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

Author

Listed:
  • Aurélie Bouchard

    (ONERA, Université Paris Saclay)

  • Magalie Buguet

    (ONERA, Université Paris Saclay)

  • Adrien Chan-Hon-Tong

    (ONERA, Université Paris Saclay)

  • Jean Dezert

    (ONERA, Université Paris Saclay)

  • Philippe Lalande

    (ONERA, Université Paris Saclay)

Abstract

Thunderstorms, the main generator of lightning on earth, are characterized by the presence of extreme atmospheric conditions (turbulence, hail, heavy rain, wind shear, etc.). Consequently, the atmospheric conditions associated with this kind of phenomenon (in particular the strike itself) can be dangerous for aviation. This study focuses on the estimation of the lightning strike risk induced by thunderstorms over the sea, in a short-range forecast, from 0 to 24 h. In this framework, three methods have been developed and compared. The first method is based on the use of thresholds and weighting functions; the second method is based on a neural network approach, and the third method is based on the use of belief functions. Each method has been applied to the same dataset comprising predictors defined from numerical weather prediction model outputs. In order to assess the different methods, a “ground truth” dataset based on lightning stroke locations supplied by the World Wide Lightning Location Network (WWLLN) has been used. The choice of one method over the others will depend on the compromise that the user is willing to accept between false alarms, missed detections, and runtimes. The first method has a very low missed detection rate but a high false alarm rate, while the other two methods have much lower false alarm rates, but at the cost of a non-negligible missed detection rate. Finally, the third method is much faster than the other two methods.

Suggested Citation

  • Aurélie Bouchard & Magalie Buguet & Adrien Chan-Hon-Tong & Jean Dezert & Philippe Lalande, 2023. "Comparison of different forecasting tools for short-range lightning strike risk assessment," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 115(2), pages 1011-1047, January.
    • Handle: RePEc:spr:nathaz:v:115:y:2023:i:2:d:10.1007_s11069-022-05546-x
    DOI: 10.1007/s11069-022-05546-x
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    References listed on IDEAS

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    1. Suman Ravuri & Karel Lenc & Matthew Willson & Dmitry Kangin & Remi Lam & Piotr Mirowski & Megan Fitzsimons & Maria Athanassiadou & Sheleem Kashem & Sam Madge & Rachel Prudden & Amol Mandhane & Aidan C, 2021. "Skilful precipitation nowcasting using deep generative models of radar," Nature, Nature, vol. 597(7878), pages 672-677, September.
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    Cited by:

    1. Rohana Rohana & Surya Hardi & Nasaruddin Nasaruddin & Yuwaldi Away & Andri Novandri, 2024. "Multi-Stage ANN Model for Optimizing the Configuration of External Lightning Protection and Grounding Systems," Energies, MDPI, vol. 17(18), pages 1-25, September.

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