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

Research output: Contribution to journalJournal articleResearchpeer-review

The occurrence of extreme (rogue) waves in the ocean is for the most part still shrouded in mystery, because 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 the Free Ocean Wave Dataset (FOWD), a freely available wave dataset and processing framework. FOWD describes the conversion of raw observations into a catalog that maps characteristic sea state parameters to observed wave quantities. Specifically, we employ a running-window approach that respects the nonstationary 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 Coastal Data Information Program (CDIP) buoy data catalog 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.

Original languageEnglish
JournalJournal of Atmospheric and Oceanic Technology
Volume38
Issue number7
Pages (from-to)1305-1322
Number of pages18
ISSN0739-0572
DOIs
Publication statusPublished - 23 Jul 2021

    Research areas

  • Wave properties, Waves, oceanic, Data mining, Data processing, Data quality control, Data science, Machine learning, ROGUE WAVES, KURTOSIS

ID: 275993811