Terrestrial water storage on the South American continent: Data from numerical simulations, observations, and deep learning

Christopher Irrgang, Jan Saynisch-Wagner, Robert Dill, Eva Boergens & Maik Thomas
In Irrgang et al. (2020), we have trained a convolutional neural network to perform a so-called downscaling task. This downscaling aims to recover the fine-structure continental water storage distribution on the South American continent from coarse-resolution space-borne gravimetry observations. Here, we share data sets that were used for training the neural network, namely (1) monthly pairs of gridded terrestrial water storage anomalies (TWSA) of the South American continent and (2) surface water storage anomalies (SWSA)...
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