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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
31 Mar 2026
Research article |
| 31 Mar 2026
| 31 Mar 2026
Predicting thunderstorm risk probability at very short time range using deep learning
DPHY, ONERA, Université Paris-Saclay, 91120, Palaiseau, France
Adrien Chan-Hon-Tong
DTIS, ONERA, Université Paris-Saclay, 91120, Palaiseau, France
Aurélie Bouchard
DPHY, ONERA, Université Paris-Saclay, 91120, Palaiseau, France
Dominique Béréziat
Sorbonne Université, CNRS, LIP6, Paris, France
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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).
In the context of aeronautics, one of the main dangers along flight paths is the presence of...
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