.  Equivalent water thickness is used to represent the water mass so it can be comparable to sea level anomalies due to water mass changes.  There are several different types of Gaussian smoothing (300-750 km radius) and image resolutions.  The time coverage for the monthly grids are determined by GRACE months.  For the list of GRACE month dates visit http://grace.jpl.nasa.gov/data/GraceMonths/.  For more information please visit http://grace.jpl.nasa.gov/data/GRACEMONTHLYMASSGRIDSOCEAN/
The twin satellites of the Gravity Recovery and Climate Experiment (GRACE), launched in March of 2002, are making detailed monthly measurements of Earth's gravity field changes. These observations can detect regional mass changes of Earth's water reservoirs over land, ice and oceans. GRACE measures gravity variations by relating it to the distance variations between the two satellites, which fly in the same orbit, separated by about 240 km at an altitude of ~450 km. The monthly ocean mass grids contain ocean bottom pressure anomalies from GRACE time-variable gravity data relative to a time-mean. The bottom pressure anomalies are given in 'equivalent water thickness'  (in NetCDF format), so can be compared to sea level anomalies due to changes of water mass (reference density is 1027 kg/m^3).  The time coverage for the monthly grids are determined by GRACE months.  For the list of GRACE month dates visit  http://grace.jpl.nasa.gov/data/GraceMonths/ . For more information please visit http://grace.jpl.nasa.gov/data/GRACEMONTHLYMASSGRIDSOCEAN/ .
The twin satellites of the Gravity Recovery and Climate Experiment (GRACE), launched in March of 2002, are making detailed monthly measurements of Earth's gravity field changes. These observations can detect regional mass changes of Earth's water reservoirs over land, ice and oceans. GRACE measures gravity variations by relating it to the distance variations between the two satellites, which fly in the same orbit, separated by about 240 km at an altitude of ~450 km. The monthly ocean mass grids contain ocean bottom pressure anomalies from GRACE time-variable gravity data relative to a time-mean. The bottom pressure anomalies are given in 'equivalent water thickness'  (in NetCDF format), so can be compared to sea level anomalies due to changes of water mass (reference density is 1027 kg/m^3).  The time coverage for the monthly grids are determined by GRACE months.  For the list of GRACE month dates visit  http://grace.jpl.nasa.gov/data/GraceMonths/ . For more information please visit http://grace.jpl.nasa.gov/data/GRACEMONTHLYMASSGRIDSOCEAN/ .
The twin satellites of the Gravity Recovery and Climate Experiment (GRACE), launched in March of 2002, are making detailed monthly measurements of Earth's gravity field changes. These observations can detect regional mass changes of Earth's water reservoirs over land, ice and oceans. GRACE measures gravity variations by relating it to the distance variations between the two satellites, which fly in the same orbit, separated by about 240 km at an altitude of ~450 km. The monthly ocean mass grids contain ocean bottom pressure anomalies from GRACE time-variable gravity data relative to a time-mean. The bottom pressure anomalies are given in 'equivalent water thickness'  (in ASCII format), so can be compared to sea level anomalies due to changes of water mass (reference density is 1027 kg/m^3).  The time coverage for the monthly grids are determined by GRACE months.  For the list of GRACE month dates visit  http://grace.jpl.nasa.gov/data/GraceMonths/ . For more information please visit http://grace.jpl.nasa.gov/data/GRACEMONTHLYMASSGRIDSOCEAN/ .
The images were generated from the monthly ocean mass grids, which contain ocean water mass given as equivalent water thickness derived from the Center of Space Research (CSR) GRACE ocean bottom pressure with an Empirical Orthogonal Function (EOF) applied.  Equivalent water thickness is used to represent the water mass so it can be comparable to sea level anomalies due to water mass changes.  There are several different types of Gaussian smoothing (300-750 km radius) and image resolutions.  The time coverage for the monthly grids are determined by GRACE months.  For the list of GRACE month dates visit http://grace.jpl.nasa.gov/data/GraceMonths/.  For information please visit http://grace.jpl.nasa.gov/data/GRACEMONTHLYMASSGRIDSOCEAN/
The images were generated from the monthly ocean mass grids, which contain ocean water mass given as equivalent water thickness derived from the Jet Propulsion Laboratory (JPL) GRACE ocean bottom pressure with an Empirical Orthogonal Function (EOF) applied.  Equivalent water thickness is used to represent the water mass so it can be comparable to sea level anomalies due to water mass changes.  There are several different types of Gaussian smoothing (300-750 km radius) and image resolutions.  The time coverage for the monthly grids are determined by GRACE months.  For the list of GRACE month dates visit http://grace.jpl.nasa.gov/data/GraceMonths/.  For information please visit http://grace.jpl.nasa.gov/data/GRACEMONTHLYMASSGRIDSOCEAN/
The twin satellites of the Gravity Recovery and Climate Experiment (GRACE), launched in March of 2002, are making detailed monthly measurements of Earth's gravity field changes. These observations can detect regional mass changes of Earth's water reservoirs over land, ice and oceans. GRACE measures gravity variations by relating it to the distance variations between the two satellites, which fly in the same orbit, separated by about 240 km at an altitude of ~450 km. The monthly ocean mass grids contain ocean bottom pressure anomalies from GRACE time-variable gravity data relative to a time-mean, but additionally filtered to project onto model-derived Empirical Orthogonal Functions (EOFs). The EOF-filter can improve the comparison of the GRACE-based ocean bottom pressure to sea surface height from altimetry.  The bottom pressure anomalies are given in 'equivalent water thickness'  (in NetCDF format), so can be compared to sea level anomalies due to changes of water mass (reference density is 1027 kg/m^3). The time coverage for the monthly grids are determined by GRACE months.  For the list of GRACE month dates visit http://grace.jpl.nasa.gov/data/GraceMonths/ . For information please visit http://grace.jpl.nasa.gov/data/GRACEMONTHLYMASSGRIDSOCEAN/ .
The twin satellites of the Gravity Recovery and Climate Experiment (GRACE), launched in March of 2002, are making detailed monthly measurements of Earth's gravity field changes. These observations can detect regional mass changes of Earth's water reservoirs over land, ice and oceans. GRACE measures gravity variations by relating it to the distance variations between the two satellites, which fly in the same orbit, separated by about 240 km at an altitude of ~450 km. The monthly ocean mass grids contain ocean bottom pressure anomalies from GRACE time-variable gravity data relative to a time-mean, but additionally filtered to project onto model-derived Empirical Orthogonal Functions (EOFs). The EOF-filter can improve the comparison of the GRACE-based ocean bottom pressure to sea surface height from altimetry.  The bottom pressure anomalies are given in 'equivalent water thickness'  (in NetCDF format), so can be compared to sea level anomalies due to changes of water mass (reference density is 1027 kg/m^3). The time coverage for the monthly grids are determined by GRACE months.  For the list of GRACE month dates visit http://grace.jpl.nasa.gov/data/GraceMonths/ . For information please visit http://grace.jpl.nasa.gov/data/GRACEMONTHLYMASSGRIDSOCEAN/ .
The twin satellites of the Gravity Recovery and Climate Experiment (GRACE), launched in March of 2002, are making detailed monthly measurements of Earth's gravity field changes. These observations can detect regional mass changes of Earth's water reservoirs over land, ice and oceans. GRACE measures gravity variations by relating it to the distance variations between the two satellites, which fly in the same orbit, separated by about 240 km at an altitude of ~450 km. The monthly ocean mass grids contain ocean bottom pressure anomalies from GRACE time-variable gravity data relative to a time-mean, but additionally filtered to project onto model-derived Empirical Orthogonal Functions (EOFs). The EOF-filter can improve the comparison of the GRACE-based ocean bottom pressure to sea surface height from altimetry.  The bottom pressure anomalies are given in 'equivalent water thickness'  (in ASCII format), so can be compared to sea level anomalies due to changes of water mass (reference density is 1027 kg/m^3). The time coverage for the monthly grids are determined by GRACE months.  For the list of GRACE month dates visit http://grace.jpl.nasa.gov/data/GraceMonths/ . For information please visit http://grace.jpl.nasa.gov/data/GRACEMONTHLYMASSGRIDSOCEAN/ .
The Post Glacial Rebound (PGR) is rebound or bounce back of the land due to continetal ice and glacial melt that occured thousands of years ago.
The Post Glacial Rebound (PGR) is rebound or bounce back of the land due to continetal ice and glacial melt that occured thousands of years ago.
The Post Glacial Rebound (PGR) is rebound or bounce back of the land due to continetal ice and glacial melt that occured thousands of years ago.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
GHRSST Level 3C North Atlantic Regional Subskin Sea Surface Temperature from the Visible Infrared Imaging Radiometer Suite (VIIRS) on board Suomi National Polar-orbiting Partnership (S-NPP) satellite
GHRSST Level 3C North Atlantic Regional Subskin Sea Surface Temperature from the Visible Infrared Imaging Radiometer Suite (VIIRS) on board Suomi National Polar-orbiting Partnership (S-NPP) satellite
GHRSST Level 3C North Atlantic Regional Subskin Sea Surface Temperature from the Visible Infrared Imaging Radiometer Suite (VIIRS) on board Suomi National Polar-orbiting Partnership (S-NPP) satellite
GHRSST Level 3C North Atlantic Regional Subskin Sea Surface Temperature from the Visible Infrared Imaging Radiometer Suite (VIIRS) on board Suomi National Polar-orbiting Partnership (S-NPP) satellite
GHRSST Level 3C North Atlantic Regional Subskin Sea Surface Temperature from the Visible Infrared Imaging Radiometer Suite (VIIRS) on board Suomi National Polar-orbiting Partnership (S-NPP) satellite
GHRSST Level 3C North Atlantic Regional Subskin Sea Surface Temperature from the Visible Infrared Imaging Radiometer Suite (VIIRS) on board Suomi National Polar-orbiting Partnership (S-NPP) satellite
GHRSST Level 3C North Atlantic Regional Subskin Sea Surface Temperature from the Visible Infrared Imaging Radiometer Suite (VIIRS) on board Suomi National Polar-orbiting Partnership (S-NPP) satellite
GHRSST Level 3C North Atlantic Regional Subskin Sea Surface Temperature from the Visible Infrared Imaging Radiometer Suite (VIIRS) on board Suomi National Polar-orbiting Partnership (S-NPP) satellite
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 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 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 2014 Environmental Performance Index (EPI) ranks 178 countries on 20 performance indicators in the following 9 policy categories: health impacts, 
air quality, water and sanitation, water resources, agriculture, forests, fisheries, biodiversity and habitat, and climate and energy. These categories track performance and progress on two broad policy objectives, environmental health and ecosystem vitality. The EPI's proximity-to-target methodology facilitates cross-country comparisons among economic and regional peer groups. The data set includes the 2014 EPI and component scores, backcast EPI scores for 2002-2012, and time-series source data. The 2014 EPI was formally released in Davos, Switzerland, at the annual meeting of the World Economic Forum on January 25, 2014. 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 2014 EPI is at http://epi.yale.edu/.
The 2014 Environmental Performance Index (EPI) ranks 178 countries on 20 performance indicators in the following 9 policy categories: health impacts, 
air quality, water and sanitation, water resources, agriculture, forests, fisheries, biodiversity and habitat, and climate and energy. These categories track performance and progress on two broad policy objectives, environmental health and ecosystem vitality. The EPI's proximity-to-target methodology facilitates cross-country comparisons among economic and regional peer groups. The data set includes the 2014 EPI and component scores, backcast EPI scores for 2002-2012, and time-series source data. The 2014 EPI was formally released in Davos, Switzerland, at the annual meeting of the World Economic Forum on January 25, 2014. 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 2014 EPI is at http://epi.yale.edu/.
The 2014 Environmental Performance Index (EPI) ranks 178 countries on 20 performance indicators in the following 9 policy categories: health impacts, 
air quality, water and sanitation, water resources, agriculture, forests, fisheries, biodiversity and habitat, and climate and energy. These categories track performance and progress on two broad policy objectives, environmental health and ecosystem vitality. The EPI's proximity-to-target methodology facilitates cross-country comparisons among economic and regional peer groups. The data set includes the 2014 EPI and component scores, backcast EPI scores for 2002-2012, and time-series source data. The 2014 EPI was formally released in Davos, Switzerland, at the annual meeting of the World Economic Forum on January 25, 2014. 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 2014 EPI is at http://epi.yale.edu/.
The 2014 Environmental Performance Index (EPI) ranks 178 countries on 20 performance indicators in the following 9 policy categories: health impacts, 
air quality, water and sanitation, water resources, agriculture, forests, fisheries, biodiversity and habitat, and climate and energy. These categories track performance and progress on two broad policy objectives, environmental health and ecosystem vitality. The EPI's proximity-to-target methodology facilitates cross-country comparisons among economic and regional peer groups. The data set includes the 2014 EPI and component scores, backcast EPI scores for 2002-2012, and time-series source data. The 2014 EPI was formally released in Davos, Switzerland, at the annual meeting of the World Economic Forum on January 25, 2014. 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 2014 EPI is at http://epi.yale.edu/.
The 2014 Environmental Performance Index (EPI) ranks 178 countries on 20 performance indicators in the following 9 policy categories: health impacts, 
air quality, water and sanitation, water resources, agriculture, forests, fisheries, biodiversity and habitat, and climate and energy. These categories track performance and progress on two broad policy objectives, environmental health and ecosystem vitality. The EPI's proximity-to-target methodology facilitates cross-country comparisons among economic and regional peer groups. The data set includes the 2014 EPI and component scores, backcast EPI scores for 2002-2012, and time-series source data. The 2014 EPI was formally released in Davos, Switzerland, at the annual meeting of the World Economic Forum on January 25, 2014. 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 2014 EPI is at http://epi.yale.edu/.
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: 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: 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 Agency for Toxic Substances and Disease Registry (ATSDR) Hazardous Waste Site Polygon Data with CIESIN Modifications, Version 2 is a database providing georeferenced 

data for 1,572 National Priorities List (NPL) Superfund sites. These were selected from the larger set of the ATSDR Hazardous Waste Site Polygon Data, Version 2 data set with polygons from 

May 26, 2010. The modified data set contains only sites that have been proposed, currently on, or deleted from the final NPL as of October 25, 2013. Of the 2,080 ATSDR polygons from 2010, 

1,575 were NPL sites but three sites were excluded - 2 in the Virgin Islands and 1 in Guam. This data set is modified by the Columbia University Center for International Earth Science 

Information Network (CIESIN). The modified polygon database includes all the attributes for these NPL sites provided in the ATSDR GRASP Hazardous Waste Site Polygon database and selected 

attributes from the EPA List 9 Active CERCLIS sites and SCAP 12 NPL sites databases. These polygons represent sites considered for cleanup under the Comprehensive Environmental Response, 

Compensation and Liability Act (CERCLA or Superfund). The Geospatial Research, Analysis, and Services Program (GRASP, Division of Health Studies, Agency for Toxic Substances and Disease 

Registry, Centers for Disease Control and Prevention) has created site boundary data using the best available information for those sites where health assessments or consultations have been 

requested.
The Agency for Toxic Substances and Disease Registry (ATSDR) Hazardous Waste Site Polygon Data with CIESIN Modifications, Version 2 is a database providing georeferenced 

data for 1,572 National Priorities List (NPL) Superfund sites. These were selected from the larger set of the ATSDR Hazardous Waste Site Polygon Data, Version 2 data set with polygons from 

May 26, 2010. The modified data set contains only sites that have been proposed, currently on, or deleted from the final NPL as of October 25, 2013. Of the 2,080 ATSDR polygons from 2010, 

1,575 were NPL sites but three sites were excluded - 2 in the Virgin Islands and 1 in Guam. This data set is modified by the Columbia University Center for International Earth Science 

