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中国夏季降水多模式集成概率预报研究
引用本文:林春泽,祁海霞,智协飞,白永清,刘琳.中国夏季降水多模式集成概率预报研究[J].湖北气象,2013(4):354-359.
作者姓名:林春泽  祁海霞  智协飞  白永清  刘琳
作者单位:[1]中国气象局武汉暴雨研究所暴雨监测预警湖北省重点实验室,武汉430074 [2]武汉中心气象台,武汉430074 [3]南京信息工程大学气象灾害省部共建教育部重点实验室,南京210044 [4]湖北省气象服务中心,武汉430074
基金项目:资助项目:公益性行业(气象)科研专项(GYHY200906007,GYHY201306056)
摘    要:基于TIGGE资料中的中国气象局(CMA)、欧洲中期天气预报中心(ECMWF)、日本气象厅(JMA)、美国国家环境预报中心(NCEP)以及英国气象局(UKMO)五个中心2007-2011年5月25日-8月31日中国地区逐日12-36 h、36-60 h、60-84 h、84-108 h、108-132 h与132-156 h累积降水集合预报资料,分别利用PoorMan (POOL)和多模式消除偏差(MBRE)两种方法对2011年各中心降水概率预报进行集成,并采用RPS和BS评分方法对预报效果进行评估。结果表明,对于12-156 h逐24 h累积降水量概率预报,多模式集成预报效果优于单模式预报效果,且多模式消除偏差概率预报效果最好;针对小雨、中雨以及大雨以上降水,PoorMan和MBRE概率预报较单中心预报效果均有提高,MBRE概率预报效果优于PoorMan方法。

关 键 词:集合预报  概率预报  多模式消除偏差

Study on multi-model ensemble probability forecast for summer precipitation in China
LIN Chunze,QI Haixia,ZHI Xiefei,BAI Yongqing,LIU Lin.Study on multi-model ensemble probability forecast for summer precipitation in China[J].Meteorology Journal of Hubei,2013(4):354-359.
Authors:LIN Chunze  QI Haixia  ZHI Xiefei  BAI Yongqing  LIU Lin
Institution:1. Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research, WHIHR, CMA, Wuhan 430074; 2. Wuhan Central Meteorological Observatory, Wuhnn 430074; 3. Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University Of lnformation Science &Technology, Nanjing 210044 ; 4. Hubei Service Center of Meteorological Science and Technology, Wuhan 430074)
Abstract:Based on the daily 12-36 h, 36-60 h, 60-84 h, 84-108 h, 108-132 h and 132-156 h ensemble precipitation probability forecasts over China (0 -60 N, 70 -140 E) from May 25 to August 31 during 2007-2011 from the global ensemble models of CMA, ECMWF, JMA, NCEP and UKMO taken from the TIGGE archives, we assembled the precipitation probability forecasts from each of the above 5 centers in 2011 by the bias-removed ensemble mean (MBRE) and the traditional PoorMan techniques (POOL), and then estimated their forecast skills by calculating the Rank Probability Score (RPS) and the Brier Score (BS). The results show that the multi-model ensemble precipitation prob-ability forecast technique has a higher forecast skill than any single model, and the MBRE technique is better than the POOL technique for the 12-156h precipitation forecasts. It is also found that both POOL and MBRE improved considerably the probabilistic forecasts compared to a single model for all light, moderate and heavy rain events. Furthermore, MBRE has a higher forecast skill than POOL.
Keywords:ensemble forecast  probabilistic forecast  multi-model bias-removed ensemble mean
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