A spatio-temporal model for Red Sea surface temperature anomalies

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.