Information Network (CIESIN). The modified polygon database includes all the attributes for these NPL sites provided in the ATSDR GRASP Hazardous Waste Site Polygon database and selected 

attributes from the EPA List 9 Active CERCLIS sites and SCAP 12 NPL sites databases. These polygons represent sites considered for cleanup under the Comprehensive Environmental Response, 

Compensation and Liability Act (CERCLA or Superfund). The Geospatial Research, Analysis, and Services Program (GRASP, Division of Health Studies, Agency for Toxic Substances and Disease 

Registry, Centers for Disease Control and Prevention) has created site boundary data using the best available information for those sites where health assessments or consultations have been 

requested.
The Agency for Toxic Substances and Disease Registry (ATSDR) Hazardous Waste Site Polygon Data with CIESIN Modifications, Version 2 is a database providing georeferenced 

data for 1,572 National Priorities List (NPL) Superfund sites. These were selected from the larger set of the ATSDR Hazardous Waste Site Polygon Data, Version 2 data set with polygons from 

May 26, 2010. The modified data set contains only sites that have been proposed, currently on, or deleted from the final NPL as of October 25, 2013. Of the 2,080 ATSDR polygons from 2010, 

1,575 were NPL sites but three sites were excluded - 2 in the Virgin Islands and 1 in Guam. This data set is modified by the Columbia University Center for International Earth Science 

Information Network (CIESIN). The modified polygon database includes all the attributes for these NPL sites provided in the ATSDR GRASP Hazardous Waste Site Polygon database and selected 

attributes from the EPA List 9 Active CERCLIS sites and SCAP 12 NPL sites databases. These polygons represent sites considered for cleanup under the Comprehensive Environmental Response, 

Compensation and Liability Act (CERCLA or Superfund). The Geospatial Research, Analysis, and Services Program (GRASP, Division of Health Studies, Agency for Toxic Substances and Disease 

Registry, Centers for Disease Control and Prevention) has created site boundary data using the best available information for those sites where health assessments or consultations have been 

requested.
The Agency for Toxic Substances and Disease Registry (ATSDR) Hazardous Waste Site Polygon Data with CIESIN Modifications, Version 2 is a database providing georeferenced 

data for 1,572 National Priorities List (NPL) Superfund sites. These were selected from the larger set of the ATSDR Hazardous Waste Site Polygon Data, Version 2 data set with polygons from 

May 26, 2010. The modified data set contains only sites that have been proposed, currently on, or deleted from the final NPL as of October 25, 2013. Of the 2,080 ATSDR polygons from 2010, 

1,575 were NPL sites but three sites were excluded - 2 in the Virgin Islands and 1 in Guam. This data set is modified by the Columbia University Center for International Earth Science 

Information Network (CIESIN). The modified polygon database includes all the attributes for these NPL sites provided in the ATSDR GRASP Hazardous Waste Site Polygon database and selected 

attributes from the EPA List 9 Active CERCLIS sites and SCAP 12 NPL sites databases. These polygons represent sites considered for cleanup under the Comprehensive Environmental Response, 

Compensation and Liability Act (CERCLA or Superfund). The Geospatial Research, Analysis, and Services Program (GRASP, Division of Health Studies, Agency for Toxic Substances and Disease 

Registry, Centers for Disease Control and Prevention) has created site boundary data using the best available information for those sites where health assessments or consultations have been 

requested.
The Agency for Toxic Substances and Disease Registry (ATSDR) Hazardous Waste Site Polygon Data with CIESIN Modifications, Version 2 is a database providing georeferenced 

data for 1,572 National Priorities List (NPL) Superfund sites. These were selected from the larger set of the ATSDR Hazardous Waste Site Polygon Data, Version 2 data set with polygons from 

May 26, 2010. The modified data set contains only sites that have been proposed, currently on, or deleted from the final NPL as of October 25, 2013. Of the 2,080 ATSDR polygons from 2010, 

1,575 were NPL sites but three sites were excluded - 2 in the Virgin Islands and 1 in Guam. This data set is modified by the Columbia University Center for International Earth Science 

Information Network (CIESIN). The modified polygon database includes all the attributes for these NPL sites provided in the ATSDR GRASP Hazardous Waste Site Polygon database and selected 

attributes from the EPA List 9 Active CERCLIS sites and SCAP 12 NPL sites databases. These polygons represent sites considered for cleanup under the Comprehensive Environmental Response, 

Compensation and Liability Act (CERCLA or Superfund). The Geospatial Research, Analysis, and Services Program (GRASP, Division of Health Studies, Agency for Toxic Substances and Disease 

Registry, Centers for Disease Control and Prevention) has created site boundary data using the best available information for those sites where health assessments or consultations have been 

requested.
The Agency for Toxic Substances and Disease Registry (ATSDR) Hazardous Waste Site Polygon Data, Version 2 consists of 2,080 polygons for selected hazardous waste sites that 

were compiled on May 26, 2010. 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. The hazardous waste site boundaries maintained by the Geospatial 

Research, Analysis, and Services Program (GRASP, Division of Health Studies, Agency for Toxic Substances and Disease Registry, Centers for Disease Control and Prevention) contain NPL and 

non-NPL hazardous waste site boundaries for which health assessments or consultations have been requested.
The Agency for Toxic Substances and Disease Registry (ATSDR) Hazardous Waste Site Polygon Data, Version 2 consists of 2,080 polygons for selected hazardous waste sites that 

were compiled on May 26, 2010. 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. The hazardous waste site boundaries maintained by the Geospatial 

Research, Analysis, and Services Program (GRASP, Division of Health Studies, Agency for Toxic Substances and Disease Registry, Centers for Disease Control and Prevention) contain NPL and 

non-NPL hazardous waste site boundaries for which health assessments or consultations have been requested.
The Agency for Toxic Substances and Disease Registry (ATSDR) Hazardous Waste Site Polygon Data, Version 2 consists of 2,080 polygons for selected hazardous waste sites that 

were compiled on May 26, 2010. 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. The hazardous waste site boundaries maintained by the Geospatial 

Research, Analysis, and Services Program (GRASP, Division of Health Studies, Agency for Toxic Substances and Disease Registry, Centers for Disease Control and Prevention) contain NPL and 

non-NPL hazardous waste site boundaries for which health assessments or consultations have been requested.
The Agency for Toxic Substances and Disease Registry (ATSDR) Hazardous Waste Site Polygon Data, Version 2 consists of 2,080 polygons for selected hazardous waste sites that 

were compiled on May 26, 2010. 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. The hazardous waste site boundaries maintained by the Geospatial 

Research, Analysis, and Services Program (GRASP, Division of Health Studies, Agency for Toxic Substances and Disease Registry, Centers for Disease Control and Prevention) contain NPL and 

non-NPL hazardous waste site boundaries for which health assessments or consultations have been requested.
The Agency for Toxic Substances and Disease Registry (ATSDR) Hazardous Waste Site Polygon Data, Version 2 consists of 2,080 polygons for selected hazardous waste sites that 

were compiled on May 26, 2010. 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. The hazardous waste site boundaries maintained by the Geospatial 

Research, Analysis, and Services Program (GRASP, Division of Health Studies, Agency for Toxic Substances and Disease Registry, Centers for Disease Control and Prevention) contain NPL and 

non-NPL hazardous waste site boundaries for which health assessments or consultations have been requested.
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 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).
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 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).
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 Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) data sets represent a series of three-year running mean grids (1998-2012) of fine 

particulate matter (solid particles and liquid droplets) that were derived from a combination of MODIS (Moderate Resolution Imaging Spectroradiometer), MISR (Multi-angle Imaging 

SpectroRadiometer) and SeaWIFS (Sea-Viewing Wide Field-of-View Sensor) AOD satellite retrievals. Together the grids provide a continuous surface of concentrations in micrograms per cubic 

meter of particulate matter 2.5 micrometers or smaller (PM2.5) for health and environmental research. For each satellite-derived PM2.5 source, the total column retrievals of AOD were 

converted to near-ground PM2.5 levels using the GEOS-Chem chemical transport model to represent local relationships between AOD and PM2.5. A global decadal (2001-2010) mean PM2.5 

concentration grid was also produced. The raster grids have a grid cell resolution of 6 arc-minutes (0.1 degree or approximately 10 km at the equator) and cover the global land surface from 

70 degrees north to 55 degrees south. Compared to SEDAC’s earlier data set, Global Annual Average PM2.5 Grids from MODIS and MISR Aerosol Optical Depth (AOD), v1 (2001-2010), this data set 

provides higher accuracy; longer temporal range; higher resolution (0.1 x 0.1 degrees); and time varying AOD to PM2.5 relationships, necessary for appropriate representation of 

trends.
The Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) data sets represent a series of three-year running mean grids (1998-2012) of fine 

particulate matter (solid particles and liquid droplets) that were derived from a combination of MODIS (Moderate Resolution Imaging Spectroradiometer), MISR (Multi-angle Imaging 

SpectroRadiometer) and SeaWIFS (Sea-Viewing Wide Field-of-View Sensor) AOD satellite retrievals. Together the grids provide a continuous surface of concentrations in micrograms per cubic 

meter of particulate matter 2.5 micrometers or smaller (PM2.5) for health and environmental research. For each satellite-derived PM2.5 source, the total column retrievals of AOD were 

converted to near-ground PM2.5 levels using the GEOS-Chem chemical transport model to represent local relationships between AOD and PM2.5. A global decadal (2001-2010) mean PM2.5 

concentration grid was also produced. The raster grids have a grid cell resolution of 6 arc-minutes (0.1 degree or approximately 10 km at the equator) and cover the global land surface from 

70 degrees north to 55 degrees south. Compared to SEDAC’s earlier data set, Global Annual Average PM2.5 Grids from MODIS and MISR Aerosol Optical Depth (AOD), v1 (2001-2010), this data set 

provides higher accuracy; longer temporal range; higher resolution (0.1 x 0.1 degrees); and time varying AOD to PM2.5 relationships, necessary for appropriate representation of 

trends.
The Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) data sets represent a series of three-year running mean grids (1998-2012) of fine 

particulate matter (solid particles and liquid droplets) that were derived from a combination of MODIS (Moderate Resolution Imaging Spectroradiometer), MISR (Multi-angle Imaging 

SpectroRadiometer) and SeaWIFS (Sea-Viewing Wide Field-of-View Sensor) AOD satellite retrievals. Together the grids provide a continuous surface of concentrations in micrograms per cubic 

meter of particulate matter 2.5 micrometers or smaller (PM2.5) for health and environmental research. For each satellite-derived PM2.5 source, the total column retrievals of AOD were 

converted to near-ground PM2.5 levels using the GEOS-Chem chemical transport model to represent local relationships between AOD and PM2.5. A global decadal (2001-2010) mean PM2.5 

concentration grid was also produced. The raster grids have a grid cell resolution of 6 arc-minutes (0.1 degree or approximately 10 km at the equator) and cover the global land surface from 

70 degrees north to 55 degrees south. Compared to SEDAC’s earlier data set, Global Annual Average PM2.5 Grids from MODIS and MISR Aerosol Optical Depth (AOD), v1 (2001-2010), this data set 

provides higher accuracy; longer temporal range; higher resolution (0.1 x 0.1 degrees); and time varying AOD to PM2.5 relationships, necessary for appropriate representation of 

trends.
The Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) data sets represent a series of three-year running mean grids (1998-2012) of fine 

particulate matter (solid particles and liquid droplets) that were derived from a combination of MODIS (Moderate Resolution Imaging Spectroradiometer), MISR (Multi-angle Imaging 

SpectroRadiometer) and SeaWIFS (Sea-Viewing Wide Field-of-View Sensor) AOD satellite retrievals. Together the grids provide a continuous surface of concentrations in micrograms per cubic 

meter of particulate matter 2.5 micrometers or smaller (PM2.5) for health and environmental research. For each satellite-derived PM2.5 source, the total column retrievals of AOD were 

converted to near-ground PM2.5 levels using the GEOS-Chem chemical transport model to represent local relationships between AOD and PM2.5. A global decadal (2001-2010) mean PM2.5 

concentration grid was also produced. The raster grids have a grid cell resolution of 6 arc-minutes (0.1 degree or approximately 10 km at the equator) and cover the global land surface from 

70 degrees north to 55 degrees south. Compared to SEDAC’s earlier data set, Global Annual Average PM2.5 Grids from MODIS and MISR Aerosol Optical Depth (AOD), v1 (2001-2010), this data set 

provides higher accuracy; longer temporal range; higher resolution (0.1 x 0.1 degrees); and time varying AOD to PM2.5 relationships, necessary for appropriate representation of 

trends.
The Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) data sets represent a series of three-year running mean grids (1998-2012) of fine 

particulate matter (solid particles and liquid droplets) that were derived from a combination of MODIS (Moderate Resolution Imaging Spectroradiometer), MISR (Multi-angle Imaging 

SpectroRadiometer) and SeaWIFS (Sea-Viewing Wide Field-of-View Sensor) AOD satellite retrievals. Together the grids provide a continuous surface of concentrations in micrograms per cubic 

meter of particulate matter 2.5 micrometers or smaller (PM2.5) for health and environmental research. For each satellite-derived PM2.5 source, the total column retrievals of AOD were 

converted to near-ground PM2.5 levels using the GEOS-Chem chemical transport model to represent local relationships between AOD and PM2.5. A global decadal (2001-2010) mean PM2.5 

concentration grid was also produced. The raster grids have a grid cell resolution of 6 arc-minutes (0.1 degree or approximately 10 km at the equator) and cover the global land surface from 

70 degrees north to 55 degrees south. Compared to SEDAC’s earlier data set, Global Annual Average PM2.5 Grids from MODIS and MISR Aerosol Optical Depth (AOD), v1 (2001-2010), this data set 

provides higher accuracy; longer temporal range; higher resolution (0.1 x 0.1 degrees); and time varying AOD to PM2.5 relationships, necessary for appropriate representation of 

trends.
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 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).
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 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 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 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 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 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 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 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 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 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 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).
The Global Mangrove Forests Distribution, 2000 data set is a compilation of the extent of mangroves forests from the Global Land Survey and the Landsat archive with hybrid supervised and unsupervised digital image classification techniques. The data are available at 30-m spatial resolution. The total area of mangroves in the year 2000 was estimated at 137,760 km2 in 118 countries and territories in the tropical and subtropical regions of the world. This is more than 12% less than previous estimates and if the current rate of loss continues, predictions suggest that 100% of these forests will be lost in the next century.
The Global Mangrove Forests Distribution, 2000 data set is a compilation of the extent of mangroves forests from the Global Land Survey and the Landsat archive with hybrid supervised and unsupervised digital image classification techniques. The data are available at 30-m spatial resolution. The total area of mangroves in the year 2000 was estimated at 137,760 km2 in 118 countries and territories in the tropical and subtropical regions of the world. This is more than 12% less than previous estimates and if the current rate of loss continues, predictions suggest that 100% of these forests will be lost in the next century.
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 Proportional Economic Loss Risks is a 2.5 minute grid of a multihazard-based 

economic loss risk as a proportion of the economic productivity of the analytical unit, the grid cell. Representation of multihazard 

risk is not based on a multihazard index but rather on combinations of hazard risk categories, drought, seismic, and hydro. The 

drought category includes drought only. The seismic category consists of earthquake and volcano hazards. Cyclones, floods, and 

landslides are included in the hydro category. For each of the six hazards considered, a binary risk surface is constructed utilizing 

the three most-at-risk deciles of each hazard's global proportional economic loss risks dataset (deciles 8-10). Each of the category 

risk surfaces are constructed by adding all the relevant hazard high-risk surfaces. These categorical risk surfaces are reclassified 

into binary high-risk surfaces. The combination of the category risk values forms a three digit identifier for determining those 

locations that are at higher-risk from multihazards. 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 Total Economic Loss Risk Deciles is a 2.5 minute grid of global multihazard 

total economic loss risks. First, for each of the considered hazards (cyclones, droughts, earthquakes, floods, landslides, and 

volcanoes), subnational distributions of Gross Domestic Product (GDP) are computed using a methodology developed from Sachs et al. 

