The dynamical-analogue ensemble method for improving operational monthly forecasting

Focusing on the monthly forecasting problem based on the Atmospheric General Circulation Model (AGCM), a method of the dynamical-analogue ensemble forecasting (DAEF) is proposed to effectively reduce prediction errors and increase prediction skills. This method aims to the intrinsic combination of the dynamical model and statistical-empirical methods, which can establish perturbation members for ensemble forecasting by extracting the historical analogue information of the atmospheric general circulation, parameterizing empirically model errors and generating the multi-time-independent analogue forcing. Applying this new ensemble method to the operational AGCM in Beijing Climate Center (BCC AGCM1), a 10-yr monthly forecasting experiment under a quasi-operational condition shows encouraging results. Compared with the operational ensemble forecasts by the BCC AGCM1, the DAEF method is capable to improve effectively prediction skills of the monthly-mean and daily atmospheric circulation forecasts in which the former almost reaches the standard, available in the BCC operation, through effectively improving predictions of the zonal mean, ultra-long waves and long waves of the circulation. The results also show that prediction errors for the DAEF are significantly reduced and its spread of the ensemble members is reasonably increased, indicating an improvement in the relationship between the prediction errors and the spread. This study suggests a big potential application of the DAEF method in the BCC monthly forecasting operation.