Performance Assessment of ERA5 Wave Data in a Swell Dominated Region

The present paper deals with a performance assessment of the ERA5 wave dataset in an ocean basin where local wind waves superimpose on swell waves. The evaluation framework relies on observed wave data collected during a coastal experimental campaign carried out offshore of the southern Oman coast i...

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Published in:Journal of marine science and engineering Vol. 8; no. 3; p. 214
Main Authors: Bruno, Maria Francesca, Molfetta, Matteo Gianluca, Totaro, Vincenzo, Mossa, Michele
Format: Journal Article
Language:English
Published: Basel MDPI AG 01-03-2020
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Abstract The present paper deals with a performance assessment of the ERA5 wave dataset in an ocean basin where local wind waves superimpose on swell waves. The evaluation framework relies on observed wave data collected during a coastal experimental campaign carried out offshore of the southern Oman coast in the Western Arabian Sea. The applied procedure requires a detailed investigation on the observed waves, and aims at classifying wave regimes: observed wave spectra have been split using a 2D partition scheme and wave characteristics have been evaluated for each wave component. Once the wave climate was defined, a detailed wave model assessment was performed. The results revealed that during the analyzed time span the ERA5 wave model overestimates the swell wave heights, whereas the wind waves’ height prediction is highly influenced by the wave developing conditions. The collected field dataset is also useful for a discussion on spectral wave characteristics during monsoon and post-monsoon season in the examined region; the recorded wave data do not suffice yet to adequately describe wave fields generated by the interaction of monsoon and local winds.
AbstractList The present paper deals with a performance assessment of the ERA5 wave dataset in an ocean basin where local wind waves superimpose on swell waves. The evaluation framework relies on observed wave data collected during a coastal experimental campaign carried out offshore of the southern Oman coast in the Western Arabian Sea. The applied procedure requires a detailed investigation on the observed waves, and aims at classifying wave regimes: observed wave spectra have been split using a 2D partition scheme and wave characteristics have been evaluated for each wave component. Once the wave climate was defined, a detailed wave model assessment was performed. The results revealed that during the analyzed time span the ERA5 wave model overestimates the swell wave heights, whereas the wind waves' height prediction is highly influenced by the wave developing conditions. The collected field dataset is also useful for a discussion on spectral wave characteristics during monsoon and post-monsoon season in the examined region; the recorded wave data do not suffice yet to adequately describe wave fields generated by the interaction of monsoon and local winds.
Author Molfetta, Matteo Gianluca
Bruno, Maria Francesca
Totaro, Vincenzo
Mossa, Michele
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  givenname: Maria Francesca
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  surname: Bruno
  fullname: Bruno, Maria Francesca
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  givenname: Matteo Gianluca
  orcidid: 0000-0002-7281-4826
  surname: Molfetta
  fullname: Molfetta, Matteo Gianluca
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  givenname: Vincenzo
  orcidid: 0000-0003-2160-421X
  surname: Totaro
  fullname: Totaro, Vincenzo
– sequence: 4
  givenname: Michele
  orcidid: 0000-0002-6477-8714
  surname: Mossa
  fullname: Mossa, Michele
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Snippet The present paper deals with a performance assessment of the ERA5 wave dataset in an ocean basin where local wind waves superimpose on swell waves. The...
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SubjectTerms arabian sea
Climate change
Coasts
Datasets
Gravitational waves
Local winds
model performance evaluation
monsoon
Monsoons
Ocean basins
Offshore
Performance assessment
Performance testing
Seasons
spectra partitioning
Swell
Wave climate
Wave data
Wave height
Wave spectra
Wind
Wind waves
Title Performance Assessment of ERA5 Wave Data in a Swell Dominated Region
URI https://www.proquest.com/docview/2382213341
https://doaj.org/article/d019d66a08714f99903f0afebfa36ea5
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