| 一种用于中国年最高(低)气温区划的新的聚类方法 |
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| 引用本文： | 刘吉峰,李世杰,丁裕国,陆其峰.一种用于中国年最高(低)气温区划的新的聚类方法[J].高原气象,2005,24(6):966-973. |
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| 作者姓名： | 刘吉峰 李世杰 丁裕国 陆其峰 |
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| 作者单位： | 中国科学院,南京地理与湖泊研究所,江苏,南京,210008;中国科学院,研究生院,北京,100039;中国科学院,南京地理与湖泊研究所,江苏,南京,210008;南京信息工程大学,江苏,南京,210044 |
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| 基金项目： | 中国科学院南京地理所知识创新工程重大项目（CXNIGLAS-A01）;国家自然科学基金项目（40471001,40275032）共同资助 |
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| 摘 要： | 采用聚类分析和旋转主分量分析相结合的方案，对我国年最高（低）气温的年际变化型态进行地理区划。这种两者相结合的分区方案可以互相补充，使区划更具客观性。结果表明，中国年最高气温和最低气温年际变化分别可划为12和11个不同类型的区域。对前者（高温）来说，各区域的年际差异比较大，气温变化的阶段性特征有所不同；对后者（低温）来说，各区域具有较强的增温一致性，但增温的特点和幅度存在明显区域差异。最高（低）气温和平均温度的关系在各区域是不同的。总体来说，年代际变化的一致性要好于年际变化的一致性。
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| 关 键 词： | 年最高(低)气温 聚类统计检验 旋转主分量分析 气候区划 |
| 文章编号： | 1000-0534（2005）06-0966-08 |
| 收稿时间： | 2004-10-15 |
| 修稿时间： | 2004-10-152005-03-24 |
A New Cluster Method Applied to Yearly Highest and Lowest Air Temperature Region Divisions in China |
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| LIU Ji-feng,LI Shi-jie,DING Yu-guo,LU Qi-feng.A New Cluster Method Applied to Yearly Highest and Lowest Air Temperature Region Divisions in China[J].Plateau Meteorology,2005,24(6):966-973. |
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| Authors: | LIU Ji-feng LI Shi-jie DING Yu-guo LU Qi-feng |
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| Institution: | 1. Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; 2. Graduate School of the Chinese Academy of Sciences, Beijing 100039, China; 3. Nanjing University of Information Science and Technology, Nanjing 210044, China |
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| Abstract: | The yearly characteristics of the highest and lowest temperatures in China are divided using method of the combine of clustering analysis with statistic test and the rotating principal component. The result shows that the two methods can supplement each other to make the region division more objective. Their yearly changes of the highest and lowest temperatures in China can be divided into 12 and 11 regions, respectively. The yearly changes and its phase characteristic are different among these areas for the highest temperature; as far as the lowest temperature, there are accordant increments of temperature. The relations between the highest-and lowest-temperatures and averaged temperature are clearly discriminating in different areas; the correlation of every decade are more prominent than those of yearly. |
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| Keywords: | Yearly highest-and lowest-temperatures Clustering analysis with statistic test The rotating principal component Climatic sub-division |
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