  Remote sensing of rainfall events is critical for both operational and
scientific needs, including for example weather forecasting, extreme flood
mitigation, water cycle monitoring, etc. Ground-based weather radars, such as
NOAA's Next-Generation Radar (NEXRAD), provide reflectivity and precipitation
estimates of rainfall events. However, their observation range is limited to a
few hundred kilometers, prompting the exploration of other remote sensing
methods, particularly over the open ocean, that represents large areas not
covered by land-based radars. Here we propose a deep learning approach to
deliver a three-class segmentation of SAR observations in terms of rainfall
regimes. SAR satellites deliver very high resolution observations with a global
coverage. This seems particularly appealing to inform fine-scale rain-related
patterns, such as those associated with convective cells with characteristic
scales of a few kilometers. We demonstrate that a convolutional neural network
trained on a collocated Sentinel-1/NEXRAD dataset clearly outperforms
state-of-the-art filtering schemes such as the Koch's filters. Our results
indicate high performance in segmenting precipitation regimes, delineated by
thresholds at 24.7, 31.5, and 38.8 dBZ. Compared to current methods that rely
on Koch's filters to draw binary rainfall maps, these multi-threshold
learning-based models can provide rainfall estimation. They may be of interest
in improving high-resolution SAR-derived wind fields, which are degraded by
rainfall, and provide an additional tool for the study of rain cells.
