  The Gravity Recovery and Climate Experiment (GRACE) satellite and its
successor GRACE Follow-On (GRACE-FO) provide valuable and accurate observations
of terrestrial water storage anomalies (TWSAs) at a global scale. However,
there is an approximately one-year observation gap of TWSAs between GRACE and
GRACE-FO. This poses a challenge for practical applications, as discontinuity
in the TWSA observations may introduce significant biases and uncertainties in
the hydrological model predictions and consequently mislead decision making. To
tackle this challenge, a Bayesian convolutional neural network (BCNN) driven by
climatic data is proposed in this study to bridge this gap at a global scale.
Enhanced by integrating recent advances in deep learning, including the
attention mechanisms and the residual and dense connections, BCNN can
automatically and efficiently extract important features for TWSA predictions
from multi-source input data. The predicted TWSAs are compared to the
hydrological model outputs and three recent TWSA prediction products. The
comparison suggests the superior performance of BCNN in providing improved
predictions of TWSAs during the gap in particular in the relatively arid
regions. The BCNN's ability to identify the extreme dry and wet events during
the gap period is further discussed and comprehensively demonstrated by
comparing with the precipitation anomalies, drought index, ground/surface water
levels. Results indicate that BCNN is capable of offering a reliable solution
to maintain the TWSA data continuity and quantify the impacts of climate
extremes during the gap.
