Abstract:Aiming at the problem that the resolution of the current wind forecasts is not high and dynamic downscaling method is less applicated to wind forecasting, this paper used the diagnostic wind field function of CALMET model and high spatial resolution terrain data to dynamically downscaling the wind forecast data outputted by WRF model. The main theory is kinematic effect of terrain. After the large scale surface wind field was adjusted by slope flows and terrain blocking effects, the wind field became finer and showed the feature corresponding to terrain. In the experiment part, we took Guangdong Province as study region, using observation data and CLDAS (CMA land data assimilation system) data to examine the simulation result by a case. The result indicated that the resolution of wind field was more precise after downscaling and contained more sophisticated information related to terrain. Correlation coefficients between the simulation and observation results of wind speed were at a high level and the RMSE (root mean square error) was much smaller. The comparison between simulation and CLDAS showed the similar result. To sum up, the combination of WRF CALMET is an outstanding downscaling method which could effectively improve the temporal spatial resolution of wind forecast data. Meanwhile it might be able to make the result closer to observation. Thus, this method could probably be a reference for wind forecasting in the future.