风能模式预报的相似误差订正

Analog Bias Correction of Numerical Model on Wind Power Prediction

采用一种基于相似误差的模式后处理方法,对2011年10月18日一2012年1月5日WRF模式24 h预报的陕西延长风电场风速进行误差订正。该方法通过寻找与当前预报相似的历史预报来进行误差订正,克服了一般基于时间顺序的误差订正方法的不足,即不能处理由于天气系统的剧烈转变引起的预报误差的快速变化。相似误差订正方法减小了预报的均方根误差和中心均方根误差,相对原始预报分别减小9%和10%左右。该方法不仅可以减小系统误差,还可以减小随机误差,从而提高预报准确率。同时,订正结果相对原始预报具有更好的Taylor图模态相关。相似误差订正方法对风能预报敏感区的订正效果更为显著,均方根误差和中心均方根误差分别减小了12%和22%左右。该方法尤其适用于基于风能模式预报的风速误差订正,同时该方法对其他的预测系统和预报变量也有很好的应用潜力。

A new post-processing method is proposed to reduce numerical weather prediction＇s systematic and random errors. The method overcomes a difficulty of a post-processing algorithm inspired by Kalman filte- ring and a 7-day running-mean correction in dealing with sudden changes of the forecast error that could be caused by rapid weather transitions. The analog forecast for a given location and time is defined as a past prediction that matches selected features of the current forecast. The method is the weighted average of observations that verifies when the best analogs are valid. The method is tested for 70-m wind speed pre- diction from Weather Research and Forecasting （WRF） model, with observations from one wind farm sited at Yanchang, Shaanxi Province for 3 months. The analog bias correction method is able to produce skillful corrections of the raw forecasts, even with large day-to-day changes in forecast error, and thus the method can predict drastic changes in forecast error. Moreover, being a prediction based solely on observations, it results in an efficient downscaling pro- cedure that eliminates representativeness discrepancies between observations and predictions. Also,it is able to reduce random errors, therefore improving the predictive skill of raw forecast. The correction method is much better, with average improvement of 9.3% and 9.8% measured by root mean square error （RMSE） and centered root mean square error （CRMSE）, respectively. Meanwhile, the meth- od shows a better pattern of correspondence between predictions and observations. Moreover, the correction method for middle wind speed （5--12 m . s-1）, which plays the most im- portant role on wind power prediction, is much better, with average improvement of 12.3% and 21.7% measured by RMSE and CRMSE, respectively. Thus the analog bias correction method is very suitable for wind power prediction. The analog bias correction method is based purely on verifying observations of past predictions that are similar to the forecast （i. e. , the analogs）, which provide physically based insight about the atmos- pheric state, thus improving the predictive skill. And it also has the potential to be applied to other predic- tion systems and variables.