Articles | Volume 7, issue 1
https://doi.org/10.5194/gi-7-113-2018
https://doi.org/10.5194/gi-7-113-2018
Research article
 | 
20 Mar 2018
Research article |  | 20 Mar 2018

A comparative study of auroral morphology distribution between the Northern and Southern Hemisphere based on automatic classification

Qiuju Yang and Ze-Jun Hu

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Cited articles

Akasofu, S. I. and Kan, J. R.: Dayside and nightside auroral arc systems, Geophys. Res. Lett., 7, 753–756, 1980. 
Chaston, C. C., Carlson, C. W., McFadden, J. P., Ergun, R. E., and Strangeway, R. J.: How important are dispersive Alfvén waves for auroral particle acceleration?, Geophys. Res. Lett., 34, L07101, https://doi.org/10.1029/2006GL029144, 2007. 
Ebihara, Y., Tanaka, Y.-M., Takasaki, S., Weatherwax, A. T., and Taguchi, M.: Quasi-stationary auroral patches observed at the South Pole Station, J. Geophys. Res., 112, A01201, https://doi.org/10.1029/2006JA012087, 2007. 
Han, D. S., Chen, X., Liu, J., Qiu, Q., Keika, K., Hu, Z. J., Liu, J. M., Hu, H. Q., and Yang, H. G.: An extensive survey of dayside diffuse aurora based on optical observations at Yellow River Station, J. Geophys. Res.-Space Phys., 120, 7447–7465, 2015. 
Han, D. S., Nishimura, Y., Lyons, L. R., Hu, H. Q., and Yang, H. G.: Throat aurora: The ionospheric signature of magnetosheath particles penetrating into the magnetosphere, Geophys. Res. Lett., 43, 1819–1827, https://doi.org/10.1002/2016GL068181, 2016. 
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Short summary
Based on the morphological characteristics of the four dayside auroral types on images at the Chinese Arctic Yellow River Station (YRS), and by extracting the local binary pattern features and using k-nearest classifier, we make an automatic classification to the auroral images of the YRS and the South Pole Station and obtain the occurrence distribution of the dayside aurora morphology. The results indicate that these auroral types present similar occurrence distributions in the two stations.