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Squall Line Identification Method Based on Convolution Neural Network
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摘要: 为了探究深度学习用于飑线识别的可行性，基于2008—2020年河南省郑州和驻马店雷达数据，采用卷积神经网络（convolutional neural network，CNN）算法构建飑线识别模型，引用临界成功指数、公平风险评分、命中率和误判率定量评价模型的识别效果，对比不同样本组成比例和网络结构对飑线识别效果的影响。结果表明：建模所用的样本组成比例对飑线识别有一定影响，通过改变采样方式和优化网络结构均能够改善样本比例不平衡的问题，提高飑线识别效果，且后者提升的幅度更大，而两种方法的结合无明显提升。测试结果表明：该模型临界成功指数为0.66，公平风险评分为0.58，命中率为0.86，误判率为0.24。研究揭示了卷积神经网络能够提取并学习飑线和非飑线回波的图像特征，对飑线有一定识别能力。Abstract: Squall line often leads to heavy rain, gale and hail, which is a difficult key problem in nowcasting. In order to explore the feasibility of deep learning for squall line identification, the training, validation and test set sample sets are established based on the radar data of Zhengzhou and Zhumadian in Henan Province during 2008-2020. The convolutional neural network (CNN) algorithm is used to construct a squall line identification model. The critical success index (CSI), equitable threat score (ETS), hit rate (POD) and false positive rate (FAR) are used to quantitatively evaluate the identification effect of the model. The influence of different sample composition and network structure on squall line identification effect are compared. The results show that the composition ratio of sample is imbalanced, because squall line accounts for very small proportion in all kinds of weather processes. This imbalance will degrade the classification performance of the identification model to squall line samples. The imbalance of sample composition can be improved by changing sampling mode and optimizing network structure, both can improve the identification efficiency, especially the latter. However, the combination of the two methods does not bring further improvement. The over fitting problem in network training can be alleviated by increasing the sparsity and randomness of the network structure. The validation set shows that CSI is 0.87, ETS is 0.82, POD is 0.96, and FAR is 0.10. Based on the test set, the echo can be correctly identified by network as non-squall line in the weak stage of convection development, and as squall line in the strong stage of squall line development. The echo intensity and spatial distribution of squall line cases differ greatly, and the samples in the test set have the image features which are not included in the training set, and therefore the identification effect reduces. The test set show that CSI is 0.66, ETS is 0.58, POD is 0.86, and FAR is 0.24. The research reveals that CNN can extract and learn the image features of squall line echo, and it has a certain ability to identify squall line.
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图 1 2018年6月26日郑州雷达探测飑线过程的识别结果
Fig. 1 Identification results of squall line detected by Zhengzhou radar on 26 Jun 2018
图 2 2019年6月3日驻马店雷达探测飑线过程的识别结果
Fig. 2 Identification results of squall line detected by Zhumadian radar on 3 Jun 2019
图 3 2020年6月24日郑州雷达探测飑线过程的识别结果
Fig. 3 Identification results of squall line detected by Zhengzhou radar on 24 Jun 2020
表 1 飑线过程发生时间
Table 1 Occurrence time of squall line processes
序号 时间 序号 时间 1 2008-05-09T06:00—11:00 12 2014-07-29T05:00—22:00 2 2008-06-03T07:00—11:00 13 2015-07-14T12:00—15:00 3 2009-05-16T12:00—15:00 14 2016-06-04T16:00—22:00 4 2009-06-03T11:00—16:00 15 2016-06-05T09:00—16:00 5 2009-06-14T11:00—16:00 16 2016-06-13T14:00—22:00 6 2011-07-26T08:00—16:00 17 2017-05-22T10:00—13:00 7 2013-06-02T10:00—14:00 18 2018-06-10T10:00—14:00 8 2013-07-04T06:00—13:00 19 2018-06-13T05:00—09:00 9 2013-07-31T20:00—24:00 20 2018-06-26T06:00—13:00 10 2013-08-01T08:00—16:00 21 2019-06-03T12:00—15:00 11 2013-08-11T09:00—18:00 22 2020-06-24T11:00—16:00 
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表 2 飑线判别依据
Table 2 Criteria of squall line discrimination
相关文献 判别依据 文献[22] ①大于12 dBZ的回波带不小于150 km，持续时间超过5 h；②大于36 dBZ的回波带长宽比不小于3:1 文献[23] ①大于20 dBZ的回波带不小于100 km，持续时间超过4 h；②大于40 dBZ的回波带长宽比不小于5:1，持续时间超过2 h 文献[24] ①大于40 dBZ的回波带呈连续或准连续且不小于100 km，持续时间超过3 h；②线性或准线性对流共有一个回波前缘 文献[25] 大于35 dBZ的回波带长宽比大于5:1，且长度大于50 km 文献[9] ①由层云(15 dBZ)连接的对流云(40 dBZ)组成的系统长轴大于100 km，持续时间超过4 h；②系统长宽比大于5:1，持续时间超过2 h
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表 3 数据集信息
Table 3 Information of datasets
数据集 飑线样本量 非飑线样本量 总样本量 训练集 1319 3035 4354 验证集 439 1011 1450 测试集 309 1075 1384
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表 4 改进方案识别结果
Table 4 Identification result of different schemes
方案 CSI ETS POD FAR 原方案 0.59 0.47 0.77 0.27 过采样 0.65 0.52 0.80 0.26 降采样 0.74 0.63 0.93 0.23 网络结构优化 0.87 0.82 0.96 0.10 降采样及网络结构优化 0.88 0.83 0.97 0.10
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表 5 训练集对比
Table 5 Comparison of training sets
训练集 飑线样本量 非飑线样本量 总样本量 原训练集 1319 3035 4354 过采样 2638 3035 5673 降采样 1319 2035 3354
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表 6 验证集与测试集识别结果
Table 6 Identification results of validation set and test set
数据集 CSI ETS POD FAR 验证集 0.87 0.82 0.96 0.10 测试集 0.66 0.58 0.86 0.24
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