Recent Advances in Lightning Research

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Meteorology".

Deadline for manuscript submissions: closed (31 May 2024) | Viewed by 3997

Special Issue Editors


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Guest Editor
Division for Electricity and Lightning Research, Uppsala University, 752 36 Uppsala, Sweden
Interests: lightning physics; physics of electrical discharges; lightning protection; electromagnetic field theory
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Guest Editor
EMC Laboratory, EPFL-SCI-STI-FR, Swiss Federal Institute of Technology (EPFL), Station 11, CH-1015 Lausanne, Switzerland
Interests: electromagnetic compatibility; lightning electromagnetics; lightning and EMP interaction with transmission lines; numerical computation of electromagnetic fields and power line communications
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
HES-SO/HEIG-VD, University of Applied Sciences and Arts Western Switzerland, 2800 Delémont, Switzerland
Interests: lightning physics

Special Issue Information

Dear Colleagues,

Lightning is a powerful electrical discharge that takes place in the Earth’s atmosphere. Events that take place during a lightning discharge are of interest to physicists, engineers, and environmental scientists. First, inside the cloud and during the leader stage of a lightning discharge, electrons begin to accelerate due to relativistic energies, leading to the production of energetic radiation including X-rays and gamma rays. In order to understand these effects, it is necessary to probe into the physics of lightning discharges. Second, during a lightning discharge that strikes the Earth, a large electric current impulse is injected into the impact point of the strike. Unless properly diverted into the ground, this current can cause significant damage at the point of impact on structures. Third, the propagation of this high-current impulse along the lightning channel gives rise to a strong electromagnetic field that interacts with human-made electrical and electronic systems causing disturbance and damage. Fourth, during the propagation of the current, the lightning channel is heated to around 30,000 K, leading to the dissociation of both oxygen and nitrogen atoms. As the channel cools, these atoms combine in different ways, leading to the production of different chemical species in the atmosphere. These chemical species are of interest in the study of the chemistry of the atmosphere and global changes in temperature. Finally,  electromagnetic fields generated by lightning flashes interact with the upper atmosphere, generating upper-atmospheric electrical discharges known as sprites, bluejets, and elves. These events are also of interest when analysing the effects of lightning on the chemistry of the atmosphere. 

Significant advances in the different areas of lightning research have been made in recent years. The aim of this Special Issue is to update the current state-of-the-art research into lightning and lightning protection.

Accordingly, the Special Issue will address all areas of lightning research, including the physics of a lightning flash and its modelling, protecting structures from lightning, systems which locate potential lightning strikes, the indirect effects of lightning on electromagnetic fields, including electromagnetic coupling models, and the ways in which the Earht's atmosphere can be modified by lightning.

We welcome contributions in the form of original research papers or review papers related to any of these subjects.

Prof. Dr. Vernon Cooray
Prof. Dr. Farhad Rachidi
Prof. Dr. Marcos Rubinstein
Guest Editors

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Keywords

  • lightning initiation
  • physics of lightning leaders and return strokes
  • return stroke, stepped-leader, and dart-leader models
  • lightning protection of structures and power lines
  • lightning electromagnetics
  • electromagnetic coupling models
  • energetic radiation from lightning
  • upper atmospheric electrical discharges

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Published Papers (5 papers)

