Deep Learning‐Based Sea Surface Roughness Parameterization Scheme Improves Sea Surface Wind Forecast

Abstract

Abstract Accurate offshore surface wind forecasting is crucial for navigation safety and disaster prevention. However, significant biases exist in forecasting sea surface winds due to the uncertainties in estimating sea surface roughness. In this study, we propose a deep learning‐based scheme (DL2023) for estimating sea surface roughness and integrate it into a regionally coupled ocean‐atmosphere‐wave model. Single‐point experiments demonstrate that DL2023 achieves a remarkable 50% reduction in the Root Mean Square Error (RMSE) compared to the four traditional schemes. During five typhoon cases in August 2020, compared to the four traditional schemes, the RMSEs of forecasted surface winds using DL2023 are reduced by 6.02%–14.75%, 11.17%–18.30%, and 11.91%–19.46% at lead times of 24, 48, and 72 hr, respectively. Thus, the DL2023 scheme, trained using data from the Atlantic Ocean, successfully improves the forecast of surface winds over the Northwest Pacific Ocean.