中国冬季气温的集合典型相关分析和预报  

Ensemble Canonical Correlation Prediction Method of Winter Temperature over China

作　　者：陈小兰[1] 吴洪宝[1] 丁留贯[1] 贺晓霞[1] 

机构地区：[1]南京信息工程大学江苏省气象灾害重点实验室,江苏南京210044

出　　处：《南京气象学院学报》2007年第5期623-631,共9页Journal of Nanjing Institute of Meteorology

基　　金：江苏省气象灾害重点实验室基金项目资助(KLME060211)

摘　　要：以欧亚大陆地面温度、北半球500 hPa高度、热带印度洋SST(sea surface temperature)以及北太平洋SST为预报因子,通过典型相关分析(canonical correlation analysis,简称CCA)建立预报关系,然后用集合典型相关分析预报(ensemble canonical correlation prediction,简称ECC)方法预报中国冬季气温,并分析预报技巧及进行独立样本检验。结果表明,不同的预报因子对各个地区有不同的预报技巧,以欧亚大陆地面温度为预报因子预报技巧较高,而ECC模式对中国冬季气温有更好的预报能力,预报技巧高于任何一个单因子场的CCA预报;采用回归法的集合平均比简单的等权集合平均预报技巧更稳定。The predictors are the surface temperature over the Eurasian continent, 500 hPa heights over the Northern Hemisphere, SST （sea surface temperature）over the tropical Indian Ocean and SST over the North Pacific. The canonical correlation analysis （CCA） prediction is used to establish forecasting relation. Then the ensemble canonical correlation prediction （ECC） method is used to predict winter temperature over China. The forecast skill is analyzed and independent samples test is also performed. The results show that different predictors have different forecast skills in various regions. Using the sea surface temperature over the North Pacific as a predictor, the skill is higher. But the ECC gives better performance, and its skill is higher than any CCA forecast of single predictor. Under the condition of the same predictors the regression ensemble forecast is better than the simple equal ensemble forecast.

关 键 词：中国冬季气温 集合预报 典型相关分析 等权集合 回归集合 

分 类 号：P457.3[天文地球—大气科学及气象学]