Model Uncertainty Representation for a Convection-Allowing Ensemble Prediction System Based on CNOP-P

作     者：Lu WANG Xueshun SHEN Juanjuan LIU Bin WANG Lu WANG;Xueshun SHEN;Juanjuan LIU;Bin WANG

作者机构：Asset Operation CentreChina Meteorological AdministrationBeijing 100081China State Key Laboratory of Severe WeatherChinese Academy of Meteorological SciencesBeijing 100081China Numerical Weather Prediction Center/National Meteorological CenterChina Meteorological AdministrationBeijing 100081China Institute of Atmosphere PhysicsChinese Academy of SciencesBeijing 100029China Center for Earth System ScienceTsinghua UniversityBeijing 100084China 

出 版 物：《Advances in Atmospheric Sciences》 (大气科学进展（英文版）)

年 卷 期：2020年第37卷第8期

页      面：817-831页

核心收录：

学科分类：07[理学] 070601[理学-气象学] 0706[理学-大气科学] 

基　　金：supported by the National Key Research and Development Program of China 

主　　题：CNOP-P convective scale model uncertainty ensemble forecastforecast 

摘      要：Formulating model uncertainties for a convection-allowing ensemble prediction system(CAEPS)is a much more challenging problem compared to well-utilized approaches in synoptic weather forecasting.A new approach is proposed and tested through assuming that the model uncertainty should reasonably describe the fast nonlinear error growth of the convection-allowing model,due to the fast developing character and strong nonlinearity of convective *** Conditional Nonlinear Optimal Perturbation related to Parameters(CNOP-P)is applied in this ***,an ensemble approach is adopted to solve the CNOP-P *** using five locally developed strong convective events that occurred in pre-rainy season of South China,the most sensitive parameters were detected based on CNOP-P,which resulted in the maximum variations in precipitation.A formulation of model uncertainty is designed by adding stochastic perturbations into these sensitive *** comparison ensemble experiments by using all the 13 heavy rainfall cases that occurred in the flood season of South China in 2017,the advantages of the CNOP-P-based method are examined and verified by comparing with the well-utilized stochastically perturbed physics tendencies(SPPT)*** results indicate that the CNOP-P-based method has potential in improving the under-dispersive problem of the current CAEPS.