Natural Hazards and Earth System Sciences
Natural Hazards and Earth System Sciences
NHESS
Articles | Volume 26, issue 3
Articles | Volume 26, issue 3
https://doi.org/10.5194/nhess-26-1603-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-26-1603-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 26, issue 3
Research article
 | 
31 Mar 2026
Research article |  | 31 Mar 2026

Predicting thunderstorm risk probability at very short time range using deep learning

Mélanie Bosc, Adrien Chan-Hon-Tong, Aurélie Bouchard, and Dominique Béréziat

Viewed

Total article views: 7,702 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
6,209 1,272 221 7,702 249 228
  • HTML: 6,209
  • PDF: 1,272
  • XML: 221
  • Total: 7,702
  • BibTeX: 249
  • EndNote: 228
Views and downloads (calculated since 23 Jul 2025)
Cumulative views and downloads (calculated since 23 Jul 2025)

Viewed (geographical distribution)

Total article views: 7,702 (including HTML, PDF, and XML) Thereof 7,607 with geography defined and 95 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

Latest update: 09 Oct 2026
Download
Short summary
In the context of aeronautics, one of the main dangers along flight paths is the presence of cumulonimbus clouds, which can generate lightning and strike aircraft causing damages. To address this issue, we have developed a data-driven AI method to predict thunderstorms risk that allows to estimate electrical activity probability at very short time range (every 5 min up to 1 h ahead).
Read more
Share
Altmetrics
Final-revised paper
Preprint