Predictive Capacity of Mesoscale Model for Short-Range Wind Speed Forecasting at Wind Power Farm

The predictive capacity of a mesoscale model for short-range wind speed forecasting at a wind power farm is investigated. The results of the Weather Research and Forecasting Model (WRF) are compared and analyzed in this paper and also compared with observation data at a wind power farm in Ulan Qab. The research shows that although the model parameterization schemes' forecasting ability varies with time, the schemes show no essential difference. The forecast level is relatively low when the weather is developing acutely. The synoptic background is the main contributor to the model's predictive capacity. At this wind farm, the daily mean forecast relative error of the WRF forecast with respect to the observation is only 11.78% in 2009, and the number of days for which the error is greater than 20% does not exceed 15%. The forecast error predominantly appears when the wind speed weakens rapidly or a Mongolian cyclone or Northeast cyclone undergoes dramatic evolution.