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Assessing marine operations with a Markov-switching autoregressive metocean model
Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment ( IF 1.8 ) Pub Date : 2020-06-13 , DOI: 10.1177/1475090220916084
Jack Paterson 1, 2 , Philipp R Thies 3 , Roman Sueur 4 , Jérôme Lonchampt 4 , Federico D’Amico 1
Affiliation  

This article presents a metocean modelling methodology using a Markov-switching autoregressive model to produce stochastic wind speed and wave height time series, for inclusion in marine risk planning software tools. By generating a large number of stochastic weather series that resemble the variability in key metocean parameters, probabilistic outcomes can be obtained to predict the occurrence of weather windows, delays and subsequent operational durations for specific tasks or offshore construction phases. To cope with the variation in the offshore weather conditions at each project, it is vital that a stochastic weather model is adaptable to seasonal and inter-monthly fluctuations at each site, generating realistic time series to support weather risk assessments. A model selection process is presented for both weather parameters across three locations, and a personnel transfer task is used to contextualise a realistic weather window analysis. Summarising plots demonstrate the validity of the presented methodology and that a small extension improves the adaptability of the approach for sites with strong correlations between wind speed and wave height. It is concluded that the overall methodology can produce suitable wind speed and wave time series for the assessment of marine operations, yet it is recommended that the methodology is applied to other sites and operations, to determine the method’s adaptability to a wide range of offshore locations.

中文翻译:

使用马尔可夫切换自回归气象海洋模型评估海洋作业

本文介绍了一种使用马尔可夫切换自回归模型生成随机风速和波高时间序列的气象海洋建模方法,以包含在海洋风险规划软件工具中。通过生成大量类似于关键气象海洋参数可变性的随机天气序列,可以获得概率结果来预测特定任务或海上施工阶段的天气窗口、延迟和后续操作持续时间的发生。为了应对每个项目的海上天气条件的变化,随机天气模型必须能够适应每个站点的季节性和月间波动,从而生成现实的时间序列以支持天气风险评估。为三个地点的两个天气参数提供了模型选择过程,并使用人员转移任务将现实天气窗口分析置于情境中。总结图证明了所提出方法的有效性,并且小范围的扩展提高了该方法对风速和波高之间具有强相关性的地点的适应性。结论是整体方法可以产生合适的风速和波浪时间序列用于评估海上作业,但建议将该方法应用于其他地点和作业,以确定该方法对广泛的海上位置的适应性. 总结图证明了所提出方法的有效性,并且小范围的扩展提高了该方法对风速和波高之间具有强相关性的地点的适应性。结论是整体方法可以产生合适的风速和波浪时间序列用于评估海上作业,但建议将该方法应用于其他地点和作业,以确定该方法对广泛的海上位置的适应性. 总结图证明了所提出方法的有效性,并且小范围的扩展提高了该方法对风速和波高之间具有强相关性的地点的适应性。结论是整体方法可以产生合适的风速和波浪时间序列用于评估海上作业,但建议将该方法应用于其他地点和作业,以确定该方法对广泛的海上位置的适应性.
更新日期:2020-06-13
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