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

Viewed

Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.

Total article views: 2,347 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,996 292 59 2,347 51 95
  • HTML: 1,996
  • PDF: 292
  • XML: 59
  • Total: 2,347
  • BibTeX: 51
  • EndNote: 95
Views and downloads (calculated since 27 Jun 2024)
Cumulative views and downloads (calculated since 27 Jun 2024)

Viewed (geographical distribution)

Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.

Total article views: 2,347 (including HTML, PDF, and XML) Thereof 2,289 with geography defined and 58 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Saved (final revised paper)

Latest update: 30 Apr 2026
Download
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.
Read more
Share