Healy, Dáire
(2023)
Non-stationary spatio-temporal extremal processes;
an analysis of extreme temperature events in Ireland.
PhD thesis, National University of Ireland Maynooth.
Abstract
Climate change has resulted in extreme events becoming more frequent, intense, and
destructive. It is essential to understand the behaviour of extreme weather processes
for societal preparedness. This thesis focuses on the development of spatio-temporal
statistical models for extremely hot summer and cold winter temperature events in
Ireland, in the context of climate change. To provide reliable estimates of extreme
temperatures of unobserved magnitudes, we rely on asymptotically justified extrapolations
provided by extreme value theory.
Modelling observational weather records is problematic for several reasons. Firstly,
these data tend to have low spatial resolution and contain spatial bias. This thesis
presents a framework to unify information about spatial extreme events using
both historical temperature observations and output from climate models to enrich
the statistical model’s topographic information while addressing spatial bias.
Secondly, observational weather records are incomplete, with sites having different
record lengths as well as missing values. To deal with this issue we develop a spatial
extreme value model which allows for an estimation of the full dependence structure
without imputing or removing spatial observations that are not fully observed.
We identify a lack of a unified measure of extremity in the spatio-temporal context.
Such tools are critical for practitioners to summarise and present risks associated
with non-stationary spatio-temporal extremal processes. To address this gap in
the literature, we develop several novel summary tools to synthesise and visually
communicate non-stationarity in the extreme values of spatio-temporal processes.
We apply our methodology to analyse the extremal behaviour of both summer maximum and winter minimum daily temperatures in Ireland. We characterise
changes on a marginal level (i.e., the behaviour at any location in Ireland) as well
as on a spatial level. For the marginal model, we use spatial covariates derived
from climate model outputs and explore several potential temporal covariates. To
model the spatial extremal dependence, we use an r-Pareto process with H¨usler-
Reiss margins in order to incorporate incomplete spatial observations and apply our
measures of spatio-temporal non-stationarity to simulations from our fitted model
of summer and winter extreme temperatures.
| Item Type: |
Thesis
(PhD)
|
| Keywords: |
Non-stationary; spatio-temporal; extremal processes;
analysis; extreme temperature events; Ireland; |
| Academic Unit: |
Faculty of Science and Engineering > Research Institutes > Hamilton Institute |
| Item ID: |
18314 |
| Depositing User: |
IR eTheses
|
| Date Deposited: |
26 Mar 2024 12:16 |
| URI: |
|
| Use Licence: |
This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available
here |
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