A Study On Multi-model Ensemble Forecast Of Surface Meteorological Elements Over China And Mid-troposphere Situation Over Europe And Asia

Based on the ensemble forecasts data from the European Centre for Medium-Range Weather Forecasts(ECMWF),the Japan Meteorological Agency(JMA),the National Centers for Environmental Prediction(NCEP),the China Meteorological Administration(CMA)and the UK Met Office(UKMO)in the TIGGE datasets,a multi-model ensemble forecast study on the surface meteorological element over China and 500 hPa situation over Europe and Asia has been conducted.The methods used in the study were Kalman filter(KF),Bias-removed ensemble mean(BREM),Weighted ensemble mean(WEM)and Ensemble mean(EMN).For the surface forecast,the root mean squared error(RMSE)became larger with the increasing of lead time.Multi-model ensemble forecast can significantly lower the forecast error of 2m temperature and 10 m wind,especially over the regions with complex topography.However,only the KF showed a more skillful performance on the forecasts of precipitation.In general,the KF performed best and it is the steadiest method among the four multi-model ensemble methods.For mid-troposphere forecast,ECMWF was the best single model forecast among all models.Compared to its forecast,the KF improved the forecast skill by over 20%.The WEM improved the forecast skill only when lead time was within 48 h,while BREM and EMN did not improve the forecast skill.What's more,after analyzing the weight of the KF and WEM,we could say that ECMWF was the main contributor in the multi-model forecasts.Besides,this study also analyzed the forecast skill of Augmented complex extended Kalman filter(ACEKF),the result showed that it improved more on zonal wind forecast,surface wind forecast and the forecast over ocean and regions with complex topography.Overall,ACEKF could improve the skill of wind forecast compared to the KF.To sum up,the KF significantly improved the forecast skill of all meteorological elements in this study by rationally optimizing the distribution of the weight of each model.The KF performed better and steadier compared to BREM,WEM and EMN.