Articles | Volume 19, issue 14
https://doi.org/10.5194/gmd-19-6497-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-6497-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
17 Jul 2026
Model description paper |
| 17 Jul 2026
| 17 Jul 2026
SNOWstorm (v1.0) – a deep-learning based model for near-surface winds and drifting snow in mountain environments
Climate System Research Group, Institute of Geography, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Erlangen, Germany
Brigitta Goger
Center for Climate Systems Modeling, ETH Zurich, Zurich, Switzerland
now at: GeoSphere Austria, Vienna, Austria
Thomas Mölg
Climate System Research Group, Institute of Geography, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Erlangen, Germany
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Wind-driven snow redistribution affects glacier mass balance. A case study of Hintereisferner glacier in Austria used high-resolution observations and simulations to model snow redistribution. Simulations matched observations, showing the potential of the model for studying snow redistribution on other mountain glaciers.
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Short summary
We present a new model to predict near-surface winds and wind-driven transport of snow in mountain regions at high resolutions. With its deep-learning based design, it is several orders of magnitude less computationally expensive compared to traditional numerical methods, while being applicable over a wide range of topographic settings and atmospheric conditions. A first application case study on a glacier in the European Alps showed good agreement with numerical simulations and observations.
We present a new model to predict near-surface winds and wind-driven transport of snow in...
