Study on Multi-Scale Blending Initial Condition Perturbations for a Regional Ensemble Prediction System  

作　　者：ZHANG Hanbin CHEN Jing ZHI Xiefei WANG Yi WANG Yanan 

机构地区：[1]College of Atmospheric Science, Nanjing University of Information Science and Technology [2]Numerical Weather Prediction Center,China Meteorological Administration [3]Key Laboratory of Meteorological Disaster, Ministry of Education,Nanjing University of Information Science and Technology [4]Earth and Atmospheric Sciences, University of Nebraska Lincoln [5]Center of Meteorological Service of Zhejiang

出　　处：《Advances in Atmospheric Sciences》2015年第8期1143-1155,共13页大气科学进展（英文版）

基　　金：supported by the National Natural Science Foundation of China (Grant No. 91437113);the Special Fund for Meteorological Scientific Research in the Public Interest (Grant Nos. GYHY201506007 and GYHY201006015);the National 973 Program of China (Grant Nos. 2012CB417204 and 2012CB955200);the Scientific Research & Innovation Projects for Academic Degree Students of Ordinary Universities of Jiangsu (Grant No. KYLX 0827)

摘　　要：An initial conditions （ICs） perturbation method was developed with the aim to improve an operational regional ensemble prediction system （REPS）. Three issues were identified and investigated： （1） the impacts of perturbation scale on the ensemble spread and forecast skill of the REPS; （2） the scale characteristic of the IC perturbations of the REPS; and （3） whether the REPS＇s skill could be improved by adding large-scale information to the IC perturbations. Numerical experiments were conducted to reveal the impact of perturbation scale on the ensemble spread and forecast skill. The scales of IC perturbations from the REPS and an operational global ensemble prediction system （GEPS） were analyzed. A ＂multi-scale blending＂ （MSB） IC perturbation scheme was developed, and the main findings can be summarized as follows： The growth rates of the ensemble spread of the REPS are sensitive to the scale of the IC perturbations; the ensemble forecast skills can benefit from large-scale perturbations; the global ensemble IC perturbations exhibit more power at larger scales, while the regional ensemble IC perturbations contain more power at smaller scales; the MSB method can generate IC perturbations by combining the small-scale component from the REPS and the large-scale component from the GEPS; the energy norm growth of the MSB-generated perturbations can be appropriate at all forecast lead times; and the MSB-based REPS shows higher skill than the original system, as determined by ensemble forecast verification.An initial conditions （ICs） perturbation method was developed with the aim to improve an operational regional ensemble prediction system （REPS）. Three issues were identified and investigated： （1） the impacts of perturbation scale on the ensemble spread and forecast skill of the REPS; （2） the scale characteristic of the IC perturbations of the REPS; and （3） whether the REPS＇s skill could be improved by adding large-scale information to the IC perturbations. Numerical experiments were conducted to reveal the impact of perturbation scale on the ensemble spread and forecast skill. The scales of IC perturbations from the REPS and an operational global ensemble prediction system （GEPS） were analyzed. A ＂multi-scale blending＂ （MSB） IC perturbation scheme was developed, and the main findings can be summarized as follows： The growth rates of the ensemble spread of the REPS are sensitive to the scale of the IC perturbations; the ensemble forecast skills can benefit from large-scale perturbations; the global ensemble IC perturbations exhibit more power at larger scales, while the regional ensemble IC perturbations contain more power at smaller scales; the MSB method can generate IC perturbations by combining the small-scale component from the REPS and the large-scale component from the GEPS; the energy norm growth of the MSB-generated perturbations can be appropriate at all forecast lead times; and the MSB-based REPS shows higher skill than the original system, as determined by ensemble forecast verification.

关 键 词：regional ensemble prediction system spectral analysis multi-scale blending initial condition perturbations 

分 类 号：P456[天文地球—大气科学及气象学]