Preprints
https://doi.org/10.5194/egusphere-2022-237
https://doi.org/10.5194/egusphere-2022-237
 
03 May 2022
03 May 2022
Status: this preprint is open for discussion.

Using snow depth observation to provide insight into the quality of regional-scale snowpack simulations for avalanche forecasting

Simon Horton1,2 and Pascal Haegeli1 Simon Horton and Pascal Haegeli
  • 1Simon Fraser University, Burnaby, BC, Canada
  • 2Avalanche Canada, Revelstoke, BC, Canada

Abstract. 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.

Simon Horton and Pascal Haegeli

Status: open (until 28 Jun 2022)

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Simon Horton and Pascal Haegeli

Model code and software

Using snow depth observation to provide insight into the quality of regional-scale snowpack simulations for avalanche forecasting (Code and data) Simon Horton and Pascal Haegeli https://osf.io/a5pek/

Simon Horton and Pascal Haegeli

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Short summary
Snowpack models can help avalanche forecasters, but are difficult to verify. We present a method for evaluating the accuracy of simulated snow profiles using readily available observations of snow depth. This method could be easily applied to understand the representativeness of available observations, the agreement between modelled and observed snow depths, and the implications for interpreting avalanche conditions.