This data set contains a high-resolution (100 m) gridded digital elevation model (DEM) for the Antarctic ice sheet and regions around the Arctic. The data can be used to initialize ice sheet models, as boundary conditions for atmospheric models, or to help with the reduction of other satellite data such as optical imagery or synthetic aperture radar (SAR). The data are derived from the ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series product (ATL11).

This data set contains land ice height changes and change rates for the Antarctic ice sheet and regions around the Arctic gridded at four spatial resolutions (1 km, 10 km, 20 km, and 40 km). The data are derived from the ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series product (ATL11).

This dataset contains gridded monthly global water storage/height anomalies relative to a time-mean, derived from GRACE and GRACE-FO and processed at JPL using the Mascon approach (RL06.3Mv04). A Coastal Resolution Improvement (CRI) filter has been applied to this data set to reduce signal leakage errors across coastlines. For most land hydrology, oceanographic as well as land-ice applications this is the recommend data set for the analysis of surface mass changes. The data are provided in a single data file in netCDF format, with water storage/height anomalies in equivalent water thickness units (cm). The data are derived from solving for monthly gravity field variations on geolocated spherical cap mass concentration functions, rather than global spherical harmonic coefficients. Additionally, realistic geophysical information is introduced during the computation to intrinsically remove correlated errors. Thus, these Mascon grids do not need to be de-correlated or smoothed, like traditional spherical harmonic gravity solutions.



The complete Mascon solution consists of 4,551 independent estimates of surface mass change that have been derived using an equal-area 3-degree grid of individual mascons. A subset of these individual mascons span coastlines, and contain mixed land and ocean mass change signals. In a post-processing step, the CRI filter is applied to those mixed land/ocean Mascons to separate land and ocean mass. The land mask used to perform this separation is provided in the same directory as this dataset, as are uncertainty values, and the gridded mascon-ID number to enable further analysis. Since the individual mascons act as an inherent smoother on the gravity field, a set of optional gain factors (for continental hydrology applications) that can be applied to the solution to study mass change signals at sub-mascon resolution is also provided within the same data directory as the Mascon data. For use-case examples and further background on the gain factors, please see Wiese, Landerer & Watkins, 2016, https://doi.org/10.1002/2016WR019344.



This RL06.3Mv04 is an updated version of the previous Tellus JPL Mascon RL06.1Mv03 (DOI, 10.5067/TEMSC-3JC63). For a detailed description on the Mascon solution, including the mathematical derivation, implementation of geophysical constraints, and solution validation, please see Watkins et al., 2015, doi: 10.1002/2014JB011547. For a detailed description of the CRI filter implementation, please see Wiese et al., 2016, doi:10.1002/2016WR019344.

This dataset contains gridded monthly global water storage/height anomalies relative to a time-mean, derived from GRACE and GRACE-FO and processed at JPL using the Mascon approach (RL06.3Mv04). These data are provided in a single data file in netCDF format, and can be used for analysis for ocean, ice, and hydrology phenomena. The data are provided in a single data file in netCDF format, with water storage/height anomalies in equivalent water thickness units (cm). The data are derived from solving for monthly gravity field variations on geolocated spherical cap mass concentration functions, rather than global spherical harmonic coefficients. Additionally, realistic geophysical information is introduced during the computation to intrinsically remove correlated errors. Thus, these Mascon grids do not need to be de-correlated or smoothed, like traditional spherical harmonic gravity solutions.



The complete Mascon solution consists of 4,551 independent estimates of surface mass change that have been derived using an equal-area 3-degree grid of individual mascons. Please note that this dataset does not correct for leakage errors across coastlines; it is therefore recommended only for users who want to apply their own algorithm to separate between land and ocean mass very near coastlines.



This RL06.3Mv04 is an updated version of the previous Tellus JPL Mascon RL06.1Mv03. For more information, please visit https://grace.jpl.nasa.gov/data/get-data/jpl_global_mascons/. For a detailed description on the Mascon processing, including the mathematical derivation, implementation of geophysical constraints, and validation, please see Watkins et al., 2015, doi: 10.1002/2014JB011547. This product is intended for expert use only; other users are encouraged to use the CRI-filtered Mascon dataset, which is available here: https://podaac.jpl.nasa.gov/dataset/TELLUS_GRAC-GRFO_MASCON_GRID_RL06.3_V4.

This data set provides spatial distributions of fast ice and glacial ice in eight fjords spanning the Southeast Greenland coast: Nansen, Kangerlusruaq, Ikertivaq, Skjoldungen, Tingmiarmiut, Napasorsvaq, Anoritup, and Kangerlluluk. Temporal coverage is discontinuous, depending on the availability and quality of images.

Fjord data were sourced from USGS EarthExplorer, Copernicus Open Access Hub, and the NSIDC. Landsat-8 and MODIS imagery for ice identification were collected from NASA Worldview and USGS EarthExplorer. Fjord, fast ice, and glacial ice boundaries were manually delineated using ArcGIS. Glacial ice was further categorized as dense glacial melange (Type 3), substantial glacial ice with large icebergs (Type 2), low-density glacial ice with large icebergs (Type 1), consistent small ice surface without large icebergs (Type 0), or glacier surface (Type 99).

This ITS_LIVE data set, part of the Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, includes quarterly estimates of Antarctic ice shelf surface elevation, thickness, basal melt rate, surface mass balance, firn air content, and associated errors, from 17 March 1992 through 16 December 2017 at 1920 m resolution.

The data were generated from four European Space Agency (ESA) satellite radar altimetry missions—ERS-1, ERS-2, Envisat, and CryoSat-2—using a novel data fusion approach and the Glacier Energy and Mass Balance model (GEMB).

This data set contains a high-resolution (100 m) gridded digital elevation model (DEM) for the Antarctic ice sheet and regions around the Arctic. The data can be used to initialize ice sheet models, as boundary conditions for atmospheric models, or to help with the reduction of other satellite data such as optical imagery or synthetic aperture radar (SAR). The data are derived from the ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series product (ATL11).

This data set contains land ice height changes and change rates for the Antarctic ice sheet and regions around the Arctic gridded at four spatial resolutions (1 km, 10 km, 20 km, and 40 km). The data are derived from the ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series product (ATL11).

The data set is a time-series of snow pit measurements obtained by the SnowEx community during the 2020 campaign. Between October 2019 and May 2020, data were collected from 454 snow pits at 12 regional locations throughout California, Colorado, Idaho, New Mexico, and Utah, USA. At each of the locations, between 1 and 11 sites covering a range of conditions (terrains, snow depths, etc.) were chosen for weekly snow pit observations. Also available are photos of the field notes and snow pit sites.

The data set is a time-series of snow pit measurements obtained by the SnowEx community during the 2021 field campaign. etween November 2020 and May 2021 data from 247 snow pits were collected at 24 unique sites distributed over 4 states (CO, ID, MT, UT) throughout the Western United States. Five of the unique sites had a single visit to establish baseline conditions, while the remaining 19 sites had 3 or more repeat visits throughout the season, with a median visit count of 11.5. On a weekly interval, a snow pit was dug approximately 1 m away from the previous week’s snow pit. Available measured parameters are: snow depth, snow temperature, snow density, stratigraphy, grain size, manual wetness, liquid water content (LWC), and snow water equivalent (SWE). Also available are photos of the field notes and snow pit sites.