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基于SVD与机器学习的华南降水预报订正方法

谢舜 孙效功 张苏平 熊朝晖 魏晓敏 崔丛欣

谢舜, 孙效功, 张苏平, 等. 基于SVD与机器学习的华南降水预报订正方法. 应用气象学报, 2022, 33(3): 293-304. DOI:  10.11898/1001-7313.20220304..
引用本文: 谢舜, 孙效功, 张苏平, 等. 基于SVD与机器学习的华南降水预报订正方法. 应用气象学报, 2022, 33(3): 293-304. DOI:  10.11898/1001-7313.20220304.
Xie Shun, Sun Xiaogong, Zhang Suping, et al. Precipitation forecast correction in South China based on SVD and machine learning. J Appl Meteor Sci, 2022, 33(3): 293-304. DOI:  10.11898/1001-7313.20220304.
Citation: Xie Shun, Sun Xiaogong, Zhang Suping, et al. Precipitation forecast correction in South China based on SVD and machine learning. J Appl Meteor Sci, 2022, 33(3): 293-304. DOI:  10.11898/1001-7313.20220304.

基于SVD与机器学习的华南降水预报订正方法

DOI: 10.11898/1001-7313.20220304
资助项目: 

国家重点研发计划 2018YFC1506606

中国气象科学研究院基本科研业务费重点项目 2019Z003

详细信息
    通信作者:

    孙效功,邮箱:xgsun@cma.gov.cn

Precipitation Forecast Correction in South China Based on SVD and Machine Learning

  • 摘要: 降水是在多种天气系统和复杂物理过程共同影响下形成的,因此降水预报难度较大。由于数值预报模式的局限性,使得模式预报产品存在一定误差。为探讨更加有效的模式预报产品误差订正方法,基于奇异值分解(SVD)与机器学习(多元线性回归、套索回归、岭回归)构建订正模型,对2007—2019年4月1日—6月30日华南前汛期欧洲中期天气预报中心(EC)模式降水预报产品进行误差订正试验。结果表明:基于SVD与机器学习相结合的订正模型能有效降低EC模式降水预报产品在华南的预报误差,均方根误差最大优化率达4.2%,累计超过69%的站点得到不同程度的优化;SVD与机器学习相结合的订正模型能很好地处理因子间共线性问题,具有更好的鲁棒性;而对多个订正模型加权集成,均方根误差优化率达5.7%,累计超过77%的站点得到优化,显然加权集成方法订正效果不仅优于EC模式预报产品,也优于参与集成的任一订正模型。
  • 图  1  华南地面气象站分布

    Fig. 1  Distribution of ground sites in South China

    图  2  订正流程

    Fig. 2  Correction process

    图  3  EC模式预报产品均方根误差

    Fig. 3  Root mean square error of EC product

    图  4  不同模型(方法)与EC模式预报产品均方根误差差值对比

    (a)模型Ⅰ, (b)模型Ⅱ, (c)模型Ⅲ,(d)模型Ⅳ, (e)加权集成方法

    Fig. 4  Comparison of root mean square error difference of different models, method to EC product before and after correction

    (a)model Ⅰ, (b)model Ⅱ, (c)model Ⅲ, (d)model Ⅳ, (e)weighted ensemble

    图  5  正负订正幅度站点数占比

    (a)模型Ⅰ, (b)模型Ⅱ, (c)模型Ⅲ, (d)模型Ⅳ, (e)加权集成方法

    Fig. 5  Ratio of positive and negative sites to the correction amplitude

    (a)model Ⅰ, (b)model Ⅱ, (c)model Ⅲ, (d)model Ⅳ, (e)weighted ensemble

    图  6  个例代表站点位置分布

    Fig. 6  Distribution of individual representative sites

    图  7  正、负订正单站对比

    (RMSE1为EC模式预报产品的均方根误差,RMSE2为加权集成订正后的均方根误差)

    Fig. 7  Comparison of positive and negative correction sites

    (RMSE1 is root mean square error of the EC product, RMSE2 is root mean square error of weighted ensemble)

    图  8  2018年6月12日和2019年5月9日两次降水个例订正

    Fig. 8  Comparison of two precipitation cases on 12 Jun 2018 and 9 May 2019 before and after correction

    表  1  前10个模态累计方差贡献

    Table  1  Cumulative variance contribution of the top 10 modes

    模态序号 累计方差贡献
    1 0.5162
    2 0.6220
    3 0.6763
    4 0.7220
    5 0.7516
    6 0.7716
    7 0.7888
    8 0.8014
    9 0.8112
    10 0.8197
    下载: 导出CSV

    表  2  不同模型(方法)订正效果

    Table  2  Correction effect in different models and method

    模型 均方根误差/(mm·d-1) 优化率/%
    模型Ⅳ 13.26 4.1
    模型Ⅱ 13.26 4.1
    模型Ⅲ 13.24 4.2
    模型Ⅰ 13.24 4.2
    加权集成方法 13.01 5.7
    下载: 导出CSV
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  • 收稿日期:  2021-11-02
  • 修回日期:  2022-01-24
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