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

Abstract Total water storage anomalies (TWSAs) describe the variations of the terrestrial water cycle, which is essential for better understanding our climate system. This study proposes a self-supervised data assimilation model with a novel loss function to provide a global TWSA product with a spatial resolution of 0.5 degrees. The model combines the hydrological simulations as well as the measurements from the Gravity Recovery and Climate Experiment (GRACE) and its follow-on (GRACE-FO) satellite missions. The efficiency of the high-resolution information is proved by closing the water balance equation while preserving large-scale accuracy inherited from the GRACE(-FO) measurements. They contribute to monitoring natural hazards locally and show potential for better understanding the impacts of natural and anthropogenic activities on the water cycle. We anticipate our approach to be generally applicable to other TWSA data sources and the resulting product to be valuable for the geoscience community and society.