In the reliability engineering and design of offshore structures, probabilistic approaches are frequently adopted. They require the estimation of extreme quantiles of oceanographic data based on the statistical information. Due to strong correlation between such random variables as, e.g., wave heights and wind speeds (WS), application of the multivariate, or bivariate in the simplest case, extreme value theory is sometimes necessary. The paper focuses on the extension of the average conditional exceedance rate (ACER) method for prediction of extreme value statistics to the case of bivariate time series. Using the ACER method, it is possible to provide an accurate estimate of the extreme value distribution of a univariate time series. This is obtained by introducing a cascade of conditioning approximations to the true extreme value distribution. When it has been ascertained that this cascade has converged, an estimate of the extreme value distribution has been obtained. In this paper, it will be shown how the univariate ACER method can be extended in a natural way to also cover the case of bivariate data. Application of the bivariate ACER method will be demonstrated for measured coupled WS and wave height data.
Statistics of Extreme Wind Speeds and Wave Heights by the Bivariate ACER Method
Contributed by the Ocean, Offshore, and Arctic Engineering Division of ASME for publication in the JOURNAL OF OFFSHORE MECHANICS AND ARCTIC ENGINEERING. Manuscript received September 30, 2013; final manuscript received December 3, 2014; published online January 20, 2015. Assoc. Editor: Lance Manuel.
- Views Icon Views
- Share Icon Share
- Cite Icon Cite
- Search Site
Naess, A., and Karpa, O. (April 1, 2015). "Statistics of Extreme Wind Speeds and Wave Heights by the Bivariate ACER Method." ASME. J. Offshore Mech. Arct. Eng. April 2015; 137(2): 021602. https://doi.org/10.1115/1.4029370
Download citation file:
- Ris (Zotero)
- Reference Manager