Probabilistic Forecasting of Summer Wind Speed in China Using Multimodel Ensembles

Abstract Wind has a crucial impact on human socio‐economic activities as well as the safety of life and property. Bayesian Model Averaging (BMA) and Ensemble Model Output Statistics (EMOS), are utilized to enhance the probabilistic forecasting skills for 10 m wind speed during the summer in China. A log‐normal distribution‐based BMA (L‐BMA) model is developed for a fair comparison with the log‐normal distribution‐based EMOS model, while the traditional gamma distribution‐based BMA (G‐BMA) model serves as a benchmark. The comparisons between the multimodel ensemble forecasts and raw ensembles demonstrate that both BMA and EMOS models improve the probabilistic forecasting skills of 10 m wind speed in China, with the EMOS model showing particularly significant improvements. The L‐BMA model generally outperforms the G‐BMA model, illustrating that the log‐normal distribution might be more appropriate for 10 m summer wind speed in China. Forecast error diagnosis is conducted through Brier Score (BS) decomposition, revealing that errors in predicting lower 10 m wind speeds primarily arise from inherent uncertainty and reliability characteristics, whereas forecast errors for higher wind speeds mainly attribute to the forecast resolution capability. The EMOS and two BMA models all decrease the reliability values, leading to lower BS values than the raw ensembles, but do not enhance the resolution capability. The analysis of a thunderstorm gale event indicates that the EMOS model provides more accurate forecasts than the raw ensembles and two BMA models.