Spatial Interpolation Methods of Average Air Temperature under Complicated Terrain
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Abstract:
By using such five methods as Inverse Distance Weighted (IDW), Ordinary Kriging (OK), Radial Basis Function (RBF), Three Dimensional and Quadratic Trend Surface, and Geographically Weighted Regression (GWR), the spatial distributions of the average temperature for 30 years （January, April, July, October and anniversary） in Yunnan Province are interpolated and contrastively analyzed on the basis of the data of 125 weather stations from 1981 to 2010 The results show that the simulation accuracies of the interpolation for three conventional methods (namely IDW, OK and RBF) are unsatisfactory under the condition of complicated geographical environments in Yunnan; the simulation results of three dimensional and quadratic trend surface and GWR to interpolate the spatial distribution of temperature are preferable. The mean absolute errors （MAE） of cross check results for three dimensional and quadratic trend surface are 043 to 102 ℃, with the root mean square errors （RMSE） being 067 to 177 ℃. MAE of the interpolation results of GWR models are less than 065 ℃ and its RMSE is below 08 ℃. The cross check results indicate that the simulation error of GWR model to simulate the spatial distribution of average temperature in Yunnan was the smallest among the five single interpolation methods. Further, by using the superposition method of “GWR interpolation + IDW residual interpolation,” the absolute errors of 64% interpolation results for tested weather stations, including monthly values and annual values, are less than 05 ℃ and the relative errors of 74% interpolation results are below 5%. Meanwhile, the determination coefficients (R2) of the regression relation between measured and interpolated values are above 09 under the condition of using the superposition method.
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Supported by:Beijing E-Tiller Technology Development Co., Ltd.
Supported by:Beijing E-Tiller Technology Development Co., Ltd.