非齐次隐马尔可夫降尺度方法对江淮流域夏季逐日降水的模拟及其评估  

Simulation and evaluation of summer daily precipitation based on nonhomogeneous hidden Markov model over the Yangtze-Huaihe River Basin

作　　者：丁梅[1] 江志红[1] 陈威霖[1] DING Mei JIANG Zhihong CHEN Weilin(Key Laboratory of Meteorological Disaster, Ministry of Education （ KLME） / Joint International Research Laboratory of Climate and Environment Change （ ILCEC） / Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disaster （CIC-FEMD ）, Nanjing University of Information Science and Technology, Nanjing 210044, China)

机构地区：[1]南京信息工程大学气象灾害教育部重点实验室/气候与环境变化国际合作联合实验室/气象灾害预报预警与评估协同创新中心,南京210044

出　　处：《气象学报》2016年第5期757-771,共15页Acta Meteorologica Sinica

基　　金：国家自然科学基金项目(41230528,41205062);国家重点研发计划项目(Grant 2016 YFA0600402);江苏高校优势学科建设工程资助项目PAPD;江苏省高校“青蓝工程”创新团队项目;公益性行业(气象)科研专项(GYHY201306024)

摘　　要：引入非齐次隐马尔可夫模型(Nonhomogeneous hidden Markov model,NHMM)统计降尺度方法,利用1961—2002年江淮流域夏季逐日降水资料、欧洲中期天气预报中心(ECMWF)的ERA-40再分析资料建立模型,检验其对东部季风区(以江淮流域为代表)夏季日降水的模拟能力,并对比BCC-CSM1.1(m)模式NHMM降尺度前后的模拟效果。结果表明,NHMM降尺度方法通过建立降水概率分布态间转移参数与大尺度环流变量的联系,对江淮流域逐日降水量具有较好的降尺度效果。模拟的各站日降水量概率分布函数(PDF)曲线与观测非常接近,布赖尔评分(Brier Score,S_B)均小于0.11%,显著性评分(Significance Score,Ss)均大于0.84;夏季总降水量、降水日数、中雨日数、降水强度和95%分位降水量指数的多年平均场偏差百分率绝对值低于10%,前3个指数的空间相关系数高于0.9;该方法对各降水指数的年际变率也有一定的模拟能力,模拟得到的各指数的区域平均年际序列与观测序列的相关系数为0.62—0.87。对BCC-CSM1.1(m)模式的模拟结果进行降尺度后,SB较降尺度前平均减小0.57%,Ss平均增大0.23,皆表明降尺度后的概率分布函数曲线更接近于观测;各降水指数在多数台站的偏差百分率绝对值由大于40%降至10%以内,空间相关系数普遍提高至0.8以上。NHMM降尺度方法能够有效提高BCC-CSM1.1(m)模式对江淮流域夏季日降水的模拟能力,相对气候模式具有显著的"增值",未来可进一步利用该方法进行气候变暖背景下的日降水变化预估。The statistical downscaling method of the nonhomogeneous hidden Markov model （NHMM） is introduced in this pa- per. Based on the observed summer daily precipitation data at 56 meteorological stations in the Yangtze-Huaihe River Basin and the ERA-40 reanalysis data of European Centre for Medium-Range Weather Forecasting （ECMWF） during 1961- 2002, the NHMM is established and the simulation capability of NHMM for summer daily precipitation over the East Asian monsoon area in China （represented by the Yangtze-Huaihe River Basin） has been assessed. Results of the NHMM have been compared with that of the BCC-CSM1. l（m）. It is found that the NHMM performs well in simulating daily precipitation over the Yangtze- Huaihe River Basin by establishing the relationship between transition probabilities of the precipitation probability distribution states and the synoptic-scale atmospheric predictors. The simulated probability distribution function （PDF） curves are close to observations at individual stations with the Brier s core（SB） less than 0.11 % and the significance score （Ss） greater than 0.84. Relative errors of summer total precipitation, number of rainy days （91 ram）, number of rainy days with daily precipitation more than 10 mm, simple daily intensity, 95th percentile value of precipitation all are less than 10 %, and the first three indices＂ spatial correlation coefficients between the NHMM outputs and observations are greater than 0.9. The NHMM can also repro- duce the interannual variability of the precipitation indices mentioned above. Correlation coefficients of the annual sequences of area- averaged precipitation indices between simulations and observations are within the range of 0.62 - 0. 87. The NHMM is also driven by predictors of BCC-CSM1.1 （m） from 1986 to 2005. After downscaling, the SB averagely decreased by 0.57% and Ss averagely in- creased by 0.23. This result indicates that the probability distribution function curves are much closer to the observations. Absolute

关 键 词：非齐次隐马尔可夫模型(NHMM) 统计降尺度 逐日降水 模拟 

分 类 号：P467[天文地球—大气科学及气象学]