Ensemble Mean Forecast Skill and Applications with the T213 Ensemble Prediction System  

作　　者：Sijia LI Yuan WANG Huiling YUAN Jinjie SONG Xin XU 

机构地区：[1]Key Laboratory of Mesoscale Severe Weather,Ministry of Education,School of Atmospheric Sciences,Nanjing University

出　　处：《Advances in Atmospheric Sciences》2016年第11期1297-1305,共9页大气科学进展（英文版）

基　　金：supported by the National Basic Research (973) Program of China (Grant No. 2013CB430106);the R&D Special Fund for Public Welfare Industry (Meteorology) (Grant Nos. GYHY201306002 and GYHY201206005);the National Natural Science Foundation of China (Grant Nos. 40830958 and 41175087);the Jiangsu Collaborative Innovation Center for Climate Change;the High Performance Computing Center of Nanjing University

摘　　要：Ensemble forecasting has become the prevailing method in current operational weather forecasting. Although ensemble mean forecast skill has been studied for many ensemble prediction systems（EPSs） and different cases, theoretical analysis regarding ensemble mean forecast skill has rarely been investigated, especially quantitative analysis without any assumptions of ensemble members. This paper investigates fundamental questions about the ensemble mean, such as the advantage of the ensemble mean over individual members, the potential skill of the ensemble mean, and the skill gain of the ensemble mean with increasing ensemble size. The average error coefficient between each pair of ensemble members is the most important factor in ensemble mean forecast skill, which determines the mean-square error of ensemble mean forecasts and the skill gain with increasing ensemble size. More members are useful if the errors of the members have lower correlations with each other, and vice versa. The theoretical investigation in this study is verified by application with the T213 EPS. A typical EPS has an average error coefficient of between 0.5 and 0.8; the 15-member T213 EPS used here reaches a saturation degree of 95%（i.e., maximum 5% skill gain by adding new members with similar skill to the existing members） for 1–10-day lead time predictions, as far as the mean-square error is concerned.Ensemble forecasting has become the prevailing method in current operational weather forecasting. Although ensemble mean forecast skill has been studied for many ensemble prediction systems（EPSs） and different cases, theoretical analysis regarding ensemble mean forecast skill has rarely been investigated, especially quantitative analysis without any assumptions of ensemble members. This paper investigates fundamental questions about the ensemble mean, such as the advantage of the ensemble mean over individual members, the potential skill of the ensemble mean, and the skill gain of the ensemble mean with increasing ensemble size. The average error coefficient between each pair of ensemble members is the most important factor in ensemble mean forecast skill, which determines the mean-square error of ensemble mean forecasts and the skill gain with increasing ensemble size. More members are useful if the errors of the members have lower correlations with each other, and vice versa. The theoretical investigation in this study is verified by application with the T213 EPS. A typical EPS has an average error coefficient of between 0.5 and 0.8; the 15-member T213 EPS used here reaches a saturation degree of 95%（i.e., maximum 5% skill gain by adding new members with similar skill to the existing members） for 1–10-day lead time predictions, as far as the mean-square error is concerned.

关 键 词：skill ensemble Ensemble questions rarely verified forecast explain saturation discussion 

分 类 号：P456.7[天文地球—大气科学及气象学]