This data set provides an estimate of the vapor pressure deficit. It infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. The Spatial Statistical Data Fusion (SSDF) surface continental United States (CONUS) products, fuse data from the Atmospheric InfraRed Sounder (AIRS) instrument on the EOS-Aqua spacecraft with data from the Cross-track Infrared and Microwave Sounding Suite (CrIMSS) instruments on the Suomi-NPP spacecraft. The CrIMSS instrument suite consists of the Cross-track Infrared Sounder (CrIS) infrared sounder and the Advanced Technology Microwave Sounder (ATMS) microwave sounder. These are all daily products on a ¼ x ¼ degree latitude/longitude grid covering the continental United States (CONUS). The SSDF algorithm infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. Performing the data fusion of two (or more) remote sensing datasets that estimate the same physical state involves four major steps: (1) Filtering input data; (2) Matching the remote sensing datasets to an in situ dataset, taken as a truth estimate; (3) Using these matchups to characterize the input datasets via estimation of their bias and variance relative to the truth estimate; (4) Performing the spatial statistical data fusion. We note that SSDF can also be performed on a single remote sensing input dataset. The SSDF algorithm only ingests the bias-corrected estimates, their latitudes and longitudes, and their estimated variances; the algorithm is agnostic as to which dataset or datasets those estimates, latitudes, longitudes, and variances originated from.

The Spatial Statistical Data Fusion (SSDF) surface continental United States (CONUS) products, fuse data from the Atmospheric InfraRed Sounder (AIRS) instrument on the EOS-Aqua spacecraft with data from the Cross-track Infrared and Microwave Sounding Suite (CrIMSS) instruments on the Suomi-NPP spacecraft. The CrIMSS instrument suite consists of the Cross-track Infrared Sounder (CrIS) infrared sounder and the Advanced Technology Microwave Sounder (ATMS) microwave sounder. This data set provides an estimate of the vapor pressure deficit. It infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. These are all daily products on a ¼ x ¼ degree latitude/longitude grid covering the continental United States (CONUS). The SSDF algorithm infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. Performing the data fusion of two (or more) remote sensing datasets that estimate the same physical state involves four major steps: (1) Filtering input data; (2) Matching the remote sensing datasets to an in situ dataset, taken as a truth estimate; (3) Using these matchups to characterize the input datasets via estimation of their bias and variance relative to the truth estimate; (4) Performing the spatial statistical data fusion. We note that SSDF can also be performed on a single remote sensing input dataset. The SSDF algorithm only ingests the bias-corrected estimates, their latitudes and longitudes, and their estimated variances; the algorithm is agnostic as to which dataset or datasets those estimates, latitudes, longitudes, and variances originated from.

This data set provides an estimate of the surface air temperature. It infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. The Spatial Statistical Data Fusion (SSDF) surface continental United States (CONUS) products, fuse data from the Atmospheric InfraRed Sounder (AIRS) instrument on the EOS-Aqua spacecraft with data from the Cross-track Infrared and Microwave Sounding Suite (CrIMSS) instruments on the Suomi-NPP spacecraft. The CrIMSS instrument suite consists of the Cross-track Infrared Sounder (CrIS) infrared sounder and the Advanced Technology Microwave Sounder (ATMS) microwave sounder. It infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. These are all daily products on a ¼ x ¼ degree latitude/longitude grid covering the continental United States (CONUS). The SSDF algorithm infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. Performing the data fusion of two (or more) remote sensing datasets that estimate the same physical state involves four major steps: (1) Filtering input data; (2) Matching the remote sensing datasets to an in situ dataset, taken as a truth estimate; (3) Using these matchups to characterize the input datasets via estimation of their bias and variance relative to the truth estimate; (4) Performing the spatial statistical data fusion. We note that SSDF can also be performed on a single remote sensing input dataset. The SSDF algorithm only ingests the bias-corrected estimates, their latitudes and longitudes, and their estimated variances; the algorithm is agnostic as to which dataset or datasets those estimates, latitudes, longitudes, and variances originated from.

