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.
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 |
|---|---|
| Pages (from-to) | 129-144 |
| Journal | Extremes |
| Volume | 24 |
| Issue number | 1 |
| Early online date | 26 Jun 2020 |
| DOIs | |
| Publication status | Published - Mar 2021 |
Funding
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’.