Date of Award


Document Type


Degree Name

Master of Science


Department of Operational Sciences

First Advisor

Edward F. Mykytka, PhD


In this thesis, a class of time series models for forecasting a hurricanes future position based on its previous positions and a generalized model of hurricane motion are examined and extended. Results of a literature review suggest that meteorological models continue to increase in complexity while few statistical approaches, such as linear regression, have been successfully applied. An exception is provided by a certain class of time series models that appear to forecast storms almost as well as current meteorological models without their tremendous complexity. A suggestion for enhancing the performance of these time series models is pursued through an examination of the forecast errors produced when these models are applied to historical storm tracks. The results uncover no patterns that can be exploited in developing an improved model and suggest that there are meteorological processes or factors at work beyond those that can be modeled with the available historical data base. The statistical structure of the time series approach is exploited to develop a practical method for determining prediction regions which probabilistically describe a hurricanes likely future position. The Monte Carlo approach used to develop these prediction ellipses is seen to be applicable for predicting areas subject to risk from hurricane landfall.

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