Atlantic Tropical Cyclone Intensity Forecasting via the Minimum Message Length Principle: A Preliminary Result

Abstract

presented at the 22nd Conference on Hurricane and Tropical Meteorology, Fort Collins -- Colorado, 19 -- 23 May 1997 1 Introduction The existing tropical cyclone intensity forecasting schemes (SHIFOR [Jarvinen and Neumann, 1979], SHIPS [DeMaria and Kaplan, 1994], SHIFOR94 [Landsea, 1995], TIPS [Fitzpatrick, 1995]) were built using the conventional multiple linear regression method. This method relies upon statistical significance test techniques which the chosen models prone to overfit the data. This inherent tendency of overfitting makes the separation of the limited available data into the training and test data sets imperative. In this abstract, a Bayesian approach using the Minimum Message Length (MML) principle [Wallace and Freeman, 1987] is applied to tropical cyclone intensity change forecasting. The MML technique builds regression models by taking a balance between the complexity of the models and the goodness of fit as a performance criterion. Because of this balancing mech..