A Dynamic Decision Model Applied to Hurricane Landfall

Abstract

The decision to prepare for an oncoming hurricane is typically framed as a static cost:loss problem, based on a strike-probability forecast. The value of waiting for updated forecasts is therefore neglected. In this paper, the problem is reframed as a sequence of interrelated decisions that more accurately represents the situation faced by a decision maker monitoring an evolving tropical cyclone. A key feature of the decision model is that the decision maker explicitly anticipates and plans for future forecasts whose accuracy improves as lead time declines. A discrete Markov model of hurricane travel is derived from historical tropical cyclone tracks and combined with the dynamic decision model to estimate the additional value that can be extracted from existing forecasts by anticipating updated forecasts, rather than incurring an irreversible preparation cost based on the instantaneous strike probability. The value of anticipating forecasts depends on the specific alternatives and cost profile of each decision maker, but conceptual examples for targets at Norfolk, Virginia, and Galveston, Texas, yield expected savings ranging up to 8% relative to repeated static decisions. In real-time decision making, forecasts of improving information quality could be used in combination with strike-probability forecasts to evaluate the trade-off between lead time and forecast accuracy, estimate the value of waiting for improving forecasts, and thereby reduce the frequency of false alarms.