Spatial models for variability of significant wave height in world oceans
SourceInternational Journal of Offshore and Polar Engineering
Google Scholar check
MetadataShow full item record
Significant wave height (Hs) is a measure of the variability of the ocean surface. Benefits from knowing the spatial and temporal characteristics of this field are multiple: It is useful to size offshore structures, to foresee the fatigue of the ship's hull depending on its route and season, to compute probabilities of risks associated with marine operations. In this paper, we describe a method for modeling the Hs in space. The method is based on the Gaussian hypothesis for the logarithms of Hs and consists of estimating the mean and the covariance structure of log(Hs) using the information provided by the total variation. We then use the estimated parameters of every area in the world to construct maps of the median and the correlation structure. These maps are used to compute the probability of Hs exceeding a predefined level, and the distribution of the length of a storm. The data used are those of the TOPEX-Poseidon satellite. Copyright © by The International Society of Offshore and Polar Engineers.
Showing items related by title, author, creator and subject.
Christofides, Tasos C.; Hadjikyriakou, M. (2015)In this article, we define the conditional convex order, that is, a stochastic ordering between random variables given a sub-σ-algebra F. For the conditional convex order, we present a few representative results. In addition, ...
Modelling of the high firing variability of real cortical neurons with the temporal noisy-leaky integrator neuron model Christodoulou, Chris C.; Clarkson, Trevor G.; Bugmann, Guido; Taylor, John G. (IEEE, 1994)Using the Temporal Noisy-Leaky Integrator (TNLI) neuron model with reset, we observed that high firing variability can be achieved for certain input parameter values which results from the temporal summation of noise in ...
Cacoullos, Theophilos; Papadatos, Nickos (2013)A random variable Z will be called self-inverse if it has the same distribution as its reciprocal Z -1. It is shown that if Z is defined as a ratio, X / Y, of two rv's X and Y (with P[X=0]=P[Y=0]=0), then Z is self-inverse ...