Glacial isostatic adjustment (GIA) is an ongoing geophysical process and is measured by gravimetry satellites like GRACE and GRACE-FO. To isolate signals of contemporary surface mass loss in the cumulative satellite gravimetry measurements, contemporary GIA rates are computed and subtracted from the satellite gravimetry observations. The GIA correction models provided here are filtered such that they are compatible with Level-3 post-processing filters applied to GRACE(-FO) data as indicated in the [product_id]. In this way, user can effectively assess the impact of the applied GIA correction, and substitute different GIA models should that be desired. This GIA dataset is mapped into 1.0-degree global grid in netCDF format.

Glacial isostatic adjustment (GIA) is an ongoing geophysical process and is measured by gravimetry satellites like GRACE and GRACE-FO. To isolate signals of contemporary surface mass loss in the cumulative satellite gravimetry measurements, contemporary GIA rates are computed and subtracted from the satellite gravimetry observations. The GIA correction models provided here are filtered such that they are compatible with Level-3 post-processing filters applied to GRACE(-FO) data as indicated in the [product_id]. In this way, user can effectively assess the impact of the applied GIA correction, and substitute different GIA models should that be desired. This GIA dataset is mapped into 0.5-degree global grid compatible with the JPL Mascon solution, provided in netCDF format.

Glacial isostatic adjustment (GIA) is an ongoing geophysical process and is measured by gravimetry satellites like GRACE and GRACE-FO. To isolate signals of contemporary surface mass loss in the cumulative satellite gravimetry measurements, contemporary GIA rates are computed and subtracted from the satellite gravimetry observations. The GIA correction models provided here are filtered such that they are compatible with Level-3 post-processing filters applied to GRACE(-FO) data as indicated in the [product_id]. In this way, user can effectively assess the impact of the applied GIA correction, and substitute different GIA models should that be desired. This GIA dataset is mapped into 1.0-degree global grid in netCDF format.

Data pertain to analysis of GRACE time series of mass change in both gridded and basin format. The GRACE data are based on the COST-G solution obtained from http://gravis.gfz-potsdam.de/home. Both 50km regular gridded data and basin-level time series are included, as well as the SAM and ENSO climate indices on which the regressions were based. The gridded datasets provided here include derived quantities from the regressions such as trends, uncertainties, and regression coefficients in NetCDF format.

This dataset is a time series of mass variability averaged over all of Antarctica. It provides the ice mass changes of Antarctica over time. The mass variability are derived from JPL GRACE Mascon Ocean, Ice, and Hydrology Equivalent Water Height CRI Filtered RL063Mv04 dataset, which can be found at https://doi.org/10.5067/TEMSC-3JC634. A more 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. The mass variability are provided as an ASCII table.

This dataset contains 50m horizontal resolution gridded digital elevation models (DEMs) of Greenland Ice Sheet outlet glaciers collected during the NASA Oceans Melting Greenland mission. Between 2016 and 2019 the GLacier and Land Ice Surface Topography Interferometer airborne (GLISTIN-A) radar measured surface elevations around the periphery of the Greenland Ice Sheet using Ka-Band (8.4 mm wavelength) single-pass interferometry. Level 2 (L2) GLISTIN-A elevation data, available on the JPL UAVSAR website (uavsar.jpl.nasa.gov), were collected each year in 81 swaths of varying lengths and 10-12km widths and then mapped to 3m horizontal grids. This Level 3 (L3) dataset was created to facilitate analysis of the year-to-year glacier surface elevation changes. Improvements over the L2 dataset include: a consistent swath numbering scheme (1 to 81) corresponding to repeated flight lines; common regular equal-area grids for each swath; filtering and flagging of outliers; an ancillary geoid layer; and UTM map projections corresponding to swath location. The interested user may generate their own L3 DEMs at different horizontal resolutions and projections using the Python 3 resample_GLISTIN_DEMs package available which will be available from https://github.com/NASA/resample_GLISTIN_DEMs

This dataset contains 50m horizontal resolution gridded digital elevation models (DEMs) of Greenland Ice Sheet outlet glaciers collected during the NASA Oceans Melting Greenland mission. Between 2016 and 2019 the GLacier and Land Ice Surface Topography Interferometer airborne (GLISTIN-A) radar measured surface elevations around the periphery of the Greenland Ice Sheet using Ka-Band (8.4 mm wavelength) single-pass interferometry. Level 2 (L2) GLISTIN-A elevation data, available on the JPL UAVSAR website (uavsar.jpl.nasa.gov), were collected each year in 81 swaths of varying lengths and 10-12km widths and then mapped to 3m horizontal grids. This Level 3 (L3) dataset was created to facilitate analysis of the year-to-year glacier surface elevation changes. Improvements over the L2 dataset include: a consistent swath numbering scheme (1 to 81) corresponding to repeated flight lines; common regular equal-area grids for each swath; filtering and flagging of outliers; an ancillary geoid layer; and UTM map projections corresponding to swath location. The interested user may generate their own L3 DEMs at different horizontal resolutions and projections using the Python 3 resample_GLISTIN_DEMs package available which will be available from https://github.com/NASA/resample_GLISTIN_DEMs

This dataset is a time series of mass variability averaged over all of the global ocean. It provides the non-steric or mass only sea level changes over time. The mass variability are derived from JPL GRACE Mascon Ocean, Ice, and Hydrology Equivalent Water Height CRI Filtered RL063Mv04 dataset, which can be found at https://podaac.jpl.nasa.gov/dataset/TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06.3_V4. A more 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. The mass variability are provided as an ASCII table.

FOR EXPERT USE ONLY. This dataset contains estimates of geopotential field derived from the Gravity Recovery and Climate Experiment (GRACE) mission measurements and a non-tidal atmospheric model produced by the German Research Centre for Geosciences (GFZ). The data are in spherical harmonics averaged over approximately a month. The primary objective of the GRACE mission is to obtain accurate estimates of the mean and time-variable components of the gravity field variations. This objective is achieved by making continuous measurements of the change in distance between twin spacecraft, co-orbiting in about 500 km altitude, near circular, polar orbit, spaced approximately 200 km apart, using a microwave ranging system. In addition to these range change, the non-gravitional forces are measured on each satellite using a high accuracy electrostatic, room-temperature accelerometer. The satellite orientation and position (and timing) are precisely measured using twin star cameras and a GPS receiver, respectively. Spatial and temporal variations in the gravity field affect the orbits (or trajectories) of the twin spacecraft differently. These differences are manifested as changes in the distance between the spacecraft, as they orbit the Earth. This change in distance is reflected in the time-of-flight of microwave signals transmitted and received nearly simultaneously between the two spacecraft. The change in this time of fight is continuously measured by tracking the phase of the microwave carrier signals. The so called dual-one-way range change measurements can be reconstructed from these phase measurements. This range change (or its numerically derived derivatives), along with other mission and ancillary data, is subsequently analyzed to extract the parameters of an Earth gravity field model.

This dataset is a time series of mass variability averaged over all of the global ocean. It provides the non-steric or mass only sea level changes over time. The mass variability are derived from JPL GRACE Mascon Ocean, Ice, and Hydrology Equivalent Water Height CRI Filtered RL06.3Mv04 dataset, which can be found at https://doi.org/10.5067/TEMSC-3JC634. A more 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. The mass variability is provided as an ASCII table.