Uncertainties of GRACE‐Based Terrestrial Water Storage Anomalies for Arbitrary Averaging Regions

Abstract

Satellite gravimetry as realised by GRACE (Gravity Recovery And Climate Experiment;Tapley et al., 2004) and GRACE-FO (GRACE-Follow-On;Landerer et al., 2020) allows for the first time to track changes in the surface mass distribution globally from space. The combined data record provides quantitative estimates on the severity of the ongoing global changes (Tapley et al., 2019), as manifested by persistent ice-mass loss in Greenland and

The Impact of New Accelerometer Transplant Data (ACH) on GRACE Follow‐On Along‐Orbit Inter‐Satellite Laser Ranging Observations and Monthly Time‐Variable Gravity and Mascon Solutions

“…In the case of L2 data, we present a comprehensive comparison of RL06 and RL06.1 solutions based on various analyses in the frequency, time and space domains and through comparison with independent data sets. In the case of L3 data, we examine the mascon solutions from JPL (Watkins et al., 2015) and NASA Goddard Space Flight Center (GSFC; Loomis, Luthcke, & Sabaka, 2019), which are directly computed from L1B data, as well as the L3 gridded mass change data from GFZ (Boergens et al., 2022; Sasgen et al., 2019; see http://gravis.gfz-potsdam.de/home), which are computed from L2 data. While monthly L2 and L3 data observe slowly varying mass changes in the Earth system, the along‐orbit analysis of inter‐satellite tracking data has helped to broaden the applications of GRACE/GRACE‐FO to rapid mass and gravity changes associated with various processes like Earth's free oscillations (Ghobadi‐Far, Han, et al., 2019), tsunamis (Ghobadi‐Far, Han, Allgeyer, et al., 2020), floods (Han, Ghobadi‐Far, et al., 2021, Han, Yeo, et al., 2021), and oceanic gyres (Ghobadi‐Far et al., 2022).…”

Global high-resolution total water storage anomalies from self-supervised data assimilation using deep learning algorithms

“…These low correlations are understandable due to insignificant hydrological signals in these drought regions since both GRACE and WGHM are not sufficiently sensitive to measure or simulate them accurately. To evaluate the basin-wise quality of the downscaled TWSAs, we rely on the GRACE measurements since the GRACE TWSAs are considered to be accurate over the effective resolution of the GRACE mission 36,37 . We first averaged GRACE and downscaled TWSAs of each individual basin to generate basin-wise time series and compute the root mean square errors (RMSEs) between these two types of time series.…”

“…4. Here, we set the spatial threshold of 200 000 km 2 to obtain more reliable GRACE estimations as reference 30,36,37 . The correlation between WGHM-and GRACE-derived trends is only 0.34 showing the major limitation of the hydrological models in capturing long-term trends.…”

Global high-resolution total water storage anomalies from self-supervised data assimilation using deep learning algorithms

“…Furthermore, comparisons with independent satellite altimetry measurements show that the high-resolution information of the downscaled product is beneficial (see the supplementary information). To evaluate the basin-wise quality of the downscaled TWSAs, we rely on the GRACE measurements since the GRACE TWSAs are considered to be accurate over the effective resolution of the GRACE mission 42,43 . We first averaged GRACE and downscaled TWSAs of each individual basin to generate basin-wise time series and compute the root mean square errors (RMSEs) between these two types of time series.…”

“…4a). Here, we set the spatial threshold of 200 000 km 2 to obtain more reliable GRACE estimations as reference 37,42,43 . The correlation between WGHM-and GRACE-derived trends is only 0.47, showing the major limitation of the hydrological models in capturing long-term trends.…”