Improving 10 m Wind Speed Forecasts over the Northwest Pacific Using a Deep Learning Network

Accurate sea surface wind forecasts are essential for marine disaster prevention, maritime economic activities, and renewable energy development. However, traditional numerical weather prediction (NWP) models often encounter limitations such as nonlinear error accumulation and systematic biases during long-lead-time integration. Consequently, this study develops a spatiotemporal deep learning post-processing framework based on state space mechanisms, utilizing ERA5 reanalysis data to correct errors in 0–72 h NWP 10 m wind speed forecasts over the Northwest Pacific and adjacent regions (0–90° N, 100–150° E). Evaluations against mainstream spatiotemporal deep learning models indicate that the proposed framework improves the forecast accuracy and spatial consistency of the NWP. Regarding overall error control, the post-processing model reduces the root mean square error (RMSE) of the raw NWP from 1.47 m/s to 1.10 m/s for 24 h forecasts. Meanwhile, during the 72 h long-lead-time integration, the pattern correlation coefficient (PCC) of the forecasted wind field is maintained at 0.86, and the overall systematic bias converges from −0.27 m/s to −0.02 m/s. Additionally, the framework effectively mitigates the over-prediction of gale-force winds, reducing the false alarm ratio (FAR) by 30–50% compared to the raw NWP. These results indicate that the proposed deep learning post-processing strategy effectively corrects underlying systematic biases in numerical models, thereby enhancing the accuracy and reliability of long-term wind field forecasts.