Spatial-temporal rainfall models based on Poisson cluster processes

Aryal, Nanda and Jones, Owen ORCID: https://orcid.org/0000-0002-7300-5510 2021. Spatial-temporal rainfall models based on Poisson cluster processes. Stochastic Environmental Research and Risk Assessment 35 , pp. 2629-2643. 10.1007/s00477-021-02046-5

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Abstract

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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Mathematics
Publisher:	Springer Verlag (Germany)
ISSN:	1436-3240
Date of First Compliant Deposit:	9 June 2021
Date of Acceptance:	7 June 2021
Last Modified:	21 Nov 2024 16:45
URI:	https://orca.cardiff.ac.uk/id/eprint/141783

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