Abstract. The self-organizing map (SOM) technique is considered and extended to assess the extremes of a multivariate sea wave climate at a site. The main purpose is to obtain a more complete representation of the sea states, including the most severe states that otherwise would be missed by a SOM. Indeed, it is commonly recognized, and herein confirmed, that a SOM is a good regressor of a sample if the frequency of events is high (e.g., for low/moderate sea states), while a SOM fails if the frequency is low (e.g., for the most severe sea states). Therefore, we have considered a trivariate wave climate (composed by significant wave height, mean wave period and mean wave direction) collected continuously at the Acqua Alta oceanographic tower (northern Adriatic Sea, Italy) during the period 1979-2008. Three different strategies derived by SOM have been tested in order to capture the most extreme events. The first contemplates a pre-processing of the input data set aimed at reducing redundancies; the second, based on the post-processing of SOM outputs, consists in a two-step SOM where the first step is applied to the original data set, and the second step is applied on the events exceeding a given threshold. A complete graphical representation of the outcomes of a two-step SOM is proposed. Results suggest that the post-processing strategy is more effective than the pre-processing one in order to represent the wave climate extremes. An application of the proposed two-step approach is also provided, showing that a proper representation of the extreme wave climate leads to enhanced quantification of, for instance, the alongshore component of the wave energy flux in shallow water. Finally, the third strategy focuses on the peaks of the storms.

Abstract. In this paper the Self-Organizing Map (SOM) technique to assess the multivariate sea wave climate at a site is analyzed and discussed with the aim of a more complete representation which includes the most severe sea states that otherwise would be missed by the standard SOM. Indeed, it is commonly recognized, and herein confirmed, that SOM is a good regressor of a sample where the density of events is high (e.g. for low/moderate and frequent sea states), while SOM fails where the density is low (e.g. for severe and rare sea states). Therefore, we have considered a trivariate wave climate (composed by significant wave height, mean wave period, and mean wave direction) collected continuously at the Acqua Alta oceanographic tower (northern Adriatic Sea, Italy) during the period 1979–2008. Three different strategies derived by the standard SOM have been tested in order to widen the range of applicability to extreme events. The first strategy contemplates a pre-processing of the input dataset with the Maximum Dissimilarity Algorithm; the second and the third strategies focus on the post-processing of SOM outputs, resulting in a two-steps SOM, where the first step is the standard SOM applied to the original dataset, and the second step is an additional SOM on the events exceeding a threshold (either taking all the events over the threshold or only the peaks of storms). Results suggest that post-processing strategies are more effective than the pre-processing one in representing the extreme wave climate, both in the time series and probability density spaces. In addition, a complete graphical representation of the outcomes of two-steps SOM as double-sided maps is proposed.