Item: HIGH-ARCTIC SNOWPACK MODELING FORCED BY NUMERICAL WEATHER PREDICTIONS AND MANUALLY OBSERVED SNOW PROFILES
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Title: HIGH-ARCTIC SNOWPACK MODELING FORCED BY NUMERICAL WEATHER PREDICTIONS AND MANUALLY OBSERVED SNOW PROFILES
Proceedings: International Snow Science Workshop 2024, Tromsø, Norway
Authors:
- Kristin Lyche [ Norwegian University of Science and Technology, Department of Hydraulic and Environmental Engineering, Trondheim, Norway ] [ Skred AS, Tromsø, Norway ]
- Holt Hancock [ University Centre in Svalbard, Department of Arctic Technology, Arctic Safety Centre, Longyearbyen, Svalbard ] [ Norwegian Geotechnical Institute, Oslo, Norway ]
- Siiri Wickström [ University Centre in Svalbard, Department of Arctic Technology, Arctic Safety Centre, Longyearbyen, Svalbard ] [ Skred AS, Tromsø, Norway ]
- Oddbjørn Bruland [ Norwegian University of Science and Technology, Department of Hydraulic and Environmental Engineering, Trondheim, Norway ]
Date: 2024-09-23
Abstract: Snow avalanches are significant natural hazards in mountain areas with a seasonal snow cover, putting people and infrastructure at risk. Snow avalanche forecasting aims to mitigate this risk, and detailed numerical modeling of snow stratigraphy is increasingly relied upon. Our work addresses the potential to improve avalanche forecasting in the high-Arctic by utilizing numerical snowpack modeling forced by numerical weather predictions and observed snow profiles. The Arctic environment presents challenges, both in terms of data collection and the expected quality of the numerical weather predictions. In this study, we validated the performance and usefulness of a model chain consisting of AROME-Arctic, manually observed snow profiles, and SNOWPACK for a site in Longyearbyen, Svalbard. We found that shortwave radiation significantly influenced the modeled snow temperature and, thereby, the metamorphism of the snowpack. Shortwave radiation showed great variability within the gridded AROMEArctic output, making the method of linking AROME-Arctic and SNOWPACK critical. The model chain’s inability to capture wind deposition and the resulting wind slabs has practical implications for operational avalanche forecasting. Our research suggests that numerical snowpack modeling forced by numerical weather predictions and manually observed snow profiles can effectively aid avalanche forecasting in the high-Arctic region. Based on the promising results, we encourage further exploration of the model chain, both for site-specific avalanche forecasting and to include the developed model in regional forecasting. As the potential of machine learning and the utilization of neural networks increases, we see possibilities in expanding the model chain to incorporate automated conclusions based on the predicted snow profile.
Object ID: ISSW2024_O3.12.pdf
Language of Article: English
Presenter(s): Kristin Lyche
Keywords: snowpack simulations, snow profiles, numerical weather predictions, avalanche forecasting
Page Number(s): 471 - 478
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