Wind Energy Science
Wind Energy Science
WES
Articles | Volume 7, issue 4
Articles | Volume 7, issue 4
https://doi.org/10.5194/wes-7-1441-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-7-1441-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 7, issue 4
Research article
 | 
13 Jul 2022
Research article |  | 13 Jul 2022

High-resolution offshore wind resource assessment at turbine hub height with Sentinel-1 synthetic aperture radar (SAR) data and machine learning

Louis de Montera, Henrick Berger, Romain Husson, Pascal Appelghem, Laurent Guerlou, and Mauricio Fragoso

Viewed

Total article views: 3,500 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,219 1,211 70 3,500 98 106
  • HTML: 2,219
  • PDF: 1,211
  • XML: 70
  • Total: 3,500
  • BibTeX: 98
  • EndNote: 106
Views and downloads (calculated since 10 May 2021)
Cumulative views and downloads (calculated since 10 May 2021)

Viewed (geographical distribution)

Total article views: 3,500 (including HTML, PDF, and XML) Thereof 3,397 with geography defined and 103 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 14 Nov 2025
Download
Short summary
A novel method for estimating offshore wind resources at turbine hub height with synthetic aperture radar (SAR) satellites is presented. The machine learning algorithm uses as input geometrical parameters of the SAR sensors and parameters related to atmospheric stability. It is trained with Doppler wind lidar vertical profiles. The extractable wind power accuracy up to 200 m is within 3 %, and SAR can resolve the coastal wind gradient, unlike the Weather Research and Forecasting numerical mode.
Read more
Share
Altmetrics
Final-revised paper
Preprint