Abstract. The climate of Mongolia is a harsh continental climate with four distinctive seasons, high annual and diurnal temperature fluctuations, and low rainfall. Because of the country’s high altitude, it is generally colder than that of other countries in the same latitude. This study focuses on evaluating the suitability of two interpolation methods in terms of their accuracy at the air temperature data in Mongolia. Four data sets of air temperature from 1982 to 2019 in 60 meteorological stations located in Mongolia and elaborated from a 90 m resolution digital elevation model (DEM), latitude and longitude using two interpolation methods. ArcGIS is used to produce the spatially distributed air temperature data by using IDW and ordinary kriging. Three statistical methods are multiple regression, RMSE and bias, which showed that the IDW the best for this data from other methods by the results that have been obtained. Statistics on the latitude, longitude and surface elevation of each of the 37 years in Mongolia at 60 meteorological stations have been statistically valid with dependent coefficients at 95–99.9%. As the average air temperature, recorded at the meteorological stations, had a statistical correlation of −0.606 with latitude, 0.295 with longitude, and −0.432 with altitude, a multiple regression equation was developed and a highly accurate map for long terms air temperature covering 1982–2019 using interpolation IDW and Kriging method. Also, the highest RMSE value for maps used IDW was 1.38 while the lowest and average values were 0.03 and 0.44, respectively, and the highest bias was 1.21, lowest 0.95, and average 1.01. As opposed to, highest RMSE value for maps that used Kriging, was 6.16, lowest 0.27 and average 1.08 while highest bias was 1.29 and lowest was 0.85, with 1.01 as average. This demonstrates that IDW offers much better accuracy as opposed to Kriging and shows less bias errors. When the air temperature map that used the IDW method is compared against the meteorological station data the significance was 0.98 and when compared against ERA5 model results, significance was 0.95 showing strong statistical significance. Also, a comparison of air temperature map, processed by Kriging method and the meteorological station data shows 0.97 statistical significance, and comparison with ERA5 model shows (validation) 0.94 significance, which is very high. The mean value of the calculated temperature regression model in Mongolia and the root mean square error 0.02–0.09 for each station indicates that the estimation method is good and can be used in the future.
Comparison of Statistical Interpolation Methods for Downscaling of Summer Temperature Data from the Korea Meteorological Administration`s Digital Forecasts
