一次人工增雨作业中CA-FCM与其他评估方案的比较研究  

Comparison between CA-FCM and other precipitation enhancement effect evaluation methods on cloud seeding operation

作　　者：房彬[1] 肖辉[2] 班显秀[1] 宫蔚蔚[3] 

机构地区：[1]辽宁省人工影响天气办公室,辽宁沈阳110016 [2]中国科学院大气物理研究所,北京100029 [3]沈阳市气象局,辽宁沈阳110168

出　　处：《气象与环境学报》2008年第4期13-18,共6页Journal of Meteorology and Environment

基　　金：国家自然科学基金(40333033);国家自然科学基金(40175001);国家"十五"科技攻关计划(2001BA610A-06-05);"奥运科技"专项(2001BA904B09);中国科学院大气物理研究所创新项目(8-4605)共同资助

摘　　要：根据2002年4月5日河南省人工催化增雨作业数据,分别采用作业区域趋势对比双比分析评估方案、区域趋势相关回归评估方案、区域趋势协变量多元回归评估方案、FCM(浮动对比区历史回归)评估方案、以降水量为协变量的CA-FCM评估方案和以降水量与整层大气可降水量为协变量的CA-FCM评估方案进行效果评估。结果表明:协变量由降水量和整层大气降水量2个组成的CA-FCM方法,由于引入了不受催化影响的物理协变量作为控制因子,以及采用了聚类分析提高了对比区和影响区相关性,其评估效果最好,显著水平高于0.05;其他评估方法评估效果较差。Based on cloud seeding data on April 5,2002 in Henan province, cloud seeding effects were evaluated by six methods, i. e. double contrast method of regional rainfall tendency control, regression analysis method of regional tendency correlation, multiple regression analysis method of regional tendency covariant, floating control historical regression method （FCM）, cluster-analysis-based floating control historical regression method （CA- FCM） using rainfall as a covariant and CA- FCM using rainfall and atmospheric perceivable water as covariants. The results indicate that the effect of CA- FCM using rainfall and atmospheric perceivable water as covariants is better, for the physical covariant that is not influenced by catalyze is used as controlling factor and cluster analysis method is employed. The significant is more than 0.05. The effects of other methods are worse.

关 键 词：CA—FCM 效果评估方案 聚类分析 统计检验 整层大气可降水量 人工增雨作业效果 

分 类 号：P481[天文地球—大气科学及气象学]