Articles | Volume 44, issue 2
https://doi.org/10.5194/angeo-44-1003-2026
https://doi.org/10.5194/angeo-44-1003-2026
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

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Cited articles

Aruliah, A. and McWhirter, I.: Aurora Cloud Sensor III Data – University College London – Kjell Henriksen Observatory, Zenodo [data set], https://doi.org/10.5281/zenodo.14931122, 2025. a, b
Bednorz, E., Kaczmarek, D., and Dudlik, P.: Atmospheric conditions governing anomalies of the summer and winter cloudiness in Spitsbergen, Theor. Appl. Climatol., 123, 1–10, https://doi.org/10.1007/s00704-014-1326-5, 2016. a
Clausen, L. B. N. and Nickisch, H.: Automatic Classification of Auroral Images From the Oslo Auroral THEMIS (OATH) Data Set Using Machine Learning, J. Geophys. Res.-Space Phys., 123, 5640–5647, https://doi.org/10.1029/2018JA025274, 2018. a, b
Clette, F. and Lefèvre, L.: SILSO Sunspot Number V2.0, WDC SILSO – Royal Observatory of Belgium (ROB), https://doi.org/10.24414/qnza-ac80, 2015. a
Donovan, E., Spanswick, E., and Chaddock, D.: AuroraX – an open data platform for aurora science, Zenodo [data set], https://doi.org/10.5281/zenodo.16583708, 2020. a, b
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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.
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