Past Projects

Forecasters had expressed concerns about the reliability of snowpack simulations in operational avalanche forecasting. This project systematically assessed how accurately regional-scale snow-pack simulations reflect critical snow layers and avalanche conditions identified by expert fore-casters.

Avalanche forecasters and researchers face challenges in interpreting the vast, complex data from snowpack observations and simulations. This project developed new computational methods to align, compare, and summarize snow profiles, making it easier to extract relevant information for avalanche hazard assessment and decision-making.

Snowpack models are powerful but often too complex to use effectively in daily avalanche forecasting. This project aimed to improve operational value by designing clear and intuitive ways to visualize model output.

Snowpack models in Canada are driven by weather forecasts, which can introduce significant uncertainty. This project aims to evaluate and improve model accuracy by assessing precipitation inputs and comparing modelled snow depth with observations.

Clustering is a useful way to summarize model output by exploring spatial and temporal snowpack patterns. This project developed and refined clustering methods to support flexible forecasting, where forecasters define region boundaries based on daily conditions.

Snowshoers and winter hikers are a growing backcountry user group, but little is known about their avalanche awareness and risk management practices. In this project, we talk to more than 500 snowshoers and winter hikers to learn more about their practices and how to better communicate to them about avalanche risk.