Modelling diurnal cycles in point rainfall properties

A hybrid model for point rainfall has been explored to model the diurnal cy cles in rainfall properties. The hybrid model is a product of two random pr ocesses: an occurrence process and an intensity process. Two occurrence pro cess models, first-order Markov chain and periodic discrete autoregressive, were compared initially. Fourier series was fitted to the properties of th e occurrence and intensity processes of the observed data in order to reduc e the number of model parameters. The Bayesian and Akaike information crite ria were used to identify the optimum number of harmonics of the Fourier se ries. Simulation results of the two hybrid models were similar, if not iden tical, and compared well with the observed. in the average sense, the intro duction of diurnal cycles in the model parameters did not improve the repro duction of the observed aggregation properties of the occurrence process. H owever, the: diurnal distributions of the aggregation statistics were signi ficantly improved by increasing the order of the Markov chain model. Also t he information criteria tend to favour higher than first-order Markov chain models. Copyright (C) 2001 John Wiley & Sons, Ltd.