(2003). Where applicable, the contributions of subnational units to national GDP estimates, the contribution ratio, are determined 

using data of varied origin. World Bank Development Indicators are substituted for GDP estimates of varied origin and the subnational 

GDP is estimated using the fore mentioned contribution ratios. A subnational, per capita GDP is derived and a final GDP estimate per 

grid cell is made based on grid cell population density. A raw, total economic loss is computed per grid cell using a regional 

economic loss rate derived from EM-DAT records. To more accurately reflect the confidence surrounding the economic loss estimate, the 

range of losses are classified into deciles, 10 classes of an approximately equal number of grid cells. A multihazard index is 

generated by summing the top three deciles of the individual hazards. 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 Global Reservoir and Dam Database, Version 1 (Revision 01) contains 6,862 records of reservoirs and their associated dams with a cumulative storage capacity of 6,197 cubic 

km. The dams were geospatially referenced and assigned to polygons depicting reservoir outlines at high spatial resolution. Dams have multiple attributes, such as name of the dam and 

impounded river, primary use, nearest city, height, area and volume of reservoir, and year of construction (or commissioning). While the main focus was to include all dams associated with 

reservoirs that have a storage capacity of more than 0.1 cubic kilometers, many smaller dams and reservoirs were added where data were available. The data were compiled by Lehner et al. 

(2011) and are distributed by the Global Water System Project (GWSP) and by the Columbia University Center for International Earth Science Information Network (CIESIN). For details please 

refer to the Technical Documentation which is provided with the data.
The Global Reservoir and Dam Database, Version 1 (Revision 01) contains 6,862 records of reservoirs and their associated dams with a cumulative storage capacity of 6,197 cubic 

km. The dams were geospatially referenced and assigned to polygons depicting reservoir outlines at high spatial resolution. Dams have multiple attributes, such as name of the dam and 

impounded river, primary use, nearest city, height, area and volume of reservoir, and year of construction (or commissioning). While the main focus was to include all dams associated with 

reservoirs that have a storage capacity of more than 0.1 cubic kilometers, many smaller dams and reservoirs were added where data were available. The data were compiled by Lehner et al. 

(2011) and are distributed by the Global Water System Project (GWSP) and by the Columbia University Center for International Earth Science Information Network (CIESIN). For details please 

refer to the Technical Documentation which is provided with the data.
The Global Reservoir and Dam (GRanD) Database, Version 1.1 contains 6,862 records of reservoirs and their associated dams with a cumulative storage capacity of 6,197 cubic km. 

The reservoirs were delineated from high spatial resolution satellite imagery and are available as polygon shape files. The only attribute for the reservoirs is the area of the reservoir. 

The associated dams data set includes multiple attributes such as name of the dam and the impounded river, primary use, nearest city, area, and year of construction (or commissioning). 

While the main focus was to include all reservoirs with a storage capacity of more than 0.1 cubic kilometers, many smaller reservoirs were added where data were available. The data were 

compiled by Lehner et al. (2011) and are distributed by the Global Water System Project (GWSP) and by the Columbia University Center for International Earth Science Information Network 

(CIESIN). For details please refer to the Technical Documentation which is provided with the data.
The Global Reservoir and Dam (GRanD) Database, Version 1.1 contains 6,862 records of reservoirs and their associated dams with a cumulative storage capacity of 6,197 cubic km. 

The reservoirs were delineated from high spatial resolution satellite imagery and are available as polygon shape files. The only attribute for the reservoirs is the area of the reservoir. 

The associated dams data set includes multiple attributes such as name of the dam and the impounded river, primary use, nearest city, area, and year of construction (or commissioning). 

While the main focus was to include all reservoirs with a storage capacity of more than 0.1 cubic kilometers, many smaller reservoirs were added where data were available. The data were 

compiled by Lehner et al. (2011) and are distributed by the Global Water System Project (GWSP) and by the Columbia University Center for International Earth Science Information Network 

(CIESIN). For details please refer to the Technical Documentation which is provided with the data.
The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1) consists of estimates of human population for the years 1990, 1995, and 2000 by 30 arc-second (1km) grid. The 

population density grids measure population per square km. A proportional allocation gridding algorithm, utilizing more than 1,000,000 national and sub-national geographic units, is used to 

assign population values to grid cells. The population count grids are divided by the land area grid to produce population densities. This dataset is produced by the Columbia University 

Center for International Earth Science Information Network (CIESIN) in collaboration with the International Food Policy Research Institute (IFPRI), The World Bank, and Centro Internacional 

de Agricultura Tropical (CIAT).
The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1) consists of estimates of human population for the years 1990, 1995, and 2000 by 30 arc-second (1km) grid. The 

population density grids measure population per square km. A proportional allocation gridding algorithm, utilizing more than 1,000,000 national and sub-national geographic units, is used to 

assign population values to grid cells. The population count grids are divided by the land area grid to produce population densities. This dataset is produced by the Columbia University 

Center for International Earth Science Information Network (CIESIN) in collaboration with the International Food Policy Research Institute (IFPRI), The World Bank, and Centro Internacional 

de Agricultura Tropical (CIAT).
The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1) consists of estimates of human population for the years 1990, 1995, and 2000 by 30 arc-second (1km) grid cells and 

associated datasets dated circa 2000. A proportional allocation gridding algorithm, utilizing more than 1,000,000 national and sub-national geographic units, is used to assign population 

values (counts, in persons) to grid cells. This dataset is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the 

International Food Policy Research Institute (IFPRI), The World Bank, and Centro Internacional de Agricultura Tropical (CIAT)..
The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1) consists of estimates of human population for the years 1990, 1995, and 2000 by 30 arc-second (1km) grid cells and 

associated datasets dated circa 2000. A proportional allocation gridding algorithm, utilizing more than 1,000,000 national and sub-national geographic units, is used to assign population 

values (counts, in persons) to grid cells. This dataset is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the 

International Food Policy Research Institute (IFPRI), The World Bank, and Centro Internacional de Agricultura Tropical (CIAT)..
The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1) consists of estimates of human population for the years 1990, 1995, and 2000 by 30 arc-second (1km) grid. The urban extent grids distinguish urban and rural areas based on a combination of population counts (persons), settlement points, and the presence of Nighttime Lights . Areas are defined as urban where contiguous lighted cells from the Nighttime Lights or approximated urban extents based on buffered settlement points for which the total population is greater than 5,000 persons. This dataset is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Food Policy Research Institute (IFPRI), The World Bank, and Centro Internacional de Agricultura Tropical (CIAT)
The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1) consists of estimates of human population for the years 1990, 1995, and 2000 by 30 arc-second (1km) grid. The urban extent grids distinguish urban and rural areas based on a combination of population counts (persons), settlement points, and the presence of Nighttime Lights . Areas are defined as urban where contiguous lighted cells from the Nighttime Lights or approximated urban extents based on buffered settlement points for which the total population is greater than 5,000 persons. This dataset is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Food Policy Research Institute (IFPRI), The World Bank, and Centro Internacional de Agricultura Tropical (CIAT)
Global Volcano Hazard Frequency and Distribution is a 2.5 minute gridded data set based upon the 

National Geophysical Data Center (NGDC) Volcano Database spanning the period of 1979 through 2000. This database includes nearly 

4,000 volcanic events categorized as moderate or above (values 2 through 8) according to the Volcano Explosivity Index (VEI). Most 

volcanoes are georeferenced to the nearest tenth or hundredth of a degree with a few to the nearest thousandth of a degree. To 

produce the final output, the frequency of a volcanic hazard is computed for each grid cell, with the data set consequently being 

classified into deciles (10 classes of approximately equal number of grid cells). The higher the grid cell value in the final output, 

the higher the relative frequency of hazard posed by volcanoes. 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 Volcano Hazard Frequency and Distribution is a 2.5 minute gridded data set based upon the 

National Geophysical Data Center (NGDC) Volcano Database spanning the period of 1979 through 2000. This database includes nearly 

4,000 volcanic events categorized as moderate or above (values 2 through 8) according to the Volcano Explosivity Index (VEI). Most 

volcanoes are georeferenced to the nearest tenth or hundredth of a degree with a few to the nearest thousandth of a degree. To 

produce the final output, the frequency of a volcanic hazard is computed for each grid cell, with the data set consequently being 

classified into deciles (10 classes of approximately equal number of grid cells). The higher the grid cell value in the final output, 

the higher the relative frequency of hazard posed by volcanoes. 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 Volcano Mortality Risks and Distribution is a 2.5 minute grid representing global volcano mortality risks. The data set was constructed using historical hazard-specific mortality loss data from the Emergency Events Database (EM-DAT) maintained by the Centre for Research on the Epidemiology of Disasters (CRED), subnational year 2000 population estimates from Gridded Population of the World, Version 3 (GPWv3), and volcano hazard data from the Global Volcano Hazard Frequency and Distribution dataset. Estimates were made as to the mortality numbers associated with volcano hazard. In turn, these mortality estimates were classified into deciles, 10 class of an approximately equal number of grid cells of increasing mortality 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 Volcano Mortality Risks and Distribution is a 2.5 minute grid representing global volcano mortality risks. The data set was constructed using historical hazard-specific mortality loss data from the Emergency Events Database (EM-DAT) maintained by the Centre for Research on the Epidemiology of Disasters (CRED), subnational year 2000 population estimates from Gridded Population of the World, Version 3 (GPWv3), and volcano hazard data from the Global Volcano Hazard Frequency and Distribution dataset. Estimates were made as to the mortality numbers associated with volcano hazard. In turn, these mortality estimates were classified into deciles, 10 class of an approximately equal number of grid cells of increasing mortality 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 Volcano Proportional Economic Loss Risk Deciles is a 2.5 minute grid of volcano 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 Volcano Proportional Economic Loss Risk Deciles is a 2.5 minute grid of volcano 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).
distributions of Gross Domestic Product (GDP) are computed using a two-fold process. Where 

applicable, the proportional contribution of subnational units are determined following the methodology of Sachs et al. (2003) and 

these proportions are used against World Bank Development Indicators to determine a GDP value for the subnational unit. Once a 

national GDP has been spatially stratified into the smallest administrative units available, it is further distributed based upon 

Gridded Population of the World, Version 3 (GPWv3) population distributions. A per capita contribution value is determined for each 

unit, and this value is multiplied by the population per grid cell. Once the GDP has been determined on a per grid cell basis, then 

the spatially variable loss rate as derived from EM-DAT historical records is used to determine the total economic loss posed to a 

grid cell by volcano hazards. The final surface does not present absolute values of total economic loss, but rather a relative decile 

(1-10) 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).
distributions of Gross Domestic Product (GDP) are computed using a two-fold process. Where 

applicable, the proportional contribution of subnational units are determined following the methodology of Sachs et al. (2003) and 

these proportions are used against World Bank Development Indicators to determine a GDP value for the subnational unit. Once a 

national GDP has been spatially stratified into the smallest administrative units available, it is further distributed based upon 

Gridded Population of the World, Version 3 (GPWv3) population distributions. A per capita contribution value is determined for each 

unit, and this value is multiplied by the population per grid cell. Once the GDP has been determined on a per grid cell basis, then 

the spatially variable loss rate as derived from EM-DAT historical records is used to determine the total economic loss posed to a 

grid cell by volcano hazards. The final surface does not present absolute values of total economic loss, but rather a relative decile 

(1-10) 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).
Gridded Population of the World, Version 3 (GPWv3) consists of estimates of human population for the years 1990, 1995, and 2000 by 2.5 arc-minute grid cells and associated 

datasets dated circa 2000. A proportional allocation gridding algorithm, utilizing more than 300,000 national and sub-national administrative units, is used to assign population values to 

grid cells. The population density grids are derived by dividing the population count grids by the land area grid and represent persons per square kilometer. The grids are available in 

various GIS-compatible data formats and geographic extents (global, continent [Antarctica not included], and country levels). GPWv3 is produced by the Columbia University Center for 

International Earth Science Information Network (CIESIN) in collaboration with Centro Internacional de Agricultura Tropical (CIAT).
Gridded Population of the World, Version 3 (GPWv3) consists of estimates of human population for the years 1990, 1995, and 2000 by 2.5 arc-minute grid cells and associated 

datasets dated circa 2000. A proportional allocation gridding algorithm, utilizing more than 300,000 national and sub-national administrative units, is used to assign population values to 

grid cells. The population density grids are derived by dividing the population count grids by the land area grid and represent persons per square kilometer. The grids are available in 

various GIS-compatible data formats and geographic extents (global, continent [Antarctica not included], and country levels). GPWv3 is produced by the Columbia University Center for 

International Earth Science Information Network (CIESIN) in collaboration with Centro Internacional de Agricultura Tropical (CIAT).
Gridded Population of the World, Version 3 (GPWv3) Subnational Administrative Boundaries are the basis of the population data products in GPWv3. Due to copyright 

restrictions, only maps of the subnational administrative boundaries are available, the underlying data cannot be released. GPWv3 is produced by the Columbia University Center for 

International Earth Science Information Network (CIESIN) in collaboration with Centro Internacional de Agricultura Tropical (CIAT).
Gridded Population of the World, Version 3 (GPWv3) Subnational Administrative Boundaries are the basis of the population data products in GPWv3. Due to copyright 

restrictions, only maps of the subnational administrative boundaries are available, the underlying data cannot be released. GPWv3 is produced by the Columbia University Center for 

International Earth Science Information Network (CIESIN) in collaboration with Centro Internacional de Agricultura Tropical (CIAT).
The Global Patterns in Human Appropriation of Net Primary Productivity (HANPP) portion of the HANPP Collection represents a digital map of human 

appropriation of net primary productivity measured in units of elemental carbon on a one-quarter degree global grid. Net primary productivity (NPP), the net amount of solar energy converted 

to plant organic matter through photosynthesis, can be measured in units of elemental carbon and represents the primary food energy source for the world's ecosystems. Humans appropriate net 

primary productivity through the consumption of food, paper, wood and fiber, which alters the composition of the atmosphere, levels of biodiversity, energy flows within food webs and the 

provision of important ecosystem services. The dataset is distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
The Global Patterns in Net Primary Productivity (NPP) portion of the HANPP Collection maps the net amount of solar energy converted to plant organic 

matter through photosynthesis. NPP is measured in units of elemental carbon and represents the primary food energy source for the world's ecosystems. This dataset is distributed by the 

