Spatial-temporal rainfall models based on poisson cluster processes

We fit stochastic spatial-temporal models to high-resolution rainfall radar data using Approximate Bayesian Computation (ABC). We consider models constructed from cluster point-processes, starting with the model of Cox, Isham and Northrop, which is the current state of the art. We then generalise this model to allow for more realistic rainfall intensity gradients and for a richer covariance structure that can capture negative correlation between the intensity and size of localised rain cells. The use of ABC is of central importance, as it is not possible to fit models of this complexity using previous approaches. We also introduce the use of Simulated Method of Moments (SMM) to initialise the ABC fit.

Automated summary

In this study, stochastic spatial-temporal models are fitted to high-resolution rainfall radar data using Approximate Bayesian Computation (ABC). The models are constructed from cluster point-processes, and the Simulated Method of Moments (SMM) is introduced to initialize the ABC fit. The use of ABC is crucial for fitting models of this complexity.