A hierarchical Bayesian spatio-temporal model for extreme precipitation events

Souparno Ghosh; Bani K. Mallick

doi:10.1002/env.1043

A hierarchical Bayesian spatio-temporal model for extreme precipitation events

Souparno Ghosh, Bani K. Mallick

Research output: Contribution to journal › Article › peer-review

40 Scopus citations

Abstract

We propose a new approach to model a sequence of spatially distributed time series of extreme values. Unlike common practice, we incorporate spatial dependence directly in the likelihood and allow the temporal component to be captured at the second level of hierarchy. Inferences about the parameters and spatio-temporal predictions are obtained via MCMC technique. The model is fitted to a gridded precipitation data set collected over 99 years across the continental U.S. © 2010 John Wiley & Sons, Ltd..

Original language	English (US)
Pages (from-to)	192-204
Number of pages	13
Journal	Environmetrics
Volume	22
Issue number	2
DOIs	https://doi.org/10.1002/env.1043
State	Published - Mar 30 2011
Externally published	Yes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUS-CI-016-04
Acknowledgements: The first author's research was partially supported by National Science foundation CMG reserach grants DMS-0724704, ATM-0620624, and by Award Number KUS-CI-016-04 made by King Abdullah University of Science and Technology (KAUST). The second author's research was supported by National Science foundation CMG reserach grants ATM-0620624. They gratefully acknowledge two referees for their constructive suggestions that led to significant improvement of the paper.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

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A hierarchical Bayesian spatio-temporal model for extreme precipitation events

Abstract

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