Simon Horton, Pascal Haegeli, Karl Klassen, James Floyer, and Grant Helgeson
Proceedings of the 2023 International Snow Science Workshop in Bend, Oregon
Publication year: 2023

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Physical snowpack modelling has become an operational forecasting tool used by Avalanche Canada over the last decade. This paper reflects on the process of adopting modelling into the forecasting workflow, highlighting both successful implementation and challenges faced. Additionally, we present an outlook on future developments based on feedback from forecasters who have used the tools. The process of adopting modelling at Avalanche Canada involved close collaboration between the SFU Avalanche Research Program and other forecasting operations. Collaborative efforts focused on developing computer infrastructure to run SNOWPACK at a regional scale, designing effective ways to present model output, and delivering ongoing training. Over time, gradual exposure to this new source of information resulted in increased trust, especially after specific cases where the model offered insights into snowpack conditions that traditional data sources could not provide. However, limitations in understanding model uncertainty and the lack of meaningful verification data currently limit the weight placed on model predictions. To address this issue, future efforts should integrate the models with traditional data sources and establish workflows to regularly monitor model output and facilitate real-time validation. Despite these challenges, physical snowpack models have the potential to improve the accuracy and reliability of avalanche forecasting. The insights gained from this process may be applicable when adopting other new technologies into forecasting programs.