Project Overview
Numeric snowpack models offer detailed predictions of snowpack evolution, but their complexity can be a barrier for operational use. Forecasters are faced with time constraints and information overload, which limited the use of early snowpack modelling tools used by Avalanche Canada in the 2010s. This project started with the idea that poor visualization, not just model limitations, was a key reason why forecasters struggled to adopt these tools.
The project brought together Simon Horton’s background in forecasting and snowpack modelling with Stan Nowak’s background in visual design and information systems. Between 2017 and 2020, they collaborated with Avalanche Canada and several heliskiing operations to test and refine visualization methods for snowpack model outputs. The goal was to develop ways to display key model information, such as the depth and distribution of different avalanche problem types, in a format that was fast to interpret and supported real-time decisions.
Using an iterative design process, we developed and tested a series of interactive prototypes with forecasters. These trials informed a set of design principles aimed at improving interpretability, highlighting relevant features, and supporting multi-scale comparisons across time and space. The results were published in Natural Hazards and Earth System Sciences (Horton et al. 2020) and have since influenced the design of Avalanche Canada’s snowpack tools, such as the current snowpack model tool at https://snowpack.avalanche.ca, and other software products used in the Canadian avalanche industry.
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Enhancing the operational value of snowpack models with visualization design principles
