A Spatiodirectional Model for Extreme Waves in the Gulf of Mexico

Author(s):  
Philip Jonathan ◽  
Kevin Ewans

The characteristics of extreme waves in hurricane dominated regions vary systematically with a number of covariates, including location and storm direction. Reliable estimation of design criteria requires incorporation of covariate effects within extreme value models. We present a spatiodirectional model for extreme waves in the Gulf of Mexico motivated by the nonhomogeneous Poisson model for peaks over threshold. The model is applied to storm peak significant wave height HS for arbitrary geographic areas from the proprietary Gulf of Mexico Oceanographic Study (GOMOS) hindcast for the US region of the Gulf of Mexico for the period of 1900–2005. At each location, directional variability is modeled using a nonparametric directional location and scale; data are standardized (or “whitened”) with respect to local directional location and scale to remove directional effects. For a suitable choice of threshold, the rate of occurrence of threshold exceedences of whitened storm peak HS with direction per location is modeled as a Poisson process. The size of threshold exceedences is modeled using a generalized Pareto form, the parameters of which vary smoothly in space, and are estimated within a roughness-penalized likelihood framework using natural thin plate spline forms in two spatial dimensions. By reparameterizing the generalized Pareto model in terms of asymptotically independent parameters, an efficient back-fitting algorithm to estimate the natural thin plate spline model is achieved. The algorithm is motivated in an appendix. Design criteria, estimated by simulation, are illustrated for a typical neighborhood of 17×17 grid locations. Applications to large areas consisting of more than 2500 grid locations are outlined.

Author(s):  
Philip Jonathan ◽  
Kevin Ewans

Statistics of storm peaks over threshold depend typically on a number of covariates including location, season and storm direction. Here, a non-homogeneous Poisson model is adopted to characterise storm peak events with respect to season for two Gulf of Mexico locations. The behaviour of storm peak significant wave height over threshold is characterised using a generalised Pareto model, the parameters of which vary smoothly with season using a Fourier form. The rate of occurrence of storm peaks is also modelled using a Poisson model with rate varying with season. A seasonally-varying extreme value threshold is estimated independently. The degree of smoothness of extreme value shape and scale, and the Poisson rate, with season, is regulated by roughness-penalised maximum likelihood; the optimal value of roughness selected by cross-validation. Despite the fact that only the peak significant wave height event for each storm is used for modelling, the influence of the whole period of a storm on design extremes for any seasonal interval is modelled using the concept of storm dissipation, providing a consistent means to estimate design criteria for arbitrary seasonal intervals. Characteristics of the 100-year storm peak significant wave height, estimated using the seasonal model, are examined and compared to those estimated ignoring seasonality.


Author(s):  
Philip Jonathan ◽  
Kevin Ewans

Statistics of storm peaks over threshold depend typically on a number of covariates including location, season, and storm direction. Here, a nonhomogeneous Poisson model is adopted to characterize storm peak events with respect to season for two Gulf of Mexico locations. The behavior of storm peak significant wave height over threshold is characterized using a generalized Pareto model, the parameters of which vary smoothly with season using a Fourier form. The rate of occurrence of storm peaks is also modeled using a Poisson model with rate varying with season. A seasonally varying extreme value threshold is estimated independently. The degree of smoothness of extreme value shape and scale and the Poisson rate with season are regulated by roughness-penalized maximum likelihood; the optimal value of roughness is selected by cross validation. Despite the fact that only the peak significant wave height event for each storm is used for modeling, the influence of the whole period of a storm on design extremes for any seasonal interval is modeled using the concept of storm dissipation, providing a consistent means to estimate design criteria for arbitrary seasonal intervals. The characteristics of the 100 year storm peak significant wave height, estimated using the seasonal model, are examined and compared with those estimated ignoring seasonality.


2018 ◽  
Vol 56 (6) ◽  
pp. 755-770 ◽  
Author(s):  
Rui Teixeira ◽  
Maria Nogal ◽  
Alan O'Connor

2015 ◽  
Vol 124 ◽  
pp. 135-147 ◽  
Author(s):  
Shankar P. Sastry ◽  
Vidhi Zala ◽  
Robert M. Kirby

2017 ◽  
Vol 6 (3) ◽  
pp. 141 ◽  
Author(s):  
Thiago A. N. De Andrade ◽  
Luz Milena Zea Fernandez ◽  
Frank Gomes-Silva ◽  
Gauss M. Cordeiro

We study a three-parameter model named the gamma generalized Pareto distribution. This distribution extends the generalized Pareto model, which has many applications in areas such as insurance, reliability, finance and many others. We derive some of its characterizations and mathematical properties including explicit expressions for the density and quantile functions, ordinary and incomplete moments, mean deviations, Bonferroni and Lorenz curves, generating function, R\'enyi entropy and order statistics. We discuss the estimation of the model parameters by maximum likelihood. A small Monte Carlo simulation study and two applications to real data are presented. We hope that this distribution may be useful for modeling survival and reliability data.


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