Since June, 2018, the Gravity Recovery and Climate Experiment Follow‐On (GRACE‐FO) is extending the 15‐year monthly mass change record of the GRACE mission, which ended in June 2017. The GRACE‐FO instrument and flight system performance has improved over GRACE. Better attitude solutions and enhanced pointing performance result in reduced fuel consumption and gravity range rate post‐fit residuals. One accelerometer requires additional calibrations due to unexpected measurement noise. The GRACE‐FO gravity and mass change fields from June 2018 through December 2019 continue the GRACE record at an equivalent precision and spatiotemporal sampling. During this period, GRACE‐FO observed large interannual terrestrial water variations associated with excess rainfall (Central US, Middle East), drought (Europe, Australia), and ice melt (Greenland). These observations are consistent with independent mass change estimates, providing high confidence that no intermission biases exist from GRACE to GRACE‐FO, despite the 11‐month gap. GRACE‐FO has also successfully demonstrated satellite‐to‐satellite laser ranging interferometry.

Time-variable gravity field models derived from observations of the Gravity Recovery and Climate Experiment (GRACE) mission, whose science operations phase ended in June 2017 after more than 15 years, enabled a multitude of studies of Earth’s surface mass transport processes and climate change. The German Research Centre for Geosciences (GFZ), routinely processing such monthly gravity fields as part of the GRACE Science Data System, has reprocessed the complete GRACE mission and released an improved GFZ GRACE RL06 monthly gravity field time series. This study provides an insight into the processing strategy of GFZ RL06 which has been considerably changed with respect to previous GFZ GRACE releases, and modifications relative to the precursor GFZ RL05a are described. The quality of the RL06 gravity field models is analyzed and discussed both in the spectral and spatial domain in comparison to the RL05a time series. All results indicate significant improvements of about 40% in terms of reduced noise. It is also shown that the GFZ RL06 time series is a step forward in terms of consistency, and that errors of the gravity field coefficients are more realistic. These findings are confirmed as well by independent validation of the monthly GRACE models, as done in this work by means of ocean bottom pressure in situ observations and orbit tests with the GOCE satellite. Thus, the GFZ GRACE RL06 time series allows for a better quantification of mass changes in the Earth system.

The application of terrestrial water storage (TWS) data observed with GRACE and GRACE‐FO often requires realistic uncertainties. For gridded TWS data, this requires the knowledge of the covariances, which can be derived from the formal, i.e., formally estimated in the parameter estimation, variance‐covariance matrix provided together with the Stokes coefficients. However, the propagation of monthly variance‐covariance matrices to TWS data is computationally expensive, so we apply a spatial covariance model for TWS data. The covariance model provides non‐homogeneous (location depending), non‐stationary (time depending), and anisotropic (orientation depending) covariances between any two given points. Further, the model accommodates wave‐like behavior of East‐West‐directed covariances, which residuals of GRACE striping errors can cause. The main application of such spatial covariances is the estimation of uncertainties for mean TWS time series for arbitrary regions such as river basins. Alternatively, regional uncertainties can be derived from the above mentioned formal variance‐covariance matrices of the Stokes coefficients. This study compares modeled basin uncertainties for GFZ RL06 and ITSG‐Grace2018 TWS data with the formal basin uncertainties from the ITSG‐Grace 2018 solution. The modeled and formal uncertainties fit both in the spatial and temporal domain. We further evaluate the modeled uncertainties by comparison to empirical uncertainties over arid regions. Here, again the appropriateness of the modeled uncertainties is shown. The results, namely the TWS uncertainties for global river basins, are available via the GravIS portal. Further, we provide a Python toolbox, which allows computing uncertainties and covariance matrices.

