基于年际增量和EOF迭代法的长江流域汛期降水预测  

Precipitation prediction during flood season in the Yangtze River Basin based on interannual increment and EOF iteration method

作　　者：沈秉璐 杨雅薇[2] 陈权亮 SHEN Binglu;YANG Yawei;CHEN Quanliang(College of Atmospheric Sciences,Chengdu University of Information Technology,Plateau Atmospheric&Environment Laboratory of Sichuan Province,Chengdu 610225;Shanghai Climate Center,Key Laboratory of Cities Mitigation and Adaptation to Climate Change in Shanghai,Shanghai 200030;Gansu Weather Modification Office,Lanzhou 730020)

机构地区：[1]成都信息工程大学大气科学学院高原大气与环境四川省重点实验室,成都610225 [2]上海市气候中心中国气象局上海城市气候变化应对重点开放实验室,上海200030 [3]甘肃省人工影响天气办公室,兰州730020

出　　处：《暴雨灾害》2022年第6期651-661,共11页Torrential Rain and Disasters

基　　金：国家自然科学基金项目(42175056);中国气象局创新发展专项(CXFZ2021Z033,CXFZ2022Z009);上海市自然科学基金(21ZR1457600)。

摘　　要：基于国家气候中心气候系统模式(Beijing Climate Center Climate System Model,BCC_CSM1.1m)和美国NCEP/NCAR的气候预测模式(The NCEP Climate Forecast System Version 2,CFSv2)分别建立针对长江流域汛期降水的动力与统计相结合的降尺度预测模型,并比较两模式对应模型的预报技巧和差异来源。分别选择两模式2月起报的500 hPa及200 hPa全球位势高度场为预报因子,结合年际增量及经验正交分解(EOF)迭代法建立降尺度模型(分别简称DY_CSM1.1m和DY_CFSv2),研究发现:(1) EOF迭代法中截断解释方差的递增增加了预报因子的协同性和稳定性,从而显著提高预报技巧,并由此确定98%的截断解释方差为模型的最优参数。(2)两模型基于最优参数的预测效果均优于模式原始的降水预测,其中DY_CSM1.1m预测技巧更高,对应29 a距平相关系数(ACC)平均评分可达0.43,尤其在长江干流区域预报效果显著提高。将两模型预测的降水年际增量百分率转换为降水距平百分率时,ACC多年平均评分降为0.27和0.22,仍高于模式原始预测。(3) DY_CSM1.1m的ACC历年评分和长江流域汛期降水年际增量均与西太平洋副热带高压的一系列指数具有高相关性(以西太平洋副高脊线位置指数为例,DY_CFSv2则无此关系),因此BCC_CSM1.1m在西太平洋地区模拟性能优于CFSv2是导致该模式降尺度后预报技巧更高的重要原因,这一点在典型洪涝年1998和2020年中得以佐证。Based on the National Climate Center Climate System Model BCC_CSM1.1m(Beijing Climate Center Climate System Model) and the NCEP/NCAR climate prediction model CFSv2(The NCEP Climate Forecast System Version 2) of the United States, two dynamic statistical downscaling prediction models of the precipitation during flood season in the Yangtze River Basin are established correspondingly. The forecasting skills and sources of differences between the two models are compared. The global geopotential height fields at 500 hPa and 200hPa produced by the two models from February are selected as the predictors, and the models are established by combining the interannual increments and the empirical orthogonal decomposition(EOF) iteration method(the test scheme named DY_CSM1.1 and DY_CFSv2). This study found that:(1) The increase of the truncated explained variance in the EOF iteration method enhances the synergy and stability of the predictors, thereby significantly improving the forecasting skills, which indicates that 98% of the truncated explained variance is the optimal parameter of the model.(2) The prediction effect of the optimal parameters of the two models is better than the original precipitation prediction of the model, and the DY_CSM1.1m prediction skill is higher, especially in the main stream of the Yangtze River. The 29-year average of the spatial anomaly correlation coefficient ACC score can reach 0.43 and 0.39, respectively. When the predicted interannual increment percentage of precipitation is converted to the flood season precipitation anomaly percentage, the ACC scores dropped to 0.27 and 0.22, but are still higher than the model’s original predictions.(3) The ACC score of DY_CSM1.1m has a high correlation with the interannual increment of the West Pacific Subtropical High Ridge Position Index(WPSHRP)(but DY_CFSv2 has no such relationship). The inter-annual increment of precipitation in the flood season in the Yangtze River Basin also has a high correlation with the inter-annual increment of WPSHRP. Ther

关 键 词：国家气候中心气候系统模式(BCC_CSM1.1m) NCEP/NCAR的气候预测模式(CFSv2) 年际增量 EOF迭代 汛期降水 

分 类 号：P456[天文地球—大气科学及气象学]