Stochastic modelling of tropical cyclone tracks

A stochastic model for the tracks of tropical cyclones that allows for the computerised generation of a large number of synthetic cyclone tracks is introduced. This will provide a larger dataset than previously available for the assessment of risks in areas affected by tropical cyclones. To improve...

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Bibliographic Details
Main Authors: Jonas Rumpf, Helga Weindl, Peter Höppe, Ernst Rauch, Volker Schmidt
Format: Article in Journal/Newspaper
Language:unknown
Subjects:
Online Access:http://hdl.handle.net/10.1007/s00186-007-0168-7
Description
Summary:A stochastic model for the tracks of tropical cyclones that allows for the computerised generation of a large number of synthetic cyclone tracks is introduced. This will provide a larger dataset than previously available for the assessment of risks in areas affected by tropical cyclones. To improve homogeneity, the historical tracks are first split into six classes. The points of cyclone genesis are modelled as a spatial Poisson point process, the intensity of which is estimated using a generalised version of a kernel estimator. For these points, initial values of direction, translation speed, and wind speed are drawn from histograms of the historical values of these variables observed in the neighbourhood of the respective points, thereby generating a first 6-h segment of a track. The subsequent segments are then generated by drawing changes in theses variables from histograms of the historical data available near the cyclone’s current location. A termination probability for the track is determined after each segment as a function of wind speed and location. In the present paper, the model is applied to historical cyclone data from the western North Pacific, but it is general enough to be transferred to other ocean basins with only minor adjustments. A version for the North Atlantic is currently under preparation. Copyright Springer-Verlag 2007 Stochastic model, Monte-Carlo simulation, Inhomogeneous Poisson point process, Generalised spatial random walk, Tropical cyclones, Risk assessment