Hubei Key Laboratory of Marine Geological Resources, China University of Geosciences, Wuhan, 430000, China
Laboratory for Regional Oceanography and Numerical Modeling, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao,
266000, China
Southern Marine Science and Engineering Guangdong Laboratory
(Guangzhou), Guangzhou, 511458, China
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2,637
1,249
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3,999
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PDF: 1,249
XML: 113
Total: 3,999
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BibTeX: 136
EndNote: 127
Views and downloads (calculated since 15 Sep 2021)
Cumulative views and downloads
(calculated since 15 Sep 2021)
Total article views: 2,776 (including HTML, PDF, and XML)
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1,826
860
90
2,776
191
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HTML: 1,826
PDF: 860
XML: 90
Total: 2,776
Supplement: 191
BibTeX: 127
EndNote: 118
Views and downloads (calculated since 03 Jan 2022)
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Total article views: 1,223 (including HTML, PDF, and XML)
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811
389
23
1,223
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Total: 1,223
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Cumulative views and downloads
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Viewed (geographical distribution)
Total article views: 3,999 (including HTML, PDF, and XML)
Thereof 3,919 with geography defined
and 80 with unknown origin.
Total article views: 2,776 (including HTML, PDF, and XML)
Thereof 2,746 with geography defined
and 30 with unknown origin.
Total article views: 1,223 (including HTML, PDF, and XML)
Thereof 1,173 with geography defined
and 50 with unknown origin.
Sea surface wind and waves are important ocean parameters that can be continuously observed by meteorological buoys. Meteorological buoys are sparse in the ocean due to their high cost of deployment and maintenance. In contrast, low-cost compact wave buoys are suited for deployment in large numbers. Although wave buoys are not designed for wind measurement, we found that deep learning can estimate wind from wave measurements accurately, making wave buoys a good-quality data source for sea wind.
Sea surface wind and waves are important ocean parameters that can be continuously observed by...