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 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 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 data set contains the results of 1 GHz ground-penetrating radar surveys conducted in Alaska, USA as part of the NASA SnowEx 2023 field campaign. The study site is a coastal tundra environment in the North Slope region of the Alaska coastal plain (Upper Kuparuk Toolik). Data were collected between 08 Mar 2023 to 15 Mar 2023, spatially coinciding with snow pit locations and along transects between snow pits. Data include two-way travel (TWT) time, calculated snow depth, and calculated snow water equivalent (SWE). Raw GPR data are available as SnowEx23 CRREL Ground Penetrating Radar Raw, Version 1.

This dataset contains ice concentration rankings of S-band images from the S-BAND marine navigation radar collected during the 2022 Salinity and Stratification at the Sea Ice Edge (SASSIE) campaign. SASSIE is a NASA experiment that aims to understand how salinity anomalies in the upper ocean generated by melting sea ice affect sea surface temperature (SST), stratification, and subsequent sea-ice growth. SASSIE involved a field campaign that sampled the transition from summer melt to autumn ice advance in the Beaufort Sea during August-October 2022, making intensive in situ and remote sensing observations within ~200km of the sea ice edge. Images from the radar were digitized and saved every 10-60 seconds on days that the R/V Wolstad was in or around ice. Ice concentration rankings ranging from 0 to 3 determined by VGG19 Machine Learning Model. L2 summary data are available in NetCDF format.

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 ITS_LIVE data set, part of the Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, consists of 240 m Antarctic Ice Sheet extent masks at roughly annual resolution from 1997 through 2021. The ice masks were generated by combining data acquired by multiple satellite-borne optical, thermal, and radar sensors. The ice thickness and velocity data used to determine the presence of ice are also provided.

This data set consists of 1 km resolution monthly land surface temperatures (MLSTs); mean annual ground temperatures (MAGTs); and estimates of permafrost extent (PE) in the High Mountain Asia region from 1 Jan 2003 – 31 Dec 2016.

The data were generated by gap-filling daily MODIS Terra/Aqua Land surface temperatures (LSTs) with downscaled Atmospheric Infra-Red Sounder (AIRS) skin surface temperatures.

The data set contains snow depth measurements from five study sites in Alaska, USA; data were collected during the March 2023 intensive observation period (IOP) as part of the NASA SnowEx 2023 field campaign. The study sites include three boreal forest sites in the Fairbanks region of central Alaska (the Bonanza Creek Experimental Forest, Caribou Poker Creek watershed, and Farmer’s Loop/Creamer’s Field) and two coastal tundra sites in the North Slope region (Arctic coastal plain and Upper Kuparuk Toolik). Snow depth measurements collected from the study sampling sites during the previous field season are available as SnowEx23 Mar22 IOP Snow Depth Measurements, Version 1.

The data set contains snow depth measurements from two regions of Alaska, USA collected during the March 2022 intensive observation period (IOP) as part of the NASA SnowEx 2023 field campaign. The study sites include three boreal forest sites in the Fairbanks region of central Alaska (the Bonanza Creek Experimental Forest, Caribou Poker Creek watershed, and Farmer’s Loop/Creamer’s Field) and a coastal tundra site in the North Slope region (Arctic coastal plain). Snow depth measurements collected from the study sampling sites during the subsequent field season are available as SnowEx23 Mar23 IOP Snow Depth Measurements, Version 1.