Columbia University Center for International Earth Science Information Network (CIESIN).
The Human Appropriation of Net Primary Productivity (HANPP) as a Percentage of Net Primary Product (NPP) portion of the HANPP Collection represents a 

map identifying regions in which human consumption of NPP is greatly in excess of production by local ecosystems. Humans appropriate net primary productivity through the consumption of 

food, paper, wood and fiber, which alters the composition of the atmosphere, levels of biodiversity, energy flows within food webs and the provision of important ecosystem services. Net 

primary productivity (NPP), the net amount of solar energy converted to plant organic matter through photosynthesis, can be measured in units of elemental carbon and represents the primary 

food energy source for the world's ecosystems. This dataset is distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
The Change in Chlorophyll-a Concentrations 1998-2007 component of the Indicators of Coastal Water Quality Collection represents a tabular time series of 

the chlorophyll-a concentrations for each grid cell, derived from the "Indicators of Coastal Water Quality: Annual Chlorophyll-a Concentration 1998-2007" dataset. Chlorophyll-a 

concentrations are derived from NASA's Sea-viewing Wide Field-of-view Sensor (SeaWiFS). The grid cells are organized by country, and the percentage of change from 1998-2007 is calculated 

for each cell. Each time series was assessed with a linear regression, and cells with statistically significant trends in chlorophyll-a concentrations are identified. The rows of the table 

are linked to a sequence grid from the "Indicators of Coastal Water Quality: Ancillary Data" collection to facilitate the mapping of the trend values for selected countries and areas of 

interest. The data are processed and compiled by the Columbia University Center for International Earth Science Information Network (CIESIN).
The Change in Chlorophyll-a Concentrations 1998-2007 component of the Indicators of Coastal Water Quality Collection represents a tabular time series of 

the chlorophyll-a concentrations for each grid cell, derived from the "Indicators of Coastal Water Quality: Annual Chlorophyll-a Concentration 1998-2007" dataset. Chlorophyll-a 

concentrations are derived from NASA's Sea-viewing Wide Field-of-view Sensor (SeaWiFS). The grid cells are organized by country, and the percentage of change from 1998-2007 is calculated 

for each cell. Each time series was assessed with a linear regression, and cells with statistically significant trends in chlorophyll-a concentrations are identified. The rows of the table 

are linked to a sequence grid from the "Indicators of Coastal Water Quality: Ancillary Data" collection to facilitate the mapping of the trend values for selected countries and areas of 

interest. The data are processed and compiled by the Columbia University Center for International Earth Science Information Network (CIESIN).
The Global Human Footprint Dataset of the Last of the Wild Project, Version 2, 2005 (LWP-2) is the Human Influence 

Index (HII) normalized by biome and realm. The HII is a global dataset of 1-kilometer grid cells, created from nine global data 

layers covering human population pressure (population density), human land use and infrastructure (built-up areas, nighttime lights, 

land use/land cover), and human access (coastlines, roads, railroads, navigable rivers). The dataset is produced by the Wildlife 

Conservation Society (WCS) and the Columbia University Center for International Earth Science Information Network (CIESIN) and is 

available in the Geographic Coordinate system.
The Global Human Footprint Dataset of the Last of the Wild Project, Version 2, 2005 (LWP-2) is the Human Influence 

Index (HII) normalized by biome and realm. The HII is a global dataset of 1-kilometer grid cells, created from nine global data 

layers covering human population pressure (population density), human land use and infrastructure (built-up areas, nighttime lights, 

land use/land cover), and human access (coastlines, roads, railroads, navigable rivers). The dataset is produced by the Wildlife 

Conservation Society (WCS) and the Columbia University Center for International Earth Science Information Network (CIESIN) and is 

available in the Geographic Coordinate system.
The Global Human Influence Index Dataset of the Last of the Wild Project, Version 2, 2005 (LWP-2) is a global dataset 

of 1-kilometer grid cells, created from nine global data layers covering human population pressure (population density), human land 

use and infrastructure (built-up areas, nighttime lights, land use/land cover), and human access (coastlines, roads, railroads, 

navigable rivers). The dataset is produced by the Wildlife Conservation Society (WCS) and the Columbia University Center for 

International Earth Science Information Network (CIESIN) and is available in Geographic Coordinate system.
The Global Human Influence Index Dataset of the Last of the Wild Project, Version 2, 2005 (LWP-2) is a global dataset 

of 1-kilometer grid cells, created from nine global data layers covering human population pressure (population density), human land 

use and infrastructure (built-up areas, nighttime lights, land use/land cover), and human access (coastlines, roads, railroads, 

navigable rivers). The dataset is produced by the Wildlife Conservation Society (WCS) and the Columbia University Center for 

International Earth Science Information Network (CIESIN) and is available in Geographic Coordinate system.
The Last of the Wild Dataset of the Last of the Wild Project, Version 2, 2005 (LWP-2) is derived from the LWP-2 Human 

Footprint Dataset. The gridded data are classified according to their raster value (wild = 0-10; not wild >10). The ten largest 

polygons of more than 5 square kilometers within each biome by realm are selected and identified. The dataset is produced by the 

Wildlife Conservation Society (WCS) and the Columbia University Center for International Earth Science Information Network (CIESIN) 

and is available in the Geographic Coordinate system.
The Last of the Wild Dataset of the Last of the Wild Project, Version 2, 2005 (LWP-2) is derived from the LWP-2 Human 

Footprint Dataset. The gridded data are classified according to their raster value (wild = 0-10; not wild >10). The ten largest 

polygons of more than 5 square kilometers within each biome by realm are selected and identified. The dataset is produced by the 

Wildlife Conservation Society (WCS) and the Columbia University Center for International Earth Science Information Network (CIESIN) 

and is available in the Geographic Coordinate system.
The Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 2 data 

set consists of country-level estimates of urban population, rural population, total population and land area country-wide and in LECZs for 

years 1990, 2000, 2010, and 2100. The LECZs were derived from Shuttle Radar Topography Mission (SRTM), 3 arc second (~90m) data which were 

post processed by ISciences LLC to include only elevations less than 20m contiguous to coastlines; and to supplement SRTM data in northern 

and southern latitudes. The population and land area statistics presented herein are summarized at the low coastal elevations of less than 

or equal to 1m, 3m, 5m, 7m, 9m, 10m, 12m, and 20m. Additionally, estimates are provided for elevations > 20m, and nationally. The spatial 

coverage of this data set includes 202 of the 232 countries and statistical areas delineated in the Gridded Rural-Urban Mapping Project 

version 1 (GRUMPv1) dataset. The 30 omitted areas were not included because they were landlocked, or otherwise lacked coastal features. 

This data set makes use of the population inputs of GRUMPv1 allocated at 3 arc seconds to match the SRTM elevations, and at 30 arc seconds 

resolution in order to reflect uncertainty levels in the product resulting from the interplay of input population data resolutions (based 

on census units) and the elevation data.. Urban and rural areas are differentiated by the GRUMPv1 Urban Extents. See data set documentation 

for specifics on the methodologies used. This data set is produced by the Columbia University Center for International Earth Science 

Information Network (CIESIN).
The Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 2 data 

set consists of country-level estimates of urban population, rural population, total population and land area country-wide and in LECZs for 

years 1990, 2000, 2010, and 2100. The LECZs were derived from Shuttle Radar Topography Mission (SRTM), 3 arc second (~90m) data which were 

post processed by ISciences LLC to include only elevations less than 20m contiguous to coastlines; and to supplement SRTM data in northern 

and southern latitudes. The population and land area statistics presented herein are summarized at the low coastal elevations of less than 

or equal to 1m, 3m, 5m, 7m, 9m, 10m, 12m, and 20m. Additionally, estimates are provided for elevations > 20m, and nationally. The spatial 

coverage of this data set includes 202 of the 232 countries and statistical areas delineated in the Gridded Rural-Urban Mapping Project 

version 1 (GRUMPv1) dataset. The 30 omitted areas were not included because they were landlocked, or otherwise lacked coastal features. 

This data set makes use of the population inputs of GRUMPv1 allocated at 3 arc seconds to match the SRTM elevations, and at 30 arc seconds 

resolution in order to reflect uncertainty levels in the product resulting from the interplay of input population data resolutions (based 

on census units) and the elevation data.. Urban and rural areas are differentiated by the GRUMPv1 Urban Extents. See data set documentation 

for specifics on the methodologies used. This data set is produced by the Columbia University Center for International Earth Science 

Information Network (CIESIN).
The Population, Landscape, And Climate Estimates, Version 2 (PLACE II) dataset contains estimates of national-level aggregations of territorial extent 

and population size by biome, climate zone, coastal proximity zone, elevation zone, and population density zone, a compendium of nearly 300 variables for 228 countries. This dataset is 

produced by the Columbia University Center for International Earth Science Information Network (CIESIN).
The Population, Landscape, And Climate Estimates, Version 3 (PLACE III) dataset contains estimates of national-level aggregations in urban, rural, and total designations of territorial extent and population size by biome, climate zone, coastal proximity zone, elevation zone, and population density zone, for 232 statistical areas (countries and other UN recognized territories). This dataset is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).
The Population, Landscape, And Climate Estimates, Version 3 (PLACE III) dataset contains estimates of national-level aggregations in urban, rural, and total designations of territorial extent and population size by biome, climate zone, coastal proximity zone, elevation zone, and population density zone, for 232 statistical areas (countries and other UN recognized territories). This dataset is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).
The Pilot 2006 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 sixteen indicators tracked in six well-established policy categories: Environmental 

Health, Air Quality, Water Resources, Productive Natural Resources, Biodiversity and Habitat, and Sustainable Energy. The Pilot 2006 EPI utilizes a proximity-to-target methodology 

focused on a core set of environmental outcomes linked to policy goals for which every government should be held accountable. By identifying specific targets and measuring how close each 

country comes to them, the EPI provides a factual 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 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 Population Exposure Estimates in Proximity to Nuclear Power Plants, Country-Level Aggregates data set consists of country-level estimates of total, urban, and rural populations and land area, country-wide, that are in proximity to a nuclear power plant. This data set was created using a global data set of point locations of nuclear power plants, with buffer zones at 30km, 75km, 150km, 300km, 600km, and 1200km, and the Global Population Count Grid Time Series Estimates, Version 1 to estimate the population within each buffer zone for the years 1990, 2000, and 2010. Global Rural-Urban Mapping Project, Version 1 (GRUMPv1) Land and Geographic Unit Area Grids were used to estimate land area within each buffer zone. The GRUMPv1 Urban Extents Grid was used to further delineate population and land area estimates within urban and rural areas. All grids used for population, land area, and urban mask were of 1 km (30 arc-second) resolution.
The Population Exposure Estimates in Proximity to Nuclear Power Plants, Country-Level Aggregates data set consists of country-level estimates of total, urban, and rural populations and land area, country-wide, that are in proximity to a nuclear power plant. This data set was created using a global data set of point locations of nuclear power plants, with buffer zones at 30km, 75km, 150km, 300km, 600km, and 1200km, and the Global Population Count Grid Time Series Estimates, Version 1 to estimate the population within each buffer zone for the years 1990, 2000, and 2010. Global Rural-Urban Mapping Project, Version 1 (GRUMPv1) Land and Geographic Unit Area Grids were used to estimate land area within each buffer zone. The GRUMPv1 Urban Extents Grid was used to further delineate population and land area estimates within urban and rural areas. All grids used for population, land area, and urban mask were of 1 km (30 arc-second) resolution.
The Population Exposure Estimates in Proximity to Nuclear Power Plants, Locations data set combines information from a global data set developed by 

Declan Butler of Nature News and the Power Reactor Information System (PRIS), an up-to-date database of nuclear reactors maintained by the International Atomic Energy Agency (IAEA). The 

locations of nuclear reactors around the world are represented as point features associated with reactor specification and performance history attributes as of March 2012.
The Population Exposure Estimates in Proximity to Nuclear Power Plants, Locations data set combines information from a global data set developed by 

Declan Butler of Nature News and the Power Reactor Information System (PRIS), an up-to-date database of nuclear reactors maintained by the International Atomic Energy Agency (IAEA). The 

locations of nuclear reactors around the world are represented as point features associated with reactor specification and performance history attributes as of March 2012.
The Population Exposure Estimates in Proximity to Nuclear Power Plants, Locations data set combines information from a global data set developed by 

Declan Butler of Nature News and the Power Reactor Information System (PRIS), an up-to-date database of nuclear reactors maintained by the International Atomic Energy Agency (IAEA). The 

locations of nuclear reactors around the world are represented as point features associated with reactor specification and performance history attributes as of March 2012.
The Global Subnational Infant Mortality Rates consists of estimates of infant mortality rates 

for the year 2000. The infant mortality rate for a region or country is defined as the number of children who die before their first 

birthday for every 1,000 live births. The data products include a shapefile (vector data) of rates, grids (raster data) of rates (per 

10,000 live births in order to preserve precision in integer format), births (the rate denominator) and deaths (the rate numerator), 

and a tabular dataset of the same and associated data. Over 10,000 national and subnational units are represented in the tabular and 

grid datasets, while the shapefile uses approximately 1,000 units in order to protect the intellectual property of source datasets 

for Brazil, China, and Mexico. This dataset is produced by the Columbia University Center for International Earth Science Information 

Network (CIESIN).
The Global Subnational Infant Mortality Rates consists of estimates of infant mortality rates 

for the year 2000. The infant mortality rate for a region or country is defined as the number of children who die before their first 

birthday for every 1,000 live births. The data products include a shapefile (vector data) of rates, grids (raster data) of rates (per 

10,000 live births in order to preserve precision in integer format), births (the rate denominator) and deaths (the rate numerator), 

and a tabular dataset of the same and associated data. Over 10,000 national and subnational units are represented in the tabular and 

grid datasets, while the shapefile uses approximately 1,000 units in order to protect the intellectual property of source datasets 

for Brazil, China, and Mexico. This dataset is produced by the Columbia University Center for International Earth Science Information 

Network (CIESIN).
The Global Subnational Prevalence of Child Malnutrition dataset consists of estimates of the 

percentage of children with weight-for-age z-scores that are more than two standard deviations below the median of the NCHS/CDC/WHO 

International Reference Population. Data are reported for the most recent year with subnational information available at the time of 

development. The data products include a shapefile (vector data) of percentage rates, grids (raster data) of rates (per thousand in 

order to preserve precision in integer format), the number of children under five (the rate denominator), and the number of 

underweight children under five (the rate numerator), and a tabular dataset of the same and associated data. This dataset is produced 

by the Columbia University Center for International Earth Science Information Network (CIESIN).
The Small Area Estimates of Poverty and Inequality dataset consists of consumption-based 

poverty, inequality and related measures for subnational administrative units in approximately twenty countries throughout Africa, 

Asia, Europe, North America, and South America. These measures are derived on a country-level basis from a combination of census and 

survey data using small area estimates techniques. The collection of data have been compiled, integrated and standardized from the 

original data providers into a unified spatially referenced and globally consistent dataset. The data products include shapefiles 

(vector data), tabular datasets (csv format), and centroids (csv file with latitude and longitude of a geographic unit and associated 

poverty estimates). Additionally, a data catalog (xls format) containing detailed information and documentation is provided. This 

dataset is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration 

with a number of external data providers.
Synthetic Assessment of Global Distribution of Vulnerability to Climate Change: Maps and Data, 2005, 2050, and 2100 consist of maps and vulnerability 

index to climate change of 100 countries based on the Vulnerability-Resilience Indicator Model (VRIM), which not only presents sensitivity to climate change stresses but allows the division 

of indicators into components that reflects sensitivity and adaptive capacity. It was produced in collaboration with the Wesleyan University, Joint Global Change Research Insitute, 

University of Illinois and the Columbia University Center for International Earth Science Information Network (CIESIN).
Synthetic Assessment of Global Distribution of Vulnerability to Climate Change: Maps and Data, 2005, 2050, and 2100 consist of maps and vulnerability 

index to climate change of 100 countries based on the Vulnerability-Resilience Indicator Model (VRIM), which not only presents sensitivity to climate change stresses but allows the division 

of indicators into components that reflects sensitivity and adaptive capacity. It was produced in collaboration with the Wesleyan University, Joint Global Change Research Insitute, 

University of Illinois and the Columbia University Center for International Earth Science Information Network (CIESIN).
U.S. Census Grids (Summary File 1), 2000 contain grids of demographic and socioeconomic data 

from the year 2000 U.S. census in ASCII and geotiff formats. The grids have a resolution of 30 arc-seconds (0.0083 decimal degrees), 

or approximately 1 square km. The gridded variables are based on census block geography from Census 2000 TIGER/Line Files and census 

variables (population, households, and housing variables). This dataset is produced by the Columbia University Center for 

