On linking atmospheric circulation patterns to extreme wave events for coastal vulnerability assessments

Atmospheric circulation patterns (CPs) are fundamental drivers of regional wave climates. A fuzzy rule-based classification algorithm has recently been developed to identify these atmospheric features. The algorithm is guided by wave heights and optimises the location, shape and strength of a set of CP classes in order to find features that drive extreme waves. This paper focuses on a method for evaluating the performance of CP classification algorithms and reducing the subjectivity in the selection of classification parameters. We suggest a method based on entropy to quantify the classification quality and provide a means to objectively define an optimal number of CP classes. We also explore the sensitivity to the temporal resolution of the data. For our case study site, the entropy measure indicates that a good quality classification requires 15–20 CP classes. However, regardless of the number of classes used, there is a persistent, common class that explains a large proportion of extreme wave events. The methods described here contribute to developing a new framework for improved statistical wave modelling in coastal vulnerability risk assessments.