Assessment of GRACE/GRACE Follow-On Terrestrial Water Storage Estimates Using an Improved Forward Modeling Method: A Case Study in Africa - Zhou, Hao; Dai, Min; Wang, Penghui; Wei, Min; Tang, Lu; Xu, Siyou; Luo, Zhicai (Frontiers in Earth Science, 2022)
Leakage errors derived from spatial filters are the major limitation for estimating terrestrial water storage via the Gravity Recovery and Climate Experiment (GRACE) mission and the recently launched GRACE Follow-On mission. Here we develop an improved forward modeling method and assess its performance of reducing leakage errors over Africa. In noise-free condition, the forward modeling method shows its outperformance in restoring signals, and the improved forward modeling method can further reduce the leakage errors along the coastline of Africa. In noise-contaminated condition, the simulated environment is set as real as possible to GRACE mission and GRACE Follow-On mission. The results based on the simulated GRACE and GRACE Follow-On solutions demonstrate the capacity of improved forward modeling method in reducing leakage errors. In case of simulated GRACE solutions, the average improvements of 24 basins over Africa are respectively 37% for annual amplitudes and 36% for trends. When compared with these simulated GRACE solutions, the improvements via simulated GRACE Follow-On solutions are minor over large and medium size river basins, but significant over small size river basins. In case of simulated GRACE Follow-On solutions, the average improvements over Africa are 39% for annual amplitudes and 41% for trends. Eventually, the improved forward modeling method is used to process GRACE spherical harmonic datasets from the Center for Space Research (CSR). The results present better agreement with those derived from the newly released mascon solutions from Jet Propulsion Laboratory (JPL) and CSR, when compared with those derived from CSR Tellus grids with scale factors. The better consistency between these model-independent approaches indicates the good performance of our improved forward modeling method and the further necessity of carefully evaluation the model-dependent approaches when using different prior hydrological models. Overall, experiments based on noise-free observations, noise-contaminated observations and GRACE datasets indicate that improved forward modeling method is capable of restoring temporal signals.