International Earth Science Information Network (CIESIN).
U.S. Census Grids (Summary File 1), 2000 contain grids of demographic and socioeconomic data 

from the year 2000 U.S. census in ASCII and geotiff formats. The grids have a resolution of 30 arc-seconds (0.0083 decimal degrees), 

or approximately 1 square km. The gridded variables are based on census block geography from Census 2000 TIGER/Line Files and census 

variables (population, households, and housing variables). This dataset is produced by the Columbia University Center for 

International Earth Science Information Network (CIESIN).
U.S. Population Grids (Summary File 1), 2000: Metropolitan Statistical Areas contain grids of 

demographic and socioeconomic data from the year 2000 U.S. census in ASCII and geotiff formats for 50 metropolitan statistical areas 

with at least one million in population. The grids have a resolution of 7.5 arc-seconds (0.002075 decimal degrees), or approximately 

250 square meters. The gridded variables are based on census block geography from Census 2000 TIGER/Line Files and census variables 

(population, households, and housing variables). This dataset is produced by the Columbia University Center for International Earth 

Science Information Network (CIESIN).
U.S. Population Grids (Summary File 1), 2000: Metropolitan Statistical Areas contain grids of 

demographic and socioeconomic data from the year 2000 U.S. census in ASCII and geotiff formats for 50 metropolitan statistical areas 

with at least one million in population. The grids have a resolution of 7.5 arc-seconds (0.002075 decimal degrees), or approximately 

250 square meters. The gridded variables are based on census block geography from Census 2000 TIGER/Line Files and census variables 

(population, households, and housing variables). This dataset is produced by the Columbia University Center for International Earth 

Science Information Network (CIESIN).
U.S. Census Grids (Summary File 3), 2000 contain grids of demographic and socioeconomic data from the year 2000 U.S. census in ASCII and geotiff formats. The grids have a resolution of 30 arc-seconds (0.0083 decimal degrees), or approximately 1 square km. The gridded variables are based on census block geography from Census 2000 TIGER/Line Files and census variables (population, households, and housing variables). This dataset is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).
U.S. Population Grids (Summary File 3), 2000: Metropolitan Statistical Areas contain grids of demographic and socioeconomic data from the year 2000 U.S. census in ASCII and geotiff formats for 50 metropolitan statistical areas with at least one million in population. The grids have a resolution of 7.5 arc-seconds (0.002075 decimal degrees), or approximately 250 square meters. The gridded variables are based on census block geography from Census 2000 TIGER/Line Files and census variables (population, households, and housing variables). This dataset is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).
U.S. Population Grids (Summary File 3), 2000: Metropolitan Statistical Areas contain grids of demographic and socioeconomic data from the year 2000 U.S. census in ASCII and geotiff formats for 50 metropolitan statistical areas with at least one million in population. The grids have a resolution of 7.5 arc-seconds (0.002075 decimal degrees), or approximately 250 square meters. The gridded variables are based on census block geography from Census 2000 TIGER/Line Files and census variables (population, households, and housing variables). This dataset is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).
The U.S. Environmental Protection Agency (EPA) National Priorities List (NPL) Sites Point Data with CIESIN Modifications, Version 2 is a modified version of the 2014 EPA 

NPL list. It includes all the sites that are proposed, currently on, or deleted from the Final NPL as of February 27, 2014. CIESIN has fixed eleven of the original coordinates by correcting 

latitude or longitude coordinates. It contains the point locations, including the eleven corrections, for 1,747 U.S. hazardous waste sites on the National Priorities List (NPL) of EPA's 

Comprehensive Environmental Response, Compensation, and Liability Information System (CERCLIS) for the fifty states, Puerto Rico, and 4 other territorial areas plus the now independent 

Palau, Federated States of Micronesia. The sites in CERCLIS are also known as Superfund Sites. The NPL is intended primarily to guide the EPA in determining which sites warrant further 

investigation.
The U.S. Environmental Protection Agency (EPA) National Priorities List (NPL) Sites Point Data with CIESIN Modifications, Version 2 is a modified version of the 2014 EPA 

NPL list. It includes all the sites that are proposed, currently on, or deleted from the Final NPL as of February 27, 2014. CIESIN has fixed eleven of the original coordinates by correcting 

latitude or longitude coordinates. It contains the point locations, including the eleven corrections, for 1,747 U.S. hazardous waste sites on the National Priorities List (NPL) of EPA's 

Comprehensive Environmental Response, Compensation, and Liability Information System (CERCLIS) for the fifty states, Puerto Rico, and 4 other territorial areas plus the now independent 

Palau, Federated States of Micronesia. The sites in CERCLIS are also known as Superfund Sites. The NPL is intended primarily to guide the EPA in determining which sites warrant further 

investigation.
The U.S. Environmental Protection Agency (EPA) National Priorities List (NPL) Sites Point Data with CIESIN Modifications, Version 2 is a modified version of the 2014 EPA 

NPL list. It includes all the sites that are proposed, currently on, or deleted from the Final NPL as of February 27, 2014. CIESIN has fixed eleven of the original coordinates by correcting 

latitude or longitude coordinates. It contains the point locations, including the eleven corrections, for 1,747 U.S. hazardous waste sites on the National Priorities List (NPL) of EPA's 

Comprehensive Environmental Response, Compensation, and Liability Information System (CERCLIS) for the fifty states, Puerto Rico, and 4 other territorial areas plus the now independent 

Palau, Federated States of Micronesia. The sites in CERCLIS are also known as Superfund Sites. The NPL is intended primarily to guide the EPA in determining which sites warrant further 

investigation.
The U.S. Environmental Protection Agency (EPA) National Priorities List (NPL) Sites Point Data with CIESIN Modifications, Version 2 is a modified version of the 2014 EPA 

NPL list. It includes all the sites that are proposed, currently on, or deleted from the Final NPL as of February 27, 2014. CIESIN has fixed eleven of the original coordinates by correcting 

latitude or longitude coordinates. It contains the point locations, including the eleven corrections, for 1,747 U.S. hazardous waste sites on the National Priorities List (NPL) of EPA's 

Comprehensive Environmental Response, Compensation, and Liability Information System (CERCLIS) for the fifty states, Puerto Rico, and 4 other territorial areas plus the now independent 

Palau, Federated States of Micronesia. The sites in CERCLIS are also known as Superfund Sites. The NPL is intended primarily to guide the EPA in determining which sites warrant further 

investigation.
The U.S. Environmental Protection Agency (EPA) National Priorities List (NPL) Sites Point Data with CIESIN Modifications, Version 2 is a modified version of the 2014 EPA 

NPL list. It includes all the sites that are proposed, currently on, or deleted from the Final NPL as of February 27, 2014. CIESIN has fixed eleven of the original coordinates by correcting 

latitude or longitude coordinates. It contains the point locations, including the eleven corrections, for 1,747 U.S. hazardous waste sites on the National Priorities List (NPL) of EPA's 

Comprehensive Environmental Response, Compensation, and Liability Information System (CERCLIS) for the fifty states, Puerto Rico, and 4 other territorial areas plus the now independent 

Palau, Federated States of Micronesia. The sites in CERCLIS are also known as Superfund Sites. The NPL is intended primarily to guide the EPA in determining which sites warrant further 

investigation.
U.S. Population Grids (Summary File 1), 2000: Houston Metropolitan Statistical Area, Alpha Version contains an ARC/INFO Workspace with grids of demographic data from the 2000 census. The grids have a resolution of 7.5 arc-seconds (0.002075 decimal degrees), or approximately 250 square meters. The gridded variables are based on census block geography from Census 2000 TIGER/Line Files and census variables (population, households, and housing variables) from Summary File 1. This dataset is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).
U.S. Population Grids (Summary File 1), 2000: Houston Metropolitan Statistical Area, Alpha Version contains an ARC/INFO Workspace with grids of demographic data from the 2000 census. The grids have a resolution of 7.5 arc-seconds (0.002075 decimal degrees), or approximately 250 square meters. The gridded variables are based on census block geography from Census 2000 TIGER/Line Files and census variables (population, households, and housing variables) from Summary File 1. This dataset is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).
U.S. Population Grids (Summary File 1), 2000: New Orleans Metropolitan Statistical Area, Alpha Version contains an ARC/INFO Workspace with grids of demographic data from the 2000 census. The grids have a resolution of 7.5 arc-seconds (0.002075 decimal degrees), or approximately 250 square meters. The gridded variables are based on census block geography from Census 2000 TIGER/Line Files and census variables (population, households, and housing variables) from Summary File 1. This dataset is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).
U.S. Population Grids (Summary File 1), 2000: New Orleans Metropolitan Statistical Area, Alpha Version contains an ARC/INFO Workspace with grids of demographic data from the 2000 census. The grids have a resolution of 7.5 arc-seconds (0.002075 decimal degrees), or approximately 250 square meters. The gridded variables are based on census block geography from Census 2000 TIGER/Line Files and census variables (population, households, and housing variables) from Summary File 1. This dataset is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).
The Urban Landsat: Cities from Space dataset contains images for 66 urban areas and the raw, 

unerlying data for 28 of these places. Each image shows a Landsat false color composite in UTM projection. The R/G/B layers 

correspond to TM/ETM+ bands 7/4/2. Each pixel is 30x30 meters in area and most images are 30x30 km in area. A 2% linear stretch has 

been applied to the images. The Landsat data files contain six reflected bands of calibrated exoatmospheric reflectance stored in 

ENVI band sequential (BSQ) format. Geographic coordinates are included in the header file. The data file contains 1000x1000x6 4 byte 

floating point numbers as indicated in the header. This dataset is distributed by the Socioeconomic Data and Applications Center 

