SNOWstorm (v1.0) – a deep-learning based model for near-surface winds and drifting snow in mountain environments
Saigger M, Goger B, Mölg T (2026)
Publication Type: Journal article
Publication year: 2026
Journal
Original Authors: Manuel Saigger, Brigitta Goger, Thomas Mölg
Book Volume: 19
Pages Range: 6497-6516
Issue: 14
Journal Issue: 14
Abstract
Wind-driven redistribution of snow and the resulting heterogeneous snow accumulation poses a major uncertainty in mountain hydrology and distributed glacier mass balance models as it is often neglected. High-quality information on the fine-scale wind structure is crucial to predict snow redistribution, but past approaches either relied on highly simplified assumptions or on computationally expensive numerical simulations, inhibiting the application for long-term studies.
To bridge this gap, we introduce SNOWstorm – the snow drift sublimation and transport model. It is designed as a deep-learning based emulator model, that is trained on data from high-resolution (Δx=50 m) numerical simulations in semi-idealized conditions, to be applicable for winter-time conditions in glaciated mountain regions in mid- to high latitudes. The model can be driven with input of standard atmospheric variables from coarse- to meso-scale numerical models and predicts near-surface wind fields, and rates of wind-driven snow mass change, drifting snow sublimation and snow transport. Validation experiments show that the model reproduces major terrain-induced flow features as well as patterns of snow redistribution. In a first real-world application study on a glacier in the European Alps, SNOWstorm predicts wind fields and drifting snow patterns comparable to nested numerical large-eddy simulations, though at more than five orders of magnitude less computational expense. The model thus shows the potential to be used in future studies on multi-seasonal influence of snow redistribution on glacier mass balance in various climatic settings.
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Switzerland (CH)How to cite
APA:
Saigger, M., Goger, B., & Mölg, T. (2026). SNOWstorm (v1.0) – a deep-learning based model for near-surface winds and drifting snow in mountain environments. Geoscientific Model Development, 19(14), 6497-6516. https://doi.org/10.5194/gmd-19-6497-2026
MLA:
Saigger, Manuel, Brigitta Goger, and Thomas Mölg. "SNOWstorm (v1.0) – a deep-learning based model for near-surface winds and drifting snow in mountain environments." Geoscientific Model Development 19.14 (2026): 6497-6516.
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