This data set provides an estimate of the surface air temperature. It infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. The Spatial Statistical Data Fusion (SSDF) surface continental United States (CONUS) products, fuse data from the Atmospheric InfraRed Sounder (AIRS) instrument on the EOS-Aqua spacecraft with data from the Cross-track Infrared and Microwave Sounding Suite (CrIMSS) instruments on the Suomi-NPP spacecraft. The CrIMSS instrument suite consists of the Cross-track Infrared Sounder (CrIS) infrared sounder and the Advanced Technology Microwave Sounder (ATMS) microwave sounder. These are all daily products on a ¼ x ¼ degree latitude/longitude grid covering the continental United States (CONUS). The SSDF algorithm infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. Performing the data fusion of two (or more) remote sensing datasets that estimate the same physical state involves four major steps: (1) Filtering input data; (2) Matching the remote sensing datasets to an in situ dataset, taken as a truth estimate; (3) Using these matchups to characterize the input datasets via estimation of their bias and variance relative to the truth estimate; (4) Performing the spatial statistical data fusion. We note that SSDF can also be performed on a single remote sensing input dataset. The SSDF algorithm only ingests the bias-corrected estimates, their latitudes and longitudes, and their estimated variances; the algorithm is agnostic as to which dataset or datasets those estimates, latitudes, longitudes, and variances originated from.

This dataset includes Aqua AIRS to MODIS 1-km collocation index product, within the framework of the Multidecadal Satellite Record of Water Vapor, Temperature, and Clouds (PI: Eric Fetzer) funded by NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, 2017. The dataset is built upon work by Wang et al. (doi: 10.3390/rs8010076) and Yue (doi:10.5194/amt-15-2099-2022). The short name for this collections is Aqua_AIRS_MODIS1km_IND

This dataset is part of MEaSUREs 2012 Program, and represent Aqua/AIRS-Aura/MLS collocation indexes, in netCDF-4 format. These data map AIRS profile indexes to those of MLS. The A-Train provides water vapor (H2O) retrievals from both the Atmospheric Infrared Sounder (AIRS) and Microwave Limb Sounder (MLS). While AIRS loses sensitivity to H2O at the elevated portions of the upper troposphere (UT), MLS cannot detect H2O below 316 hPa. Therefore, to obtain a full profile of H2O in the whole column of air, this dataset manages to join the two products together by utilizing their own averaging kernels (AK). In doing so, the dataset builds a solid H2O of the whole column of air, which will help understand the H2O budget and many processes governing the humidity around the upper troposphere and lower stratosphere (UTLS). The short name for this collections is AIRS_MLS_IND

The TROPESS AIRS-Aqua L2 Deuterated Water Vapor for Reanalysis Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of semi-heavy water (HDO), and formal uncertainties measured by the AIRS instrument on the EOS Aqua satellite. The reanalysis stream summary product is global for the time period from 2002-09-01 to 2020-03-31. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 13.5 km (AIRS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.

The TROPESS CrIS-SNPP L2 Deuterated Water Vapor for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of semi-heavy water (HDO), and formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream standard product is global for the time period from 2021-02-01 to 2021-05-21, when the CrIS-SNPP processing was discontinued. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.

The TROPESS CrIS L2 Deuterated Water Vapor for Reanalysis Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of semi-heavy water (HDO), and formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. The reanalysis stream summary product is global for the time period from 2015-12-01 to 2021-05-21, when the CrIS-SNPP processing was discontinued for HDO. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.

This dataset includes SNPP VIIRS-CrIS collocation index product, within the framework of the Multidecadal Satellite Record of Water Vapor, Temperature, and Clouds (PI: Eric Fetzer) funded by NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, 2017. The dataset is built upon work by Wang et al. (doi: 10.3390/rs8010076) and Yue (doi:10.5194/amt-15-2099-2022). The short name for this collections is SNPP_CrIS_VIIRS750m_IND