(SEDAC) at Center for International Earth Science Information Network (CIESIN), Columbia University.
PALSAR_Radiometric_Terrain_Corrected_high_res
PALSAR_Radiometric_Terrain_Corrected_low_res
UAVSAR Repeat Pass Interferometry Amplitude Scene
UAVSAR Repeat Pass Interferometry Ground Projected Amplitude Scene
UAVSAR Repeat Pass Interferometry Scene Browse
UAVSAR Repeat Pass Interferometry Scene DEM TIFF
UAVSAR Repeat Pass Interferometry Ground Projected Scene
UAVSAR PolSAR Scene Browse
UAVSAR PolSAR Scene Complex
UAVSAR PolSAR Scene DEM TIFF
UAVSAR PolSAR Scene HDF5
UAVSAR PolSAR Scene Incidence Angle
UAVSAR PolSAR Scene Pauli Decomposition
UAVSAR PolSAR Scene Projected
UAVSAR PolSAR Scene Projected Multilook 3x3
UAVSAR PolSAR Scene Projected Multilook 5x5
UAVSAR PolSAR Scene Slope
UAVSAR PolSAR Scene Stokes
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.
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.
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 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 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 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 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 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 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-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-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-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 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 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-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 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 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 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.
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.
AMSR/ADEOS-II global swath brightness temperatures are resampled at resolutions of 56 km, 38 km, 21 km, 12 km, and 5.4 km.
Aquarius L2 Swath Single Orbit Soil Moisture Data
Aquarius L3 Gridded 1-Degree Annual Soil Moisture Data
Aquarius L3 Gridded 1-Degree Daily Soil Moisture Data
Aquarius L3 Gridded 1-Degree Monthly Soil Moisture Data
Aquarius L3 Gridded 1-Degree Seasonal Soil Moisture Data
Aquarius L3 Gridded 1-Degree Weekly Soil Moisture Data
This data set is comprised of several parameters from in situ measurements collected for the Cloud and Land Surface Interaction Campaign 2007 (CLASIC07).
This data set is comprised of several parameters from in situ measurements collected for the Cloud and Land Surface Interaction Campaign 2007 (CLASIC07).
This data set includes in situ vegetation data collected during the Cloud and Land Surface Interaction Campaign 2007 (CLASIC07) campaign. Sampling was designed to coincide with satellite overpasses, such as Landsat's Thematic Mapper (TM) 5 and the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on NASA's Terra satellite (MODIS/Terra), which can be then used to estimate vegetation water content on the regional scale.
This data set includes in situ vegetation data collected during the Cloud and Land Surface Interaction Campaign 2007 (CLASIC07) campaign. Sampling was designed to coincide with satellite overpasses, such as Landsat's Thematic Mapper (TM) 5 and the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on NASA's Terra satellite (MODIS/Terra), which can be then used to estimate vegetation water content on the regional scale.
This data set consists of land cover classification data derived from satellite imagery as part of the Cloud and Land Surface Interaction Campaign 2007 (CLASIC07). ResourceSat-1 AWiFS images of the study area were retrieved for the period of April through August 2007. The land use classification image provides information about vegetation present in the study area at a resolution of 56 meters.
This data set consists of land cover classification data derived from satellite imagery as part of the Cloud and Land Surface Interaction Campaign 2007 (CLASIC07). ResourceSat-1 AWiFS images of the study area were retrieved for the period of April through August 2007. The land use classification image provides information about vegetation present in the study area at a resolution of 56 meters.
This data set contains backscatter data obtained by the Passive Active L-band System (PALS) microwave aircraft radar instrument as part of the Cloud and Land Surface Interaction Campaign 2007 (CLASIC07).
This data set contains backscatter data obtained by the Passive Active L-band System (PALS) microwave aircraft radar instrument as part of the Cloud and Land Surface Interaction Campaign 2007 (CLASIC07).
This data set contains brightness temperature data obtained by the Passive Active L-band System (PALS) microwave aircraft radiometer instrument as part of the Cloud and Land Surface Interaction Campaign 2007 (CLASIC07).
This data set contains brightness temperature data obtained by the Passive Active L-band System (PALS) microwave aircraft radiometer instrument as part of the Cloud and Land Surface Interaction Campaign 2007 (CLASIC07).
This data set contains soil texture data obtained for the Cloud and Land Surface Interaction Campaign 2007 (CLASIC07). The original data were extracted from a multi-layer soil characteristics database for the conterminous United States called CONUS-Soil and generated for the regional study area. Data are representative of the conditions present in the regional study area during the general timeline of the CLASIC07 campaign.
This data set contains soil texture data obtained for the Cloud and Land Surface Interaction Campaign 2007 (CLASIC07). The original data were extracted from a multi-layer soil characteristics database for the conterminous United States called CONUS-Soil and generated for the regional study area. Data are representative of the conditions present in the regional study area during the general timeline of the CLASIC07 campaign.
The Vegetation Water Content (VWC) map for the Cloud and Land Surface Interaction Campaign 2007 (CLASIC07) was derived by calculating Normalized Difference Water Index (NDWI) from ResourceSat-1 satellite imagery.
The Vegetation Water Content (VWC) map for the Cloud and Land Surface Interaction Campaign 2007 (CLASIC07) was derived by calculating Normalized Difference Water Index (NDWI) from ResourceSat-1 satellite imagery.
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 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 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.
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.
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.
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.
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.
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.
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.
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.
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.
This data set contains geolocated elevations captured over Antarctica using the Sigma Space photon counting lidar. The data were collected by scientists working on the International Collaborative Exploration of the Cryosphere through Airborne Profiling (ICECAP) project, which was funded by the National Science Foundation (NSF), the Antarctic Climate and Ecosystems Collaborative Research Center, and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.
This data set contains geolocated elevations captured over Antarctica using the Sigma Space photon counting lidar. The data were collected by scientists working on the International Collaborative Exploration of the Cryosphere through Airborne Profiling (ICECAP) project, which was funded by the National Science Foundation (NSF), the Antarctic Climate and Ecosystems Collaborative Research Center, and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.
This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, provides a daily record of Arctic sea ice characteristics for the years 1979 through 2012 derived from passive microwave brightness temperatures. Characteristics include the location of sea ice cover, sea ice age, day of melt onset, and status of melt onset. Data are gridded in the 25 km Equal-Area Scalable Earth Grid (EASE-Grid) 2.0 and provided as netCDF files.
This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, provides a daily record of Arctic sea ice characteristics for the years 1979 through 2012 derived from passive microwave brightness temperatures. Characteristics include the location of sea ice cover, sea ice age, day of melt onset, and status of melt onset. Data are gridded in the 25 km Equal-Area Scalable Earth Grid (EASE-Grid) 2.0 and provided as netCDF files.
This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, provides a daily record of Arctic sea ice characteristics for the years 1979 through 2012 derived from passive microwave brightness temperatures. Characteristics include the location of sea ice cover, sea ice age, day of melt onset, and status of melt onset. Data are gridded in the 25 km Equal-Area Scalable Earth Grid (EASE-Grid) 2.0 and provided as netCDF files.
MODIS/Aqua Sea Ice Extent 5-Min L2 Swath 1km
MODIS/Aqua Sea Ice Extent 5-Min L2 Swath 1km
MODIS/Aqua Sea Ice Extent 5-Min L2 Swath 1km
MODIS/Aqua Sea Ice Extent 5-Min L2 Swath 1km
MODIS/Aqua Sea Ice Extent 5-Min L2 Swath 1km
MODIS/Aqua Sea Ice Extent and IST Daily L3 Global 4km EASE-Grid Day
MODIS/Aqua Sea Ice Extent and IST Daily L3 Global 4km EASE-Grid Day
MODIS/Aqua Sea Ice Extent and IST Daily L3 Global 4km EASE-Grid Day
MODIS/Aqua Sea Ice Extent and IST Daily L3 Global 4km EASE-Grid Day
MODIS/Aqua Sea Ice Extent and IST Daily L3 Global 4km EASE-Grid Day
MODIS/Aqua Sea Ice Extent Daily L3 Global 1km EASE-Grid Day
MODIS/Aqua Sea Ice Extent Daily L3 Global 1km EASE-Grid Day
MODIS/Aqua Sea Ice Extent Daily L3 Global 1km EASE-Grid Day
MODIS/Aqua Sea Ice Extent Daily L3 Global 1km EASE-Grid Day
MODIS/Aqua Sea Ice Extent Daily L3 Global 1km EASE-Grid Day
MODIS/Aqua Sea Ice Extent Daily L3 Global 1km EASE-Grid Night
MODIS/Aqua Sea Ice Extent Daily L3 Global 1km EASE-Grid Night
MODIS/Aqua Sea Ice Extent Daily L3 Global 1km EASE-Grid Night
MODIS/Aqua Sea Ice Extent Daily L3 Global 1km EASE-Grid Night
MODIS/Aqua Sea Ice Extent Daily L3 Global 1km EASE-Grid Night
MODIS/Aqua Snow Cover 5-Min L2 Swath 500m
MODIS/Aqua Snow Cover 5-Min L2 Swath 500m
MODIS Snow Cover Climate Modeling Grid (CMG), 8-Day Level 3 Global Product at 0.05Deg Resolution
MODIS Snow Cover Climate Modeling Grid (CMG), 8-Day Level 3 Global Product at 0.05Deg Resolution
MODIS/Aqua Snow Cover 8-Day L3 Global 500m SIN Grid
MODIS/Aqua Snow Cover 8-Day L3 Global 500m SIN Grid
MODIS Snow Cover Climate Modeling Grid (CMG), Daily Level 3 Global Product at 0.05Deg Resolution
MODIS Snow Cover Climate Modeling Grid (CMG), Daily Level 3 Global Product at 0.05Deg Resolution
MODIS/Aqua Snow Cover Daily L3 Global 500m SIN Grid
MODIS/Aqua Snow Cover Daily L3 Global 500m SIN Grid
MODIS Snow Cover Climate Modeling Grid (CMG), Monthly Level 3 Global Product at 0.05Deg Resolution
MODIS Snow Cover Climate Modeling Grid (CMG), Monthly Level 3 Global Product at 0.05Deg Resolution
MODIS/Terra Sea Ice Extent 5-Min L2 Swath 1km
MODIS/Terra Sea Ice Extent 5-Min L2 Swath 1km
MODIS/Terra Sea Ice Extent 5-Min L2 Swath 1km
MODIS/Terra Sea Ice Extent 5-Min L2 Swath 1km
MODIS/Terra Sea Ice Extent 5-Min L2 Swath 1km
MODIS/Terra Sea Ice Extent and IST Daily L3 Global 4km EASE-Grid Day
MODIS/Terra Sea Ice Extent and IST Daily L3 Global 4km EASE-Grid Day
MODIS/Terra Sea Ice Extent and IST Daily L3 Global 4km EASE-Grid Day
MODIS/Terra Sea Ice Extent and IST Daily L3 Global 4km EASE-Grid Day
MODIS/Terra Sea Ice Extent and IST Daily L3 Global 4km EASE-Grid Day
MODIS/Terra Sea Ice Extent Daily L3 Global 1km EASE-Grid Day
MODIS/Terra Sea Ice Extent Daily L3 Global 1km EASE-Grid Day
MODIS/Terra Sea Ice Extent Daily L3 Global 1km EASE-Grid Day
MODIS/Terra Sea Ice Extent Daily L3 Global 1km EASE-Grid Day
MODIS/Terra Sea Ice Extent Daily L3 Global 1km EASE-Grid Day
MODIS/Terra Sea Ice Extent Daily L3 Global 1km EASE-Grid Night
MODIS/Terra Sea Ice Extent Daily L3 Global 1km EASE-Grid Night
MODIS/Terra Sea Ice Extent Daily L3 Global 1km EASE-Grid Night
MODIS/Terra Sea Ice Extent Daily L3 Global 1km EASE-Grid Night
MODIS/Terra Sea Ice Extent Daily L3 Global 1km EASE-Grid Night
MODIS/Terra Snow Cover 5-Min L2 Swath 500m
MODIS/Terra Snow Cover 5-Min L2 Swath 500m
MODIS Snow Cover Climate Modeling Grid (CMG), 8-Day Level 3 Global Product at 0.05Deg Resolution
MODIS Snow Cover Climate Modeling Grid (CMG), 8-Day Level 3 Global Product at 0.05Deg Resolution
MODIS/Terra Snow Cover 8-Day L3 Global 500m SIN Grid
MODIS/Terra Snow Cover 8-Day L3 Global 500m SIN Grid
MODIS Snow Cover Climate Modeling Grid (CMG), Daily Level 3 Global Product at 0.05Deg Resolution
MODIS Snow Cover Climate Modeling Grid (CMG), Daily Level 3 Global Product at 0.05Deg Resolution
MODIS/Terra Snow Cover Daily L3 Global 500m SIN Grid
MODIS/Terra Snow Cover Daily L3 Global 500m SIN Grid
MODIS Snow Cover Climate Modeling Grid (CMG), Monthly Level 3 Global Product at 0.05Deg Resolution
MODIS Snow Cover Climate Modeling Grid (CMG), Monthly Level 3 Global Product at 0.05Deg Resolution
Data for the Near Real-Time SSM/I EASE-Grid Daily Global Ice Concentration and Snow Extent product are provided in two 25-km grids, in an azimuthal, equal area projection (the NSIDC SL and NL EASE-Grids).
Data for the Near Real-Time SSM/I EASE-Grid Daily Global Ice Concentration and Snow Extent product are provided in two 25-km grids, in an azimuthal, equal area projection (the NSIDC SL and NL EASE-Grids).
Data for the Near-Real-Time SSMIS EASE-Grid Daily Global Ice Concentration and Snow Extent product are provided in two 25-km grids, in an azimuthal, equal area projection (the NSIDC SL and NL EASE-Grids).
Data for the Near-Real-Time SSMIS EASE-Grid Daily Global Ice Concentration and Snow Extent product are provided in two 25-km grids, in an azimuthal, equal area projection (the NSIDC SL and NL EASE-Grids).
Nimbus Ice Edge Points from Nimbus Visible Imagery L2, CSV
Nimbus Ice Edge Points from Nimbus Visible Imagery L2, CSV
Nimbus Image Dissection Camera System Remapped Visible Imagery Daily L3, GeoTIFF
Nimbus Image Dissection Camera System Remapped Visible Imagery Daily L3, HDF5
Nimbus Image Dissection Camera System Visible Imagery L1, HDF5
Nimbus Temperature-Humidity Infrared Radiometer 11.5 um Daily L3, GeoTIFF
Nimbus Temperature-Humidity Infrared Radiometer 11.5 um Daily L3, GeoTIFF
Nimbus Temperature-Humidity Infrared Radiometer 11.5 um Daily L3, HDF5
Nimbus Temperature-Humidity Infrared Radiometer 11.5 um Daily L3, HDF5
Nimbus Temperature-Humidity Infrared Radiometer 11.5 um Swath L1, HDF5
Nimbus Temperature-Humidity Infrared Radiometer 11.5 um Swath L1, HDF5
Nimbus Temperature-Humidity Infrared Radiometer 6.7 um Water Vapor Daily L3, GeoTIFF
Nimbus Temperature-Humidity Infrared Radiometer 6.7 um Water Vapor Daily L3, GeoTIFF
Nimbus Temperature-Humidity Infrared Radiometer 6.7 um Water Vapor Daily L3, HDF5
Nimbus Temperature-Humidity Infrared Radiometer 6.7 um Water Vapor Daily L3, HDF5
Nimbus Temperature-Humidity Infrared Radiometer 6.7 um Water Vapor Swath L1, HDF5
Nimbus Temperature-Humidity Infrared Radiometer 6.7 um Water Vapor Swath L1, HDF5
This data set includes several parameters that were obtained from field surveys as part of the Soil Moisture Active Passive Validation Experiment 2008 (SMAPVEX08).
This data set includes several parameters that were obtained from field surveys as part of the Soil Moisture Active Passive Validation Experiment 2008 (SMAPVEX08).
This data set includes in situ vegetation data collected during the Soil Moisture Active Passive Validation Experiment 2008 (SMAPVEX08) campaign. Sampling was designed to coincide with satellite overpasses, such as Landsat's Thematic Mapper (TM) 5 and the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on NASA's Terra satellite (MODIS/Terra), which can be then used to estimate vegetation water content on the regional scale.
This data set includes in situ vegetation data collected during the Soil Moisture Active Passive Validation Experiment 2008 (SMAPVEX08) campaign. Sampling was designed to coincide with satellite overpasses, such as Landsat's Thematic Mapper (TM) 5 and the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on NASA's Terra satellite (MODIS/Terra), which can be then used to estimate vegetation water content on the regional scale.
This data set consists of land cover classification data derived from satellite imagery and of data obtained in the field as part of the Soil Moisture Active Passive Validation Experiment 2008 (SMAPVEX08).
This data set consists of land cover classification data derived from satellite imagery and of data obtained in the field as part of the Soil Moisture Active Passive Validation Experiment 2008 (SMAPVEX08).
This data set contains backscatter data obtained by the Passive Active L-band System (PALS) microwave aircraft radar instrument as part of the Soil Moisture Active Passive Validation Experiment 2008 (SMAPVEX08).
This data set contains backscatter data obtained by the Passive Active L-band System (PALS) microwave aircraft radar instrument as part of the Soil Moisture Active Passive Validation Experiment 2008 (SMAPVEX08).
This data set contains brightness temperatures obtained by the Passive Active L-band System (PALS) microwave aircraft radiometer instrument as part of the Soil Moisture Active Passive Validation Experiment 2008 (SMAPVEX08).
This data set contains brightness temperatures obtained by the Passive Active L-band System (PALS) microwave aircraft radiometer instrument as part of the Soil Moisture Active Passive Validation Experiment 2008 (SMAPVEX08).
This data set contains soil texture data that were extracted from a multi-layer soil characteristics database for the conterminous United States and generated for each regional study area. Data are representative of the conditions present in the regional study areas during the general timeline of the Soil Moisture Active Passive Validation Experiment 2008 (SMAPVEX08) campaign.
This data set contains soil texture data that were extracted from a multi-layer soil characteristics database for the conterminous United States and generated for each regional study area. Data are representative of the conditions present in the regional study areas during the general timeline of the Soil Moisture Active Passive Validation Experiment 2008 (SMAPVEX08) campaign.
Surface roughness data for this data set were collected at several field sites as part of the Soil Moisture Active Passive Validation Experiment 2008 (SMAPVEX08) campaign.
