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气象:2008,34(9):73-80
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我国极端气温指数的时空变化与分区研究
(1.南京大学大气科学系,南京 210093;2.鞍山市气象台;3.南京信息工程大学;4.鞍山市气象台;5.民航江苏空中交通管理分局)
Research of Temporal Spatial Variation and Distribution of Extreme Temperatures Index in China
(1.Atmospheric Science department of Nanjing University, Nanjing 210093;2. Anshan Meteorological Observatory;3.Nanjing University of Information Science and Technology;4.Anshan Meteorological Observatory;5.Jiangsu Air Traffic Management Branch Bureau of CAAC)
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投稿时间:2007-12-18    修订日期:2008-05-04
中文摘要: 利用1961—2000年全国550个台站的逐日最高气温、最低气温资料计算出热浪指数和暖夜指 数并对两个指数分别进行时空变化分析,结果表明极端气温指数的前2种模态基本代表了该 指数的空间分布特征。热浪指数和暖夜指数的第一特征向量的荷载场空间分布基本一致,全 区为一致的增加和减少趋势,并且在时间变化上存在着明显年际和年代际变化特征。利用RE OF和CAST聚类分析相结合的方法对热浪指数和暖夜指数进行分区,将全国热浪指数和暖夜指 数分别分成11个和10个变化区。经过验证发现该方法既克服了前者确定荷载值界限的主观性 ,又避免了后者选择气候中心的不确定性,使区划结果更具有客观性。
Abstract:Using the daily maximum temperature and minimum temperature data from 550 statio ns for the period 1960-2000, the heat wave days index (HWDI) and the warm night index (Tn90) are calculated and their temporal and spatial variation are analyze d. The first two modes are able to represent the spatial distribution characteri stics. The spatial distribution of first eigenvector of the two indexes is gener ally similar, with the same increase and decrease trend. Their inter annual and inter decadal variability in temporal characteristic is remarkable. The change s of heat index and warm night index can be divided into 11 and 10 districts by using REOF and CAST Clustering Analysis Method, which can overcome the subjectivity of the boun dary of the load values by REOF and the uncertainty of climate center by CAST an d make the division more objective.
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向旬,王冀,王绪鑫,徐琪,2008.我国极端气温指数的时空变化与分区研究[J].气象,34(9):73-80.
Xiang Xun,Wang Ji,Wang Xuxin,Xu Qi,2008.Research of Temporal Spatial Variation and Distribution of Extreme Temperatures Index in China[J].Meteor Mon,34(9):73-80.