Downscaling Hourly Air Temperature of WRF Simulations Over Complex Topography: A Case Study of Chongli District in Hebei Province, China

Abstract Accurate and high‐resolution air temperature prediction is important in many different applications. Hourly air temperature forecasting in mountainous areas is necessary and important because mountainous areas are becoming increasingly important areas of human activities. At present, scientists successfully employ numerical weather prediction (NWP) models, such as the Weather Research and Forecasting (WRF) model, to achieve reliable forecasts. However, air temperature forecasting and modeling over complex geographical zones are still difficult tasks. The WRF model is a mesoscale model and does not adequately account for the influence of terrain on the air temperature. It is important to downscale larger‐scale models to a much finer scale. In this paper, a statistical temperature downscaling method based on geographically weighted regression (GWR) and diurnal temperature cycle (DTC) models is proposed. A statistical downscaling scheme of WRF simulation data is designed to forecast the hourly air temperature from 1‐km spatial resolution to 30 m, up to 24 hr in advance. The combined downscaling model's root‐mean‐square error (RMSE) decreased by 0.87°C at the automatic weather station (AWS) level and 0.62°C over the domain when compared to WRF simulations, and the mean absolute error (MAE) decreased by 0.71°C and 0.51°C, respectively, at these two levels. The results reveal that the combined downscaling model performs very well in correcting and downscaling the air temperature in WRF simulations in the study areas.