Wind Energy Science
Wind Energy Science
WES
Articles | Volume 8, issue 4
Articles | Volume 8, issue 4
https://doi.org/10.5194/wes-8-621-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-8-621-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 8, issue 4
Research article
 | 
28 Apr 2023
Research article |  | 28 Apr 2023

Vertical extrapolation of Advanced Scatterometer (ASCAT) ocean surface winds using machine-learning techniques

Daniel Hatfield, Charlotte Bay Hasager, and Ioanna Karagali

Viewed

Total article views: 2,033 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,572 406 55 2,033 39 36
  • HTML: 1,572
  • PDF: 406
  • XML: 55
  • Total: 2,033
  • BibTeX: 39
  • EndNote: 36
Views and downloads (calculated since 09 Nov 2022)
Cumulative views and downloads (calculated since 09 Nov 2022)

Viewed (geographical distribution)

Total article views: 2,033 (including HTML, PDF, and XML) Thereof 1,964 with geography defined and 69 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 13 Dec 2024
Download
Short summary
Wind observations at heights relevant to the operation of modern offshore wind farms, i.e. 100 m and more, are required to optimize their positioning and layout. Satellite wind retrievals provide observations of the wind field over large spatial areas and extensive time periods, yet their temporal resolution is limited and they are only representative at 10 m height. Machine-learning models are applied to lift these satellite winds to higher heights, directly relevant to wind energy purposes.
Read more
Altmetrics
Final-revised paper
Preprint