A STATISTICAL-METHOD FOR FORECASTING RAINFALL OVER PUERTO-RICO

Using results from a factor analysis regionalization of nontropical st orm 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 dat aset, while the predictive model is derived from 3 yr of surface and r ainfall data. The work is an initial attempt at improving objective gu idance for operational rainfall forecasting in Puerto Rico. Surface da ta from two first-order stations are used as input to a partially adap tive classification tree to predict the occurrence of heavy rain. Resu lts from a case study show that the methodology has skill above climat ology-the leading contender;in such cases. The algorithm also achieves skill over persistence. Comparisons of forecast skill with a linear d iscriminant analysis suggest that classification trees are an easier a nd 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 p repping the environment for rainfall. Generalizations of the findings and a discussion of a more realistic forecast setting in which to appl y the technology for improving tropical rainfall forecasts are given.