Multi-scale regionalization based mining of spatio-temporal teleconnection patterns between anomalous sea and land climate events  

作　　者：XU Feng SHI Yan DENG Min GONG Jian-ya LIU Qi-liang JIN Rui 徐枫;石岩;邓敏;龚健雅;刘启亮;金瑞

机构地区：[1]Department of Geo-informatics,Central South University,Changsha 410083,China [2]State Key Laboratory of Information Engineering in Surveying(Mapping&Remote Sensing,Wuhan University),Wuhan 430079,China [3]Collaborative Innovation Center of Geospatial Technology,Wuhan University,Wuhan 430079,China

出　　处：《Journal of Central South University》2017年第10期2438-2448,共11页中南大学学报（英文版）

基　　金：Projects(41601424,41171351)supported by the National Natural Science Foundation of China;Project(2012CB719906)supported by the National Basic Research Program of China(973 Program);Project(14JJ1007)supported by the Hunan Natural Science Fund for Distinguished Young Scholars,China;Project(2017M610486)supported by the China Postdoctoral Science Foundation;Projects(2017YFB0503700,2017YFB0503601)supported by the National Key Research and Development Foundation of China

摘　　要：Climate sequences can be applied to defining sensitive climate zones, and then the mining of spatio-temporal teleconnection patterns is useful for learning from the past and preparing for the future. However, scale-dependency in this kind of pattern is still not well handled by existing work. Therefore, in this study, the multi-scale regionalization is embedded into the spatio-temporal teleconnection pattern mining between anomalous sea and land climatic events. A modified scale-space clustering algorithm is first developed to group climate sequences into multi-scale climate zones. Then, scale variance analysis method is employed to identify climate zones at characteristic scales, indicating the main characteristics of geographical phenomena. Finally, by using the climate zones identified at characteristic scales, a time association rule mining algorithm based on sliding time windows is employed to discover spatio-temporal teleconnection patterns. Experiments on sea surface temperature, sea level pressure, land precipitation and land temperature datasets show that many patterns obtained by the multi-scale approach are coincident with prior knowledge, indicating that this method is effective and reasonable. In addition, some unknown teleconnection patterns discovered from the multi-scale approach can be further used to guide the prediction of land climate.Climate sequences can be applied to defining sensitive climate zones, and then the mining of spatio-temporal teleconnection patterns is useful for learning from the past and preparing for the future. However, scale-dependency in this kind of pattern is still not well handled by existing work. Therefore, in this study, the multi-scale regionalization is embedded into the spatio-temporal teleconnection pattern mining between anomalous sea and land climatic events. A modified scale-space clustering algorithm is first developed to group climate sequences into multi-scale climate zones. Then, scale variance analysis method is employed to identify climate zones at characteristic scales, indicating the main characteristics of geographical phenomena. Finally, by using the climate zones identified at characteristic scales, a time association rule mining algorithm based on sliding time windows is employed to discover spatio-temporal teleconnection patterns. Experiments on sea surface temperature, sea level pressure, land precipitation and land temperature datasets show that many patterns obtained by the multi-scale approach are coincident with prior knowledge, indicating that this method is effective and reasonable. In addition, some unknown teleconnection patterns discovered from the multi-scale approach can be further used to guide the prediction of land climate.

关 键 词：CLIMATE sequences ANOMALOUS climatic EVENTS SPATIO-TEMPORAL teleconnection patterns MULTI-SCALE REGIONALIZATION 

分 类 号：P461[天文地球—大气科学及气象学] P732.5