Using the Inverse of Expected Error Variance to Determine Weights of Individual Ensemble Members: Application to Temperature Prediction

The inverse of expected error variance is utilized to determine weights of individual ensemble members based on the THORPEX (The Observing System Research and Predictability Experiment) Interactive Grand Global Ensemble (TIGGE) forecast datasets. The weights of all ensemble members are thus calculated for summer 2012, with the NCEP final operational global analysis (FNL) data as the truth. Based on the weights of all ensemble members, the variable weighted ensemble mean (VWEM) of temperature of summer 2013 is derived and compared with that from the simple equally weighted ensemble mean. The results show that VWEM has lower root-mean-square error (RMSE) as well as absolute error, and has improved the temperature prediction accuracy. The improvements are quite notable over the Tibetan Plateau and its surrounding areas; specifically, a relative improvement rate of RMSE of more than 24% in 2-m temperature is demonstrated. Moreover, the improvement rates vary slightly with the prediction lead-time (24–96 h). It is suggested that the VWEM approach be employed in operational ensemble prediction to provide guidance for weather forecasting and climate prediction.