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基于迁移CNN的江淮持续性强降水环流分型

蔡金圻 谭桂容 牛若芸

蔡金圻, 谭桂容, 牛若芸. 基于迁移CNN的江淮持续性强降水环流分型. 应用气象学报, 2021, 32(2): 233-244. DOI: 10.11898/1001-7313.20210208. DOI: 10.11898/1001-7313.20210208
引用本文: 蔡金圻, 谭桂容, 牛若芸. 基于迁移CNN的江淮持续性强降水环流分型. 应用气象学报, 2021, 32(2): 233-244. DOI: 10.11898/1001-7313.20210208. DOI: 10.11898/1001-7313.20210208
Cai Jinqi, Tan Guirong, Niu Ruoyun. Circulation pattern classification of persistent heavy rainfall in jianghuai region based on the transfer learning cnn model. J Appl Meteor Sci, 2021, 32(2): 233-244. DOI:  10.11898/1001-7313.20210208
Citation: Cai Jinqi, Tan Guirong, Niu Ruoyun. Circulation pattern classification of persistent heavy rainfall in jianghuai region based on the transfer learning cnn model. J Appl Meteor Sci, 2021, 32(2): 233-244. DOI:  10.11898/1001-7313.20210208

基于迁移CNN的江淮持续性强降水环流分型

DOI: 10.11898/1001-7313.20210208
资助项目: 

国家重点研发计划 2018YFC1507703

国家重点研发计划 2018YFC1505804

详细信息
    通信作者:

    谭桂容, tanguirong@nuist.edu.cn

Circulation pattern classification of persistent heavy rainfall in jianghuai region based on the transfer learning cnn model

  • 摘要: 利用新建的1981—2018年区域持续性强降水个例集、1981—2018年中国逐日降水量及NCEP/NCAR全球再分析资料,运用江淮地区持续性强降水典型模态个例样本及残差神经网络(CNN),通过迁移学习分步训练建立针对江淮强降水的环流客观分型模型;并运用该模型对1981—2015年全国持续性强降水个例的环流进行客观分型,比较其与相似量(R)分型、余弦相似系数(COS)分型的效果,且对2016—2018年逐日环流进行客观识别与分型。结果表明:迁移CNN在拟合准确率达到100%后,测试集损失函数很快稳定,准确率较高,比R分型、COS分型效果好。在强降水客观分型中,迁移CNN所得各型与典型模态降水之间的相关系数远高于R分型、COS分型,其中不一致型个例分析表明迁移CNN所得各型与典型模态降水间的相关系数明显高于R分型、COS分型。在独立样本分型中,迁移CNN所得各型与典型模态降水的相关系数也均高于R分型、COS分型,且对非持续性强降水环流分型也存在一定的识别能力。
  • 图  1  江淮地区持续性强降水个例EOF分解的前3个模态

    Fig. 1  Three leading EOF models of persistent heavy rain in Jianghuai Region

    图  2  根据时间系数合成的江淮地区典型模态降水分布

    Fig. 2  Composite rainfall of typical mode patterns from EOF time coefficient in Jianghuai Region

    图  3  迁移CNN训练集与测试集的损失函数和准确率

    (a)增加训练样本前损失函数, (b)增加训练样本前准确率, (c)增加训练样本后损失函数, (d)增加训练样本后准确率

    Fig. 3  The loss and accuracy of training dataset and test dataset of the transfer learning CNN model

    (a)the loss before adding training samples, (b)the accuracy before adding training samples, (c)the loss after adding training samples, (d)the accuracy after adding training samples

    图  4  迁移CNN的训练流程

    Fig. 4  Frame of training of transfer learning CNN model

    图  5  迁移CNN分型

    Fig. 5  Frame of pattern classification of the transfer learning CNN model

    图  6  迁移CNN,R分型和COS分型得到的江淮地区强降水分布

    Fig. 6  Composite heavy rain patterns in Jianghuai Region by the transfer learning CNN model, R typing and COS typing

    图  7  迁移CNN,R分型和COS分型得到的各型500 hPa高度场的方差

    Fig. 7  The variance between 500 hPa geopotential height fields of different rainfall types of transfer learning CNN model, R typing and COS typing

    图  8  个例日样本中迁移CNN, R分型和COS分型得到的各型与典型模态500 hPa高度场的方差

    Fig. 8  The variance of geopotential height field at 500 hPa between transfer learning CNN model, R typing, COS typing and typical mode patterns in samples

    图  9  个例日样本中迁移CNN, R分型和COS分型3种方法得到的江淮地区强降水分布

    Fig. 9  Composite heavy rain patterns in Jianghuai Region by the transfer learning CNN model, R typing and COS typing in samples

    表  1  迁移CNN, R分型和COS分型得到的各型与典型模态强降水之间的相关系数

    Table  1  The correlation coefficients between the transfer learning CNN model, R typing, COS typing and heavy rainfall of typical mode patterns

    相关类型 分型方法 Ⅰ型 Ⅱ型 Ⅲ型
    CNN 0.771 0.986 0.913
    平均场相关 R 0.770 0.946 0.702
    COS 0.671 0.423 0.791
    CNN 0.348 0.224 0.382
    个例日相关 R 0.233 0.140 0.129
    COS 0.198 0.025 0.146
    下载: 导出CSV

    表  2  个例日样本中迁移CNN,R分型和COS分型得到的各型与典型模态强降水之间的相关系数

    Table  2  The correlation coefficients between the transfer learning CNN model, R typing, COS typing and heavy rainfall of typical mode patterns in samples

    相关类型 分型方法 Ⅰ型 Ⅱ型 Ⅲ型
    CNN 0.877 0.979 0.984
    平均场相关 R 0.069 0.513 -0.144
    COS -0.559 0.332 -0.077
    CNN 0.352 0.238 0.467
    个例日相关 R -0.034 0.023 0.060
    COS -0.221 -0.010 0.037
    下载: 导出CSV

    表  3  不同部分样本中迁移CNN,R分型和COS分型得到的各型与典型模态强降水之间的相关系数

    Table  3  The correlation coefficients between transfer learning CNN model, R typing, COS typing and heavy rainfall of typical mode patterns in samples of different part

    相关类型 分型方法 Ⅰ型 Ⅱ型 Ⅲ型
    CNN 0.832 0.818 0.541
    平均场相关 R 0.592 0.199 0.294
    COS 0.485 0.800 -0.224
    CNN 0.238 0.090 0.258
    个例日相关 R 0.233 0.003 0.132
    COS 0.123 0.048 0.058
    下载: 导出CSV
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  • 收稿日期:  2020-09-16
  • 修回日期:  2020-12-25
  • 刊出日期:  2021-03-31

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