A Bayesian-Hierarchical Space-Time Model for Significant Wave Height Data

Bad weather and rough seas contribute significantly to the risk to maritime transportation. This stresses the importance of taking severe sea state conditions adequately into account, with due treatment of the uncertainties involved, in ship design and operation. Hence, there is a need for appropriate stochastic models describing the variability of sea states. This paper presents a Bayesian hierarchical space-time stochastic model for significant wave height. The model has been fitted by data for an area in the North Atlantic ocean and aims at describing the temporal and spatial variability of significant wave height in this area. It could also serve as foundation for further extensions used for long-term prediction of significant wave height and future return periods of extreme significant wave heights. The model will be outlined in this paper, and the results will be discussed. Furthermore, a discussion of possible model extensions will be presented.Copyright © 2011 by ASME

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