Annales Geophysicae
Annales Geophysicae
ANGEO
Articles | Volume 44, issue 2
Articles | Volume 44, issue 2
https://doi.org/10.5194/angeo-44-1003-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/angeo-44-1003-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 44, issue 2
Regular paper
 | 
01 Oct 2026
Regular paper |  | 01 Oct 2026

High-latitude auroral and cloudiness occurrence from automatic image classification

Noora Partamies and Mikko Syrjäsuo

Viewed

Total article views: 553 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
271 258 24 553 18 16
  • HTML: 271
  • PDF: 258
  • XML: 24
  • Total: 553
  • BibTeX: 18
  • EndNote: 16
Views and downloads (calculated since 29 May 2026)
Cumulative views and downloads (calculated since 29 May 2026)

Viewed (geographical distribution)

Total article views: 553 (including HTML, PDF, and XML) Thereof 540 with geography defined and 13 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 02 Oct 2026
Download
Short summary
We developed a method to prune colour all-sky images into classes of clear or cloudy skies with and without aurora using supervised learning and pre-trained convolutional neural network. We investigate a 10-year database of auroral images taken from Svalbard. The method accuracy is well over 90 %, and the results show that about 2/3 of auroral images are cloudy with the cloudiest month being November. Aurora are most often observed in the morning hours independent on the solar activity.
Read more
Share