Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 15, issue 1
Articles | Volume 15, issue 1
https://doi.org/10.5194/amt-15-1-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-15-1-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 1
Research article
 | 
03 Jan 2022
Research article |  | 03 Jan 2022

Wind speed and direction estimation from wave spectra using deep learning

Haoyu Jiang

Viewed

Total article views: 3,470 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,309 1,063 98 3,470 182 102 85
  • HTML: 2,309
  • PDF: 1,063
  • XML: 98
  • Total: 3,470
  • Supplement: 182
  • BibTeX: 102
  • EndNote: 85
Views and downloads (calculated since 15 Sep 2021)
Cumulative views and downloads (calculated since 15 Sep 2021)

Viewed (geographical distribution)

Total article views: 3,470 (including HTML, PDF, and XML) Thereof 3,397 with geography defined and 73 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 30 Jun 2025
Download
Short summary
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.
Read more
Share