Key points are not available for this paper at this time.
Data Assimilation (DA) of time-variable satellite gravity observations, e.g., from the Gravity Recovery and Climate Experiment (GRACE), GRACE-Follow On (GRACE-FO) and future gravity missions, can be applied to constrain the vertical sum of water storage simulations of Global Hydrological Models (GHMs). However, the state-of-the-art DA of these measured Terrestrial Water Storage (TWS) changes into models is often performed regionally, and if globally, at low spatial resolution. To perform a reliable global DA system with GRACE(-FO), several major challenges must be addressed, (1) whats the accuracy of GRACE(-FO), (2) whats the spatial correlation of GRACE(-FO) grids, (3) how to resolve the numerical instability and inefficiency of global DA. In this study, we present a detailed analysis of GRACE(-FO)s accuracy and spatial resolution from a new perspective, and reveal their dynamic change over space and time. Then, we develop a Python-based open-source PyGLDA system that allows performing DA globally at a fine scale with high numerical efficiency. Case studies will be demonstrated at Danube River Basin and at a global scale, using the monthly TWS fields of GRACE (2002-2010) and the W3RA water balance model at 0.1-degree/daily spatial-temporal resolution.
Yang et al. (Mon,) studied this question.