Establishment and Evaluation of an Ensemble Bias Correction Framework for the Short-Term Numerical Forecasting on Lower Atmospheric Ducts

Based on the COAWST (Coupled Ocean–Atmosphere–Wave–Sediment Transport) model, this study developed an atmospheric refractivity forecasting model incorporating ensemble bias correction by combining five bias correction algorithms with the Bayesian Model Averaging (BMA) method. Hindcast tests conducted over the Yellow Sea and Bohai Sea regions demonstrated that the ensemble bias correction enhanced both forecasting accuracy and adaptability. On the one hand, the corrected forecasting outperformed the original COAWST model in terms of mean error (ME), root mean square error (RMSE), and correlation coefficient (CC), with the RMSE reduced by approximately 20% below 3000 m altitude. On the other hand, the corrected forecasting reduced the uncertainty associated with the performance of different algorithms. In particular, during typhoon events, the corrected forecasting maintained stable bias characteristics across different height layers through dynamic weight adjustment. Throughout the hindcast period, the ME of the corrected forecasting was lower than that of any single bias correction algorithm. Moreover, compared with other ensemble methods, the corrected forecasting developed in this study achieved more flexible weight allocation through Bayesian optimization, resulting in lower ME. In addition, the corrected forecasting maintained an improvement of approximately 28% in bias reduction even at a 72 h forecasting lead time, demonstrating their robustness and reliability under complex weather conditions.