Spatial Interpolation of Daily Maximum and Minimum Air Temperature Based on Meteorological Model Analyses and Independent Observations

Abstract

Abstract Hourly meteorological forecast model initializations are used to guide the spatial interpolation of daily cooperative network station data in the northeastern United States. The hourly model data are transformed to daily maximum and minimum temperature values and interpolated to the station points after standardization to station elevation based on the model temperature lapse rate. The resulting bias (interpolation − observation) is computed and then interpolated back to the model grids, allowing daily adjustment of the temperature fields based on independent observations. These adjusted data can then be interpolated to the resolution of interest. For testing, the data are interpolated to stations that were withheld during the construction of the bias field. The use of the model initializations as a basis for interpolation improves upon the conventional interpolation of elevation-adjusted station data alone. When inverse-distance-weighted interpolation is used in conjunction with data from a 40-km-model grid, mean annual absolute errors averaged 5% smaller than those from interpolation of station data alone for maximum and minimum temperature, which is a significant decrease. Using data from a 20-km-model grid reduces mean absolute error during June by 10% for maximum temperature and 16% for minimum temperature. Adjustment for elevation based on the model temperature lapse rate improved the interpolation of maximum temperature, but had little effect on minimum temperature. Winter minimum temperature errors were related to snow depth, a feature that likely contributed to the relatively high autocorrelation exhibited by the daily errors.