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 language | English (US) |
|---|---|
| Pages (from-to) | 129-144 |
| Number of pages | 16 |
| Journal | Extremes |
| Volume | 24 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jun 26 2020 |
| Externally published | Yes |
Bibliographical note
KAUST Repository Item: Exported on 2021-04-14Acknowledged KAUST grant number(s): OSR-CRG2017-3434
Acknowledgements: We would like to thank the referees and Associate Editor for their helpful comments. Christian Rohrbeck is beneficiary of an AXA Research Fund postdoctoral grant. Emma Simpson’s work is supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. OSR-CRG2017-3434. Ross Towe is supported by Engineering and Physical Sciences Research Council (Grant Number: EP/P002285/1) ‘The Role of Digital Technology in Understanding, Mitigating and Adapting to Environmental Change’.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.
ASJC Scopus subject areas
- Engineering (miscellaneous)
- Economics, Econometrics and Finance (miscellaneous)
- Statistics and Probability