Squall Line Identification Method Based on Convolution Neural Network

Jin Ziqi; Wang Xinmin; Bao Yansong; Li Han; Wei Ming; Lu Mingyue

doi:10.11898/1001-7313.20210506

Vol.32, NO.5, 2021

Turn off MathJax

Article Contents

Article Navigation > Journal of Applied Meteorological Science > 2021 > 32(5): 580-591

Jin Ziqi, Wang Xinmin, Bao Yansong, et al. Squall line identification method based on convolution neural network. J Appl Meteor Sci, 2021, 32(5): 580-591. DOI: 10.11898/1001-7313.20210506.

Citation:

Jin Ziqi, Wang Xinmin, Bao Yansong, et al. Squall line identification method based on convolution neural network. J Appl Meteor Sci, 2021, 32(5): 580-591. DOI: 10.11898/1001-7313.20210506.

Jin Ziqi, Wang Xinmin, Bao Yansong, et al. Squall line identification method based on convolution neural network. J Appl Meteor Sci, 2021, 32(5): 580-591. DOI: 10.11898/1001-7313.20210506.

Citation:

Jin Ziqi, Wang Xinmin, Bao Yansong, et al. Squall line identification method based on convolution neural network. J Appl Meteor Sci, 2021, 32(5): 580-591. DOI: 10.11898/1001-7313.20210506.

PDF( 27070 KB)

Squall Line Identification Method Based on Convolution Neural Network

DOI: 10.11898/1001-7313.20210506

Jin Ziqi^{1, 2, 3},
Wang Xinmin^{1
,
,},
Bao Yansong^{2, 3},
Li Han¹,
Wei Ming^{2, 3},
Lu Mingyue⁴

1.
CMA·Henan Key Laboratory of Agrometeorological Support and Applied Technique/Henan Meteorological Observatory, Zhengzhou 450003
2.
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration/Joint Laboratory of Meteorological Environment Satellite Engineering and Application, Nanjing University of Information Science & Technology, Nanjing 210044
3.
School of Atmospheric Physics, Nanjing University of Information Science & Technology, Nanjing 210044
4.
School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044

Received Date: 2021-03-01
Rev Recd Date: 2021-05-12
Publish Date: 2021-09-30

Abstract

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.
- squall line;
- identification;
- convolution neural network;
- radar echo;
- sample imbalance

FullText(HTML)

References(34)

Figures and Tables

Fig. 1 Identification results of squall line detected by Zhengzhou radar on 26 Jun 2018

DownLoad: Full-Size Img PowerPoint

Fig. 2 Identification results of squall line detected by Zhumadian radar on 3 Jun 2019

DownLoad: Full-Size Img PowerPoint

Fig. 3 Identification results of squall line detected by Zhengzhou radar on 24 Jun 2020

