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:已知合成孔径雷达能够提供地表风速的高分辨率估计值。 这些估计值通常依赖于一个地球物理模型函数(GMF),该模型函数很难算出降雨等非风过程。 另一方面,进化神经网络有能力使用背景信息,并表明它们有能力划定降雨面积。通过仔细建立由哥白尼哨兵1号飞行任务(与GMF和大气模型风速并列)和降雨量估计值相结合的大型搜索和救援观测数据集,我们得以培训出一个风速测算器,在雨下差减少,浮标的原地风速测量显示根平方差,估计降雨量在1毫米/小时以上(重写3毫米/小时)下减少27%(重写45%)。这些结果表明深学习模型有能力纠正合成孔径雷达产品中与降雨有关的错误。</s>