Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 16, issue 18
Articles | Volume 16, issue 18
https://doi.org/10.5194/amt-16-4165-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-16-4165-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 18
Research article
 | 
19 Sep 2023
Research article |  | 19 Sep 2023

Retrieval of surface solar irradiance from satellite imagery using machine learning: pitfalls and perspectives

Hadrien Verbois, Yves-Marie Saint-Drenan, Vadim Becquet, Benoit Gschwind, and Philippe Blanc

Viewed

Total article views: 1,530 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,148 320 62 1,530 79 47 35
  • HTML: 1,148
  • PDF: 320
  • XML: 62
  • Total: 1,530
  • Supplement: 79
  • BibTeX: 47
  • EndNote: 35
Views and downloads (calculated since 28 Mar 2023)
Cumulative views and downloads (calculated since 28 Mar 2023)

Viewed (geographical distribution)

Total article views: 1,530 (including HTML, PDF, and XML) Thereof 1,511 with geography defined and 19 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 16 Nov 2024
Download
Short summary
Solar surface irradiance (SSI) estimations inferred from satellite images are essential to gain a comprehensive understanding of the solar resource, which is crucial in many fields. This study examines the recent data-driven methods for inferring SSI from satellite images and explores their strengths and weaknesses. The results suggest that while these methods show great promise, they sometimes dramatically underperform and should probably be used in conjunction with physical approaches.
Read more