The Effect of Local Gradients on Increasing of Climatic Data Interpolation Accuracy by Geographically Weighted Regression

Due to spatial correlations between climatic variables and natural features like altitude, the gradients finding is the first option in zoning of them. Various gradients in large area and lack of knowledge of them is resulted in applying of geostatistical and deterministic interpolation techniques by researchers. Geographically Weighted Regression (GWR) is a method which can identify local regressions as well and in spite of gradient increases interpolation accuracy. Accordingly, this research was conducted in order to evaluation of GWR’s accuracy than another interpolation methods based on 30 years average of air temperature and relative humidity data of 240 synoptic and climatologically station in Iran. The results of this research on air temperature which was a function of altitude showed that GWR method had significant difference than other methods, while there was no significant difference on relative humidity and These results may be due to no specific correlation between altitude and relative humidity.