TY - JOUR
T1 - Lightning Strike Location Identification Based on 3D Weather Radar Data
AU - Lu, Mingyue
AU - Zhang, Yadong
AU - Ma, Zaiyang
AU - Yu, Manzhu
AU - Chen, Min
AU - Zheng, Jianqin
AU - Wang, Menglong
N1 - Publisher Copyright:
© Copyright © 2021 Lu, Zhang, Ma, Yu, Chen, Zheng and Wang.
PY - 2021/8/4
Y1 - 2021/8/4
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85113133040&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85113133040&partnerID=8YFLogxK
U2 - 10.3389/fenvs.2021.714067
DO - 10.3389/fenvs.2021.714067
M3 - Article
AN - SCOPUS:85113133040
SN - 2296-665X
VL - 9
JO - Frontiers in Environmental Science
JF - Frontiers in Environmental Science
M1 - 714067
ER -