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://podaac.jpl.nasa.gov/dataset/TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06.3_V3. 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.

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 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.

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 RL06.1Mv04 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 is provided as an ASCII table.

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 RL06.1Mv04 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.

This data set contains the monthly Global Ocean Mass Anomalies (goma) since 04/2002, as measured by the GRACE and GRACE Follow-On (G/GFO) satellite missions. The data are averaged over the global ocean domain, at monthly intervals (note: data gaps exist). This file contains the goma time series based on the spherical harmonic gravity fields provided by the G/GFO SDS centers: JPL, CSR, GFZ. The data are frequently updated as new monthly observations are acquired by the GFO mission. The processing of the spherical harmonics gravity field coefficients is as follows: (1) GAD + GSM: the monthly de-aliasing product GAD is added back to the GSM L2 gravity fields; (2) [GSM + GAD] coefficients are averaged over the global ocean with a coastal buffer of 300 km (to avoid land-ocean leakage); (3) the spatial mean of atmospheric loading of the entire global ocean domain is removed (via the GAA L2 data product). A GIA correction using the ICE-6GD model (Peltier et al., 2018) is applied.

This data set contains the monthly Global Ocean Mass Anomalies (goma) since 04/2002, as measured by the GRACE and GRACE Follow-On (G/GFO) satellite missions. The data are averaged over the global ocean domain, at monthly intervals (note: data gaps exist). This file contains the goma time series based on the spherical harmonic gravity fields provided by the G/GFO SDS centers: JPL, CSR, GFZ. The data are frequently updated as new monthly observations are acquired by the GFO mission. The processing of the spherical harmonics gravity field coefficients is as follows: (1) GAD + GSM: the monthly de-aliasing product GAD is added back to the GSM L2 gravity fields; (2) [GSM + GAD] coefficients are averaged over the global ocean with a coastal buffer of 300 km (to avoid land-ocean leakage); (3) the spatial mean of atmospheric loading of the entire global ocean domain is removed (via the GAA L2 data product). A GIA correction using the ICE-6GD model (Peltier et al., 2018) is applied.

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.

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 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).