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Research

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16 pages, 6945 KiB  
Article
Deep Learning for High-Speed Lightning Footage—A Semantic Segmentation Network Comparison
by Tyson Cross, Jason R. Smit, Carina Schumann, Tom A. Warner and Hugh G. P. Hunt
Atmosphere 2024, 15(8), 873; https://doi.org/10.3390/atmos15080873 - 23 Jul 2024
Viewed by 520
Abstract
We present a novel deep learning approach to a unique image processing application: high-speed (>1000 fps) video footage of lightning. High-speed cameras enable us to observe lightning with microsecond resolution, characterizing key processes previously analyzed manually. We evaluate different semantic segmentation networks (DeepLab3+, [...] Read more.
We present a novel deep learning approach to a unique image processing application: high-speed (>1000 fps) video footage of lightning. High-speed cameras enable us to observe lightning with microsecond resolution, characterizing key processes previously analyzed manually. We evaluate different semantic segmentation networks (DeepLab3+, SegNet, FCN8s, U-Net, and AlexNet) and provide a detailed explanation of the image processing methods for this unique imagery. Our system architecture includes an input image processing stage, a segmentation network stage, and a sequence classification stage. The ground-truth data consists of high-speed videos of lightning filmed in South Africa, totaling 48,381 labeled frames. DeepLab3+ performed the best (93–95% accuracy), followed by SegNet (92–95% accuracy) and FCN8s (89–90% accuracy). AlexNet and U-Net achieved below 80% accuracy. Full sequence classification was 48.1% and stroke classification was 74.1%, due to the linear dependence on the segmentation. We recommend utilizing exposure metadata to improve noise misclassifications and extending CNNs to use tapped gates with temporal memory. This work introduces a novel deep learning application to lightning imagery and is one of the first studies on high-speed video footage using deep learning. Full article
(This article belongs to the Special Issue Recent Advances in Lightning Research)
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19 pages, 1006 KiB  
Article
Can Bayesian Networks Improve Ground-Strike Point Classification?
by Wandile Lesejane, Hugh G. P. Hunt, Carina Schumann and Ritesh Ajoodha
Atmosphere 2024, 15(7), 776; https://doi.org/10.3390/atmos15070776 - 28 Jun 2024
Viewed by 391
Abstract
Studying cloud-to-ground lightning strokes and ground-strike points provides an alternative method of lightning mapping for lightning risk assessment. Various k-means algorithms have been used to verify the ground-strike points from lightning locating systems, producing results with room for improvement. This paper proposes using [...] Read more.
Studying cloud-to-ground lightning strokes and ground-strike points provides an alternative method of lightning mapping for lightning risk assessment. Various k-means algorithms have been used to verify the ground-strike points from lightning locating systems, producing results with room for improvement. This paper proposes using Bayesian networks (BNs), a model not previously used for this purpose, to classify lightning ground-strike points. A Bayesian network is a probabilistic graphical model that uses Bayes’ theorem to represent the conditional dependencies of variables. The networks created for this research were trained from the data using a score-based structure-learning procedure and the Bayesian information criterion score function. The models were evaluated using confusion matrices and kappa indices and produced accuracy values ranging from 86% to 94% and kappa indices of up to 0.76. While BN models do not outperform k-means algorithms, they offer an alternative by not requiring predetermined distances. However, the easy implementation of the k-means approach means that no significant gain is made by implementing the more complex Bayesian network approach. Full article
(This article belongs to the Special Issue Recent Advances in Lightning Research)
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20 pages, 14948 KiB  
Article
Inter-Comparison of Lightning Measurements in Quasi-Linear Convective Systems
by Jacquelyn Ringhausen, Vanna Chmielewski and Kristin Calhoun
Atmosphere 2024, 15(3), 309; https://doi.org/10.3390/atmos15030309 - 29 Feb 2024
Viewed by 1136
Abstract
Data from four lightning networks collected during three quasi-linear convective systems (QLCS) are used to understand the differences in detection for optimizing their combined use. Additionally, using unique aspects from each network provides a more complete picture of lightning in a thunderstorm. The [...] Read more.
Data from four lightning networks collected during three quasi-linear convective systems (QLCS) are used to understand the differences in detection for optimizing their combined use. Additionally, using unique aspects from each network provides a more complete picture of lightning in a thunderstorm. The four lightning networks examined include a Lightning Mapping Array (LMA), the Earth Networks Total Lightning Network (ENTLN), the Geostationary Lightning Mapper (GLM), and the National Lightning Detection Network (NLDN). The data from each network are inter-matched and locations where each network uniquely detected a flash versus all are analyzed in reference to three QLCSs, including two QLCSs that occurred in the Southeast (22 March 2022 and 30 March 2022) during the Propagation, Evolution, and Rotation in Linear Systems (PERiLS) field campaign, and one case from Oklahoma (26 February 2023). Unique aspects of the lightning provided by each network are examined, including flash initiation altitude, size, type, and energy. Lightning flash trends and characteristics for each QLCS are similar between networks in general, but deviate in certain conditions and locations. Times of decreased matching between networks were associated with localized increases in lightning rates, smaller flash sizes, and lower-energy flashes. The differences in each network’s performance across the QLCSs demonstrates the importance of understanding the limitations in each and the advantage of using multiple networks. Full article
(This article belongs to the Special Issue Recent Advances in Lightning Research)
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Review

