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To provide guidance to the general public, clinicians, and avalanche professionals about best practices, the Wilderness Medical Society convened an expert panel to revise the evidence-based guidelines for the prevention, rescue, and resuscitation of avalanche and nonavalanche snow burial victims. The original panel authored the Wilderness Medical Society Practice Guidelines for Prevention and Management of Avalanche and Nonavalanche Snow Burial Accidents in 2017. A second panel was convened to update these guidelines and make recommendations based on quality of supporting evidence.

The combination of numerical weather prediction and snowpack models has potential to provide valuable information about snow avalanche conditions in remote areas. However, the output of snowpack models is sensitive to precipitation inputs, which can be difficult to verify in mountainous regions. To examine how existing observation networks can help interpret the accuracy of snowpack models, we compared snow depths predicted by a weather-snowpack model chain with data from automated weather stations and manual observations. Data from the 2020-21 winter were compiled for 21 avalanche forecast regions across western Canada covering a range of climates and observation networks. To perform regional-scale comparisons, snowpack model simulations were run at select grid points from the HRDPS numerical weather prediction model to represent conditions at treeline elevations and observed snow depths were interpolated to the same locations. Snow depths in the Coast Mountain range were systematically overpredicted, while snow depths in many parts of the interior Rocky Mountain range were underpredicted. The impact of these biases had a greater impact on the simulated avalanche conditions in the interior ranges, where faceting was more sensitive to snow depth. To put the comparisons in context, the quality of the observations were assessed with uncertainties in the interpolations and by checking whether snow depth increases during stormy periods were consistent with the forecast avalanche hazard. While some regions had high quality observations, many regions had large uncertainties, suggesting in some situations the modelled snow depths could be more reliable than the observations. The analysis provides insights into the potential for validating weather and snowpack models with readily available observations, and for how avalanche forecasters can better interpret the accuracy of snowpack simulations. [ABSTRACT FROM AUTHOR]

Snowpack models simulate the evolution of the snow stratigraphy based on meteorological inputs and have the potential to support avalanche risk management operations with complementary information relevant for their avalanche hazard assessment, especially in data-sparse regions or at times of unfavorable weather and hazard conditions. However, the adoption of snowpack models in operational avalanche forecasting has been limited, predominantly due to missing data processing algorithms and uncertainty around model validity. Thus, to enhance the usefulness of snowpack models for the avalanche industry, numerical methods are required that evaluate and summarize snowpack model output in accessible and relevant ways. We present algorithms that compare and assess generic snowpack data from both human observations and models, which consist of multidimensional sequences describing the snow characteristics of grain type, hardness, and age. Our approach exploits Dynamic Time Warping, a well-established method in the data sciences, to match layers between snow profiles and thereby align them. The similarity of the aligned profiles is then evaluated by our independent similarity measure based on characteristics relevant for avalanche hazard assessment. Since our methods provide the necessary quantitative link to data clustering and aggregating methods, we demonstrate how snowpack model output can be grouped and summarized according to similar hazard conditions. By emulating aspects of the human avalanche hazard assessment process, our methods aim to promote the operational application of snowpack models so that avalanche forecasters can begin to build an understanding of how to interpret and trust operational snowpack simulations. [ABSTRACT FROM AUTHOR]

Snowpack models simulate the evolution of the snow stratigraphy based on meteorological inputs and have the potential to support avalanche risk management operations with complementary information relevant to their avalanche hazard assessment, especially in data-sparse regions or at times of unfavorable weather and hazard conditions. However, the adoption of snowpack models in operational avalanche forecasting has been limited, predominantly due to missing data processing algorithms and uncertainty around model validity. Thus, to enhance the usefulness of snowpack models for the avalanche industry, numerical methods are required that evaluate and summarize snowpack model output in accessible and relevant ways. We present algorithms that compare and assess generic snowpack data from both human observations and models. Our approach exploits Dynamic Time Warping, a well-established method in the data sciences, to match layers between snow profiles and thereby align them. The similarity of the aligned profiles is then evaluated by our independent similarity measure based on characteristics relevant for avalanche hazard assessment. Since our methods provide the necessary quantitative link to data clustering and aggregating methods, we demonstrate how snowpack model output can be grouped and summarized according to similar hazard conditions. Through emulating a human avalanche hazard assessment approach, our methods aim to promote the operational application of snowpack models so that avalanche forecasters can begin to build understanding in how to interpret and when to trust operational snowpack simulations. [ABSTRACT FROM AUTHOR]

