Directional Spectra-based Clustering for Visualizing Patterns of Ocean Waves and Winds
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Permanent link to this recordhttp://hdl.handle.net/10754/631109
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AbstractThe energy distribution of wind-driven ocean waves is of great interest in marine science. Discovering the generating process of ocean waves is often challenging and the direction is the key for a better understanding. Typically, wave records are transformed into a directional spectrum which provides information about the wave energy distribution across different frequencies and directions. Here, we propose a new time series clustering method for a series of directional spectra in order to extract the spectral features of ocean waves and develop informative visualization tools to summarize identified wave clusters. We treat directional distributions as functional data of directions, and construct a directional functional boxplot to display the main directional distribution of the wave energy within a cluster. We also trace back when these spectra were observed, and we present color-coded clusters on a calendar plot to show their temporal variability. For each identified wave cluster, we analyze wind speed and wind direction hourly to investigate the link between wind data and wave directional spectra. The performance of the proposed clustering method is evaluated by simulations and illustrated by a real-world dataset from the red sea.
CitationEuán C, Sun Y (2019) Directional Spectra-based Clustering for Visualizing Patterns of Ocean Waves and Winds. Journal of Computational and Graphical Statistics: 1–15. Available: http://dx.doi.org/10.1080/10618600.2019.1575745.
SponsorsWe thank Dr. Ibrahim Hoteit from Earth Fluid Modeling and Prediction group for sharing the Red Sea data set that was used in this paper.
PublisherInforma UK Limited
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