Statistical Models for Extreme Weather Events
Creators: name: Sharkey, Paul
Publisher: name: Lancaster University
Year: 2018

description: Western Europe is typically prone to extreme weather events during the
      winter months, which typically take the form of windstorms or flooding.
      The storm Desmond brought strong winds and heavy rain to Ireland, northern
      England and Scotland in December 2015, resulting in an estimated $500
      million worth of damage and extensive flooding, particularly in the region
      of Cumbria. Accurate modelling of such extreme weather events is necessary
      to ensure that the societal and infrastructural risk associated with these
      phenomena is minimised.
      
      
      In statistical modelling, extreme value analysis is typically used to
      model the rate and size of extreme weather events. Typically,
      practitioners can use the outputs of such an analysis to design flood
      defences to a standard such that there is only a small probability that
      defences are breached in a given year. These models can be applied at
      individual sites or adapted to address questions related to the spatial
      extent of an event, which is important for policy makers eager to reduce
      the economic and societal impacts associated with extreme weather.
      
      
      One aim of this thesis is to improve inference with regarding to existing
      extreme value methodology. First, we propose a reparameterisation of the
      likelihood corresponding to the Poisson process model for excesses above a
      high threshold, which improves mixing in a Bayesian framework and ensures
      more rapid convergence of the parameter chains in a Markov Chain Monte
      Carlo routine. The Poisson process model is often preferred for modelling
      extremes of non-stationary processes as the parameters are invariant to
      the
      
      choice of threshold; our approach may increase the possibility of this
      model being used more widely. Second, we propose an adjustment to the
      likelihood when implementing a spatial hierarchical model for extremes,
      which accounts for the dependence in the data when estimating model
      uncertainty. In both cases, the improvement in inference should increase
      confidence among practitioners of the outputs obtained from extreme value
      models.
      
      
      The main influence of extreme weather events in winter is from the passage
      of low-pressure extratropical cyclones from the North Atlantic. The second
      aim of this thesis is to quantify the risk associated with extreme wind
      speed events, which we call windstorms, arising from an extratropical
      cyclone system. First, we develop a model capturing the spatial variation
      of the track associated with the cyclone, from which we can simulate
      synthetic tracks with the same statistical characteristics of the observed
      record. Second, we describe an approach for modelling the spatial extent
      and severity of windstorms relative to the storm track, from which we can
      provide improved estimates of risk associated with windstorms at
      individual sites and jointly over a spatial domain. The methods described
      in this thesis can be used to address multiple questions related
      
      to windstorm risk, that is not available using current methodology.