Synthetic Aperture Radar is known to be able to provide high-resolution estimates of surface wind speed. These estimates usually rely on a Geophysical Model Function (GMF) that has difficulties accounting for non-wind processes such as rain events. Convolutional neural network, on the other hand, have the capacity to use contextual information and have demonstrated their ability to delimit rainfall areas. By carefully building a large dataset of SAR observations from the Copernicus Sentinel-1 mission, collocated with both GMF and atmospheric model wind speeds as well as rainfall estimates, we were able to train a wind speed estimator with reduced errors under rain. Collocations with in-situ wind speed measurements from buoys show a root mean square error that is reduced by 27% (resp. 45%) under rainfall estimated at more than 1 mm/h (resp. 3 mm/h). These results demonstrate the capacity of deep learning models to correct rain-related errors in SAR products.

翻译：合成孔径雷达能够提供高分辨率的海洋表面风速估计，但此类估计通常依赖难以处理降雨等非风过程的地球物理模型函数。相比之下，卷积神经网络具备利用空间上下文信息的能力，并在降雨区域识别方面展现出显著优势。通过系统构建Copernicus Sentinel-1任务的大规模SAR观测数据集，并联合地球物理模型函数、大气模式风速及降雨估计数据，我们成功训练出在降雨条件下具有更低误差的风速估计模型。与浮标实测风速的对比验证表明：当降雨强度超过1毫米/小时（3毫米/小时）时，该模型均方根误差分别降低27%（45%）。研究结果充分证明了深度学习模型对SAR产品中降雨相关误差的修正能力。