Modelling and Statistical Analysis of ocean-wave data using transformed gaussian processes

Rychlik, Igor; Johannesson, Pär; Leadbetter, M. R.

doi:10.1016/s0951-8339(96)00017-2

Cited by 69 publications

(36 citation statements)

References 14 publications

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“…This is an important requirement for engineering safety assessment (Rychlik et al 1997). Algorithms to estimate N, p and ϑ are given in the Appendix B.…”

Section: Estimation Of Parameters In the Hybrid Modelmentioning

confidence: 99%

Spatio-temporal modelling of wind speed variations and extremes in the Caribbean and the Gulf of Mexico

Rychlik

Mao

2018

Theor Appl Climatol

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The wind speed variability in the North Atlantic has been successfully modelled using a spatio-temporal transformed Gaussian field. However, this type of model does not correctly describe the extreme wind speeds attributed to tropical storms and hurricanes. In this study, the transformed Gaussian model is further developed to include the occurrence of severe storms. In this new model, random components are added to the transformed Gaussian field to model rare events with extreme wind speeds. The resulting random field is locally stationary and homogeneous. The localized dependence structure is described by time-and space-dependent parameters. The parameters have a natural physical interpretation. To exemplify its application, the model is fitted to the ECMWF ERA-Interim reanalysis data set. The model is applied to compute longterm wind speed distributions and return values, e.g., 100-or 1000-year extreme wind speeds, and to simulate random wind speed time series at a fixed location or spatio-temporal wind fields around that location.

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“…This is an important requirement for engineering safety assessment (Rychlik et al 1997). Algorithms to estimate N, p and ϑ are given in the Appendix B.…”

Section: Estimation Of Parameters In the Hybrid Modelmentioning

confidence: 99%

Spatio-temporal modelling of wind speed variations and extremes in the Caribbean and the Gulf of Mexico

Rychlik

Mao

2018

Theor Appl Climatol

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“…Zeroth-order approximations. For the time waves, in parallel to (19), let u −1 00 (τ ) = {u ∈ R : X time 00 (τ, u) = 0} be the reference coordinate for the particle that is at 0 at time t. Then W time 00 (τ, u −1 00 (τ )) is the Lagrange time wave in the zeroth-order model. Simulated examples are shown in Figure 6, for comparison with Figure 5.…”

Section: G Lindgren Where Y (T K + τ ) = (Y (T K + τ 1 ) (Ymentioning

confidence: 99%

“…The transformation takes the instantaneous water level and rescales it, using a nonlinear transformation, to become a wave process for which the one-dimensional marginal distribution is Gaussian. We then treat the transformed process as if it were a Gaussian process, obtain the relevant distributions, and transform back to the original scale; for examples of the statistical properties, see [19]. However, this procedure is purely empirical and is not based on any physical theory, and it does not solve the problem with front-back asymmetry, which is particularly important for the understanding and description of wave steepness.…”

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confidence: 99%

Slepian models for the stochastic shape of individual Lagrange sea waves

Lindgren

2006

Adv. Appl. Probab.

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Gaussian wave models have been successfully used since the early 1950s to describe the development of random sea waves, particularly as input to dynamic simulation of the safety of ships and offshore structures. A drawback of the Gaussian model is that it produces stochastically symmetric waves, which is an unrealistic feature and can lead to unconservative safety estimates. The Gaussian model describes the height of the sea surface at each point as a function of time and space. The Lagrange wave model describes the horizontal and vertical movements of individual water particles as functions of time and original location. This model is physically based, and a stochastic version has recently been advocated as a realistic model for asymmetric water waves. Since the stochastic Lagrange model treats both the vertical and the horizontal movements as Gaussian processes, it can be analysed using methods from the Gaussian theory. In this paper we present an analysis of the stochastic properties of the first-order stochastic Lagrange waves model, both as functions of time and as functions of space. A Slepian model for the description of the random shape of individual waves is also presented and analysed.

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“…Different strategies to choose the function g have been proposed and studied in [8][9][10] and [11]. An advantage with the transformed Gaussian models is that they are easy to simulate from and that the fatigue damage can be related to Gaussian loads where a lot of results are known.…”

Section: Introductionmentioning

confidence: 99%