Visualizing Uncertainty in Hurricane Forecasts with Animated Risk Trajectories
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Description: Accepted for publication in Weather, Climate, and Society (2023): Abstract: Hurricane forecasts are often communicated through visualizations depicting the possible future track of the storm. The Cone of Uncertainty (COU) is the most commonly used visualization, but it is prone to misinterpretation and constrains the information that can be communicated. Our study investigated conveying uncertain hurricane forecast paths using a set of animated icons, each representing an instance of a possible storm path. We refer to this new visualization as Animated Risk Trajectories (ARTs). We measured non-experts’ perception of risk when viewing simplified, hypothetical hurricane forecasts presented as ARTs or COUs. To measure perception of risk for each visualization type, experiments were designed to have participants make decisions to evacuate individual towns at varying distances from the most likely forecast path of a storm. The ARTs led to greater appreciation for risk in areas that would fall beyond the boundaries of the cone. Non-experts were sensitive to the visual properties of the distribution of the icons, including their density and whether the distribution was unimodal or bimodal. This supports the suggestion that ARTs can have value in communicating spatial-temporal uncertainty. Significance Statement: Due to the inherent uncertainties in weather forecasts and emergency management planning, communicating hurricane risk to the public is a unique challenge for decision makers. Our study investigated the effect of conveying uncertainty in hurricane forecast tracks using an idealized distribution of animated icons (Animated Risk Trajectories or ARTs), which correspond to potential hurricane tracks that evolve over time. This visualization was compared to the standard Cone of Uncertainty graphic from the National Hurricane Center. We found that ARTs offered flexibility in conveying information about hurricane risk such as the magnitude of the risk via the number of icons and the location of the risk via the distribution of icons.