Simon Horton and Pascal Haegeli
EGUsphere [preprint],, in review
Publication year: 2022

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The combination of numerical weather prediction and snowpack models has potential to provide valuable information about snow avalanche conditions in remote areas. However, the output of snowpack models is sensitive to precipitation inputs, which can be difficult to verify in mountainous regions. To examine how existing observation networks can help interpret the accuracy of snowpack models, we compared snow depths predicted by a weather-snowpack model chain with data from automated weather stations and manual observations. Data from the 2020–21 winter were compiled for 21 avalanche forecast regions across western Canada covering a range of climates and observation networks. To perform regional-scale comparisons, snowpack model simulations were run at select grid points from the HRDPS numerical weather prediction model to represent conditions at treeline elevations and observed snow depths were interpolated to the same locations. Snow depths in the Coast Mountain range were systematically overpredicted, while snow depths in many parts of the interior Rocky Mountain range were underpredicted. The impact of these biases had a greater impact on the simulated avalanche conditions in the interior ranges, where faceting was more sensitive to snow depth. To put the comparisons in context, the quality of the observations were assessed with uncertainties in the interpolations and by checking whether snow depth increases during stormy periods were consistent with the forecast avalanche hazard. While some regions had high quality observations, many regions had large uncertainties, suggesting in some situations the modelled snow depths could be more reliable than the observations. The analysis provides insights into the potential for validating weather and snowpack models with readily available observations, and for how avalanche forecasters can better interpret the accuracy of snowpack simulations.