Selection of Optimal Tropical Cyclone Motion Guidance Using an Objective Classification Tree Methodology

Abstract

A number of tropical cyclone track forecast aids are available to the forecasters at the Joint Typhoon Warning Center (JTWC) at Guam. These aids typically provide conflicting guidance and no single aid provides consistently superior guidance in every situation. The basic assumption in this study is that synoptic factors and storm-related parameters can be used to predict the performance of each objective aid under various situations. The algorithm of Breiman et al. is used to derive objectively a classification tree to select which of eight objective aids has the lowest 72-h forecast error. The path by which each case traverses the classification tree consists of a series of branches or ?decisions.? These branches, which ultimately result in the selection of an objective aid to be utilized in each case, may be physically interpreted in most cases. The branches of the classification tree in this study are highly dependent upon empirical orthogonal function coefficient values of the environmental wind fields, especially those at 700 mb, which are used to represent the synoptic forcing. The tree correctly classifies 44% (23%) of the dependent (independent) sample cases compared to 13.5% by random chance. The mean 72-h forecast error is 537 km for the dependent sample and 592 km for the independent sample, whereas the corresponding CLIPER errors are 703 and 635 km, respectively. The JTWC errors for a nearly homogeneous sample are 721 and 654 km, respectively. Discriminant analysis is presented as an alternate classification method. The discriminant analysis functions correctly classify 37% (18%) of the dependent (independent) sample cases, with forecast errors of 559 km and 636 km for the dependent and independent samples, respectively. Monte Carlo simulations of the process for selecting the aid with the minimum 72-h forecast error indicate that the selection process includes a large random contribution. Inclusion of more objective aids leads to greater reductions in forecast errors, but this does not provide an appropriate estimate of the potential accuracy of the optimal aid selection process. The classification tree provides an objective method by which conflicting guidance may be better utilized.