Short-term wind speed forecasting bias correction in the Hangzhou area of China based on a machine learning model

准确的风速预报具有重要的社会意义.在本研究中,使用名为WSFBC-XGB的XGBoost机器学习模型对中国浙江省杭州市自动气象站的短期风速预报误差进行校正.WSFBC-XGB使用本地数值天气预报系统的产品作为输入.将WSFBC-XGB校正的结果与传统MOS(模型输出统计)方法校正的结果进行了比较.结果表明:WSFBC-XGB预报风速的均方根误差(RMSE)/准确率(ACC)分别比NWP和MOS降低/提高了26.1%和7.64%/35.6%和7.02%;对于90%的站点WSFBC-XGB的RMSE/ACC均小于/高于MOS.此外,采用平均杂质减少法对WSFBC-XGB的可解释性进行分析,以帮助用户增加对模型的信任.结果表明:10米风速(47.35%),10米风的经向分量(12.73%),日循环(9.97%)和1000百帕风的经向分量(7.45%)是前4个最重要的特征.WSFBC-XGB模型将有助于提高短期风速预报的准确性,为大型户外活动提供支持.

Accurate wind speed forecasting is of great societal importance.In this study,the short-term wind speed forecasting bias at automatic meteorological stations in Hangzhou,Zhejiang Province,China,was corrected using an XGBoost machine learning model called WSFBC-XGB.The products of the local NWP(numerical weather prediction)system were used as the inputs of WSFBC-XGB.The WSFBC-XGB-corrected results were compared with those corrected using the traditional MOS(model output statistics)method.Results showed that WSFBC-XGB performed better than MOS,with the root-mean-square errors(RMSEs)/accuracy rates of the wind speed forecasting(ACCs)of WSFBC-XGB being reduced/promoted by 26.1%and 7.64%/35.6%and 7.02%relative to NWP and MOS,respectively.The RMSEs/ACCs of WSFBC-XGB were smaller/higher than those of MOS at 90%stations.In addition,the mean decrease in impurity method was used to analyze the interpretability of WSFBC-XGB to help users gain trust in the model.Results showed that the four most important features were the wind speed at 10 m(47.35%),meridional component of wind at 10 m(12.73%),diurnal cycle(9.97%),and meridional component of wind at 1000 hPa(7.45%).The WSFBC-XGB model will help improve the accuracy of short-term wind speed forecasting and provide support for large-scale outdoor activities.