This paper introduces two copula-based interpolation methods to produce air temperature maps in a data-scarce area: a spatial copula interpolator including covariates, and a mixed copula interpolator. Daily mean air temperature was used from weather stations and ERA_Interim reanalysis weather data at 174 locations in the Qazvin Plain, Iran. The results showed an improved performance of the new methods to describe both spatial variability and co-variability between variables. The methods are potentially useful for other sparsely and irregularly distributed weather data.

The data is minimum and maximum air temperature from two sources: weather stations in Iran, ECMWF Era-interim archive. The study area is between 35.44º and 36.68º latitudes (N) and 49.09º and 50.92º longitudes (E) and includes 24 weather stations . The European Centre for Medium-Range Weather Forecasts (ECMWF) provides reanalysis weather data using the ERA-Interim data assimilation system . The air temperature is available at three hourly intervals at 10 × 15 grid cells at a 0.125º lat/lon resolution. The daily mean air temperature is determined by averaging the minimum and maximum temperatures for each of the 150 grid cells and 24 weather stations, in total 174 locations, at each day of June in 2014.