Surface roughness data for this data set were collected at several field sites as part of the Soil Moisture Active Passive Validation Experiment 2008 (SMAPVEX08) campaign.
The Vegetation Water Content (VWC) map for the Soil Moisture Active Passive Validation Experiment 2008 (SMAPVEX08) was derived by calculating Normalized Difference Water Index (NDWI) from Satellite Pour l'Observation de la Terre-4 (SPOT-4) overpasses on 11 October 2008. In addition, samples from a range of vegetation types were used to compare VWC and NDWI to the satellite imagery.
The Vegetation Water Content (VWC) map for the Soil Moisture Active Passive Validation Experiment 2008 (SMAPVEX08) was derived by calculating Normalized Difference Water Index (NDWI) from Satellite Pour l'Observation de la Terre-4 (SPOT-4) overpasses on 11 October 2008. In addition, samples from a range of vegetation types were used to compare VWC and NDWI to the satellite imagery.
This data set contains in situ soil moisture data collected with coring devices at several agricultural sites as a part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12).
This data set contains in situ soil texture data collected with coring devices at several sites as part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12).
This data set contains in situ vegetation data collected at several agricultural sites as a part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12).
This data set contains in situ vegetation data collected at several forest sites as a part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12).
This data set consists of land cover classification data derived from satellite imagery as part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12).
This data set contains backscatter data obtained by the Passive Active L-band System (PALS) microwave aircraft instrument as a part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12).
This data set contains brightness temperature data obtained by the Passive Active L-band System (PALS) microwave aircraft instrument as a part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12).
This data set contains in situ soil moisture data collected at several agricultural sites as a part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12).
This data set contains in situ soil moisture data collected at several forested sites as a part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12).
This data set consists of soil texture classification data derived from field surveys as part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12).
This data set consists of soil texture classification data derived from field surveys as part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12).
This data set contains surface roughness data collected at several agricultural sites as a part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12).
This data set contains surface roughness data collected at several forested sites as a part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12).
This data set contains backscatter data obtained by the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) instrument as partof the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12).
This data set contains the vegetation water content map derived by calculating Normalized Difference Water Index (NDWI) from SPOT and RapidEye satellite imagery as part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12).
CERES-MODIS-CALIPSO-CloudSat CCCM data set is an integrated product 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 ERBE-like Monthly Geographical Averages (ES-4) 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  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 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.
International Satellite Cloud Climatology Project (ISCCP) Stage D1 3-Hourly Cloud Product - Revised Algorithm in Hierarchical Data Format
International Satellite Cloud Climatology Project (ISCCP) Stage D2 Monthly Cloud Product - Revised Algorithm in Hierarchical Data Format
NARSTO EPA_SS_ATLANTA 1999 Air Chemistry, Particulate Matter, and Met Data
NARSTO EPA_SS_ATLANTA 1999 UAH Mobile Integrated Profiling System Data
NARSTO EPA_SS_ATLANTA Rapid Single-Particle Mass Spectrometer Data
NARSTO EPA_SS_BALTIMORE JHU LIDAR Backscatter and Mixing Height Data
NARSTO EPA_SS_BALTIMORE JHU Meteorological Data
NARSTO EPA Supersite in Baltimore, Maryland Rapid Single-Particle Mass Spectrometer Data
NARSTO EPA Supersite in Baltimore Metal Conc of and Cytokines Induced by SEAS PM2.5 Samples
NARSTO EPA_SS_FRESNO Aethalometer Multi-Wavelength Carbon Data
NARSTO EPA_SS_FRESNO Elemental Carbon in 2.5 um Aerosol Fraction
NARSTO EPA Supersite in Fresno Meteorological Data
NARSTO EPA_SS_FRESNO Particle-bound Polycyclic Aromatic Compound Data
NARSTO EPA Supersite in Fresno PM2.5 Particulate Nitrate and Sulfate Data
NARSTO EPA_SS_FRESNO PM 2.5 Organic and Elemental Carbon Data
NARSTO EPA_SS_FRESNO TEOM Particulate Mass Concentration Data
NARSTO EPA Supersite in Houston, Texas Differential Mobility Analyzer Data
NARSTO EPA_SS_HOUSTON Ammonia and Nitric Acid Data
NARSTO EPA_SS_HOUSTON PM2.5 Nitrate, Sulfate, and Carbon Data
NARSTO EPA Supersite in Houston, Texas Rapid Single-Particle Mass Spectrometer Data
NARSTO EPA_SS_HOUSTON TEXAQS2000 TNRCC CAMS Air Quality Data
NARSTO EPA Supersite in Houston, Texas DOE G-1 Air Chemistry, Aerosol, and Meteorology Data
NARSTO EPA Supersite in Houston, Texas Formaldehyde and Hydrogen Peroxide Data
NARSTO EPA Supersite in Houston, Texas PM .25 Organic Speciation Data
NARSTO EPA Supersite in Houston, Texas Formaldehyde and Hydrogen Peroxide Data
NARSTO EPA Supersite in Houston, Texas Size-specfic PM Mass Concentration Data
NARSTO EPA Supersite in Houston, Texas Washburn Tunnel Data
NARSTO EPA_SS_LOS_ANGELES Aethalometer Elemental Carbon Data
NARSTO EPA_SS_LOS_ANGELES Aerodynamic Particle Size Data
NARSTO EPA_SS_LOS_ANGELES PM 2.5 Particulate Carbon Data
NARSTO EPA_SS_LOS_ANGELES PM 2.5 Particulate Nitrate Data
NARSTO EPA_SS_LOS_ANGELES Scanning Mobility Particle Size Data
NARSTO EPA_SS_LOS_ANGELES Tapered Element Oscillating Microbalance PM 2.5 Mass Concentration Data
NARSTO EPA Supersite in New York Air Chemistry, Particulate Matter, and Met Data
NARSTO EPA Supersite in Pittsburgh Gac Conc and PM Physical Properties Data
NARSTO EPA_SS_PITTSBURGH Meteorological Data
NARSTO EPA Supersite in Pittsburgh Particulate Matter Composition Data
NARSTO EPA Supersite in Pittsburgh, Pennsylvania Rapid Single-Particle Mass Spectrometer Data
NARSTO ICARTT NEAX 2004 DOE G-1 Air Chemistry,Aerosol, and Met Data
NARSTO 1998 Model-Intercomparison Study Verification Data
This data set contains selected NEXRAD weather images from the northeastern United States for the summer of 1995. The NEXRAD weather radar images were used to supplement the air quality monitoring, meteorological, and emissions data collected across the northeastern United States.
NARSTO PAC2001 Cassiar Tunnel Gaseous and Particle Mass and Composition Data
NARSTO PAC2001 CFS Cessna VOC, Particle Count, Ozone, and Meteorological Data
NARSTO PAC2001 NRC-IAR Convair 580 Particle Count, Ozone, Meteorological Data
NARSTO PAC2001 Golden Ears Provincal Park Gaseous and Particle Data
NARSTO PAC2001 GVRD and CAPMoN Supplemental Air Quality Data
NARSTO PAC2001 Langley Site Gaseous, Particle, and Meteorological Data
NARSTO PAC2001 Slocan Park Site Gaseous, Particle, and Meteorological Data
NARSTO PAC2001 Sumas Eadle Ridge Gaseous, Particle, and Meteorological Data
NARSTO_SHEMP_CANADA PM Composition Data
NARSTO SOS99NASH DOE G-1 Air Chemistry Data
NARSTO SOS99NASH TVA Surface Meteorology and Chemistry Data
NARSTO SOS99 Nashville WP-3D Orion Air Chemistry Data
NARSTO SOS99 Nashville WP-3D Orion Air Chemistry Data
NARSTO_SOS_SC_UPSTATE PM 2.5 Compostion Data
NARSTO_STIFS_CANADA PM Inorganic Volatile Organic Compounds Data
NARSTO Texas PM2.5 Sampling and Analysis Study: 1997-1998 data for 13 sites near Houston
MODIS/Aqua Aerosol 5-Min L2 Swath 10km
MODIS/Aqua Aerosol 5-Min L2 Swath 10km
MODIS/Aqua Aerosol 5-Min L2 Swath 10km
This Level 1B collection contains calibrated and geolocated radiances at-aperture for all 36 MODIS spectral bands at 1km resolution.
This Level 1B collection contains calibrated and geolocated radiances at-aperture for all 36 MODIS spectral bands at 1km resolution.
This Level 1B collection contains calibrated and geolocated radiances at-aperture for all 36 MODIS spectral bands at 1km resolution.
This Level 1B collection contains calibrated and geolocated radiances at-aperture for MODIS spectral bands 1 and 2 at 250m resolution.
This Level 1B collection contains calibrated and geolocated radiances at-aperture for MODIS spectral bands 1 and 2 at 250m resolution.
This Level 1B collection contains calibrated and geolocated radiances at-aperture for MODIS spectral bands 1 and 2 at 250m resolution.
This Level 1B collection contains calibrated and geolocated radiances at-aperture for MODIS spectral bands 1 through 7 at 500m resolution.
This Level 1B collection contains calibrated and geolocated radiances at-aperture for MODIS spectral bands 1 through 7 at 500m resolution.
This Level 1B collection contains calibrated and geolocated radiances at-aperture for MODIS spectral bands 1 through 7 at 500m resolution.
MODIS/Aqua Cloud Mask and Spectral Test Results 5-Min L2 Swath 250m and 1km
MODIS/Aqua Cloud Mask and Spectral Test Results 5-Min L2 Swath 250m and 1km
MODIS/Aqua Cloud Mask and Spectral Test Results 5-Min L2 Swath 250m and 1km
Level 2 daily global Cloud particle phase, effective particle radius, and cloud optical thickness derived from VNIR radiances. Cloud top temp, ht., effective emissivity, phase,&cloud fraction produced with IR retrieval methods at 5X5 1-km resolution.
Level 2 daily global Cloud particle phase, effective particle radius, and cloud optical thickness derived from VNIR radiances. Cloud top temp, ht., effective emissivity, phase,&cloud fraction produced with IR retrieval methods at 5X5 1-km resolution.
Level 2 daily global Cloud particle phase, effective particle radius, and cloud optical thickness derived from VNIR radiances. Cloud top temp, ht., effective emissivity, phase,&cloud fraction produced with IR retrieval methods at 5X5 1-km resolution.
Level 2 Coarse Resolution Sea Ice Cover 5-Min L2 5km...
Level 2 Coarse Resolution Snow Cover 5-Min L2 5km...
Level 2 Coarse Resolution Thermal Anomalies/Fire 5-Min L2 5km...
Level 3 Daily Aerosol Optical Thickness Daily L3 Global 0.05Deg CMG...
Geolocation collection contains geodetic latitude and longitude, surface height above geoid, solar zenith and azimuth angles, satellite zenith and azimuth angles, and a land/sea mask for each 1km sample.
Level 2 Land Surface Reflectance ...
Level 3 Land Surface Reflectance Daily L3 Global 0.05Deg CMG...
Level 1A collection contains packetized raw detector counts for the 36 MODIS spectral bands as well as engineering and spacecraft ancillary data. Only bands 20 to 36 are used to collect measurements in night mode, while all bands are used in day mode.
MODIS/Aqua NRT value-added Aerosol Optical Depth Produc
MODIS/Aqua NRT value-added Aerosol Optical Depth Produc
MODIS/Aqua NRT value-added Aerosol Optical Depth Produc
Level 1A collection contains digitized raw detector counts for the 36 MODIS spectral bands as well as engineering and spacecraft ancillary data. Only bands 20 to 36 are used to collect measurements in night mode, while all bands are used in day mode.
Level 2 Sea Ice Cover 5-Min L2 1km...
Level 2 Snow Cover 5-Min L2 500m...
Level 2 Daily global Total Ozone Burden, Atmospheric Stability, Temperature and Moisture Profiles at 20 vertical levels for temperature and 15 levels for moisture, and Atmospheric Water Vapor at 5x5 1-km resolution when at least 9 FOVs are cloud free.
Level 2 Daily global Total Ozone Burden, Atmospheric Stability, Temperature and Moisture Profiles at 20 vertical levels for temperature and 15 levels for moisture, and Atmospheric Water Vapor at 5x5 1-km resolution when at least 9 FOVs are cloud free.
Level 2 Daily global Total Ozone Burden, Atmospheric Stability, Temperature and Moisture Profiles at 20 vertical levels for temperature and 15 levels for moisture, and Atmospheric Water Vapor at 5x5 1-km resolution when at least 9 FOVs are cloud free.
Level 2 Thermal Anomalies/Fire 5-Min L2 1km...
This Level 2 data collection contains derived precipitable column water vapor amounts, during daytime using a near-infrared over clear land areas and above clouds over both land and ocean; water vapor estimates are provided over clear ocean areas.
This Level 2 data collection contains derived precipitable column water vapor amounts, during daytime using a near-infrared over clear land areas and above clouds over both land and ocean; water vapor estimates are provided over clear ocean areas.
This Level 2 data collection contains derived precipitable column water vapor amounts, during daytime using a near-infrared over clear land areas and above clouds over both land and ocean; water vapor estimates are provided over clear ocean areas.
MODIS/Terra Aerosol 5-Min L2 Swath 10km
MODIS/Terra Aerosol 5-Min L2 Swath 10km
MODIS/Terra Aerosol 5-Min L2 Swath 10km
Level 3 Daily Aerosol Optical Thickness Daily L3 Global 0.05Deg CMG...
This Level 1B collection contains calibrated and geolocated radiances at-aperture for all 36 MODIS spectral bands at 1km resolution.
This Level 1B collection contains calibrated and geolocated radiances at-aperture for all 36 MODIS spectral bands at 1km resolution.
This Level 1B collection contains calibrated and geolocated radiances at-aperture for all 36 MODIS spectral bands at 1km resolution.
This Level 1B collection contains calibrated and geolocated radiances at-aperture for MODIS spectral bands 1 and 2 at 250m resolution.
This Level 1B collection contains calibrated and geolocated radiances at-aperture for MODIS spectral bands 1 and 2 at 250m resolution.
This Level 1B collection contains calibrated and geolocated radiances at-aperture for MODIS spectral bands 1 and 2 at 250m resolution.
This Level 1B collection contains calibrated and geolocated radiances at-aperture for MODIS spectral bands 1 through 7 at 500m resolution.
This Level 1B collection contains calibrated and geolocated radiances at-aperture for MODIS spectral bands 1 through 7 at 500m resolution.
This Level 1B collection contains calibrated and geolocated radiances at-aperture for MODIS spectral bands 1 through 7 at 500m resolution.
MODIS/Terra Cloud Mask and Spectral Test Results 5-Min L2 Swath 250m and 1km
MODIS/Terra Cloud Mask and Spectral Test Results 5-Min L2 Swath 250m and 1km
MODIS/Terra Cloud Mask and Spectral Test Results 5-Min L2 Swath 250m and 1km
Level 2 daily global Cloud particle phase, effective particle radius, and cloud optical thickness derived from VNIR radiances. Cloud top temp, ht., effective emissivity, phase,&cloud fraction produced with IR retrieval methods at 5x5 1-km resolution.
Level 2 daily global Cloud particle phase, effective particle radius, and cloud optical thickness derived from VNIR radiances. Cloud top temp, ht., effective emissivity, phase,&cloud fraction produced with IR retrieval methods at 5x5 1-km resolution.
Level 2 daily global Cloud particle phase, effective particle radius, and cloud optical thickness derived from VNIR radiances. Cloud top temp, ht., effective emissivity, phase,&cloud fraction produced with IR retrieval methods at 5x5 1-km resolution.
Level 2 Coarse Resolution Sea Ice Cover 5-Min L2 5km...
Level 2 Coarse Resolution Snow Cover 5-Min L2 5km...
Level 2 Coarse Resolution Thermal Anomalies/Fire 5-Min L2 5km...
Geolocation collection contains geodetic latitude and longitude, surface height above geoid, solar zenith and azimuth angles, satellite zenith and azimuth angles, and a land/sea mask for each 1km sample.
Level 2 Land Surface Reflectance ...
Level 3 Land Surface Reflectance Daily L3 Global 0.05Deg CMG...
Level 1A collection contains packetized raw detector counts for the 36 MODIS spectral bands as well as engineering and spacecraft ancillary data. Only bands 20 to 36 are used to collect measurements in night mode, while all bands are used in day mode.
MODIS/Terra Near Real Time (NRT) Level 3 Daily Rolling-8-Day Surface Reflectance Global 250m Products
MODIS/Terra Near Real Time (NRT) Level 3 Daily Rolling-8-Day Surface Reflectance Global 250m Products
MODIS/Terra Near Real Time (NRT) Surface Reflectance Rolling 8-Day L3 Global 500m SIN Grid Products
MODIS/Terra Near Real Time (NRT) Surface Reflectance Rolling 8-Day L3 Global 500m SIN Grid Products
MODIS/Terra NRT value-added Aerosol Optical Depth Produc
MODIS/Terra NRT value-added Aerosol Optical Depth Produc
MODIS/Terra NRT value-added Aerosol Optical Depth Produc
Level 1A collection contains digitized raw detector counts for the 36 MODIS spectral bands as well as engineering and spacecraft ancillary data. Only bands 20 to 36 are used to collect measurements in night mode, while all bands are used in day mode.
Level 2 Sea Ice Cover 5-Min L2 1km...
Level 2 Snow Cover 5-Min L2 500m...
Level 2 Daily global Total Ozone Burden, Atmospheric Stability, Temperature and Moisture Profiles at 20 vertical levels for temperature and 15 levels for moisture, and Atmospheric Water Vapor at 5x5 1-km resolution when at least 9 FOVs are cloud free.
Level 2 Daily global Total Ozone Burden, Atmospheric Stability, Temperature and Moisture Profiles at 20 vertical levels for temperature and 15 levels for moisture, and Atmospheric Water Vapor at 5x5 1-km resolution when at least 9 FOVs are cloud free.
Level 2 Daily global Total Ozone Burden, Atmospheric Stability, Temperature and Moisture Profiles at 20 vertical levels for temperature and 15 levels for moisture, and Atmospheric Water Vapor at 5x5 1-km resolution when at least 9 FOVs are cloud free.
Level 2 Thermal Anomalies/Fire 5-Min L2 1km...
This Level 2 data collection contains derived precipitable column water vapor amounts, during daytime using a near-infrared over clear land areas and above clouds over both land and ocean; water vapor estimates are provided over clear ocean areas.
This Level 2 data collection contains derived precipitable column water vapor amounts, during daytime using a near-infrared over clear land areas and above clouds over both land and ocean; water vapor estimates are provided over clear ocean areas.
This Level 2 data collection contains derived precipitable column water vapor amounts, during daytime using a near-infrared over clear land areas and above clouds over both land and ocean; water vapor estimates are provided over clear ocean areas.
MODIS/Terra Near Real Time (NRT) Global MODIS vegetation indices Products
