Lightning Flashes: Detection, Forecasting and Hazards

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

Deadline for manuscript submissions: closed (30 May 2023) | Viewed by 3817

Special Issue Editors


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Guest Editor
Division of Meteorological Sensors and Satellites, General Coordination of Earth Sciences, National Institute for Space Research (INPE), Sao Jose dos Campos 12227-010, Brazil
Interests: climatology; atmospheric chemistry; atmospheric electricity; lightning physics; nowcasting

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Guest Editor
Vale Institute of Technology Sustainable Development (ITV DS) - Environmental Technology Group, Belém 66055-090, Brazil
Interests: severe storms; lightning; forecast; nowcasting; lightning monitoring and warning system

Special Issue Information

Dear Colleagues,

Severe storms that usually produce high lightning activity are responsible for hundreds of deaths and billions of dollars of damage annually worldwide. Unlike other hydrometeorological events, these severe storms are randomly distributed over large continental areas affecting populations of any social class. Thus, higher impacts occur on the less favored parts of the population, which are much more vulnerable to losses and deaths. In this context, there is a significant motivation within the scientific community to improve the forecasting techniques of severe storms by using high-resolution numerical models together with high-quality observational data. In particular, the almost real-time detection of lightning activity is important for a wide variety of applications and for the development of new nowcasting techniques. Climatological lightning data are also essential to understand humanity’s influence on the climate and, conversely, how these climate changes can affect the behavior of severe storms in the long term.

Epoch-making advances have been made in recent decades. New state-of-the-art geostationary satellites and high-tech ground-based lightning detection systems are producing high-precision, high-quality, and high-resolution lightning datasets over the whole planet. Promising constellations of low-orbit small-satellites will soon provide observational data with unprecedented quality and accuracy. Quantum computing and new computing architectures have significantly improved the numerical weather prediction models, including artificial intelligence techniques, thus yielding new and exciting insights into the nature of severe storms and how to predict them. Moreover, accessing the impacts of severe storms on the population will help us to understand its vulnerabilities, leading to more effective mitigation and adaptation actions. 

Based on this discussion, we are planning a Special Issue dedicated to multi-disciplinary contributions in all areas related to lightning: detection techniques and/or systems, nowcasting and/or forecasting methods, hazard characterization, severe storm signatures and life cycle development (micro-physics, electrification, thermodynamics, and dynamics).

We welcome contributions of various article types such as original research and reviews.

Dr. Kleber Pinheiro Naccarato
Dr. Ana Paula Paes Dos Santos
Guest Editors

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Keywords

  • lightning
  • nowcasting
  • detection
  • hazards
  • numerical models
  • severe storms
  • warning
  • vulnerability
  • climate changes.

Published Papers (3 papers)

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Research

20 pages, 16549 KiB  
Article
Lightning Nowcasting Using Solely Lightning Data
by Ehsan Mansouri, Amirhosein Mostajabi, Chong Tong, Marcos Rubinstein and Farhad Rachidi
Atmosphere 2023, 14(12), 1713; https://doi.org/10.3390/atmos14121713 - 21 Nov 2023
Viewed by 1026
Abstract
Lightning is directly or indirectly responsible for significant human casualties and property damage worldwide. A timely prediction of its occurrence can enable authorities and the public to take necessary precautionary actions resulting in diminishing the potential hazards caused by lightning. In this paper, [...] Read more.
Lightning is directly or indirectly responsible for significant human casualties and property damage worldwide. A timely prediction of its occurrence can enable authorities and the public to take necessary precautionary actions resulting in diminishing the potential hazards caused by lightning. In this paper, based on the assumption that atmospheric phenomena behave in a continuous manner, we present a model based on residual U-nets where the network architecture leverages this inductive bias by combining information passing directly from the input to the output with the necessary required changes to the former, predicted by a neural network. Our model is trained solely on lightning data from geostationary weather satellites and can be used to predict the occurrence of future lightning. Our model has the advantage of not relying on numerical weather models, which are inherently slow due to their sequential nature, enabling it to be used for near-future prediction (nowcasting). Moreover, our model has similar performance compared to other machine learning based lightning predictors in the literature while using significantly less amount of data for training, limited to lightning data. Our model, which is trained for four different lead times of 15, 30, 45, and 60 min, outperforms the traditional persistence baseline by 4%, 12%, and 22% for lead times of 30, 45, and 60 min, respectively, and has comparable accuracy for 15 min lead time. Full article
(This article belongs to the Special Issue Lightning Flashes: Detection, Forecasting and Hazards)
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25 pages, 11887 KiB  
Article
Spider Lightning Characterization: Integrating Optical, NLDN, and GLM Detection
by Gilbert Green and Naomi Watanabe
Atmosphere 2023, 14(7), 1191; https://doi.org/10.3390/atmos14071191 - 24 Jul 2023
Viewed by 1233
Abstract
Here, we investigate the characteristics of spider lightning analyzing individual lightning flashes as well as the overall electric storm system. From July to November 2022, optical camera systems captured the visually spectacular spider lightning in Southwest Florida. The aspects and activities of the [...] Read more.
Here, we investigate the characteristics of spider lightning analyzing individual lightning flashes as well as the overall electric storm system. From July to November 2022, optical camera systems captured the visually spectacular spider lightning in Southwest Florida. The aspects and activities of the discharges were analyzed by merging the video images with lightning flash data from the National Detection Lightning Network (NLDN) and the Geostationary Lightning Mapper (GLM). Spider lightning discharges primarily occurred during the later stages of the overall lightning activity when there was a decrease in the flash count and flash locations were drifting apart. The propagation path of the spider discharge was predominantly luminous and exhibited an extended duration, ranging from 300 ms to 1720 ms, with most of the path remaining continuously illuminated. Occasionally, observed discharges produced cloud-to-ground flashes (CG) along their propagation paths. This study represents the first attempt to utilize video images, NLDN, and GLM data to investigate the correlation between visual observed spider lightning events and detection networks. These combined datasets facilitated the characterization of the observed spider lightning discharges. Full article
(This article belongs to the Special Issue Lightning Flashes: Detection, Forecasting and Hazards)
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14 pages, 1091 KiB  
Article
Lightning Risk Warning Method Using Atmospheric Electric Field Based on EEWT-ASG and Morpho
by Xiang Li, Ling Yang, Qiyuan Yin, Zhipeng Yang and Fangcong Zhou
Atmosphere 2023, 14(6), 1002; https://doi.org/10.3390/atmos14061002 - 9 Jun 2023
Cited by 1 | Viewed by 885
Abstract
The current methods for lightning risk warnings that are based on atmospheric electric field (AEF) data have a tendency to rely on single features, which results in low robustness and efficiency. Additionally, there is a lack of research on canceling warning signals, contributing [...] Read more.
The current methods for lightning risk warnings that are based on atmospheric electric field (AEF) data have a tendency to rely on single features, which results in low robustness and efficiency. Additionally, there is a lack of research on canceling warning signals, contributing to the high false alarm rate (FAR) of these methods. To overcome these limitations, this study proposes a lightning risk warning method that incorporates enhanced empirical Wavelet transform-Adaptive Savitzky–Golay filter (EEWT-ASG) and one-dimensional morphology, using time-frequency domain features obtained through the Wavelet transform (WT). The proposed method achieved a probability of detection (POD) of 77.11%, miss alarm rate (MAR) of 22.89%, FAR of 40.19%, and critical success index (CSI) of 0.51, as evaluated on 83 lightning events. This method can issue a warning signal up to 22 min in advance for lightning processes. Full article
(This article belongs to the Special Issue Lightning Flashes: Detection, Forecasting and Hazards)
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