Spatial-Temporal Rainfall Models Based On Poisson Cluster Processes

Abstract We fit stochastic spatial-temporal models to high-resolution rainfall radar data using Approximate Bayesian Computation (ABC). As a baseline we fit a model of Cox, Isham and Northrop, which we then generalise in a variety of ways. Of central importance is the use of ABC, 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.