Developing Prediction Regions for a Time Series Model for Hurricane Forecasting
Abstract
In this thesis, a class of time series models for forecasting a hurricane's 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 hurricane's 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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Dec 01, 1993
- Accession Number
- ADA273777
Entities
People
- William Cheman
Organizations
- Air Force Institute of Technology