Improving avalanche forecasts in data-sparse areas with physical snowpack modelling

Assessing dangerous avalanche conditions requires a reliable stream of weather and snowpack data, which can be difficult and expensive to collect in many remote areas of Canada. Snowpack conditions can be simulated in these areas by coupling weather forecast models with physical snowpack models, however, this method has had limited adoption by avalanche forecasters. The proposed project will increase the adoption of snowpack models by developing a dashboard that allows Avalanche Canada forecasters to visualize spatial snowpack patterns, alarm them of critical changes, and provide an assessment of the model’s accuracy. Novel methods of comparing model output with snow observations will be investigated and spatial clustering methods will offer a new dynamic view of regional snowpack patterns. The project will improve the accuracy and quality of Avalanche Canada’s public safety products and warnings in data-sparse areas.

Faculty Supervisor:

Pascal Haegeli


Simon Horton


Avalanche Canada


Environmental sciences



Simon Fraser University



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