Assimilation of ocean wave spectra and atmospheric circulation patterns to improve wave modelling

The modelling of waves associated with extreme events is fundamental to coastal engineering design and coastal vulnerability assessments. The storm systems that drive extreme wave events are associated with specific atmospheric circulation patterns (CPs). In this paper the link between these circulation patterns and wave spectra is explored as a means to improve wave modelling in engineering applications. The methodology involves partitioning wave spectra into low frequency swell and locally generated wind waves. The origin(s) of the swell waves can then be estimated in order to link them to the atmospheric circulation pattern(s) that generated them. A method based on fuzzy logic and fuzzy sets is used to identify and classify the atmospheric circulation patterns. Finally the spectral characteristics associated with specific circulation patterns can be obtained. The methodology is tested using a case study on the east coast of South Africa. The atmospheric circulation patterns driving low frequency swell events resemble those previously identified as the dominant drivers of significant wave events in the region. The link between wave spectra and CPs can be used to study the impacts of specific CPs on the coastline. For example the spectra associated with swell produced by tropical cyclones can be used to model their potential impacts. This new methodology may improve the inputs to spectral wave models and aid studies of climate change impacts. It may also help in identifying statistically independent storm events and improve multivariate statistical models of such events. •Linking wave events to atmospheric circulation patterns can improve wave models.•Wave data is partitioned into swell and local wind-waves to track their origins.•Circulation patterns are categorized using fuzzy logic methods.•Wave spectra are then linked to their driver circulation patterns.•The methods can be used for design and vulnerable assessments.