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Media type:
E-Article
Title:
SWRL Net: A Spectral, Residual Deep Learning Model for Improving Short-Term Wave Forecasts
Contributor:
Mooneyham, Jonny;
Crosby, Sean C.;
Kumar, Nirnimesh;
Hutchinson, Brian
imprint:
American Meteorological Society, 2020
Published in:Weather and Forecasting
Language:
Not determined
DOI:
10.1175/waf-d-19-0254.1
ISSN:
0882-8156;
1520-0434
Origination:
Footnote:
Description:
<jats:title>Abstract</jats:title><jats:p>Skillful nearshore wave forecasts are critical for providing timely alerts of hazardous wave events that impact navigation or recreational beach use. While typical forecasts provide bulk wave parameters (wave height and period), spectral details are needed to correctly predict wave and associated circulation dynamics in the nearshore region. Currently, global wave models, such as WAVEWATCH III (WW3), make spectral predictions, but do not assimilate regional buoy observations. Here, Spectral Wave Residual Learning Network (SWRL Net), a fully convolutional neural network, is trained to take recent WW3 forecasts and buoy observations, and produce corrections to frequency-directional WW3 spectra, transformed into directional buoy moments, for up to 24 h in the future. SWRL Net is trained with 10 years of collocated NOAA’s WW3 CFSR reanalysis predictions and buoy observations at three locations offshore of the U.S. western coast. At buoy locations SWRL Net residual corrections result in wave height root-mean-square error (RMSE) reductions of 23%–50% in the first 6 h and 10%–20% thereafter. Sea frequencies (5–10 s) show the most improvement compared to swell (12–20 s). SWRL Net reduces mean direction RMSE by 28%–54% and mean period RMSE by 20%–56% over 24 forecast hours. While each model is trained and tested at independent locations, SWRL Net exhibits generalization when introduced to data from other locations, suggesting future development may be composed of training sets from multiple locations.</jats:p>