Abstract
Abstract. 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.
Highlights
Numerical environmental and weather prediction models have dramatically transformed the accuracy of weather forecasts and the role of weather forecasters since the 1980s (Benjamin et al, 2019)
Morin et al (2020) aptly conclude that while it was important for researchers to focus on improving the accuracy of snowpack models, we are at a point where addressing issues with the design of operational tools is critical for making snowpack models truly valuable for avalanche forecasting
Reflecting the broad adoption of the conceptual model of avalanche hazard (CMAH) (Statham et al, 2018), the proposition of using snowpack models to characterize avalanche problems across forecast regions has gained more interest from the Canadian forecasting community than previous snowpack model tools that focused on individual stratigraphy profiles
Summary
Numerical environmental and weather prediction models have dramatically transformed the accuracy of weather forecasts and the role of weather forecasters since the 1980s (Benjamin et al, 2019). Snowpack models can produce snow stratigraphy profiles for multiple parameters (e.g. grain size, hardness, temperature) at different time intervals at potentially hundreds or thousands of locations These data can be so complex and voluminous that it becomes challenging for operational forecasters to make sense of them in their raw form using conventional methods such as viewing manual snow stratigraphy profiles. As avalanche forecasting requires substantial cognitive effort to continuously maintain a mental model of conditions (Maguire and Percival, 2018), introducing additional complex data can disrupt this process and have adverse effects on performance Based on their analysis, Morin et al (2020) aptly conclude that while it was important for researchers to focus on improving the accuracy of snowpack models, we are at a point where addressing issues with the design of operational tools is critical for making snowpack models truly valuable for avalanche forecasting. We provide examples of visualizations where these principles are applied with snowpack model data (Sect. 3), followed by suggestions for steps towards operational applications (Sect. 4) and conclusions (Sect. 5)
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