A new rainfall model based on the Neyman‐Scott process using cubic copulas

A classical way to model rainfall is to use a Poisson process. Authors generally employed cluster of rectangular pulses to reproduce the hierarchical structure of rainfall storms. Although independence between cell intensity and duration turned out to be a nonrealistic assumption, only a few models link these variables. In this paper, a Neyman‐Scott cluster process considering dependence between cell depth and duration is developed. We introduce this link with a cubic copula. Copulas are multivariate distributions modeling the dependence structure between variables, preserving the marginal distributions. Thanks to this flexibility, we are able to introduce a global concept of dependence between cell depth and duration. We derive the aggregated moments (first‐, second‐, and third‐order moments) from the new model for several families of polynomial copulas and perform an application on Belgium and American data.