Uncertainties in the SNOWPACK multilayer snow model for a Canadian avalanche context: sensitivity to climatic forcing data. (4th March 2017) - Côté, Kevin; Madore, Jean-Benoît; Langlois, Alexandre
Abstract: As interest in outdoor activities in remote areas is increasing, there is a strong need for improved avalanche forecasting at the regional scale. Due to important logistical and safety matters, avalanche terrain measurements (avalanche observations, snowpack profiles, and stability tests) are not always possible for practitioners/forecasters. An interesting alternative would be to analyze the snowpack without these challenges by using snow model outputs. The SNOWPACK model is currently used operationally for avalanche forecasting and research in the Swiss Alps. Thus, this paper presents a summary of analyses that have been conducted to assess the potential of using the SNOWPACK model driven with both in-situ and forecasted meteorological data in three different Canadian climate and geomorphological contexts. A comparison of meteorological data from in-situ and predicted datasets for two winters shows that the GEMLAM weather model is the most accurate for the three climatic contexts of this project, but also showed a bias proportional to precipitation intensity/rate. Snow simulations forced with GEMLAM are the closest to field measurements. Finally, predictions of persistent weak layers have been validated using the InfoEx platform from Avalanche Canada. Crust and surface hoar formation dates agree with the information reported in InfoEx .