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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.
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