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

Horton, Simon; Haegeli, Pascal

doi:https://doi.org/10.5194/tc-16-3393-2022

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.

https://doi.org/10.5194/tc-16-3393-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 16, issue 8

Research article

|

29 Aug 2022

Research article |

| 29 Aug 2022

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

Simon Horton and Pascal Haegeli

Model code and software

Using snow depth observations to provide insight into the quality of snowpack simulations for regional-scale avalanche forecasting Simon Horton and Pascal Haegeli https://doi.org/10.17605/OSF.IO/A5PEK

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.