Abstract
Using results from a factor analysis regionalization of nontropical storm convective rainfall over the island of Puerto Rico, a statistical methodology is investigated for its potential to forecast rain events over limited areas. Island regionalization is performed on a 15-yr dataset, while the predictive model is derived from 3 yr of surface and rainfall data. The work is an initial attempt at improving objective guidance for operational rainfall forecasting in Puerto Rico. Surface data from two first-order stations are used as input to a partially adaptive classification tree to predict the occurrence of heavy rain. Results from a case study show that the methodology has skill above climatology—the leading contender in such cases. The algorithm also achieves skill over persistence. Comparisons of forecast skill with a linear discriminant analysis suggest that classification trees are an easier and more natural way to handle this kind of forecast problem. Synthesis of results confirms the notion that despite the very local nature of tropical convection, synoptic-scale disturbances are responsible for prepping the environment for rainfall. Generalizations of the findings and a discussion of a more realistic forecast setting in which to apply the technology for improving tropical rainfall forecasts are given.
Corresponding author address: Matthew M. Carter, Department of Meteorology, The Florida State University, Tallahassee, FL 32306-3034.
Email: carter@huey.met.fsu.edu
