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基于机器学习的华北气温多模式集合预报的订正方法

门晓磊 焦瑞莉 王鼎 赵晨光 刘亚昆 夏江江 李昊辰 严中伟 孙建华 王立志

门晓磊, 焦瑞莉, 王鼎, 赵晨光, 刘亚昆, 夏江江, 李昊辰, 严中伟, 孙建华, 王立志. 基于机器学习的华北气温多模式集合预报的订正方法[J]. 气候与环境研究, 2019, 24(1): 116-124. doi: 10.3878/j.issn.1006-9585.2018.18049
引用本文: 门晓磊, 焦瑞莉, 王鼎, 赵晨光, 刘亚昆, 夏江江, 李昊辰, 严中伟, 孙建华, 王立志. 基于机器学习的华北气温多模式集合预报的订正方法[J]. 气候与环境研究, 2019, 24(1): 116-124. doi: 10.3878/j.issn.1006-9585.2018.18049
Xiaolei MEN, Ruili JIAO, Ding WANG, Chenguang ZHAO, Yakun LIU, Jiangjiang XIA, Haochen LI, Zhongwei YAN, Jianhua SUN, Lizhi WANG. A Temperature Correction Method for Multi-model Ensemble Forecast in North China Based on Machine Learning[J]. Climatic and Environmental Research, 2019, 24(1): 116-124. doi: 10.3878/j.issn.1006-9585.2018.18049
Citation: Xiaolei MEN, Ruili JIAO, Ding WANG, Chenguang ZHAO, Yakun LIU, Jiangjiang XIA, Haochen LI, Zhongwei YAN, Jianhua SUN, Lizhi WANG. A Temperature Correction Method for Multi-model Ensemble Forecast in North China Based on Machine Learning[J]. Climatic and Environmental Research, 2019, 24(1): 116-124. doi: 10.3878/j.issn.1006-9585.2018.18049

基于机器学习的华北气温多模式集合预报的订正方法

doi: 10.3878/j.issn.1006-9585.2018.18049
基金项目: 

中国科学院战略性先导科技专项 A-XDA19030403

中国科学院战略性先导科技专项 XDA19040202

详细信息
    作者简介:

    门晓磊, 男, 1993年出生, 硕士研究生, 主要从事机器学习和深度学习方向的研究。E-mail:1160727887@qq.com

    通讯作者:

    夏江江, E-mail:xiajj@tea.ac.cn

  • 中图分类号: P457.3

A Temperature Correction Method for Multi-model Ensemble Forecast in North China Based on Machine Learning

Funds: 

Strategic Priority Research Program of Chinese Academy of Sciences A-XDA19030403

Strategic Priority Research Program of Chinese Academy of Sciences XDA19040202

  • 摘要: 模式预报的订正是决定局地天气预报结果的一个重要步骤,基于机器学习的后处理模型近年来开始崭露头角。本文发展了基于岭回归(Ridge)、随机森林(Random Forest,RF)和深度学习(Deep Learning,DL)的3种后处理模型,基于中国气象局(CMA)的BABJ模式、欧洲中期天气预报中心(ECMWF)的ECMF模式、日本气象厅(JMA)的RJTD模式和NCEP的KWBC模式这4个数值天气预报模式2014年2月至2016年9月(训练期)近地面2 m气温预报和实况资料确定各模型参数,进而对2016年10月至2017年9月(预报期)华北地区(38°N~43°N,113°E~119°E)的逐日地面2 m气温预报进行了多模式集合预报分析。采用均方根误差对预报效果进行评估,这3种后处理模型的预报效果和4个数值天气预报模式以及通常的多模式集合平均(Ensemble Mean,EMN)的预报效果的对比表明:1)随着预报时长增加,4个数值预报模式及各种后处理模型的均方根误差均呈上升趋势;但区域平均而言,Ridge、RF和DL的预报效果在任何预报时长上都明显优于EMN和单个天气预报模式;特别是前几天的短期预报DL的预报效果更好,中后期预报Ridge的预报效果略好。2)华北地区的东南部均方根误差较小,其余格点上均方根误差较高,从空间分布而言,DL的订正预报效果最好,3种机器学习模型的误差在1.24~1.26℃之间,而EMN的误差达1.69℃。3)夏季各种方法的预报效果都较好,冬季预报效果都较差;但是Ridge、RF和DL的预报效果明显优于EMN,这3种模型预报的平均均方根误差在2.15~2.18℃之间,而EMN的平均均方根误差达2.45℃。
  • 图  1  深层神经网络构建的多模式集合温度预报模型

    Figure  1.  Multi-model ensemble temperature prediction model constructed by deep neural network

    图  2  4个模式以及4种后处理模型的均方根误差(RMSE)随预报时长的变化

    Figure  2.  Root-mean-square errors (RMSEs) in four models and four post-processing models as a function of forecast lead time

    图  3  4种预报订正处理模型的均方根误差空间分布

    Figure  3.  RMSEs spatial distributions in four post-predictive processing models

    图  4  4种预报订正处理模型的均方根误差的时间序列(2016年10月至2017年9月)

    Figure  4.  Time series of RMSEs for four forecast correction processing models (Oct 2016−Sep 2017)

    图  5  4个数值预报模式的均方根误差的时间序列(2016年10月至2017年9月)

    Figure  5.  Time series of RMSEs for four numerical weather prediction models (Oct 2016−Sep 2017)

    表  1  4个模式以及4种后处理模型的均方根误差随预报时长的线性趋势

    Table  1.   Trends of RMSEs in four models and four postrocessing models with the forecast lead time

    ℃ d−1
    模式或模型名称 线性趋势
         BABJ 0.22
         ECMF 0.29
         RJTD 0.22
         KWBC 0.27
         EMN 0.20
         Ridge 0.23
         RF 0.23
         DL 0.23
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-04-02
  • 网络出版日期:  2018-07-04
  • 刊出日期:  2019-01-20

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