Lightning Strike Location Identification Based on 3D Weather Radar Data

被引:7
|
作者
Lu, Mingyue [1 ]
Zhang, Yadong [1 ]
Ma, Zaiyang [2 ,3 ,4 ]
Yu, Manzhu [5 ]
Chen, Min [2 ,3 ,4 ]
Zheng, Jianqin [6 ]
Wang, Menglong [1 ]
机构
[1] Nanjing Univ Informat Sci & Technol, Collaborat Innovat Ctr Forecast & Evaluat Meteoro, Nanjing, Peoples R China
[2] Nanjing Normal Univ, Key Lab Virtual Geog Environm, Minist Educ PRC, Nanjing, Peoples R China
[3] State Key Lab Cultivat Base Geog Environm Evolut, Nanjing, Peoples R China
[4] Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing, Peoples R China
[5] Penn State Univ, Dept Geog, University Pk, PA 16802 USA
[6] Wenzhou Meteorol Bur, Wenzhou, Peoples R China
关键词
lightning strike location; identification; convolutional neural network; 3D weather radar; machine learning; CLASSIFICATION; MODEL; PREDICTION; IMPACT;
D O I
10.3389/fenvs.2021.714067
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Lightning is an instantaneous, intense, and convective weather phenomenon that can produce great destructive power and easily cause serious economic losses and casualties. It always occurs in convective storms with small spatial scales and short life cycles. Weather radar is one of the best operational instruments that can monitor the detailed 3D structures of convective storms at high spatial and temporal resolutions. Thus, extracting the features related to lightning automatically from 3D weather radar data to identify lightning strike locations would significantly benefit future lightning predictions. This article makes a bold attempt to apply three-dimensional radar data to identify lightning strike locations, thereby laying the foundation for the subsequent accurate and real-time prediction of lightning locations. First, that issue is transformed into a binary classification problem. Then, a suitable dataset for the recognition of lightning strike locations based on 3D radar data is constructed for system training and evaluation purposes. Furthermore, the machine learning methods of a convolutional neural network, logistic regression, a random forest, and k-nearest neighbors are employed to carry out experiments. The results show that the convolutional neural network has the best performance in identifying lightning strike locations. This technique is followed by the random forest and k-nearest neighbors, and the logistic regression produces the worst manifestation.
引用
收藏
页数:10
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