摘要
利用辽宁和吉林省24座测风塔风速观测资料,应用线性回归方法对高分辨率中尺度模式近地层风速预报产品进行订正。首先通过4组不同的订正实验分析训练样本长度、样本滚动方式等对订正效果的影响,确定单点订正最佳方案,并综合线性方法在东北地区不同下垫面条件下的适用性;然后应用24座测风塔已确定的单点订正关系,尝试区域风速的平面订正,并基于剩余23座测风塔资料对全场订正效果进行评估。结果表明:训练样本的长度对订正效果影响较明显,在东北地区训练样本长度取20 d效果最佳;当训练样本长度取最优天数时,滚动系数的订正效果与固定系数的订正效果基本一致;各种下垫面通过线性订正均能取得较明显提高,其中丘陵地区效果最明显,通过订正均方根误差整体降低1.61 m·s-1,平原地区为0.95 m·s-1,沿海地区为0.91 m·s-1;平面风速订正实验显示,订正关系平面外推可取得明显的订正效果,全场平均绝对误差降低0.20 m·s-1,该方法可为订正资料匮乏区域的预报提供参考。
Based on the wind speed observation data of 24 wind towers in Liaoning and Jilin provinces,the linear regression method was adopted to revise wind speed forecast biases of the high-resolution mesoscale model.Firstly,the impacts of the training sample duration and rolling method on correction effectiveness were studied to determine the optimal scheme by four different calibration experiments,and the applicability of the station correction method on different underlying surfaces was synthetically analyzed.Then,the determined station correction relation from the 24 wind towers was used to correct gridded forecast wind field data,and the other 23 wind towers data were employed to assess the correction effect.The results show that the duration of the training sample has a direct impact on the correction effect.In the experiment area,the duration of 20-day for the training sample can achieve the best effect.When the training sample duration is 20 days,the correction effects of different sample selection methods are consistent.The prediction effect under various underlying surfaces can be significantly improved with the linear correcting method,and the improvement is the most obvious in the hilly area with the root mean square error(RMSE)reducing by 1.61 m·s-1.The RMSEs in the plain and coastal areas decrease by 0.95 m·s-1 and 0.91 m·s-1,respectively.The overall correction experiments of the gridded wind speed data indicate that the extrapolation of the correction relation can achieve an obvious correction effect with the RMSE reducing by 0.20 m·s-1.Therefore,the method can be effectively applied to the region where observation is scarce and will be available for modifying grid wind speed data in the future.
作者
王洁
郭鹏
何晓凤
刘善峰
WANG Jie;GUO Peng;HE Xiao-feng;LIU Shan-feng(Huafeng Meteorological Media Group Co.,Ltd,Beijing 100081 China;Beijing JiuTian Meteorology Science&Technology Co.,Ltd,Beijing,100081 China;Beijing HuaXinTianLi Energy Meteorological Science and Technology Center,Beijing,100081 China;Electric Power Research Institute,State Grid Electric Power of He′nan Province,Zhengzhou 450052,China)
出处
《气象与环境学报》
2020年第6期115-121,共7页
Journal of Meteorology and Environment
基金
国家重点研发计划项目(2018YFC1507801)资助。
关键词
风速数值预报
误差订正
线性回归
Numerical forecast of wind speed
Bias correction
Linear regression
