Department of Atmospheric and Cryospheric Sciences, University of Innsbruck, Innsbruck, Austria
Tobias Hell
Data Lab Hell GmbH, Zirl, Austria
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Total article views: 422 (including HTML, PDF, and XML)
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Cumulative views and downloads
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Total article views: 86 (including HTML, PDF, and XML)
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Cumulative views and downloads
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Total article views: 336 (including HTML, PDF, and XML)
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336
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336
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Total: 336
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Views and downloads (calculated since 27 Jun 2024)
Cumulative views and downloads
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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: 422 (including HTML, PDF, and XML)
Thereof 391 with geography defined
and 31 with unknown origin.
Total article views: 86 (including HTML, PDF, and XML)
Thereof 83 with geography defined
and 3 with unknown origin.
Total article views: 336 (including HTML, PDF, and XML)
Thereof 308 with geography defined
and 28 with unknown origin.
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.
As lightning is a brief and localized event, it is not explicitly resolved in atmospheric...