Vol.32, NO.2, 2021
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Article Navigation >  Journal of Applied Meteorological Science  > 2021  >  32(2): 233-244
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.
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Circulation Pattern Classification of Persistent Heavy Rainfall in Jianghuai Region Based on the Transfer Learning CNN Model

DOI: 10.11898/1001-7313.20210208
  • 1).

    Key Laboratory of Meteorological Disaster, Ministry of Education(KLME), Joint International Research Laboratory of Climate and Environment Change(ILCEC), Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing 210044

  • 2).

    National Meteorological Center, Beijing 100081

  • Received Date: 2020-09-16
  • Rev Recd Date: 2020-12-25
  • Publish Date: 2021-03-31
    Abstract
  • Newly reconstructed dataset of regional persistent historical heavy rain events in 1981-2018, corresponding daily rainfall data of 2474 observational stations in China, and NCEP/NCAR global reanalysis data of daily geopotential height field are used to study the persistent heavy rain events in Jianghuai Region.Based on 72 persistent heavy rainfall cases, typical rain patterns and circulation fields are refined by empirical orthogonal function(EOF). And the corresponding time coefficient is obtained by projecting rainfall of individual days to the typical rain patterns, and the training and test dataset samples are determined by the time coefficient. Using residual neural network(CNN), a transfer learning CNN classification model of Jianghuai persistent heavy rainfall is established by three transfer learning processes. Compared with the analog quantity(R) and Cosine similarity coefficient(COS) methods, the transfer learning CNN model has the highest classification accuracy on the test dataset.CNN, R and COS methods are used to objectively classify the circulation of all persistent heavy rain cases and to synthesize the distribution of various types of rainfall and circulation during 1981-2015. The statistical analysis shows that the transfer learning CNN model is better at classification. By comparing the correlation coefficients between rain distribution of each type and typical rain patterns, it shows that the transfer learning CNN model performs better than the R and COS methods. The variance between different types of geopotential height fields at 500 hPa obtained by the CNN model is the largest and the CNN model can better distinguish the circulation fields of different types of heavy rainfall.The analysis of samples with inconsistent objective classification of three methods shows that the correlation coefficients of various patterns of rainfall of the transfer learning CNN model are significantly higher than those of R typing and COS typing methods. The spatial distribution of various rainfall patterns of CNN model can clearly show the characteristics of the three typical heavy rain patterns, while the results obtained by R typing and COS typing methods are almost opposite to the typical rain patterns except for type Ⅱ. Considering classification of independent samples in 2016-2018, the correlation coefficients between the rain distribution of each type and typical rain patterns obtained by the transfer learning CNN model are much higher than the R and COS methods. The transfer learning CNN model has certain advantages over R typing and COS typing methods in classification and also has a certain ability to distinguish the non-continuous heavy rainfall circulation pattern.
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  • Fig. 1  Three leading EOF models of persistent heavy rain in Jianghuai Region

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

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    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

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    Fig. 4  Frame of training of transfer learning CNN model

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

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

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

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    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

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

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    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
    DownLoad: Download CSV

    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
    DownLoad: Download CSV

    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
    DownLoad: Download CSV
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    • Received : 2020-09-16
    • Accepted : 2020-12-25
    • Published : 2021-03-31
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