DownLoad: Full-Size Img PowerPoint

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

DownLoad: Download CSV

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

DownLoad: Download CSV

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

DownLoad: Download CSV

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

DownLoad: Download CSV

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

DownLoad: Download CSV

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

DownLoad: Download CSV

References

[1]	Yao J Q, Dai J H, Yao Z Q. Case analysis of the formation and evolution of 12 July 2004 severe squall line. Journal of Applied Meteorological Science, 2005, 16(6): 746-752. doi: 10.3969/j.issn.1001-7313.2005.06.005
[2]	Liang J Y, Sun J H. Formation mechanism of disastrous gale in a squall line process in June 2009. Chinese Journal of Atmospheric Sciences, 2012, 36(2): 316-336. https://www.cnki.com.cn/Article/CJFDTOTAL-DQXK201202010.htm
[3]	Luo Q, Zheng Y G, Chen M. Evolution and mechanism of a rare strong bow squall line in northern Beijing in 2017. Acta Meteorologica Sinica, 2019, 77(3): 371-386. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB201903001.htm
[4]	Rinehart R E, Garvey E T. Three-dimensional storm motion detection by conventional weather radar. Nature, 1978, 273(5660): 287-289. doi: 10.1038/273287a0
[5]	Dixon M, Wiener G. TITAN: Thunderstorm identification, tracking, analysis, and nowcasting-A radar-based methodology. Journal of Atmospheric and Oceanic Technology, 1993, 10(6): 785-797. doi: 10.1175/1520-0426(1993)010<0785:TTITAA>2.0.CO;2
[6]	Johnson J T, MacKeen P L, Witt A, et al. The storm cell identification and tracking algorithm: An enhanced WSR-88D algorithm. Weather and Forecasting, 1998, 13(2): 263-276. doi: 10.1175/1520-0434(1998)013<0263:TSCIAT>2.0.CO;2
[7]	Han F, Wo W F. Design and implementation of SWAN2.0 platform. Journal of Applied Meteorological Science, 2018, 29(1): 25-34. doi: 10.11898/1001-7313.20180103
[8]	Zheng Y G, Zhou K H, Sheng J, et al. Advances in techniques of monitoring, forecasting and warning of severe convective weather. Journal of Applied Meteorological Science, 2015, 26(6): 641-657. doi: 10.11898/1001-7313.20150601
[9]	Yang J, Liu L P, Xia W M, et al. Automatic identification of linear mesoscale convective system based on dynamic template function. Meteorological Monthly, 2014, 40(11): 1389-1397. doi: 10.7519/j.issn.1000-0526.2014.11.012
[10]	Cheng L Z, He J X, Zeng X J. Squall line radar echo recognition based on Wavelet and Hu moment. Journal of Chengdu University of Information Technology, 2017, 32(4): 369-374. https://www.cnki.com.cn/Article/CJFDTOTAL-CDQX201704005.htm
[11]	Li Z, Li G C, Liu L P, et al. Optimal identification of squall line and analysis of thunderstorm and gale. Plateau Meteorology, 2017, 36(3): 801-810. https://www.cnki.com.cn/Article/CJFDTOTAL-GYQX201703019.htm
[12]	Tang W, Zhou Y, Dong H, et al. Current situation and international comparison of artificial intelligence technology in meteorological field in China. Advances in Meteorological Science and Technology, 2019, 9(5): 55-56;62. doi: 10.3969/j.issn.2095-1973.2019.05.009
[13]	Huang X G, Fei J F, Chen P Y. A neural network approach to predict tropical cyclone intensity. Journal of Applied Meteorological Science, 2009, 20(6): 699-705. doi: 10.3969/j.issn.1001-7313.2009.06.007
[14]	Han F, Yang L, Zhou C X, et al. An Experimental study of the short-time heavy rainfall event forecast based on ensemble learning and sounding data. Journal of Applied Meteorological Science, 2021, 32(2): 188-199. doi: 10.11898/1001-7313.20210205
[15]	Zhang Z H, Miao C S, Zeng Z H, et al. Improvement and application of artificial neural networks to cloud classification. Journal of Applied Meteorological Science, 2012, 23(3): 355-363. doi: 10.3969/j.issn.1001-7313.2012.03.012
[16]	Zhang X F, Wang Z C, Mao J J, et al. Experiments on improving temperature and humidity profile retrieval for ground-based microwave radiometer. Journal of Applied Meteorological Science, 2020, 31(4): 385-396. doi: 10.11898/1001-7313.20200401
[17]	Zheng Y P, Li G Y, Li Y. Survey of application of deep learning in image recognition. Computer Engineering and Applications, 2019, 55(12): 20-36. doi: 10.3778/j.issn.1002-8331.1903-0031
[18]	Guo H Y, Chen M X, Han L, et al. Experiment on high resolution near prediction of severe convection based on deep learning. Acta Meteorologica Sinica, 2019, 77(4): 715-727. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB201904009.htm
[19]	Han F, long M S, Li Y A, et al. Application of cyclic neural network in radar nowcasting. Journal of Applied Meteorological Science, 2019, 30(1): 61-69. doi: 10.11898/1001-7313.20190106
[20]	Gu J, Wang Z, Kuen J, et al. Recent advances in convolutional neural networks. Pattern Recognition, 2018, 77: 354-377. doi: 10.1016/j.patcog.2017.10.013
[21]	Zhang J, Howard K, Gourley J. Constructing three-dimensional multiple-radar reflectivity mosaics: Examples of convective storms and stratiform rain echoes. Journal of Atmospheric and Oceanic Technology, 2005, 22(1): 30-42. doi: 10.1175/JTECH-1689.1
[22]	Chen G T J, Chou H C. General characteristics of squall lines observed in TAMEX. Monthly Weather Review, 1993, 121(3): 726-733. doi: 10.1175/1520-0493(1993)121<0726:GCOSLO>2.0.CO;2
[23]	Geerts B. Mesoscale convective systems in the southeast United States during 1994-95: A survey. Weather and Forecasting, 1998, 13(3): 860-869. doi: 10.1175/1520-0434(1998)013<0860:MCSITS>2.0.CO;2
[24]	Parker M D, Johnson R H. Organizational modes of midlatitude mesoscale convective systems. Monthly Weather Review, 2000, 128(10): 3413-3436. doi: 10.1175/1520-0493(2001)129<3413:OMOMMC>2.0.CO;2
[25]	Yu X D. Doppler Weather Radar Principle and Application. Beijing: China Meteorological Press, 2006.
[26]	Hu T. Research on Fish Recognition Based on Deep Learning. Hangzhou: Zhejiang University of Technology, 2019.
[27]	Simonyan K, Zisserman A. Very Deep Convolutional Networks for Large-scale Image Recognition. arXiv preprint arXiv: 1409.1556, 2014.
[28]	Kingma D P, Ba J L. Adam: A Method for Stochastic Optimization. arXiv: 1412.6980, 2014.
[29]	Cai J Q, Tan G R, Niu R Y. Circulation pattern classification of persistent heavy rainfall in jianghuai region based on the transfer learning cnn model. Journal of Applied Meteorological Science, 2021, 32(2): 233-244. doi: 10.11898/1001-7313.20210208
[30]	Tao X M, Hao S Y, Zhang D X, et al. Unbalanced support vector machine based on sample characteristics under sampling. Control and Decision, 2013, 28(7): 978-984. https://www.cnki.com.cn/Article/CJFDTOTAL-KZYC201307004.htm
[31]	Li Y D, Hao Z B, Lei H. A review of convolutional neural networks. Journal of Computer Applications, 2016, 36(9): 2508-2515;2565. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJY201609029.htm
[32]	Srivastava N, Hinton G, Krizhevsky A, et al. Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 2014, 15(1): 1929-1958. http://dl.acm.org/citation.cfm?id=2670313
[33]	Goodfellow I J, Warde-Farley D, Mirza M, et al. Maxout Networks. arXiv preprint arXiv: 1302.4389, 2013.
[34]	Zeiler M D, Fergus R. Stochastic Pooling for Regularization of Deep Convolutional Neural Networks. arXiv preprint arXiv: 1301.3557, 2013.