A nonparametric stochastic model is developed and tested for the simulation of tropical cyclone tracks. Tropical cyclone tracks demonstrate continuity and memory over many time and space steps. Clusters of tracks can be coherent, and the separation between clusters may be marked by geographical locations where groups of tracks diverge as a result of the physics of the underlying process. Consequently, their evolution may be non-Markovian. Markovian simulation models, as are often used, may produce tracks that potentially diverge or lose memory quicker than happens in nature. This is addressed here through a model that simulates tracks by randomly sampling track segments of varying length, selected from historical tracks. For performance evaluation, a spatial grid is imposed on the domain of interest. For each grid box, long-term tropical cyclone risk is assessed through the annual probability distributions of the number of storm hours, landfalls, winds, and other statistics. Total storm length is determined at birth by local distribution, and movement to other tropical cyclone segments by distance to neighbor tracks, comparative vector, and age of track. The model is also applied to the conditional simulation of hurricane tracks from specific positions for hurricanes that were not included in the model fitting so as to see whether the probabilistic coverage intervals properly cover the subsequent track. Consequently, tests of both the long-term probability distributions of hurricane landfall and of event simulations from the model are provided.
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