A stochastic cluster model of daily rainfall sequences

Abstract
A two‐level point stochastic model for the rainfall occurrences at a given rainfall station is constructed in the time dimension. The model is a cluster process of the Neyman‐Scott type. The model has the rainfall‐generating mechanisms as its primary level and the rainfalls that are generated by these mechanisms as the secondary level. It uses infinite superposition of rainfalls and has a very flexible dependence structure. The model is fitted to daily rainfall sequences in Indiana after these are stationarized by a transformation. The fit of the model is then tested in terms of its correlation and marginal probability characteristics. The present form of the Neyman‐Scott cluster model is time homogeneous. Therefore the Neyman‐Scott process, as presented in this paper, may be of practical use only for modeling the stationary rainfall occurrences.

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