Machine learning-assisted sea surface wind dataset (MLAWind)

The product is developed under the leadership of Weihao Guo, Rongwang Zhang, and Xin Wang from South China Sea Institute of Oceanology, Chinese Academy of Sciences. The product has a horizontal spatial resolution of 1°×1° and covers the period from 1950 to 2023 with a monthly average temporal resolution. Based on the Random Forest machine learning algorithm and the SHapley Additive exPlanations (SHAP) interpretable module, the product effectively integrates in-situ observations with satellite data. It achieves comparable accuracy to widely-used sea surface wind products, while significantly improving El Niño-Southern Oscillation (ENSO) forecast skill in boreal spring.