Lightning Strike Location Identification Based on 3D Weather Radar Data Article Swipe
Mingyue Lu
,
Yadong Zhang
,
Zaiyang Ma
,
Manzhu Yu
,
Min Chen
,
Jianqin Zheng
,
Menglong Wang
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3389/fenvs.2021.714067
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3389/fenvs.2021.714067
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.
Related Topics
Concepts
Lightning (connector)
Radar
Lightning detection
Convective storm detection
Random forest
Meteorology
Lightning strike
Identification (biology)
Weather radar
Storm
Convolutional neural network
Computer science
Remote sensing
Artificial neural network
Environmental science
Artificial intelligence
Geology
Thunderstorm
Geography
Power (physics)
Telecommunications
Physics
Biology
Quantum mechanics
Botany
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fenvs.2021.714067
- https://www.frontiersin.org/articles/10.3389/fenvs.2021.714067/pdf
- OA Status
- gold
- Cited By
- 11
- References
- 48
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3187878061
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3187878061Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3389/fenvs.2021.714067Digital Object Identifier
- Title
-
Lightning Strike Location Identification Based on 3D Weather Radar DataWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-08-04Full publication date if available
- Authors
-
Mingyue Lu, Yadong Zhang, Zaiyang Ma, Manzhu Yu, Min Chen, Jianqin Zheng, Menglong WangList of authors in order
- Landing page
-
https://doi.org/10.3389/fenvs.2021.714067Publisher landing page
- PDF URL
-
https://www.frontiersin.org/articles/10.3389/fenvs.2021.714067/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.frontiersin.org/articles/10.3389/fenvs.2021.714067/pdfDirect OA link when available
- Concepts
-
Lightning (connector), Radar, Lightning detection, Convective storm detection, Random forest, Meteorology, Lightning strike, Identification (biology), Weather radar, Storm, Convolutional neural network, Computer science, Remote sensing, Artificial neural network, Environmental science, Artificial intelligence, Geology, Thunderstorm, Geography, Power (physics), Telecommunications, Physics, Biology, Quantum mechanics, BotanyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
11Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 1, 2023: 5, 2022: 3Per-year citation counts (last 5 years)
- References (count)
-
48Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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