Enhancing the operational value of snowpack models with visualization design principles

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.

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

“…To visualize the grain types of snow layers, this manuscript uses the hazard-focused color coding suggested by Horton et al (2020a). We abbreviate grain types with the following acronyms: precipitation particles (PP), decomposing fragments (DF), surface hoar (SH), depth hoar (DH), facets (FC), rounding facets (FCxr), round grains (RG), rain crust (IFrc), sun crust (IFsc), or temperature/melt-freeze crust (MFcr), and melt forms (MF).…”

“…To be useful for forecasters and fit into their already busy forecasting days, such a validation suite must present the information in an intuitive way that integrates seamlessly with their existing practices (Horton et al, 2020a). Hence, we suggest to present simulated layers in a grouped format similar to human layers of concern.…”

Using snow depth observations to provide insight into the quality of snowpack simulations for regional-scale avalanche forecasting

“…Grain types are colour-coded following the suggestions of Horton et al (2020a) rather than Fierz et al (2009) to better highlight features relevant to avalanche conditions (i.e., new snow and weak layers). The maritime climate in the Coast range resulted in thick layers of new snow, rounded grains, and melt forms.…”

A data exploration tool for averaging and accessing large data sets of snow stratigraphy profiles useful for avalanche forecasting

“…While avalanche forecasters have developed meaningful strategies for synthesizing limited numbers of manual snowpack observations, the potential volume of data generated by snowpack simulations is too vast for human processing (Morin et al, 2020). While effective visualization designs can help guide human perception to data features that prompt human reasoning (Horton et al, 2020b), visualizations of large data sets that include both spatial and temporal dimensions remain challenging. Since computer-based tools excel at applying repetitive tasks to big data sets, numerical data aggregation algorithms have the potential to allow avalanche forecasters to make better use of large-scale snowpack simulations.…”