Using Mesoscale Simulations to Train Statistical Models of Tropical Cyclone Intensity over Land

Abstract

Abstract The decay of tropical cyclones after landfall is a key factor in estimating the extent of the hazard overland. Yet our current understanding of this decay is challenged by the low frequency of past events. Consequently, one cannot rely solely upon the historical record when attempting to quantify robustly the inland penetration of tropical cyclones. Thus, a framework designed to complement the historical record of landfalling storms by means of numerical modeling is introduced. Historical meteorological situations that could potentially have led to a landfall on the coast of the Gulf of Mexico are targeted and, using a bogus vortex technique in conjunction with a mesoscale model, a large number of landfalling hurricanes are simulated. The numerical ensemble constitutes a more comprehensive sample of possible landfalling hurricanes: it encompasses the range of events observed in the past but is not constrained to it. This allows us to revisit existing statistical models of the decay of tropical cyclones after landfall. A range of statistical models trained on the numerical ensemble of storms are evaluated on their ability to reproduce the inland decay of historical storms. These models have more skill at predicting tropical cyclone intensity over land than similar models trained exclusively on historical data.