Forecasting snow avalanches requires a reliable stream of field observations, which are often difficult and expensive to collect. Despite the increasing capability of simulating snowpack conditions with physical models, models have seen limited adoption by avalanche forecasters. Feedback from forecasters suggests that model data are presented in ways that are difficult to interpret and irrelevant to operational needs. We apply a visualization design framework to enhance the value of snowpack models to avalanche forecasters. An established risk-based avalanche forecasting workflow is used to define the ways forecasters solve problems with snowpack data. We suggest that model data be visualized in ways that directly support common forecasting tasks such as identifying snowpack features related to avalanche problems and locating avalanche problems in terrain at relevant spatial scales. Examples of visualizations that support these tasks and follow established perceptual and cognitive principles from the field of information visualization are presented. Interactive designs play a critical role in understanding these complex datasets and are well suited for forecasting workflows. Although extensive user testing is still needed to evaluate the effectiveness of these designs, visualization design principles open the door to more relevant and interpretable applications of snowpack model for avalanche forecasters. This work sets the stage for implementing snowpack models into visualization tools where forecasters can test their operational value and learn their capabilities and deficiencies. [ABSTRACT FROM AUTHOR]

Forecasting snow avalanches requires a reliable stream of field observations, which are often difficult and expensive to collect. Despite the increasing capability of simulating snowpack conditions with physical models, models have seen limited adoption by avalanche forecasters. Feedback from forecasters suggest model data is presented in ways that are difficult to interpret and irrelevant to operational needs. We apply a visualization design framework to enhance the value of snowpack models to avalanche forecasters. An established risk-based workflow for avalanche forecasting is used to define the ways forecasters solve problems with snowpack data. We address common forecasting tasks such as identifying snowpack features related to avalanche problems, summarizing snowpack features within a forecast area, and locating problems in terrain. Examples of visualizations that support these tasks are presented and follow established perceptual and cognitive principles from the field of information visualization. Interactive designs play a critical role in understanding these complex datasets and are well suited for forecasting workflows. Preliminary feedback suggests these design principles produce visualizations that are more relevant and interpretable for avalanche forecasters, but additional operational testing is needed to evaluate their effectiveness. By addressing issues with interpretability and relevance, this work sets the stage for implementing snowpack models into workstations where forecasters can test their operational value and learn their capabilities and deficiencies. [ABSTRACT FROM AUTHOR]

Abstract: Snow avalanche danger can vary considerably within the forecast regions, especially large regions. Experienced recreationists routinely use the regional forecast along with local observations to estimate the local avalanche danger. However, some less experienced recreationists are unsure how to interpret the various field observations. To assess a systematic approach, we conducted a field study during the winters of 2006–07 and 2007–08 in the Columbia Mountains of western Canada. Experienced observers rated the local avalanche danger and made 24 observations of weather, avalanche activity and simple manual snowpack tests on approximately 130 location-days. Since the local danger was often rated separately for the elevation bands alpine, treeline, and below treeline, the observations could be applied to 272 individual local danger ratings. Fourteen of the potential predictors yielded significant rank correlations with the local avalanche danger. Reflecting their larger scale, many of the weather variables correlated better with the regional danger forecast than with the local rating. In contrast, some snowpack observations including the hand shear and ski pole test correlated better at the local scale than the regional scale. Classification trees using the regional rating plus three of the local observations exhibited a better agreement with the local danger rating than did the regional rating by itself. [Copyright &y& Elsevier]

The article focuses on the North American Public Avalanche Danger Scale. It aims to show a relative measure of avalanche danger corresponding with a set of definitions for each of the five danger levels, while a danger rating used for ranking avalanche danger for a period of time over a specific region. The scale consists of signal words, numbers, words, colors, and scale. On the scale's travel advice column, the avalanche conditions are described with corresponding advice offered. A discussion on the impact of the scale on the avalanche risk management.