Forcing the snow-cover model SNOWPACK with forecasted weather data

• Published in: The Cryosphere
• Main Authors: S. Bellaire, J. B. Jamieson, C. Fierz
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2011
• Subjects: Environmental sciences; GE1-350; Geology; QE1-996.5; British Columbia; Canada; The Cryosphere
• Online Access: https://doi.org/10.5194/tc-5-1115-2011; https://doaj.org/article/cd5fbc8dcbbd4fd6ad8b900b4c750c10

Avalanche danger is often estimated based on snow cover stratigraphy and snow stability data. In Canada, single forecasting regions are very large (>50 000 km 2 ) and snow cover data are often not available. To provide additional information on the snow cover and its seasonal evolution the Swiss snow cover model SNOWPACK was therefore coupled with a regional weather forecasting model GEM15. The output of GEM15 was compared to meteorological as well as snow cover data from Mt. Fidelity, British Columbia, Canada, for five winters between 2005 and 2010. Precipitation amounts are most difficult to predict for weather forecasting models. Therefore, we first assess the capability of the model chain to forecast new snow amounts and consequently snow depth. Forecasted precipitation amounts were generally over-estimated. The forecasted data were therefore filtered and used as input for the snow cover model. Comparison between the model output and manual observations showed that after pre-processing the input data the snow depth and new snow events were well modelled. In a case study two key factors of snow cover instability, i.e. surface hoar formation and crust formation were investigated at a single point. Over half of the relevant critical layers were reproduced. Overall, the model chain shows promising potential as a future forecasting tool for avalanche warning services in Canadian data sparse areas and could thus well be applied to similarly large regions elsewhere. However, a more detailed analysis of the simulated snow cover structure is still required.