Smart distance searching and DEM-informed interpolation of surface air temperature of climatology in China

Statistical interpolation of the temperature for the missing points is one of the most popular approaches for generating high spatial resolution data sets. However, many interpolation methods used by previous studies are purely mathematic ways, without geographical significance being considered. In the present study the authors interpolate the monthly and annual mean temperature using 726-station observations in China, utilizing improved methods by taking into account geographical factors such as latitude, longitude, altitude. In addition, a smart distance-searching technique is adopted, which helps select the optimum stations on which the guess values at missing points are generated. Results show that the methods used here have evident advantages over the previous approaches. The mean absolute error of ordinary inverse-distance-squared (IDS) technique is in the range of 1.44-1.63degC, on average 1.52degC. The improved method yields a mean of 0.53-0.92degC, on average 0.69degC. Errors have been reduced as much as 50%