Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 16, issue 21
Articles | Volume 16, issue 21
https://doi.org/10.5194/amt-16-5305-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-16-5305-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 21
Research article
 | 
09 Nov 2023
Research article |  | 09 Nov 2023

A neural-network-based method for generating synthetic 1.6 µm near-infrared satellite images

Florian Baur, Leonhard Scheck, Christina Stumpf, Christina Köpken-Watts, and Roland Potthast

Viewed

Total article views: 1,855 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,426 357 72 1,855 85 101
  • HTML: 1,426
  • PDF: 357
  • XML: 72
  • Total: 1,855
  • BibTeX: 85
  • EndNote: 101
Views and downloads (calculated since 18 Apr 2023)
Cumulative views and downloads (calculated since 18 Apr 2023)

Viewed (geographical distribution)

Total article views: 1,855 (including HTML, PDF, and XML) Thereof 1,765 with geography defined and 90 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 27 Nov 2025
Download
Short summary
Near-infrared satellite images have information on clouds that is complementary to what is available from the visible and infrared parts of the spectrum. Using this information for data assimilation and model evaluation requires a fast, accurate forward operator to compute synthetic images from numerical weather prediction model output. We discuss a novel, neural-network-based approach for the 1.6 µm near-infrared channel that is suitable for this purpose and also works for other solar channels.
Read more
Share