Accurately quantifying global mass changes at the Earth's surface is essential for understanding climate system dynamics and their evolution. Satellite gravimetry, as realized with the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) missions, is the only currently operative remote sensing technique that can track large-scale mass variations, making it a unique monitoring opportunity for various geoscientific disciplines. To facilitate easy accessibility of GRACE and GRACE-FO (GRACE/-FO in the following) results (also beyond the geodetic community), the Helmholtz Centre for Geosciences (GFZ) developed the Gravity Information Service (GravIS) portal (https://gravis.gfz.de, last access: 21 January 2025). This work aims to introduce the user-friendly mass anomaly products provided at GravIS that are specifically processed for hydrology, glaciology, and oceanography applications. These mass change data, available in both a gridded representation and as time series for predefined regions, are routinely updated when new monthly GRACE/-FO gravity field models become available. The associated GravIS web portal visualizes and describes the products, demonstrating their usefulness for various studies and applications in the geosciences. Together with GFZ's complementary information portal https://www.globalwaterstorage.info/ (last access: 21 January 2025), GravIS supports widening the dissemination of knowledge about satellite gravimetry in science and society and highlights the significance and contributions of the GRACE/-FO missions for understanding changes in the climate system. The GravIS products, divided into several data sets corresponding to their specific application, are available at https://doi.org/10.5880/GFZ.GRAVIS_06_L2B (Dahle and Murböck, 2019), https://doi.org/10.5880/COST-G.GRAVIS_01_L2B (Dahle and Murböck, 2020), https://doi.org/10.5880/GFZ.GRAVIS_06_L3_ICE (Sasgen et al., 2019), https://doi.org/10.5880/COST-G.GRAVIS.5880/GFZ.GRAVIS_01_L3_ICE (Sasgen et al., 2020), https://doi.org/10.5880/GFZ.GRAVIS_06_L3_TWS (Boergens et al., 2019), https://doi.org/10.5880/COST-G.GRAVIS_01_L3_TWS (Boergens et al., 2020a), https://doi.org/10.5880/GFZ.GRAVIS_06_L3_OBP (Dobslaw et al., 2019), and https://doi.org/10.5880/COST-G.GRAVIS_01_L3_OBP (Dobslaw et al., 2020a).

Gridded terrestrial water storage (TWS) variations observed by GRACE or GRACE-FO typically show a spatial correlation structure that is both anisotropic (direction-dependent) and non-homogeneous (latitude-dependent). We introduce a new correlation model to represent this structure. This correlation model allows GRACE and GRACE-FO data users to get realistic correlations of the TWS grids without the need to derive them from the formal spherical harmonic uncertainties. Further, we found that the modelled correlations fit the spatial structure of uncertainties to a greater extent in a simulation environment. The model is based on a direction-dependent Bessel function of the first kind which allows to model the longer correlation lengths in the longitudinal direction via a shape parameter, and also to account for residual GRACE striping errors that might remain after spatial filtering. The global scale and shape parameters vary with latitude by means of even Legendre polynomials. The correlation between two points transformed to covariance by scaling with the standard deviations of each point. The covariance model is valid on the sphere which is empirically verified with a Monte-Carlo approach. The covariance model is subsequently applied to 5 years of simulated GRACE-FO data which allow for immediate validation with true uncertainties from the differences between the input mass signal and the recovered gravity fields. Four different realisations of the point standard deviations were tested: two based on the formal errors provided with the simulated Stokes coefficients, and two based on empirical standard deviations, where the first is spatially variant and temporally invariant, and the second spatially invariant and temporally variant. These four different covariance models are applied to compute TWS time series uncertainties for both the fifty largest discharge basins and regular grid cells over the continents. These four models are compared with the true uncertainties available in the simulations. The two empirically-based covariance models provide more realistic TWS uncertainties than the ones based on the formal errors. Especially, the empirically-based covariance models are better in reflecting the spatial pattern of the uncertainties of the simulated GRACE-FO data including their latitude dependence. However, these modelled uncertainties are in general too large. But with only one global scaling factor, a statistical test confirms the equivalence between the empirically-based covariance model with temporally variable point standard deviations and the true uncertainties. Thus at the end, this covariance model represents the closest fit in the simulation environment. The simulated GRACE-FO data are assumed to be very realistic which is why we recommend the new covariance model to be further investigated for the characterisation of real GRACE and GRACE-FO terrestrial water storage data.

The Gravity Recovery and Climate Experiment (GRACE) satellite mission has provided global long-term observations of mass transport in the Earth system with applications in numerous geophysical fields. In this paper, we targeted the in-orbit performance of the GRACE key instruments, the ACCelerometers (ACC) and the MicroWave ranging Instrument (MWI). For the ACC data, we followed a transplant approach analyzing the residual accelerations from transplanted accelerations of one of the two satellites to the other. For the MWI data, we analyzed the post-fit residuals of the monthly GFZ GRACE RL06 solutions with a focus on stationarity. Based on the analyses for the two test years 2007 and 2014, we derived stochastic models for the two instruments and a combined ACC+MWI stochastic model. While all three ACC axes showed worse performance than their preflight specifications, in 2007, a better ACC performance than in 2014 was observed by a factor of 3.6 due to switched-off satellite thermal control. The GRACE MWI noise showed white noise behavior for frequencies above 10 mHz around the level of $1.5\times {10}^{-6} \mathrm{m}/\sqrt{\mathrm{Hz}}$. In the combined ACC+MWI noise model, the ACC part dominated the frequencies below 10 mHz, while the MWI part dominated above 10 mHz. We applied the combined ACC+MWI stochastic models for 2007 and 2014 to the monthly GFZ GRACE RL06 processing. This improved the formal errors and resulted in a comparable noise level of the estimated gravity field parameters. Furthermore, the need for co-estimating empirical parameters was reduced.