The economic impact of tropical cyclones depends on two factors: obtaining accurate forecasts with enough lead time to allow for appropriate preparedness, and the capacity of a given asset’s infrastructure for preparedness. Based on these factors, decisions are made to either protect or not protect life and property. These decisions are usually represented in a cost/loss ratio framework, which is a prototype decision problem that has been widely used to investigate the optimal use and economic value of weather information. A cost is incurred when protective action is taken, but a loss is experienced if protective action is not taken and damage occurs.

The cost/loss ratio scenario is typically a static (i.e., one-time) decision problem. But the ultimate impact of a tropical cyclone is determined by a series of decisions in which weather information and the natural variability of cyclones are critical components. Therefore, in order to more accurately represent the problem of a decision-maker facing an evolving tropical cyclone, the cost/loss ration scenario must be extended to include multiple interrelated decisions. At each successive warning period and forecast update, the decision-maker can revisit an earlier decision not to protect, and choose again whether or not to protect assets based on the new information.

This study proposes replacing the static "protect or don't protect" decision-making model with a dynamic "protect or wait" model which allows for the incorporation of new information. Information value, increasing in forecast accuracy as lead time to landfall declines, is investigated. This includes climatological information on landfall position, and probabilities of landfall based on stochastic models that utilize climatological data. The ways these additional types of information could be used to optimize the series of protect-or-wait decisions are explored, in the context of maximizing the economic value associated with adding this information to the decision process. The model accounts for variabilities in the frequency of tropical cyclone occurrences plus variability in the size of the impacts of each tropical cyclone.