Dynamically Aggregating Avalanche Forecast Regions Based on Simulated Snow Profiles
Abstract
A potential application for avalanche forecasting with snowpack models is to group gridded simulations into areas with similar snowpack conditions. We present and test a method to aggregate small forecast regions into larger regions by analyzing simulated snow profiles from a weather-snowpack model chain. The study focuses on the Columbia Mountains of Canada, where avalanche forecasters have split the range into 33 small subregions. Representative snow profiles were produced for each subregion by averaging gridded SNOWPACK simulations over each day of the 2020-21 winter. Then a clustering algorithm was applied to dynamically group the subregions into large regions each day based on the similarity of the 33 representative profiles. The workflow applies recently developed snow profile averaging and clustering methods that focus on features relevant to avalanche hazard (e.g., new snow, weak layers). We test and illustrate the capabilities of this approach for several cases where local hazard assessments revealed variable avalanche conditions across the Columbia Mountains. Avalanche danger ratings and problems were taken from roughly 50 professional operations to find examples where regional-scale variability was caused by factors such as variable snowfall amounts or the presence/absence of specific weak layers. The ability of the model-based groups to reproduce these patterns is discussed to show the potential value and limitations of using snowpack models to identify regional-scale variability in avalanche conditions.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2022
- Bibcode:
- 2022AGUFM.C33B..06H