FOWD: A Free Ocean Wave Dataset for Data Mining and Machine Learning

The occurrence of extreme (rogue) waves in the ocean is for the most part still shrouded in mystery, as the rare nature of these events makes them difficult to analyze with traditional methods. Modern data mining and machine learning methods provide a promising way out, but they typically rely on th...

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Bibliographic Details
Published inJournal of atmospheric and oceanic technology Vol. 38; no. 7; p. 1305
Main Authors Häfner, Dion, Gemmrich, Johannes, Jochum, Markus
Format Journal Article
LanguageEnglish
Published Boston American Meteorological Society 01.07.2021
Subjects
Buoy data
Buoys
Catalogues
Coastal processes
Data analysis
Data mining
Data science
Datasets
Elevation
Kurtosis
Learning algorithms
Machine learning
Metadata
Methods
Ocean waves
Oceans
Quality control
Reproducibility
Rogue waves
Sea state
Sea states
Subject specialists
Surface water waves
Time series
Wave height
United States > US
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Summary:The occurrence of extreme (rogue) waves in the ocean is for the most part still shrouded in mystery, as the rare nature of these events makes them difficult to analyze with traditional methods. Modern data mining and machine learning methods provide a promising way out, but they typically rely on the availability of massive amounts of well-cleaned data. To facilitate the application of such data-hungry methods to surface ocean waves, we developed FOWD, a freely available wave dataset and processing framework. FOWD describes the conversion of raw observations into a catalogue that maps characteristic sea state parameters to observed wave quantities. Specifically, we employ a running window approach that respects the non-stationary nature of the oceans, and extensive quality control to reduce bias in the resulting dataset. We also supply a reference Python implementation of the FOWD processing toolkit, which we use to process the entire CDIP buoy data catalogue containing over 4 billion waves. In a first experiment, we find that, when the full elevation time series is available, surface elevation kurtosis and maximum wave height are the strongest univariate predictors for rogue wave activity. When just a spectrum is given, crest-trough correlation, spectral bandwidth, and mean period fill this role.
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ISSN:0739-0572
1520-0426
DOI:10.1175/JTECH-D-20-0185.1