Accuracy Comparison of Spatial Interpolation Methods for Estimation of Air Temperatures in South Korea.

Because of complex terrain, micro- as well as meso-climate variability is extreme by locations in Korea. In particular, air temperature of agricultural fields is influenced by topographic features of the surroundings making accurate interpolation of regional meteorological data from point-measured data. This study was conducted to compare spatial interpolation methods to estimate air temperature in agricultural fields surrounded by rugged terrains in South Korea. Four spatial interpolation methods including Inverse Distance Weighting (IDW), Spline, Ordinary Kriging and Cokriging were tested to estimate monthly air temperature of unobserved stations. Monthly measured data sets (minimum and maximum air temperature) from 588 automatic weather system(AWS) locations in South Korea were used to generate the gridded air temperature surface. As a result, temperature lapse rate improved accuracy of interpolation methods and spline produced the lowest RMSE of spatial interpolation methods in both maximum and minimum air temperature estimation.