Articles | Volume 12, issue 1
https://doi.org/10.5194/gi-12-71-2023
https://doi.org/10.5194/gi-12-71-2023
18 Apr 2023
 | 18 Apr 2023

Auroral alert version 1.0: two-step automatic detection of sudden aurora intensification from all-sky JPEG images

Masatoshi Yamauchi and Urban Brändström

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Cited articles

Akasofu, S.-I.: The development of the auroral substorm, Planet. Space Sci., 12, 273–282, https://doi.org/10.1016/0032-0633(64)90151-5, 1964. 
Akasofu, S.-I.: Physics of magnetospheric substorms, in: Astrophysics and space science library, vol. 47, Reidel, Dordrecht, https://doi.org/10.1007/978-94-010-1164-8, 1977. 
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001. 
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., 123, 5640–5647,https://doi.org/10.1029/2018JA025274, 2018.  
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Potential users of all-sky aurora images even include power companies, tourists, and aurora enthusiasts. However, these potential users are normally not familiar with interpreting these images. To make them comprehensive for more users, we developed an automatic evaluation system of auroral activity level. The method involves two steps: first making a simple set of numbers that describes the auroral activity and then further simplifying them into several levels (Level 6 is an auroral explosion).
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