Articles | Volume 19, issue 5
https://doi.org/10.5194/tc-19-1849-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-19-1849-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 May 2025
Research article |
| 14 May 2025
| 14 May 2025
Predicting avalanche danger in northern Norway using statistical models
Department of Physics and Technology, University of Tromsø, Tromsø, Norway
Rune Grand Graversen
Department of Physics and Technology, University of Tromsø, Tromsø, Norway
Norwegian Meteorological Institute, Tromsø Office, Norway
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We train machine-learning models to predict avalanche problems from meteorological and snow-cover data in northern Norway. A major part of the work is the estimation of avalanche-problem changes throughout the 21st century based on future climate projections. We find that while the avalanche danger generally declines towards 2100, the avalanche characteristics will likely change, meaning fewer dry but more wet avalanches, having potential implications for the avalanche-danger forecast quality.
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Short summary
In this study we optimise and train a random forest model to predict avalanche danger in northern Norway based on meteorological reanalysis data. The model performance is at the low end compared to recent similar studies. A hindcast of the frequency of avalanche days (based on the avalanche-danger level) is performed from 1970 to 2024, and a correlation is found with the Arctic Oscillation. This has potential implications for longer-term avalanche predictability.
In this study we optimise and train a random forest model to predict avalanche danger in...
