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气象:2019,45(3):426-436
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基于机器学习的数值天气预报风速订正研究
孙全德,焦瑞莉,夏江江,严中伟,李昊辰,孙建华,王立志,梁钊明
(北京信息科技大学,北京 100101;中国科学院大气物理研究所,北京 100029;北京大学,北京 100871;中国气象科学研究院灾害天气国家重点实验室,北京 100081)
Adjusting Wind Speed Prediction of Numerical Weather Forecast Model Based on Machine Learning Methods
SUN Quande,JIAO Ruili,XIA Jiangjiang,YAN Zhongwei,LI Haochen,SUN Jianhua,WANG Lizhi,LIANG Zhaoming
(Beijing Information Science and Technology University, Beijing 100101;Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029;Peking University, Beijing 100871;State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081)
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投稿时间:2018-05-28    修订日期:2018-10-15
中文摘要: 对风速进行准确预测是精细化天气预报服务(如风能发电、冬季奥运会赛场条件保障等)的重要环节。本文基于三种机器学习算法(LASSO回归、随机森林和深度学习),对数值天气预报模式ECMWF预测的华北地区近地面10 m风速进行订正。首先利用LASSO回归算法提取对10 m风速有重要影响的气象要素特征集,将其作为三种机器学习算法的输入,建立相应模型对ECMWF预测的风速进行订正。用提取后的气象要素特征集建模有助于减少计算量和存储开销,并减小模型的复杂性,从而提高模型的泛化能力。将订正结果与传统订正方法模式输出统计(model output statistics,MOS)得到的订正结果进行对比。结果表明,三种机器学习算法的订正效果均好于MOS方法,显示了机器学习方法在改善局地精准气象预报方面的潜力。
Abstract:Accurate prediction of wind speed is crucial for local weather forecasting services (e.g., dealing with wind power industry and the Olympic Winter Game). Based on three machine learning algorithms (LASSO regression, random forest and deep learning), this paper demonstrates three models for adjusting the 10 m wind speed in North China predicted by the numerical weather forecast model of ECMWF. Firstly, the LASSO regression algorithm is applied to identify the features which significantly affect the near surface wind speed, among all the available meteorological elements. The extracted feature set is used as input for each machine learning algorithm to establish a model to adjust the ECMWF predicted wind speed. Feature extraction helps to reduce the amount of computation, storage overhead and the complexity of the model, hence to facilitate the generalization of the model. The results of the three machine learning algorithms are compared with that of the traditional MOS method. All the three machine learning methods show a better performance in adjusting the wind speed than that of MOS, indicating great potential of the machine learning methods in improving local weather forecast.
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基金项目:中国科学院战略性先导科技专项(A类 XDA19030403)和北京信息科技大学2017年度“实培计划”共同资助
引用文本:
孙全德,焦瑞莉,夏江江,严中伟,李昊辰,孙建华,王立志,梁钊明,2019.基于机器学习的数值天气预报风速订正研究[J].气象,45(3):426-436.
SUN Quande,JIAO Ruili,XIA Jiangjiang,YAN Zhongwei,LI Haochen,SUN Jianhua,WANG Lizhi,LIANG Zhaoming,2019.Adjusting Wind Speed Prediction of Numerical Weather Forecast Model Based on Machine Learning Methods[J].Meteor Mon,45(3):426-436.