This global Level-3 data set provides the percentage of snow-covered land and cloud-covered land observed daily, within 0.05° (approx. 5 km at the equator) MODIS/VIIRS Climate Modeling Grid (CMG) cells. Percentages are computed from snow cover observations in the 'VIIRS/JPSS1 Snow Cover Daily L3 Global 375m SIN Grid' data set (DOI:10.5067/UAJGR7WVWDDI).

This data set contains daily snow cover derived from radiance data acquired by the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Joint Polar Satellite System's first satellite (JPSS-1). The data is a gridded composite, generated from 6 minute swaths, and projected to a 375 m Sinusoidal grid. Snow cover is identified using the Normalized Difference Snow Index (NDSI) and a series of screens designed to alleviate errors and flag uncertain snow cover detections.

This data set contains daily 'cloud-free' snow cover produced from the VIIRS/JPSS-1 Snow Cover Daily L3 Global 375m SIN Grid, Version 2 snow cover product. A cloud-gap-filled algorithm is utilized to replace ‘cloud-covered’ pixels with ‘cloud-free pixels’ for the purpose of estimating the snow cover that may exist under current cloud cover. The data are provided daily and mapped to a 375 m sinusoidal grid.

This data set characterizes snow microstructure for 6 snow pits from the SnowEx 2020 Grand Mesa Intensive Observation Period (February 2020) using microcomputed tomography (micro-CT). Included with this data set are two- and three-dimensional microstructural analysis of DMP/DEP casted and un-casted snow samples, available as .xlsx and .txt file, and visual representations of the three-dimensional snow structure, available is .bmp image files. Containers of snow were collected at 6 snow pits in approximately 17 cm intervals. There were approximately 5-8 discrete containers per pit and each container had an ~2 cm overlap with the sample below. A 6-cm section of snow was dissected from each container of snow, which were analyzed in ~2 cm sub-sections. Each sub-section was scanned in three intervals using a micro-CT instrument. The three interval scans comprise multiple slices, and were combined into the reconstructed final scan used for calculating the snow microstructural data.

This data set includes: (1) fine-scale snow and land cover maps from two mountainous study sites in the Western U.S., produced using machine-learning models trained to extract land cover data from WorldView-2 and WorldView-3 stereo panchromatic and multispectral images; (2) binary snow maps derived from the land cover maps; and (3) 30 m and 465 m fractional snow-covered area (fSCA) maps, produced via downsampling of the binary snow maps. The land cover classification maps feature between three and six classes common to mountainous regions and integral for accurate stereo snow depth mapping: illuminated snow, shaded snow, vegetation, exposed surfaces, surface water, and clouds. Also included are Landsat and MODSCAG fSCA map products. The source imagery for these data are the Maxar WorldView-2 and Maxar WorldView-3 Level-1B 8-band multispectral images, orthorectified and converted to top-of-atmosphere reflectance. These Level-1B images are available under the NGA NextView/EnhancedView license.

The Primary Ancillary Data Geo-Colocated to OMI/Aura VIS 1-Orbit L2 Swath 13x24km (OMVANC) provides selected parameters from GEOS-5 Forward Processing for Instrument Teams (FP-IT) assimilated product produced by the Global Modeling and Assimilation Office (GMAO) co-located in space and time with the OMI UV-2 swath.

The fields in this product include snow cover, sea ice cover, land cover, terrain height, row anomaly flag, and pixel area. The OMI team also provides a corresponding product for the OMI UV2 swath, OMUANC. This product has been generated for convenient use by the OMI/Aura team in their L2 algorithms, and for research where those L2 products are used. The original GEOS-5 FP-IT data are reported on a 0.625 deg longitude by 0.5 deg latitude grid, whereas the OMI UV-2 spatial resolution is 13km x 24km at nadir.

The OMVANC files are in netCDF4 format which is compatible with most netCDF and HDF5 readers and tools. Each file is approximately 45mb in size. The lead for this product is Zachary Fasnacht of SSAI. Joanna Joiner is the responsible NASA official.

The Primary Ancillary Data Geo-Colocated to OMI/Aura UV2 1-Orbit L2 Swath 13x24km (OMUANC) provides selected parameters from GEOS-5 Forward Processing for Instrument Teams (FP-IT) assimilated product produced by the Global Modeling and Assimilation Office (GMAO) co-located in space and time with the OMI UV-2 swath.

The fields in this product include snow cover, sea ice cover, land cover, terrain height, row anomaly flag, and pixel area. The OMI team also provides a corresponding product for the OMI VIS swath, OMVANC. This product has been generated for convenient use by the OMI/Aura team in their L2 algorithms, and for research where those L2 products are used. The original GEOS-5 FP-IT data are reported on a 0.625 deg longitude by 0.5 deg latitude grid, whereas the OMI UV-2 spatial resolution is 13km x 24km at nadir.

The OMUANC files are in netCDF4 format which is compatible with most netCDF and HDF5 readers and tools. Each file is approximately 45mb in size. The lead for this product is Zachary Fasnacht of SSAI. Joanna Joiner is the responsible NASA official.

The U.S. Climate Risk Projections by County, 2040-2049 data set contains a projection for 2040-2049 risk for the entire contiguous U.S. at the county level with a novel climate risk index integrating multiple hazards, exposures and vulnerabilities. Multiple hazards such as weather and climate are characterized as a frequency of heat wave, cold spells, drought, and heavy precipitation events along with anomalies of temperature and precipitation using high resolution (4 km) downscaled climate projections. Exposure is characterized by projections of population, infrastructure, and built surfaces prone to multiple hazards including sea level rise and storm surges. Vulnerability is characterized by projections of demographic groups most sensitive to climate hazards. This approach can guide planners in targeting counties at most risk and where adaptation strategies to reduce exposure or protect vulnerable populations might be best applied.

This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, contains monthly ice velocity mosaics for the Greenland Ice Sheet. The data are derived from Synthetic Aperture Radar (SAR) data, obtained by TerraSAR-X/TanDEM-X and Sentinel-1A and -1B, and from optical imagery acquired by Landsat 8 and Landsat 9.

See Greenland Ice sheet Mapping Project (GrIMP) for related data.

This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, contains quarterly (three-month interval) ice velocity mosaics for the Greenland Ice Sheet. This data set is derived from Synthetic Aperture Radar (SAR) data, obtained by TerraSAR-X/TanDEM-X and Sentinel-1A and -1B, and from optical imagery acquired by Landsat 8 and Landsat 9.

See Greenland Ice sheet Mapping Project (GrIMP) for related data.