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A spatio-temporal model for Red Sea surface temperature anomalies

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A spatio-temporal model for Red Sea surface temperature anomalies. / Rohrbeck, Christian; Simpson, Emma; Towe, Ross.
In: Extremes, Vol. 24, 01.03.2021, p. 129–144.

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

Harvard

Rohrbeck, C, Simpson, E & Towe, R 2021, 'A spatio-temporal model for Red Sea surface temperature anomalies', Extremes, vol. 24, pp. 129–144. https://doi.org/10.1007/s10687-020-00383-2

APA

Rohrbeck, C., Simpson, E., & Towe, R. (2021). A spatio-temporal model for Red Sea surface temperature anomalies. Extremes, 24, 129–144. https://doi.org/10.1007/s10687-020-00383-2

Vancouver

Rohrbeck C, Simpson E, Towe R. A spatio-temporal model for Red Sea surface temperature anomalies. Extremes. 2021 Mar 1;24:129–144. Epub 2020 Jun 26. doi: 10.1007/s10687-020-00383-2

Author

Rohrbeck, Christian ; Simpson, Emma ; Towe, Ross. / A spatio-temporal model for Red Sea surface temperature anomalies. In: Extremes. 2021 ; Vol. 24. pp. 129–144.

Bibtex

@article{70abff679da64735ac258d1718baeac5,
title = "A spatio-temporal model for Red Sea surface temperature anomalies",
abstract = "This paper details the approach of team Lancaster to the 2019 EVA data challenge, dealing with spatio-temporal modelling of Red Sea surface temperature anomalies. We model the marginal distributions and dependence features separately; for the former, we use a combination of Gaussian and generalised Pareto distributions, while the dependence is captured using a localised Gaussian process approach. We also propose a space-time moving estimate of the cumulative distribution function that takes into account spatial variation and temporal trend in the anomalies, to be used in those regions with limited available data. The team's predictions are compared to results obtained via an empirical benchmark. Our approach performs well in terms of the threshold-weighted continuous ranked probability score criterion, chosen by the challenge organiser.",
author = "Christian Rohrbeck and Emma Simpson and Ross Towe",
year = "2021",
month = mar,
day = "1",
doi = "10.1007/s10687-020-00383-2",
language = "English",
volume = "24",
pages = "129–144",
journal = "Extremes",
issn = "1386-1999",
publisher = "Springer Netherlands",

}

RIS

TY - JOUR

T1 - A spatio-temporal model for Red Sea surface temperature anomalies

AU - Rohrbeck, Christian

AU - Simpson, Emma

AU - Towe, Ross

PY - 2021/3/1

Y1 - 2021/3/1

N2 - This paper details the approach of team Lancaster to the 2019 EVA data challenge, dealing with spatio-temporal modelling of Red Sea surface temperature anomalies. We model the marginal distributions and dependence features separately; for the former, we use a combination of Gaussian and generalised Pareto distributions, while the dependence is captured using a localised Gaussian process approach. We also propose a space-time moving estimate of the cumulative distribution function that takes into account spatial variation and temporal trend in the anomalies, to be used in those regions with limited available data. The team's predictions are compared to results obtained via an empirical benchmark. Our approach performs well in terms of the threshold-weighted continuous ranked probability score criterion, chosen by the challenge organiser.

AB - This paper details the approach of team Lancaster to the 2019 EVA data challenge, dealing with spatio-temporal modelling of Red Sea surface temperature anomalies. We model the marginal distributions and dependence features separately; for the former, we use a combination of Gaussian and generalised Pareto distributions, while the dependence is captured using a localised Gaussian process approach. We also propose a space-time moving estimate of the cumulative distribution function that takes into account spatial variation and temporal trend in the anomalies, to be used in those regions with limited available data. The team's predictions are compared to results obtained via an empirical benchmark. Our approach performs well in terms of the threshold-weighted continuous ranked probability score criterion, chosen by the challenge organiser.

U2 - 10.1007/s10687-020-00383-2

DO - 10.1007/s10687-020-00383-2

M3 - Journal article

VL - 24

SP - 129

EP - 144

JO - Extremes

JF - Extremes

SN - 1386-1999

ER -