Abstract
Spatial interpolation of meteorological parameters, closely related to the earth surface, plays important roles in climatological study. However, most of traditional spatial interpolation methods ignore the geographic semantics of interpolation sample points in practical application. This paper attempts to propose an improved inverse-distance weighting interpolation algorithm considering geographic semantics (S-IDW), which adds geographic semantic similarity to the traditional IDW formula and adjusts weight coefficient. In the interpolation process, the geographic semantic differences between sample points and estimation points are considered comprehensively. In this study, 3 groups of land surface temperature data from 2 different areas were selected for experiments, and several commonly used spatial interpolation methods were compared. Experimental results indicated that S-IDW outperformed IDW and several existing spatial interpolation methods, but there were also some abnormal value and interpolation outliers. This method provides a new insight toward the estimation accuracy, data missing, and error correction of spatial attributes related to meteorological parameters.
Highlights
Spatial interpolation of meteorological parameters is to obtain relatively accurate descriptions of spatial attributes related to climatological dynamics and weather patterns by using some reasonably located samples [1]
In order to explore the spatial interpolation accuracy in different areas and different land surface temperatures, LST at diverse time intervals were selected in the 2 study areas, and 3 distinct LST conditions of high temperature, low temperature, and normal temperature were used to carry out experiments. e interpolation accuracy of traditional numerical interpolation methods is often closely related to the density and sparsity of the discrete points
Through the mathematical statistics analysis and Pearson correlation analysis of the five interpolation methods, it is indicated that the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared of errors (RMSE) of S-IDW are closer to the best fitting values between measured and estimated values under ideal conditions than the other 4 interpolation methods
Summary
Spatial interpolation of meteorological parameters is to obtain relatively accurate descriptions of spatial attributes related to climatological dynamics and weather patterns by using some reasonably located samples [1]. Spatial interpolation method is widely used to transform discrete observation data into continuous surface so as to better measure the spatial distribution pattern of data elements [2]. Familiar spatial interpolation methods, such as IDW, Kriging, Spline, and trend surface method, have been widely used in different fields. Kriging method can adopt different variogram forms and parameters for different sampling data points, with certain flexibility. It loses the high efficiency of the original inverse-distance weighting method by first determining the variogram form and fitting the parameters of variogram. Spline method is not suitable for sparse and finite sampling points and is often used for high-density sample point interpolation [5]. e trend surface method relies
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