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The Use of Geographically Weighted Regression for the Relationship among Extreme Climate Indices in China
Joint Authors
Wang, Chunhong
Yan, Xiaodong
Zhang, Jiang-She
Source
Mathematical Problems in Engineering
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-15, 15 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2011-11-17
Country of Publication
Egypt
No. of Pages
15
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Wang, Chunhong& Zhang, Jiang-She& Yan, Xiaodong. 2011. The Use of Geographically Weighted Regression for the Relationship among Extreme Climate Indices in China. Mathematical Problems in Engineering،Vol. 2012, no. 2012, pp.1-15.
https://search.emarefa.net/detail/BIM-1029558
Modern Language Association (MLA)
Wang, Chunhong…[et al.]. The Use of Geographically Weighted Regression for the Relationship among Extreme Climate Indices in China. Mathematical Problems in Engineering No. 2012 (2012), pp.1-15.
https://search.emarefa.net/detail/BIM-1029558
American Medical Association (AMA)
Wang, Chunhong& Zhang, Jiang-She& Yan, Xiaodong. The Use of Geographically Weighted Regression for the Relationship among Extreme Climate Indices in China. Mathematical Problems in Engineering. 2011. Vol. 2012, no. 2012, pp.1-15.
https://search.emarefa.net/detail/BIM-1029558
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1029558