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