Exploring groundwater and soil water storage changes across the CONUS at 12.5 km resolution by a Bayesian integration of GRACE data into W3RA

Bayesian convolutional neural networks for predicting the terrestrial water storage anomalies during GRACE and GRACE-FO gap

“…This has led to approximately one year of data gap (July 2017-May 2018) (Li et al, 2020), leading to discontinuity in the time series and thus impeding full utilization and analysis of the data Yi and Sneeuw, 2021). Particularly, considering that the TWSA observations are usually assimilated into hydrological models for higher reliability (Li et al, 2019;Mehrnegar et al, 2020;Mehrnegar et al, 2021;Nie et al, 2019;Soltani et al, 2021;Yin et al, 2020;Zaitchik et al, 2008), discontinuity in the time series observations may introduce significant biases and uncertainties in the model predictions and consequently mislead decision making . This is especially the case when there existed climate extremes during the gap as they usually cause abnormal changes in the TWSA signals.…”

Hydrological Droughts of 2017–2018 Explained by the Bayesian Reconstruction of GRACE(‐FO) Fields

“…The latter two compartments cannot be measured by other remote sensing techniques. Therefore, GRACE and GRACE‐FO have been used widely for studying global large‐scale hydrometeorological events (see, e.g., Boergens, Güntner, et al., 2020; Chen et al., 2010; Eicker et al., 2016; Forootan et al., 2019; Humphrey et al., 2016; B. Li et al., 2019; X. Li et al., 2022; Liu et al., 2020; Long et al., 2013, 2014; Mehrnegar et al., 2021; Thomas et al., 2014; Yan et al., 2021).…”

A New 1‐Hourly ERA5‐Based Atmosphere De‐Aliasing Product for GRACE, GRACE‐FO, and Future Gravity Missions

“…The Gravity Recovery and Climate Experiment (GRACE, 2002–2017) mission (Tapley et al., 2004), together with its successor GRACE follow‐on (2018‐onwards) mission (Kornfeld et al., 2019), have collected numerous valuable observations that allow mapping the Earth's time‐variable gravity field. Applications that utilize these fields (e.g., Dahle et al., 2014) have broadened our knowledge in interdisciplinary science including studying water variability in aquifers (Famiglietti & Rodell, 2013; Mehrnegar et al., 2021; Ramillien et al., 2011; Schumacher et al., 2018), continental ice‐sheets (Sasgen et al., 2013; Velicogna et al., 2020), and geo‐dynamics (Panet et al., 2018) at meso‐scale, that is, 10 to few 100 kilometers (see other examples in, e.g., Kusche et al., 2012; Tapley et al., 2019).…”

“…• Hourly ERA5 reanalysis is employed to generate the atmosphere dealiasing product for the first time, through a refined 3-D vertical integration method • The use of input fields from ERA5 and increasing the sampling rate of atmospheric products to 1 h are recommended for Gravity Recovery and Climate Experiment-Follow On and future missions • New sets of hourly atmospheric non-tidal de-aliasing and tidal components including , , , , , , , , , , ] E S S P K N M L T R T R S that utilize these fields (e.g., Dahle et al, 2014) have broadened our knowledge in interdisciplinary science including studying water variability in aquifers (Famiglietti & Rodell, 2013;Mehrnegar et al, 2021;Ramillien et al, 2011;Schumacher et al, 2018), continental ice-sheets (Sasgen et al, 2013;Velicogna et al, 2020), and geo-dynamics (Panet et al, 2018) at meso-scale, that is, 10 to few 100 kilometers (see other examples in, e.g., Kusche et al, 2012;Tapley et al, 2019).…”