MODIS/Terra Near Real Time (NRT) Global MODIS vegetation indices Products
MODIS/Terra Near Real Time (NRT) Global MODIS vegetation indices Products
MODIS/Terra Near Real Time (NRT) Global MODIS vegetation indices Products
MODIS/Terra and Aqua Combined NRT value-added Aerosol Optical Depth Produc
MODIS/Terra and Aqua Combined NRT value-added Aerosol Optical Depth Produc
MODIS/Terra and Aqua Combined NRT value-added Aerosol Optical Depth Produc
The Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument on the Global Change Observation Mission - Water 1 (GCOM-W1) provides global passive microwave measurements of terrestrial, oceanic, and atmospheric parameters for the investigation of global water and energy cycles.  Near real-time (NRT) products are generated within 3 hours of the last observations in the file, by the Land Atmosphere Near real-time Capability for EOS (LANCE) at the AMSR Science Investigator-led Processing System (AMSR SIPS), which is collocated with the Global Hydrology Resource Center (GHRC) DAAC.  The GCOM-W1 AMSR2 Level-3 Snow Water Equivalent (SWE) data set contains snow water equivalent (SWE) data and quality assurance flags mapped to Northern and Southern Hemisphere 25 km Equal-Area Scalable Earth Grids (EASE-Grids).  Data are stored in HDF-EOS5 format and are available via HTTP from the EOSDIS LANCE system at https://lance.nsstc.nasa.gov/amsr2-science/data/level3/daysnow/.  If data latency is not a primary concern, please consider using science quality products.  Science products are created using the best available ancillary, calibration and ephemeris information.  Science quality products are an internally consistent, well-calibrated record of the Earth's geophysical properties to support science.  The AMSR SIPS plans to start producing initial AMSR2 standard science quality data products in late 2015 and they will be available from the NSIDC DAAC.  Notice: All LANCE AMSR2 data should be used with the understanding that these are preliminary products.  Cross calibration with AMSR-E products has not been performed.  As updates are made to the L1R data set, those changes will be reflected in this higher level product.
The Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument on the Global Change Observation Mission - Water 1 (GCOM-W1) provides global passive microwave measurements of terrestrial, oceanic, and atmospheric parameters for the investigation of global water and energy cycles.  Near real-time (NRT) products are generated within 3 hours of the last observations in the file, by the Land Atmosphere Near real-time Capability for EOS (LANCE) at the AMSR Science Investigator-led Processing System (AMSR SIPS), which is collocated with the Global Hydrology Resource Center (GHRC) DAAC.  The GCOM-W1 AMSR2 Level-3 Snow Water Equivalent (SWE) data set contains snow water equivalent (SWE) data and quality assurance flags mapped to Northern and Southern Hemisphere 25 km Equal-Area Scalable Earth Grids (EASE-Grids).  Data are stored in HDF-EOS5 format and are available via HTTP from the EOSDIS LANCE system at https://lance.nsstc.nasa.gov/amsr2-science/data/level3/daysnow/.  If data latency is not a primary concern, please consider using science quality products.  Science products are created using the best available ancillary, calibration and ephemeris information.  Science quality products are an internally consistent, well-calibrated record of the Earth's geophysical properties to support science.  The AMSR SIPS plans to start producing initial AMSR2 standard science quality data products in late 2015 and they will be available from the NSIDC DAAC.  Notice: All LANCE AMSR2 data should be used with the understanding that these are preliminary products.  Cross calibration with AMSR-E products has not been performed.  As updates are made to the L1R data set, those changes will be reflected in this higher level product.
The Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument on the Global Change Observation Mission - Water 1 (GCOM-W1) provides global passive microwave measurements of terrestrial, oceanic, and atmospheric parameters for the investigation of global water and energy cycles.  Near real-time (NRT) products are generated within 3 hours of the last observations in the file, by the Land Atmosphere Near real-time Capability for EOS (LANCE) at the AMSR Science Investigator-led Processing System (AMSR SIPS), which is collocated with the Global Hydrology Resource Center (GHRC) DAAC.  The GCOM-W1 AMSR2 Level-3 Snow Water Equivalent (SWE) data set contains snow water equivalent (SWE) data and quality assurance flags mapped to Northern and Southern Hemisphere 25 km Equal-Area Scalable Earth Grids (EASE-Grids).  Data are stored in HDF-EOS5 format and are available via HTTP from the EOSDIS LANCE system at https://lance.nsstc.nasa.gov/amsr2-science/data/level3/daysnow/.  If data latency is not a primary concern, please consider using science quality products.  Science products are created using the best available ancillary, calibration and ephemeris information.  Science quality products are an internally consistent, well-calibrated record of the Earth's geophysical properties to support science.  The AMSR SIPS plans to start producing initial AMSR2 standard science quality data products in late 2015 and they will be available from the NSIDC DAAC.  Notice: All LANCE AMSR2 data should be used with the understanding that these are preliminary products.  Cross calibration with AMSR-E products has not been performed.  As updates are made to the L1R data set, those changes will be reflected in this higher level product.
The Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument on the Global Change Observation Mission - Water 1 (GCOM-W1) provides global passive microwave measurements of terrestrial, oceanic, and atmospheric parameters for the investigation of global water and energy cycles.  Near real-time (NRT) products are generated within 3 hours of the last observations in the file, by the Land Atmosphere Near real-time Capability for EOS (LANCE) at the AMSR Science Investigator-led Processing System (AMSR SIPS), which is collocated with the Global Hydrology Resource Center (GHRC) DAAC.  The GCOM-W1 AMSR2 Level-3 Snow Water Equivalent (SWE) data set contains snow water equivalent (SWE) data and quality assurance flags mapped to Northern and Southern Hemisphere 25 km Equal-Area Scalable Earth Grids (EASE-Grids).  Data are stored in HDF-EOS5 format and are available via HTTP from the EOSDIS LANCE system at https://lance.nsstc.nasa.gov/amsr2-science/data/level3/daysnow/.  If data latency is not a primary concern, please consider using science quality products.  Science products are created using the best available ancillary, calibration and ephemeris information.  Science quality products are an internally consistent, well-calibrated record of the Earth's geophysical properties to support science.  The AMSR SIPS plans to start producing initial AMSR2 standard science quality data products in late 2015 and they will be available from the NSIDC DAAC.  Notice: All LANCE AMSR2 data should be used with the understanding that these are preliminary products.  Cross calibration with AMSR-E products has not been performed.  As updates are made to the L1R data set, those changes will be reflected in this higher level product.
The Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument on the Global Change Observation Mission - Water 1 (GCOM-W1) provides global passive microwave measurements of terrestrial, oceanic, and atmospheric parameters for the investigation of global water and energy cycles.  Near real-time (NRT) products are generated within 3 hours of the last observations in the file, by the Land Atmosphere Near real-time Capability for EOS (LANCE) at the AMSR Science Investigator-led Processing System (AMSR SIPS), which is collocated with the Global Hydrology Resource Center (GHRC) DAAC.  The GCOM-W1 AMSR2 Level-3 Snow Water Equivalent (SWE) data set contains snow water equivalent (SWE) data and quality assurance flags mapped to Northern and Southern Hemisphere 25 km Equal-Area Scalable Earth Grids (EASE-Grids).  Data are stored in HDF-EOS5 format and are available via HTTP from the EOSDIS LANCE system at https://lance.nsstc.nasa.gov/amsr2-science/data/level3/daysnow/.  If data latency is not a primary concern, please consider using science quality products.  Science products are created using the best available ancillary, calibration and ephemeris information.  Science quality products are an internally consistent, well-calibrated record of the Earth's geophysical properties to support science.  The AMSR SIPS plans to start producing initial AMSR2 standard science quality data products in late 2015 and they will be available from the NSIDC DAAC.  Notice: All LANCE AMSR2 data should be used with the understanding that these are preliminary products.  Cross calibration with AMSR-E products has not been performed.  As updates are made to the L1R data set, those changes will be reflected in this higher level product.
The Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument on the Global Change Observation Mission - Water 1 (GCOM-W1) provides global passive microwave measurements of terrestrial, oceanic, and atmospheric parameters for the investigation of global water and energy cycles.  Near real-time (NRT) products are generated within 3 hours of the last observations in the file, by the Land Atmosphere Near real-time Capability for EOS (LANCE) at the AMSR Science Investigator-led Processing System (AMSR SIPS), which is collocated with the Global Hydrology Resource Center (GHRC) DAAC.  The GCOM-W1 AMSR2 Level-2B rain and ocean products include global precipitation and ocean parameters (not including Sea Surface Temperatures), calculated by the Goddard PROFiling algorithm (GPROF) 2010 version using as input the resampled brightness temperature (Level-1R) data provided by the Japanese Aerospace Exploration Agency (JAXA).  Data are stored in HDF-EOS5 format and are available via HTTP from the EOSDIS LANCE system at https://lance.nsstc.nasa.gov/amsr2-science/data/level2/rainocean/.  If data latency is not a primary concern, please consider using science quality products.  Science products are created using the best available ancillary, calibration and ephemeris information.  Science quality products are an internally consistent, well-calibrated record of the Earth's geophysical properties to support science.  The AMSR SIPS plans to start producing initial AMSR2 standard science quality data products in late 2015 and they will be available from the NSIDC DAAC.  Notice: All LANCE AMSR2 data should be used with the understanding that these are preliminary products.  Cross calibration with AMSR-E products has not been performed.  As updates are made to the L1R data set, those changes will be reflected in this higher level product.
The Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument on the Global Change Observation Mission - Water 1 (GCOM-W1) provides global passive microwave measurements of terrestrial, oceanic, and atmospheric parameters for the investigation of global water and energy cycles.  Near real-time (NRT) products are generated within 3 hours of the last observations in the file, by the Land Atmosphere Near real-time Capability for EOS (LANCE) at the AMSR Science Investigator-led Processing System (AMSR SIPS), which is collocated with the Global Hydrology Resource Center (GHRC) DAAC.  The GCOM-W1 AMSR2 Level-2B rain and ocean products include global precipitation and ocean parameters (not including Sea Surface Temperatures), calculated by the Goddard PROFiling algorithm (GPROF) 2010 version using as input the resampled brightness temperature (Level-1R) data provided by the Japanese Aerospace Exploration Agency (JAXA).  Data are stored in HDF-EOS5 format and are available via HTTP from the EOSDIS LANCE system at https://lance.nsstc.nasa.gov/amsr2-science/data/level2/rainocean/.  If data latency is not a primary concern, please consider using science quality products.  Science products are created using the best available ancillary, calibration and ephemeris information.  Science quality products are an internally consistent, well-calibrated record of the Earth's geophysical properties to support science.  The AMSR SIPS plans to start producing initial AMSR2 standard science quality data products in late 2015 and they will be available from the NSIDC DAAC.  Notice: All LANCE AMSR2 data should be used with the understanding that these are preliminary products.  Cross calibration with AMSR-E products has not been performed.  As updates are made to the L1R data set, those changes will be reflected in this higher level product.
The Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument on the Global Change Observation Mission - Water 1 (GCOM-W1) provides global passive microwave measurements of terrestrial, oceanic, and atmospheric parameters for the investigation of global water and energy cycles.  Near real-time (NRT) products are generated within 3 hours of the last observations in the file, by the Land Atmosphere Near real-time Capability for EOS (LANCE) at the AMSR Science Investigator-led Processing System (AMSR SIPS), which is collocated with the Global Hydrology Resource Center (GHRC) DAAC.  The GCOM-W1 AMSR2 Level-2B rain and ocean products include global precipitation and ocean parameters (not including Sea Surface Temperatures), calculated by the Goddard PROFiling algorithm (GPROF) 2010 version using as input the resampled brightness temperature (Level-1R) data provided by the Japanese Aerospace Exploration Agency (JAXA).  Data are stored in HDF-EOS5 format and are available via HTTP from the EOSDIS LANCE system at https://lance.nsstc.nasa.gov/amsr2-science/data/level2/rainocean/.  If data latency is not a primary concern, please consider using science quality products.  Science products are created using the best available ancillary, calibration and ephemeris information.  Science quality products are an internally consistent, well-calibrated record of the Earth's geophysical properties to support science.  The AMSR SIPS plans to start producing initial AMSR2 standard science quality data products in late 2015 and they will be available from the NSIDC DAAC.  Notice: All LANCE AMSR2 data should be used with the understanding that these are preliminary products.  Cross calibration with AMSR-E products has not been performed.  As updates are made to the L1R data set, those changes will be reflected in this higher level product.
The Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument on the Global Change Observation Mission - Water 1 (GCOM-W1) provides global passive microwave measurements of terrestrial, oceanic, and atmospheric parameters for the investigation of global water and energy cycles.  Near real-time (NRT) products are generated within 3 hours of the last observations in the file, by the Land Atmosphere Near real-time Capability for EOS (LANCE) at the AMSR Science Investigator-led Processing System (AMSR SIPS), which is collocated with the Global Hydrology Resource Center (GHRC) DAAC.  The GCOM-W1 AMSR2 Level-2B rain and ocean products include global precipitation and ocean parameters (not including Sea Surface Temperatures), calculated by the Goddard PROFiling algorithm (GPROF) 2010 version using as input the resampled brightness temperature (Level-1R) data provided by the Japanese Aerospace Exploration Agency (JAXA).  Data are stored in HDF-EOS5 format and are available via HTTP from the EOSDIS LANCE system at https://lance.nsstc.nasa.gov/amsr2-science/data/level2/rainocean/.  If data latency is not a primary concern, please consider using science quality products.  Science products are created using the best available ancillary, calibration and ephemeris information.  Science quality products are an internally consistent, well-calibrated record of the Earth's geophysical properties to support science.  The AMSR SIPS plans to start producing initial AMSR2 standard science quality data products in late 2015 and they will be available from the NSIDC DAAC.  Notice: All LANCE AMSR2 data should be used with the understanding that these are preliminary products.  Cross calibration with AMSR-E products has not been performed.  As updates are made to the L1R data set, those changes will be reflected in this higher level product.
The Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument on the Global Change Observation Mission - Water 1 (GCOM-W1) provides global passive microwave measurements of terrestrial, oceanic, and atmospheric parameters for the investigation of global water and energy cycles.  Near real-time (NRT) products are generated within 3 hours of the last observations in the file, by the Land Atmosphere Near real-time Capability for EOS (LANCE) at the AMSR Science Investigator-led Processing System (AMSR SIPS), which is collocated with the Global Hydrology Resource Center (GHRC) DAAC.  The GCOM-W1 AMSR2 Level-2B rain and ocean products include global precipitation and ocean parameters (not including Sea Surface Temperatures), calculated by the Goddard PROFiling algorithm (GPROF) 2010 version using as input the resampled brightness temperature (Level-1R) data provided by the Japanese Aerospace Exploration Agency (JAXA).  Data are stored in HDF-EOS5 format and are available via HTTP from the EOSDIS LANCE system at https://lance.nsstc.nasa.gov/amsr2-science/data/level2/rainocean/.  If data latency is not a primary concern, please consider using science quality products.  Science products are created using the best available ancillary, calibration and ephemeris information.  Science quality products are an internally consistent, well-calibrated record of the Earth's geophysical properties to support science.  The AMSR SIPS plans to start producing initial AMSR2 standard science quality data products in late 2015 and they will be available from the NSIDC DAAC.  Notice: All LANCE AMSR2 data should be used with the understanding that these are preliminary products.  Cross calibration with AMSR-E products has not been performed.  As updates are made to the L1R data set, those changes will be reflected in this higher level product.
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 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 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 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 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 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 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 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 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.