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28 pages, 8540 KiB  
Review
Earthquake Lights Observed in Japan—Possible Underlying Mechanisms
by Yuji Enomoto
Atmosphere 2024, 15(8), 916; https://doi.org/10.3390/atmos15080916 - 31 Jul 2024
Viewed by 597
Abstract
In Japan, a country prone to earthquakes, numerous damaging earthquakes have been recorded throughout history, often accompanied by descriptions of mysterious earthquake lights (EQL), which may involve various mechanisms. In this article, the possible mechanisms for different types of EQL in 11 cases [...] Read more.
In Japan, a country prone to earthquakes, numerous damaging earthquakes have been recorded throughout history, often accompanied by descriptions of mysterious earthquake lights (EQL), which may involve various mechanisms. In this article, the possible mechanisms for different types of EQL in 11 cases are reviewed among 21 selected earthquakes. These involve preseismic physicochemical variations in the geological structure of the fault in the lithosphere, which contains deep Earth gases such as radon, methane, and others, as a primary factor for EQL generation. Additionally, various seismic, atmospheric, hydrospheric, and ionospheric variations interact with each other, resulting in the visualization of characteristic anomalous phenomena, such as glowing or shining ground, mountains, offshore areas, and skies of various colors. These phenomena appear momentarily but can sometimes last for extended periods. Because EQL often appear just before an earthquake, their study might be significant for earthquake prediction. Additionally, EQL involving methane flames in the ground is an important research topic as it relates to public safety. Was what they witnessed paranormal? Full article
(This article belongs to the Special Issue Recent Advances in Lightning Research)
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Other

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12 pages, 2619 KiB  
Case Report
Implication of Subsequent Leaders in the Gigantic Jet
by Wen-Qian Chang, Yan-Mou Lai, Cheng-Ling Kuo, Janusz Mlynarczyk and Zhong-Yi Lin
Atmosphere 2024, 15(7), 781; https://doi.org/10.3390/atmos15070781 - 29 Jun 2024
Viewed by 474
Abstract
Most of the lightning appears below the cloud or inside the cloud. Unlike conventional lightning, blue jets and gigantic jets (GJ) produce upward discharge since electric discharge occurs as a form of cloud-to-air leader. We analyzed a gigantic jet recorded in the 2022 [...] Read more.
Most of the lightning appears below the cloud or inside the cloud. Unlike conventional lightning, blue jets and gigantic jets (GJ) produce upward discharge since electric discharge occurs as a form of cloud-to-air leader. We analyzed a gigantic jet recorded in the 2022 Taiwan campaign. For our color photograph recorded in the observation, high spatial resolution (150 m) at a close distance (140 km) resolves the important spatial features of the GJ phenomena. First, the GJ propagated upwardly as the fully developed jet with a maximum height of ~80 km above the cloud top ~17 km. After the fully developed stage, the subsequent leader reached its top height of ~30 km with a width of 0.5–1.0 km. The subsequent leader attempted but failed to develop from leader to fully developed jet. The subsequent leader may be interpreted as a negative stepped leader associated with cloud rebrightening, similar to the subsequent stroke in the multi-stroke lightning. Besides, the relatively higher IC flash rates associated with the rise of cloud tops benefit the required meteorological conditions for developing gigantic jets. Full article
(This article belongs to the Special Issue Recent Advances in Lightning Research)
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