An efficient and accurate Python tool for uncertainty quantification of GRACE based TWS

GRACE-derived TWS (terrestrial water storage) uncertainty provides crucial stochastic information for geophysical studies that assimilate GRACE data. The recent advances of assimilation studies demand to quantifying GRACE-derived TWS at a finer spatial resolution. However, traditional methods like the rigorous error propagation and the empirical modelling have their own limitations at either efficiency or flexibility towards the increasing demand of relevant assimilation studies. The Monte Carlo method, which has never been applied for GRACE uncertainty quantification before, is surprisingly found to achieve high flexibility, satisfied accuracy and unprecedented efficiency. The simplicity and generality of this method also allow one to easily customize it to accommodate user-specific GRACE study without the need to be professional to GRACE data processing. This method expects to bridge the gap and advance the data assimilation with GRACE in hydrological models.