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Keywords:

  • rainfall;
  • diurnal cycles;
  • Markov chain;
  • hybrid;
  • aggregation;
  • Fourier series;
  • Bayesian;
  • Akaike

Abstract

A hybrid model for point rainfall has been explored to model the diurnal cycles in rainfall properties. The hybrid model is a product of two random processes: an occurrence process and an intensity process. Two occurrence process models, first-order Markov chain and periodic discrete autoregressive, were compared initially. Fourier series was fitted to the properties of the occurrence and intensity processes of the observed data in order to reduce the number of model parameters. The Bayesian and Akaike information criteria were used to identify the optimum number of harmonics of the Fourier series. Simulation results of the two hybrid models were similar, if not identical, and compared well with the observed. In the average sense, the introduction of diurnal cycles in the model parameters did not improve the reproduction of the observed aggregation properties of the occurrence process. However, the diurnal distributions of the aggregation statistics were significantly improved by increasing the order of the Markov chain model. Also the information criteria tend to favour higher than first-order Markov chain models. Copyright © 2001 John Wiley & Sons, Ltd.