Tailoring Hurricane-related Decision Support for Varying Risk Tolerances

When hurricanes threaten, making decisions that lead to the safety and well-being of thousands of people is a difficult charge. Key decisions are often based on unique thresholds which are sensitive to specific vulnerabilities. Many times, emergency managers use decision aids that rely on the National Weather Service to provide the essential weather information for enacting triggered decisions. They need briefings on the latest forecast, but also require information which factors in uncertainty relative to each of the associated hurricane hazards. In order to make effective decisions that have an appropriate margin of safety, they need an expression of the most likely scenario along with an expression of the reasonable worst case scenario. Absent this, empirical rules of thumb are simply applied, perhaps adding a category of wind or an additional few feet of storm surge to the forecast according to historical tolerances for risk. In short, enacted decisions which have safety in mind are predicated on the extent to which preparations should be made and when they should be completed.

To objectivize the process of accommodating varying risk tolerances among decision-makers, probability information for both hurricane-related wind and storm surge have been made available to forecasters. They are provided in cumulative-form and incremental-form to address both risk and timing decisions. Using such probability information can be intimidating, even for more experienced forecasters and decision-makers. This paper will explore how hurricane hazard probability information can be used explicitly for supporting sophisticated decision-making and implicitly for the less sophisticated, whether for a community at large or a specific site. It will address quantitative vs. qualitative means for yielding a reasonable worst case scenario according to critical thresholds, a reasonable best case scenario, and methods for conveying threat trends for different locations. Forecaster tools and associated logic will also be presented. These tools enable forecasters to dynamically engage exceedance probability data for tailored threat depictions. Finally, challenges related to subsequent public messaging will be outlined with initial considerations from ongoing social science studies