Improving Multi-model Ensemble Probabilistic Prediction of Yangtze River Valley Summer Rainfall

作     者：LI Fang LIN Zhongda 

作者机构：International Center for Climate and Environmental Sciences Institute of Atmospheric PhysicsChinese Academy of Sciences State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid DynamicsInstitute of Atmospheric Physics Chinese Academy of Sciences 

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

年 卷 期：2015年第32卷第4期

页      面：497-504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：co-supported by the National Natural Science Foundation (Grant Nos. 41005052 and 41375086) the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA05110201) the National Basic Research Program of China (Grant No. 2010CB950403) 

主　　题：probability density function seasonal prediction multi-model ensemble Yangtze River valley summer rainfall Bayesian scheme 

摘      要：Seasonal prediction of summer rainfall over the Yangtze River valley（YRV） is valuable for agricultural and industrial production and freshwater resource management in China, but remains a major challenge. Earlier multi-model ensemble（MME） prediction schemes for summer rainfall over China focus on single-value prediction, which cannot provide the necessary uncertainty information, while commonly-used ensemble schemes for probability density function（PDF） prediction are not adapted to YRV summer rainfall prediction. In the present study, an MME PDF prediction scheme is proposed based on the ENSEMBLES hindcasts. It is similar to the earlier Bayesian ensemble prediction scheme, but with optimization of ensemble members and a revision of the variance modeling of the likelihood function. The optimized ensemble members are regressed YRV summer rainfall with factors selected from model outputs of synchronous 500-h Pa geopotential height as predictors. The revised variance modeling of the likelihood function is a simple linear regression with ensemble spread as the predictor. The cross-validation skill of 1960–2002 YRV summer rainfall prediction shows that the new scheme produces a skillful PDF prediction, and is much better-calibrated, sharper, and more accurate than the earlier Bayesian ensemble and raw ensemble.