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

Abstract

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.

A quantitative module of avalanche hazard—comparing forecaster assessments of storm and persistent slab avalanche problems with information derived from distributed snowpack simulations

“…The data sets used in this study consist of snowpack simulations and operational avalanche hazard assessments from avalanche forecasters in western Canada over ten winter seasons (2013-2022) similar to Herla et al (2023).…”

“…All simulated snow profiles were valid between 4-5 PM local time representing flat field conditions. For a detailed description of the snowpack simulations used for this study, the interested reader is referred to Herla et al (2023). Informal conversations with forecasters suggest that the hazard assessments are most supported by observations for the treeline elevation band.…”

“…Informal conversations with forecasters suggest that the hazard assessments are most supported by observations for the treeline elevation band. Since previous research found most agreement between simulations and assessments also for the treeline elevation band (Herla et al, 2023), we limit the data set for the present study to grid points between 1800-2100 m asl.…”

“…A large body of research provides insights into the validation of snowpack simulations from a variety of different angles (Schirmer et al, 2010;Bellaire and Jamieson, 2013;Schmucki et al, 2014;Magnusson et al, 2015;Vernay et al, 2015;Quéno et al, 2016;Bellaire et al, 2017;Revuelto et al, 2018;Calonne et al, 2020;Menard et al, 2021;Viallon-Galinier et al, 2020;Morin et al, 2020). Recently, Horton and Haegeli (2022) and Herla et al (2023) validated the simulations on a large scale for their capabilities of representing both new snow amounts and critical avalanche layers, two of the most important aspects for the practitioner community.…”

Exploring the potential of forest snow modelling at the tree and snowpack layer scale

“…Yet, besides validation data, new approaches to quantitatively analyze the multidimensional information accessible through the simulations also need to be developed. Existing layer matching algorithms target the comparison of observations and simulations at the same locations, but fewer approaches exist to quantitatively compare profiles from different locations (Herla et al, 2023).…”