An Objective Approach to Generating Multi-Physics Ensemble Precipitation Forecasts Based on the WRF Model

作     者：Chenwei SHEN Qingyun DUAN Wei GONG Yanjun GAN Zhenhua DI Chen WANG Shiguang MIAO Chenwei SHEN;Qingyun DUAN;Wei GONG;Yanjun GAN;Zhenhua DI;Chen WANG;Shiguang MIAO

作者机构：State Key Laboratory of Earth Surface Processes and Resource EcologyFaculty of Geographical ScienceBeijing Normal UniversityBeijing 100875 State Key Laboratory of Hydrology–Water Resources and Hydraulic Engineering and College of Hydrology&Water ResourcesHohai UniversityNanjing 210098 State Key Laboratory of Severe WeatherChinese Academy of Meteorological SciencesChina Meteorological AdministrationBeijing 100081 South China Botanical GardenChinese Academy of SciencesGuangzhou 510650 Institute of Urban MeteorologyChina Meteorological AdministrationBeijing 100081 

出 版 物：《Journal of Meteorological Research》 (气象学报（英文版）)

年 卷 期：2020年第34卷第3期

页      面：601-620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 Chinese Academy of Sciences Strategic Pioneering Program(XDA20060401) China Meteorological Administration Special Public Welfare Research Fund(GYHY201506002) National Basic Research Program of China(2015CB953703) Intergovernment Key International S&T Innovation Cooperation Program(2016YFE0102400) 

主　　题：ensemble precipitation forecast Weather Research and Forecasting(WRF)model multi-physics verification bootstrapping 

摘      要：Selecting proper parameterization scheme combinations for a particular application is of great interest to the Weather Research and Forecasting(WRF)model *** study aims to develop an objective method for identifying a set of scheme combinations to form a multi-physics ensemble suitable for short-range precipitation forecasting in the Greater Beijing *** ensemble is created by using statistical techniques and some *** initial sample of 90 scheme combinations was first generated by using Latin hypercube sampling(LHS).Then,after several rounds of screening,a final ensemble of 40 combinations were *** ensemble forecasts generated for both the training and verification cases using these combinations were evaluated based on several verification metrics,including threat score(TS),Brier score(BS),relative operating characteristics(ROC),and ranked probability score(RPS).The results show that TS of the final ensemble improved by 9%-33%over that of the initial *** reliability was improved for rain≤10 mm day^-1,but decreased slightly for rain10 mm day^-1 due to insufficient *** resolution remained about the *** final ensemble forecasts were better than that generated from randomly sampled scheme *** results suggest that the proposed approach is an effective way to select a multi-physics ensemble for generating accurate and reliable forecasts.