Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 17, issue 21
Articles | Volume 17, issue 21
https://doi.org/10.5194/gmd-17-7569-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-7569-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 21
Model description paper
 | Highlight paper
 | 
30 Oct 2024
Model description paper | Highlight paper |  | 30 Oct 2024

A three-stage model pipeline predicting regional avalanche danger in Switzerland (RAvaFcast v1.0.0): a decision-support tool for operational avalanche forecasting

Alessandro Maissen, Frank Techel, and Michele Volpi

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Operational avalanche forecasting has so far been done almost exclusively by human forecasters. For the first time, an automated machine learning approach allows to reach forecasting skills close to human forecasters.
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Short summary
By harnessing AI models, this work enables processing large amounts of data, including weather conditions, snowpack characteristics, and historical avalanche data, to predict human-like avalanche forecasts in Switzerland. Our proposed model can significantly assist avalanche forecasters in their decision-making process, thereby facilitating more efficient and accurate predictions crucial for ensuring safety in Switzerland's avalanche-prone regions.
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