Identification of regional parameters of a stochastic model for rainfall disaggregation

This paper demonstrates how the Gyasi-Agyei-Willgoose hybrid model for point processes could be regionalised for daily rainfall disaggregation using limited high resolution data within a region of interest. Their model is a product of the binary non-randomised Bartlett-Lewis rectangular pulse model and a lognormal autoregressive model used as a jitter. The computationally efficient multinormal approximation to parameter uncertainty is used to group the monthly parameter values of the binary model. For central Queensland, Australia, it has been established that the parameters of cell origins and the duration of the rectangular pulse of the binary model could have constant values for all months. Second harmonic Fourier series is used to represent the seasonal variation of the parameter governing the storm lifetime. The storm arrival rate is a function of the daily dry probability and the other parameters. Additive properties of random variables with finite variances were used to scale down the daily mean and variance of the historical data to the simulation timescale, values required by the jitter model. The results of using observed daily rainfall statistics to capture sub-daily statistics by the regionalised model are very encouraging. The model is therefore a valuable tool for disaggregating daily rainfall data. (C) 1999 Elsevier Science B.V. All rights reserved.