Synthetic metocean time series generation for offshore operability and design based on multivariate Markov model

Probabilistic assessment of expected time for occurrence of operative weather windows for critical Installation analysis, when only a short time series is available, would largely benefit from a methodology to generate additional sea state time series given the observed one. Additional time series should have not only the same statistical properties but also a different but consistent temporal sequence of weather windows. In the present paper, a first order multivariate Markov chain model is developed. The model is demonstrated to be effective to generate synthetic time series with the same statistical properties of the original one. More notably, this procedure is demonstrated to correctly represent persistence and autocovariance properties of the original time series. This feature is particularly relevant not only for Installation purpose, but also for other Ocean Engineering applications, like pipe-soil interaction, sediment transport, coastal erosion and free-span analysis.