Abstract. Avalanche problems are used in avalanche forecasting to describe snowpack, weather, and terrain factors that require distinct risk management techniques. Although they have become an effective tool for assessing and communicating avalanche hazard, their definitions leave room for interpretation and inconsistencies. This study uses conditional inference trees to explore the application of avalanche problems over eight winters in Glacier National Park, Canada. The influence of weather and snowpack variables on each avalanche problem type were explored by analyzing a continuous set of weather and snowpack variables produced with a numerical weather prediction model and a physical snow cover model. The decision trees suggest forecasters’ assessments are not only based on a physical analysis of weather and snowpack conditions, but also contextual information about the time of season, location, and interactions with other avalanche problems. The decision trees show clearer patterns when new avalanche problems were added to hazard assessments compared to when problems were removed. Despite discrepancies between modelled variables and field observations, the model-generated variables produced intuitive explanations for conditions influencing most avalanche problem types. For example, 72 h snowfall was the most significant variable for storm slab avalanche problems, skier penetration depth was the most significant variable for dry loose avalanche problems, and slab density was the most significant variable for persistent slab avalanche problems. The explanations for wind slab and cornice avalanche problems were less intuitive, suggesting potential inconsistencies in their application as well as shortcomings of the model-generated data. The decision trees illustrate how forecasters apply avalanche problems and can inform discussions about improved operational practices and the development of data-driven decision aids.

中文翻译：

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用决策树和模型生成的天气和积雪变量检查雪崩问题的操作使用

摘要。雪崩问题用于雪崩预测中，以描述需要不同风险管理技术的积雪，天气和地形因素。尽管它们已成为评估和传达雪崩危害的有效工具，但其定义为解释和前后矛盾留有余地。这项研究使用条件推理树来探索加拿大冰川国家公园八个冬季雪崩问题的应用。通过分析由数值天气预报模型和物理积雪模型产生的连续的天气和积雪变量集，探索了天气和积雪变量对每种雪崩问题类型的影响。决策树表明，预报员的评估不仅基于对天气和积雪状况的物理分析，而且还提供有关季节时间，位置以及与其他雪崩问题的相互作用的上下文信息。与移除问题相比，将新的雪崩问题添加到危害评估中时，决策树显示出更清晰的模式。尽管建模变量和现场观测值之间存在差异，但模型生成的变量对影响大多数雪崩问题类型的条件提供了直观的解释。例如，降雪72小时是风暴平板雪崩问题的最大变量，滑雪者的穿透深度是干燥松散雪崩问题的最大变量，平板密度是持续平板雪崩问题的最大变量。对风板和檐口雪崩问题的解释不太直观，提示其应用中可能存在的不一致之处以及模型生成的数据的缺点。决策树说明了预报员如何应用雪崩问题，并可以为有关改进操作方法和数据驱动决策辅助工具开发的讨论提供信息。