Empirical Probability Models to Predict Precipitation Levels over Puerto Rico Stations

Abstract

A new algorithm is proposed to predict the level of rainfall (above normal, normal, and below normal) in Puerto Rico that relies on probability and empirical models. The algorithm includes a theoretical probability model in which parameters are expressed as regression equations containing observed meteorological variables. Six rainfall stations were used in this study to implement and assess the reliability of the models. The stations, located throughout Puerto Rico, have monthly records that extend back 101 yr. The maximum likelihood method is used to estimate the parameters of the empirical probability models. A variable selection (VS) algorithm identifies the minimum number of variables that maximize the correlation between predictors and a predictand. The VS algorithm is used to identify the initial point and the maximum likelihood is optimized by using the sequential quadratic programming algorithm. Ten years of cross validation were applied to the results from six stations. The proposed method outperforms both climatology and damped persistence models. Results suggest that the methodology implemented here can be used as a potential tool to predict the level of rainfall at any station located on a tropical island, assuming that at least 50 yr of monthly rainfall observations are available. Model analyses show that meteorological indices can be used to predict rainfall stages.