Now showing 1 - 4 of 4
  • Publication
    Improving the Laplace transform integration method
    (Wiley, 2015-11-03) ;
    We consider the Laplace transform filtering integration scheme applied to the shallow water equations, and demonstrate how it can be formulated as a finite difference scheme in the time domain. In addition, we investigate a more accurate treatment of the non linear terms. The advantages of the resulting algorithms are demonstrated by means of numerical integrations.
    Scopus© Citations 2  309
  • Publication
    Laplace transform integration of the shallow-water equations. Part 2: Lagrangian formulation and orographic resonance
    (Wiley, 2011-04) ;
    In this paper we combine the Laplace transform (LT) scheme with a semi- Lagrangian advection scheme, and implement it in a shallow water model. It is compared to a reference model using the semi-implicit (SI) scheme, with both Eulerian and Lagrangian advection. We show that the LT scheme is accurate and computationally competitive with these reference schemes. We also show, both analytically and numerically, that the LT scheme is free from the problem of orographic resonance that is found with semi-implicit schemes.
    Scopus© Citations 6  205
  • Publication
    Laplace transform integration of the shallow-water equations. Part 1: Eulerian formulation and Kelvin waves
    (Wiley, 2011-04) ;
    A filtering integration scheme is developed, using a modification of the contour used to invert the Laplace transform (LT). It is shown to eliminate components with frequencies higher than a specified cut-off value. Thus it is valuable for integrations of the equations governing atmospheric flow. The scheme is implemented in a shallow water model with an Eulerian treatment of advection. It is compared to a reference model using the semi-implicit (SI) scheme. The LT scheme is shown to treat dynamically important Kelvin waves more accurately than the SI scheme.
    Scopus© Citations 8  320
  • Publication
    Spatial Bayesian hierarchical modelling of extreme sea states
    A Bayesian hierarchical framework is used to model extreme sea states, incorporating a latent spatial process to more effectively capture the spatial variation of the extremes. The model is applied to a 34-year hindcast of significant wave height off the west coast of Ireland. The generalised Pareto distribution is fitted to declustered peaks over a threshold given by the 99.8th percentile of the data. Return levels of significant wave height are computed and compared against those from a model based on the commonly-used maximum likelihood inference method. The Bayesian spatial model produces smoother maps of return levels. Furthermore, this approach greatly reduces the uncertainty in the estimates, thus providing information on extremes which is more useful for practical applications.
    Scopus© Citations 9  322