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A Bayesian spatio-temporal model for precipitation extremes - STOR team contribution to the EVA2017 challenge

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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A Bayesian spatio-temporal model for precipitation extremes - STOR team contribution to the EVA2017 challenge. / Barlow, Anna; Rohrbeck, Christian; Sharkey, Paul et al.
In: Extremes, Vol. 21, No. 3, 09.2018, p. 431-439.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Barlow, A, Rohrbeck, C, Sharkey, P, Shooter, R & Simpson, E 2018, 'A Bayesian spatio-temporal model for precipitation extremes - STOR team contribution to the EVA2017 challenge', Extremes, vol. 21, no. 3, pp. 431-439. https://doi.org/10.1007/s10687-018-0330-z

APA

Barlow, A., Rohrbeck, C., Sharkey, P., Shooter, R., & Simpson, E. (2018). A Bayesian spatio-temporal model for precipitation extremes - STOR team contribution to the EVA2017 challenge. Extremes, 21(3), 431-439. https://doi.org/10.1007/s10687-018-0330-z

Vancouver

Barlow A, Rohrbeck C, Sharkey P, Shooter R, Simpson E. A Bayesian spatio-temporal model for precipitation extremes - STOR team contribution to the EVA2017 challenge. Extremes. 2018 Sept;21(3):431-439. Epub 2018 Jun 18. doi: 10.1007/s10687-018-0330-z

Author

Barlow, Anna ; Rohrbeck, Christian ; Sharkey, Paul et al. / A Bayesian spatio-temporal model for precipitation extremes - STOR team contribution to the EVA2017 challenge. In: Extremes. 2018 ; Vol. 21, No. 3. pp. 431-439.

Bibtex

@article{20e9ef33eded41c28c622d0e3c10d6df,
title = "A Bayesian spatio-temporal model for precipitation extremes - STOR team contribution to the EVA2017 challenge",
abstract = "This paper concerns our approach to the EVA2017 challenge, the aim of which was to predict extreme precipitation quantiles across several sites in the Netherlands. Our approach uses a Bayesian hierarchical structure, which combines Gamma and generalised Pareto distributions. We impose aspatio-temporal structure in the model parameters via an autoregressive prior.Estimates are obtained using Markov chain Monte Carlo techniques and spatial interpolation. This approach has been successful in the context of the challenge, providing reasonable improvements over the benchmark.",
keywords = "Bayesian hierarchical modelling, Extreme value analysis, Markov chain Monte Carlo , Precipitation extremes, Spatio-temporal dependence ",
author = "Anna Barlow and Christian Rohrbeck and Paul Sharkey and Robert Shooter and Emma Simpson",
note = "The final publication is available at Springer via http://dx.doi.org/10.1007/s10687-018-0330-z ",
year = "2018",
month = sep,
doi = "10.1007/s10687-018-0330-z",
language = "English",
volume = "21",
pages = "431--439",
journal = "Extremes",
issn = "1386-1999",
publisher = "Springer Netherlands",
number = "3",

}

RIS

TY - JOUR

T1 - A Bayesian spatio-temporal model for precipitation extremes - STOR team contribution to the EVA2017 challenge

AU - Barlow, Anna

AU - Rohrbeck, Christian

AU - Sharkey, Paul

AU - Shooter, Robert

AU - Simpson, Emma

N1 - The final publication is available at Springer via http://dx.doi.org/10.1007/s10687-018-0330-z

PY - 2018/9

Y1 - 2018/9

N2 - This paper concerns our approach to the EVA2017 challenge, the aim of which was to predict extreme precipitation quantiles across several sites in the Netherlands. Our approach uses a Bayesian hierarchical structure, which combines Gamma and generalised Pareto distributions. We impose aspatio-temporal structure in the model parameters via an autoregressive prior.Estimates are obtained using Markov chain Monte Carlo techniques and spatial interpolation. This approach has been successful in the context of the challenge, providing reasonable improvements over the benchmark.

AB - This paper concerns our approach to the EVA2017 challenge, the aim of which was to predict extreme precipitation quantiles across several sites in the Netherlands. Our approach uses a Bayesian hierarchical structure, which combines Gamma and generalised Pareto distributions. We impose aspatio-temporal structure in the model parameters via an autoregressive prior.Estimates are obtained using Markov chain Monte Carlo techniques and spatial interpolation. This approach has been successful in the context of the challenge, providing reasonable improvements over the benchmark.

KW - Bayesian hierarchical modelling

KW - Extreme value analysis

KW - Markov chain Monte Carlo

KW - Precipitation extremes

KW - Spatio-temporal dependence

U2 - 10.1007/s10687-018-0330-z

DO - 10.1007/s10687-018-0330-z

M3 - Journal article

VL - 21

SP - 431

EP - 439

JO - Extremes

JF - Extremes

SN - 1386-1999

IS - 3

ER -