Preprints
https://doi.org/10.5194/egusphere-2023-420
https://doi.org/10.5194/egusphere-2023-420
13 Apr 2023
 | 13 Apr 2023

A Large-scale Validation of Snowpack Simulations in Support of Avalanche Forecasting Focusing on Critical Layers

Florian Herla, Pascal Haegeli, Simon Horton, and Patrick Mair

Abstract. Avalanche warning services increasingly employ large-scale snow stratigraphy simulations to improve their insight into the current state of the snowpack. These simulations contain information about thin, persistent critical avalanche layers that are buried within the snowpack and are fundamental drivers of avalanche hazard. However, the data volume, data complexity, and unknown validity have so far limited the value of the simulations for operational decisions. We attribute this at least partially to a lack of research that validates the simulations for their capability to represent the existence and instability of known critical layers at the regional scale. To address this knowledge gap, we present methods that enable meaningful comparisons between regional assessments of avalanche forecasters and snowpack simulations that are distributed across entire forecast regions. We applied these methods to operational data sets of ten winter seasons and three public forecast regions in western Canada and thereby quantified the performance of the Canadian weather and snowpack model chain to represent persistent critical avalanche layers. We found that the overall probability of detecting a known critical layer in the simulations can be as high as 75 % when accepting a low probability of 40 % that any simulated layer is actually of operational concern in reality. Furthermore, we explored patterns that characterize which layers were represented well and which were not. Faceted layers, for example, were captured well but also caused most false alarms, whereas surface hoar layers tended to be less prevalent but in return were mostly of operational concern when modeled. Overall, our results suggest that the simulations provide a valuable starting point for targeted field observations as well as a rich complementary information source that can help alert forecasters about the existence of specific critical layers or provide an independent perspective on their instability. However, we do not believe that the existing model chain is sufficiently reliable to generate assessments purely based on simulations. We conclude by presenting our vision of a real-time operational validation suite that can help forecasters develop a better understanding of the simulations' strengths and weaknesses by continuously comparing assessments and simulations in a user-friendly manner.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Florian Herla, Pascal Haegeli, Simon Horton, and Patrick Mair

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-420', Jürg Schweizer, 05 May 2023
    • AC1: 'Reply on RC1', Florian Herla, 16 Jun 2023
  • CC1: 'Comment on egusphere-2023-420', Ron Simenhois, 06 May 2023
    • AC2: 'Reply on CC1', Florian Herla, 16 Jun 2023
  • RC2: 'Comment on egusphere-2023-420', Anonymous Referee #2, 02 Apr 2024
    • AC3: 'Reply on RC2', Florian Herla, 08 May 2024
Florian Herla, Pascal Haegeli, Simon Horton, and Patrick Mair

Model code and software

Critical Layer Validation—Data and Code F. Herla, P. Haegeli, S. Horton, and P. Mair https://doi.org/10.17605/OSF.IO/W7PJY

Florian Herla, Pascal Haegeli, Simon Horton, and Patrick Mair

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Latest update: 16 Jun 2024
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Short summary
Snowpack simulations are increasingly employed by avalanche warning services to inform about critical avalanche layers buried in the snowpack. However, validity concerns limit their operational value. We present methods that enable meaningful comparisons between snowpack simulations and regional assessments of avalanche forecasters to quantify the performance of the Canadian weather and snowpack model chain to represent thin critical avalanche layers on a large scale and in real-time.