Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 19, issue 10
Articles | Volume 19, issue 10
https://doi.org/10.5194/amt-19-3539-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-19-3539-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 10
Research article
 | 
29 May 2026
Research article |  | 29 May 2026

Extraction of spatially confined small-scale waves from high-resolution all-sky airglow images based on machine learning

Sabine Wüst, Jakob Strutz, Patrick Hannawald, Jonas Steffen, Rainer Lienhart, and Michael Bittner

Viewed

Total article views: 2,709 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,518 1,000 191 2,709 8 182 158
  • HTML: 1,518
  • PDF: 1,000
  • XML: 191
  • Total: 2,709
  • Supplement: 8
  • BibTeX: 182
  • EndNote: 158
Views and downloads (calculated since 13 Oct 2025)
Cumulative views and downloads (calculated since 13 Oct 2025)

Viewed (geographical distribution)

Total article views: 2,709 (including HTML, PDF, and XML) Thereof 2,653 with geography defined and 56 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 16 Jun 2026
Download
Short summary
Since June 2019, an infrared camera has been scanning the nearly entire sky (diameter: 500 km) above DLR Oberpfaffenhofen (48.09° N, 11.28° E), Germany, every night providing images of the OH* airglow layer (height: 85–87 km), with a high spatial and temporal resolution (150 m, 2 min). We analysed three years of data for spatially confined small-scale wave structures with a machine learning approach. We derived seasonal variations and deduced that wave breaking is mostly observed in summer.
Read more
Share