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

ABSTRACT: 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.
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.
FOWD: A Free Ocean Wave Dataset for Data Mining and Machine Learning
http://arxiv.org/abs/2011.12071v2