Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 369539, 15 pages
http://dx.doi.org/10.1155/2012/369539
Research Article

The Use of Geographically Weighted Regression for the Relationship among Extreme Climate Indices in China

Chunhong Wang,1,2 Jiangshe Zhang,1,2 and Xiaodong Yan3

1School of Science, Xi'an Jiaotong University, Xi'an 710049, China
2State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, China
3Key Laboratory of Regional Climate-Environment in Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Science, Beijing 100029, China

Received 9 May 2011; Accepted 2 September 2011

Academic Editor: Weihai Zhang

Copyright © 2012 Chunhong Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The changing frequency of extreme climate events generally has profound impacts on our living environment and decision-makers. Based on the daily temperature and precipitation data collected from 753 stations in China during 1961–2005, the geographically weighted regression (GWR) model is used to investigate the relationship between the index of frequency of extreme precipitation (FEP) and other climate extreme indices including frequency of warm days (FWD), frequency of warm nights (FWN), frequency of cold days (FCD), and frequency of cold nights (FCN). Assisted by some statistical tests, it is found that the regression relationship has significant spatial nonstationarity and the influence of each explanatory variable (namely, FWD, FWN, FCD, and FCN) on FEP also exhibits significant spatial inconsistency. Furthermore, some meaningful regional characteristics for the relationship between the studied extreme climate indices are obtained.