Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 18, issue 4
Articles | Volume 18, issue 4
https://doi.org/10.5194/gmd-18-1017-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-1017-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 4
Methods for assessment of models
 | 
24 Feb 2025
Methods for assessment of models |  | 24 Feb 2025

Explaining neural networks for detection of tropical cyclones and atmospheric rivers in gridded atmospheric simulation data

Tim Radke, Susanne Fuchs, Christian Wilms, Iuliia Polkova, and Marc Rautenhaus

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Short summary
In our study, we built upon previous work to investigate the patterns artificial intelligence (AI) learns to detect atmospheric features like tropical cyclones (TCs) and atmospheric rivers (ARs). As primary objective, we adopt a method to explain the AI used and investigate the plausibility of learned patterns. We find that plausible patterns are learned for both TCs and ARs. Hence, the chosen method is very useful for gaining confidence in the AI-based detection of atmospheric features.
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