Preprints
https://doi.org/10.5194/egusphere-2024-1609
https://doi.org/10.5194/egusphere-2024-1609
08 Jul 2024
 | 08 Jul 2024
Status: this preprint is open for discussion.

Clustering simulated snow profiles to form avalanche forecast regions

Simon Horton, Florian Herla, and Pascal Haegeli

Abstract. This study presents a statistical clustering method that allows avalanche forecasters to explore patterns in simulated snow profiles. The method uses fuzzy analysis clustering to group small regions into larger forecast regions by considering snow profile characteristics, spatial arrangements, and temporal trends. We developed the method, tuned parameters, and present clustering results using operational snowpack model data and human hazard assessments from the Columbia Mountains of western Canada during the 2022–23 winter season. The clustering results from simulated snow profiles closely matched actual forecast regions, effectively partitioning areas based on major patterns in avalanche hazard, such as varying danger ratings or avalanche problem types. By leveraging the uncertain predictions of fuzzy analysis clustering, this method can provide avalanche forecasters with a straightforward approach to interpreting complex snowpack model output and identifying regions of uncertainty. We provide practical and technical considerations to help integrate these methods into operational forecasting practices.

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Simon Horton, Florian Herla, and Pascal Haegeli

Status: open (until 02 Sep 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on egusphere-2024-1609', Frank Techel, 17 Jul 2024 reply
Simon Horton, Florian Herla, and Pascal Haegeli

Data sets

Clustering simulated snow profiles to form avalanche forecast regions – Code and Data Simon Horton, Florian Herla, and Pascal Haegeli https://osf.io/4u2az/

Model code and software

Clustering simulated snow profiles to form avalanche forecast regions – Code and Data Simon Horton, Florian Herla, and Pascal Haegeli https://osf.io/4u2az/

Simon Horton, Florian Herla, and Pascal Haegeli

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Short summary
We present a method for avalanche forecasters to analyze patterns in snowpack model simulations. It uses fuzzy clustering to group small regions into larger forecast areas based on snow characteristics, location, and time. Tested in the Columbia Mountains during winter 2022–23, it accurately matched real forecast regions and identified major avalanche hazard patterns. This approach simplifies complex model outputs, helping forecasters make informed decisions.