A spatio-temporal model for Red Sea surface temperature anomalies

Christian Rohrbeck, Emma S. Simpson, Ross P. Towe

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish (US)
Pages (from-to)129-144
Number of pages16
JournalExtremes
Volume24
Issue number1
DOIs
StatePublished - Jun 26 2020
Externally publishedYes

ASJC Scopus subject areas

  • Engineering (miscellaneous)
  • Economics, Econometrics and Finance (miscellaneous)
  • Statistics and Probability

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