Comparison of different forecasting tools for short-range lightning strike risk assessment

Bouchard, Aurélie; Buguet, Magalie; Chan-Hon-Tong, Adrien; Dezert, Jean; Lalande, P.

doi:10.1007/s11069-022-05546-x

Cited by 4 publications

(2 citation statements)

References 69 publications

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“…Ground-based networks have been employed since the 1950s to detect lightning (Bouchard et al 2023). The National Lightning Detection Network TM (hereafter NLDN), operated by Vaisala, Inc. (Orville 2008) and the Earth Networks Total Lightning Network ® (hereafter ENTLN), operated by Earth Networks, Incorporated (Liu and Heckman 2011) are two major ground-based systems used in North America.…”

Section: Lightning Detection Networkmentioning

confidence: 99%

Comparing ground-based lightning detection networks near wildfire points-of-origin

Hatchett,

Nauslar,

Brown

2024

Nat Hazards

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Lightning detection and attribution to wildfire ignitions is a critical component of fire management worldwide to both reduce hazards of wildfire to values-at-risk and to enhance the potential for wildland fire to provide resource benefits in fire-adapted ecosystems. We compared two operational ground-based lightning detection networks used by fire managers to identify cloud-to-ground strokes within operationally-relevant distances (1.6 km) of the origins of 4408 western United States lightning-ignited wildfires spanning May–September 2020. Applying two sets of constraints–varying holdover time and applying a quality control measure–we found strokes were co-detected near 55–65% of fires, increasing to 65–79% for detection by at least one network, with neither network detecting lightning near 1024–1666 fires. Because each network detected strokes near 136–376 unique fires, the use of both networks is suggested to increase the probability of identifying potential fire starts. Given the number of fires with network-unique detections and no detections by either network, improvements in lightning detection networks are recommended given increasing fire hazard.

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Section: Lightning Detection Networkmentioning

confidence: 99%

Comparing ground-based lightning detection networks near wildfire points-of-origin

Hatchett,

Nauslar,

Brown

2024

Nat Hazards

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“…It is found that the main activity center of lightning is related to the supercooled cloud layer, and the negative charge area of cloudto-ground lightning is mainly located in the temperature level of −5 to −25 °C (Bouchard et al, 2023). Considering that 40-dBZ echo intensity at the height of −10 °C can be used as the best factor to predict the occurrence of the first lightning, Maribel analyzed several convective cells and found that the condition for lightning in the cell is that the top height of the echo above 40 dBZ in the cell must be higher than 7 km.…”

Section: The Echo Intensity Thresholdmentioning

confidence: 99%

Optimization of the lightning warning model for distribution network lines based on multiple meteorological factor thresholds

Wan,

Fu,

et al. 2023

Front. Energy Res.

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Lightning is one of the frequent natural disasters, which seriously affects the secure and stable operation of the power system, especially the distribution network lines with weak reliability. In order to improve the power supply reliability of the distribution network, higher requirements are put forward for the accuracy of lightning warning. Therefore, this paper establishes a lightning warning model based on comprehensive multi-meteorological factor thresholds and analyzes the meteorological factor data such as atmospheric field strength, echo intensity, echo-top height, and vertical cumulative liquid water content under thunderstorm weather. The threshold value of each factor warning is obtained, and the corresponding threshold weight is calculated by the entropy weight method. According to the weight of each threshold, the comprehensive threshold index of lightning warning is obtained, and the lightning warning is based on this index. A total of 105 lightning data from May to June 2022 in Nanchang city were analyzed as samples. The thresholds of atmospheric field strength, echo intensity, echo-top height, and vertical cumulative liquid water content were 1.2 kV/m, 40 dBZ, 8 km, and 5.2 kg·m−2, respectively. The corresponding weights of each factor were 0.4188, 0.2056, 0.2105, and 0.165, respectively. This model was used to warn a thunderstorm event in July 2022 in Nanchang area. The success rate of the model warning was 0.91, the false alarm rate (FAR) was 0.11, and the critical success index (CSI) was 0.80. Compared with the single-factor threshold lightning warning model, the warning FAR is decreased by 6%, and CSI is increased by 14% while ensuring the high warning success rate.

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Review of Natural Hazard Risks for Wind Farms

Patil¹,

Pathak²,

Alduse³

2023

Energies

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Technological advancement in recent years has resulted in larger and taller wind turbines (WTs) with enhanced power generation capacities. Application of natural hazard risk quantification for WTs helps stakeholders plan, design, install, and operate wind farms safely and profitably. This study focuses on a review of the risks to WTs from earthquakes, strong wind, hurricanes, tsunamis, and lightning. The structural failure of the blades, towers, and foundations in response to these hazards was investigated. Furthermore, research from the past few decades covering modes of failures, such as foundation overturning, tower tilting, tower buckling, blade buckling, deformations, and delamination of blades, was investigated. It was found that the methodologies used by researchers include analytical, statistical, and data-based models, as well as experimental research. This study shows that, while seismic, wind, and hurricane risks have been explored with analytical, experimental, and statistical models in the past, future research could focus on the latest methods involving data-based models, integration of monitored data, and physics-based models. Tsunami risk assessment focuses on experimental methods, and future research may benefit from data-integrated models and a focus on the transient nature of the risks.

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Comparison of different forecasting tools for short-range lightning strike risk assessment

Cited by 4 publications

References 69 publications

Comparing ground-based lightning detection networks near wildfire points-of-origin

Comparing ground-based lightning detection networks near wildfire points-of-origin

Optimization of the lightning warning model for distribution network lines based on multiple meteorological factor thresholds

Review of Natural Hazard Risks for Wind Farms

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