Identifying lightning processes in ERA5  soundings with deep learning

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

doi:https://doi.org/10.5194/gmd-18-1141-2025

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

Data sets

The ERA5 Global Reanalysis Hans Hersbach et al. https://doi.org/10.1002/qj.3803

ALDIS cloud to ground lightning strike occurrence aggregated to spatiotemporal ERA5 cells (summer months 2010 to 2019) Thorsten Simon et al. https://doi.org/10.5281/zenodo.13164463

ERA5 hourly data on single levels from 1959 to present H. Hersbach et al. https://doi.org/10.24381/cds.adbb2d47

Model code and software

xai_lightningprocesses Gregor Ehrensperger et al. https://doi.org/10.5281/zenodo.13907708

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.