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Seasonal Modelling of Multivariate Distributions of Metocean Parameters

[+] Author Affiliations
Sébastien Fouques, Dag Myrhaug

Norwegian University of Science and Technology, Trondheim, Norway

Finn Gunnar Nielsen

Norsk Hydro ASA, Bergen, Norway

Paper No. OMAE2002-28048, pp. 55-65; 11 pages
  • ASME 2002 21st International Conference on Offshore Mechanics and Arctic Engineering
  • 21st International Conference on Offshore Mechanics and Arctic Engineering, Volume 2
  • Oslo, Norway, June 23–28, 2002
  • Conference Sponsors: Ocean, Offshore, and Arctic Engineering Division
  • ISBN: 0-7918-3612-6 | eISBN: 0-7918-3599-5
  • Copyright © 2002 by ASME


Statistical information about the joint occurrence of metocean parameters is of importance for many offshore activities. For instance, in marine operations, environmental limitations may be brought about by both wind and wave conditions. Thus, knowledge of their joint occurrence is important as the persistence duration and the seasonal dependence of wind and waves appear to be of large interest. However, such a modelling becomes difficult as the number of considered variables increases, especially when utilizing a common parameterization of some conditional distributions. This paper proposes a general methodology that aims at modelling seasonal joint distributions of n such parameters from their correlation structure and the n marginal distributions fitted by generalized gamma ones. Two methods are proposed in order to derive an approximate joint distribution from the modelled margins. The first one matches the correlation matrix only, whereas the second one, which is based on a multivariate Hermite polynomials expansion of the multinormal distribution, is able to match joint moments of order higher than two. However, more restrictive conditions are shown by the latter. An application to the simple example of the joint occurrence of significant wave height and the mean wind velocity at the 10m elevation is used to illustrate the methods. Eventually, examples of applications like simultaneous persistence of wind and wave conditions as well as seastate forecasting from statistics are given.

Copyright © 2002 by ASME
Topics: Modeling



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