淮河流域夏季极端降水事件的统计预测模型研究  

Statistical Prediction Model for Summer Extreme Precipitation Events over Huaihe River Valley

作　　者：陈红[1] 

机构地区：[1]中国科学院大气物理研究所,北京100029

出　　处：《气候与环境研究》2013年第2期221-231,共11页Climatic and Environmental Research

基　　金：国家重点基础研究发展计划项目2009CB421407;公益性行业(气象)科研专项GYHY200906018;中国科学院战略性先导科技专项XDA05110201;国家科技支撑计划项目2007BAC29B03

摘　　要：采用年际增量预测方法,通过考察与淮河流域夏季极端降水事件发生频次(HRF)年际增量相关的环流,确定了5个预测因子:冬季北太平洋涛动、12月南极涛动、春季3～4月南印度洋气压、春季3～4月白令海气压、春季3～4月印尼—澳洲附近经向风垂直切变;然后利用这5个预测因子,通过多元线性回归方法建立HRF年际增量的预测模型,进而预测HRF。交叉检验表明,在1962～2005年的后报中,这个预测模型对HRF显示了较高的预测技巧,预测结果与实测间的相关系数为0.67,表现出较高的预测潜力,对淮河流域夏季极端降水事件的预测具有较大的应用价值。The atmospheric circulation anomalies in winter and spring associated with the year-to-year increments of summer extreme precipitation event frequency over the Huaihe River valley （HRF） were analyzed during 1962-2005 to identify five key HRF predictors by using a year-to-year incremental approach. These indicators include the North Pacific Oscillation （NPO） in winter, the Antarctic Oscillation （AAO） in December, sea level pressure over the region of the south Indian Ocean and Bering Sea in March and April （MA）, and the meridional wind shear between 850 hPa and 200 hPa over the Indo-Australian plate region in MA. A prediction model for year-to-year increments of HRF is established by using the multi-linear regression method. The predicted value of each year＇s increment of HRF is added to the observed value within a particular year to yield the HRF forecast. Cross-validation testing shows that the prediction model has a high skill for HRF with a correlation coefficient of 0.67 during 1962-2005. Thus, this prediction model has a high potential for accurate HRF forecasting.

关 键 词：淮河 极端降水事件 预测模型 年际增量 

分 类 号：P456[天文地球—大气科学及气象学]