Multi-model ensemble forecasting of 10-m wind speed over eastern China based on machine learning optimization

Abstract

Wind substantially impacts human activity and electricity generation. Thus, accurately forecasting the short-term wind speed is of profound societal and economic significance. Based on 100 weather stations in eastern China, the authors first evaluate the performance of the 10-m wind forecast products from five operational forecast models. Among them, the Japan Meteorological Agency (JMA) model performs best in reducing the forecasting errors. Then, the authors establish a 10-m wind speed multimodel ensemble forecast based on the five numerical models’ outputs and machine learning methods, combining dynamic and statistical methods. Feature engineering and machine learning algorithm optimization are conducted for each site separately. The forecast performance of this method is compared to the JMA model and multimodel ensemble forecast by ridge regression at lead times of 24–96 h. The results demonstrate that the multimodel ensemble method based on machine learning optimization can reduce the forecast error of JMA by more than 39%, and the improvement in forecast skill is most evident in November. In addition, it performs better than the ensemble forecast by ridge regression.