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Reduction of rain-induced errors for wind speed estimation on SAR observations using convolutional neural networks
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.
Rain regime segmentation of Sentinel-1 observation learning from NEXRAD collocations with Convolution Neural Networks
SAR satellites deliver very high resolution observations with a global coverage.
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