  A new stochastic model for daily precipitation occurrence processes observed
at multiple locations is developed. The modeling concept is to use the
indicator function and the elliptical shape of multivariate Gaussian
distribution to represent the joint probabilities of daily precipitation
occurrences. By using this concept, the number of parameters needed for
precipitation occurrence modeling can be largely reduced when compared to the
commonly used two-state Markov chain approach. With this parameter reduction,
the modeling of spatio-temporal dependence of daily precipitation occurrence
processes observed at different locations is no longer difficult. Results of an
illustrative application using the precipitation record available from a
network of ten raingauges in the southern Quebec region, also demonstrate the
accuracy and the feasibility of the proposed model.
