Statistical modelling of sequences of no-rain days

The stochastic models, developed to simulate long-term hydrological data, can be subdivided in “driven data” models, which reproduce the principal characteristics of the available data series, and “physically based” models, which schematize the generating mechanism of atmospheric precipitation. The initial step of a “driven data” stochastic model, able to adequately simulate the sequences of wet and dry days, is the definition of the statistics of the model. In this paper, various statistical models for sequences of no-rain days are firstly presented: the models are based on an approach which considers the arrival of rainfall events as a Poisson process, homogenous or not. Moreover, the first results of an application of one of these models to the daily rainfall series registered at the Cosenza rain gauge (Calabria, Southern Italy) are also shown. In particular, the model applied is a non-homogeneous Poisson model which considers the rainfall as a pulse of random duration.