Geospatially enhanced convolutional neural network for HY-2B scatterometer-based marine wind field retrieval.

Precise monitoring of sea surface wind fields is imperative for understanding ocean-atmosphere interactions and extreme weather evolution, where spaceborne scatterometers provide unique observational capabilities but face inherent limitations in accuracy and efficiency using the geophysical model function (GMF). We developed convolutional neural network-based models (CNN-WSPD and CNN-WDIR respectively for sea surface wind speed and direction retrievals) integrating HY-2B scatterometer data and ECMWF reanalysis with geographical coordinates. The innovation lies in embedding positional features into multi-angle backscatter processing, enhancing physical consistency across diverse marine environments.