Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 18, issue 4
Articles | Volume 18, issue 4
https://doi.org/10.5194/gmd-18-1141-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-1141-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 4
Development and technical paper
 | 
26 Feb 2025
Development and technical paper |  | 26 Feb 2025

Identifying lightning processes in ERA5 soundings with deep learning

Gregor Ehrensperger, Thorsten Simon, Georg J. Mayr, and Tobias Hell

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Short summary
As lightning is a brief and localized event, it is not explicitly resolved in atmospheric models. Instead, expert-based auxiliary descriptions are used to assess it. This study explores how AI can improve our understanding of lightning without relying on traditional expert knowledge. We reveal that AI independently identified the key factors known to experts as essential for lightning in the Alps region. This shows how knowledge discovery could be sped up in areas with limited expert knowledge.
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