Circulation Pattern Classification of Persistent Heavy Rainfall in Jianghuai Region Based on the Transfer Learning CNN Model

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.Th