The knowledge of sea state and wind conditions is of central importance for many offshore and nearshore operations. In this paper, we make a complete survey of stochastic models for sea state and wind time series. We begin with methods based on Gaussian processes, then non-parametric resampling methods for time series are introduced followed by various parametric models. We also propose an original statistical method, based on Monte Carlo goodness-of-fit tests, for model validation and comparison and this method is illustrated on an example of multivariate sea state time series.
The paper discusses short-and long-term probability models of ocean waves. The Gaussian theory is reviewed, and nonlinear short-term probability distributions are derived from a narrow band second-order model. The nonlinearity has different impact on different measurement techniques, and this is further demonstrated for wave data from the WAVEMOD Crete measurement campaign and laser data from the North Sea. Finally, we give some examples on how the short-term statistics may be used to estimate the probability distributions for the maximum waves during individual storms as well as in a wave climate described by long-term distributions.
To date the estimation of long-term wave energy production at a given deployment site has commonly been limited to a consideration of the significant wave height H s and mean energy period T e . This paper addresses the sensitivity of power production from wave energy converters to the wave groupiness and spectral bandwidth of sea states. Linear and non-linear systems are implemented to simulate the response of converters equipped with realistic power take-off devices in real sea states. It is shown in particular that, when the converters are not much sensitive to wave directionality, the bandwidth characteristic is appropriate to complete the set of overall wave parameters describing the sea state for the purpose of estimating wave energy production.
SUMMARYIn this article, an original Markov-switching autoregressive model is proposed to describe the space-time evolution of wind fields. At first, a non-observable process is introduced in order to model the motion of the meteorological structures. Then, conditionally to this process, the evolution of the wind fields is described using autoregressive models with time-varying coefficients. The proposed model is calibrated and validated on data in the North Atlantic.
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