Articles | Volume 16, issue 8
https://doi.org/10.5194/tc-16-3393-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-16-3393-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
29 Aug 2022
Research article |
| 29 Aug 2022
| 29 Aug 2022
Using snow depth observations to provide insight into the quality of snowpack simulations for regional-scale avalanche forecasting
School of Resource and Environmental Management, Simon Fraser University, Burnaby, BC, Canada
Avalanche Canada, Revelstoke, BC, Canada
Pascal Haegeli
School of Resource and Environmental Management, Simon Fraser University, Burnaby, BC, Canada
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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.
Snowpack models can help avalanche forecasters but are difficult to verify. We present a method...
