Ding, G., He, F., Zhang, X., and Chen, X.: A new auroral boundary
determination algorithm based on observations from TIMED/GUVI and
DMSP/SSUSI, J. Geophys. Res.-Space, 122, 2162–2173, https://doi.org/10.1002/2016JA023295, 2017.
Feldstein, Y. I. and Starkov, G. V.: Dynamics of auroral belt and polar
geomagnetic disturbances, Planet. Space Sci., 15, 209–229, https://doi.org/10.1016/0032-0633(67)90190-0, 1967.
Fitzgibbon, A., Pilu, M., and Fisher, R. B.: Direct least square fitting of
ellipses, IEEE TPAMI, 21, 476–480, https://doi.org/10.1109/34.765658, 1999.
Hinton, G. E.: Training products of experts by minimizing contrastive
divergence, Neural Comput., 14, 1771–1800, https://doi.org/10.1162/089976602760128018, 2002.
Hinton, G. E.: A practical guide to training restricted Boltzmann machines,
Momentum, 9, 599–619, https://doi.org/10.1007/978-3-642-35289-8_32,
2012.
Hinton, G. E., Osindero, S., and Teh, Y. W.: A fast learning algorithm for
deep belief nets, Neural Computat., 18, 1527–1554, https://doi.org/10.1162/neco.2006.18.7.1527, 2006.
Holzworth, R. H. and Meng, C.: Mathematical representation of the auroral
oval, Geophys. Res. Lett., 2, 377–380, https://doi.org/10.1029/GL002i009p00377, 1975.
Holzworth, R. H. and Meng, C.: Auroral boundary variations and the
interplanetary magnetic field, Planet. Space Sci., 32, 25–29, https://doi.org/10.1016/0032-0633(84)90038-2, 1984.
Hu, Z., Yang, Q., Liang, J., Hu, H., Zhang, B., and Yang, H.: Variation and
modeling of ultraviolet auroral oval boundaries associated with
interplanetary and geomagnetic parameters, Space Weather, 15, 606–622,
https://doi.org/10.1002/2016SW001530, 2017.
Huang, C., Andre, D. A., Sofko, G. J., and Koustov, A. V.: Evolution of
ionospheric multicell convection during northward interplanetary magnetic
field with
|Bz/
By| > 1, J. Geophys. Res., 105,
7095–27107, https://doi.org/10.1029/2000JA000163, 2000.
Karlson, K. A., Oieroset, M., Moen, J., and Sandholt, P. E.: A statistical
study of flux transfer event signatures in the dayside aurora: The IMF
By-related prenoon-postnoon asymmetry, J. Geophys. Res., 101, 59–68, https://doi.org/10.1029/95JA02590, 1996.
King, J. and Papitashvili, N.: One min and 5-min solar wind data sets at the
Earth's bow shock nose, available at:
http://omniweb.gsfc.nasa.gov/html/HROdocum.html (last access: 18 December 2018), 2006.
Krizhevsky, A., Sutskever, I., and Hinton, G. E.: Imagenet classification with deep
convolutional neural networks, Adv. Neur. In., 25,
1097–1105, https://doi.org/10.1145/3065386, 2012.
Liu, H., Gao, X., Han, B., and Yang, X.: An automatic MSRM method with a
feedback based on shape information for auroral oval segmentation, Springer,
Berlin, Heidelberg, 8261, 748–755, https://doi.org/10.1007/978-3-642-42057-3_94,2013.
Loomis, B.: On the geographical distribution of auroras in the northern
hemisphere, Am. J. Sci. Arts, 30, 89–94, 1890.
Łukaszyk, S.: A new concept of probability metric and its applications in
approximation of scattered data sets, Comput. Mech., 33,
299–304, https://doi.org/10.1007/s00466-003-0532-2, 2004.
Makita, K., Meng, C. I., and Akasofu, S. I.: The shift of the auroral
electron precipitation boundaries in the dawn-dusk sector in association
with geomagnetic activity and interplanetary magnetic field, J. Geophys.
Res.-Space, 88, 7967–7981, https://doi.org/10.1029/JA088iA10p07967, 1983.
Milan, S. E., Evans, T. A., and Hubert, B.: Average auroral configuration parameterized by geomagnetic activity and solar wind conditions, Ann. Geophys., 28, 1003-1012, https://doi.org/10.5194/angeo-28-1003-2010, 2010.
Niu, Y., Zhang, X., He, F., and Jiang, Y.: Statistical characteristics of the
equatorial boundary of the nightside auroral particle precipitation, Sci.
China Earth Sci., 58, 1602–1608, https://doi.org/10.1007/s11430-015-5090-x, 2015.
Peng, Z., Wang, C., Hu, Y., Kan, J., and Yang, Y.: Simulations of observed
auroral brightening caused by solar wind dynamic pressure enhancements under
different interplanetary magnetic field conditions, J. Geophys. Res.-Space,
116, https://doi.org/10.1029/2010JA016318, 2011.
Provan, G., Yeoman, T. K., and Cowley, S. W. H.: The influence of the IMF
By
component on the location of pulsed flows in the dayside ionosphere observed
by an HF radar, Geophys. Res. Lett., 26, 521–524, https://doi.org/10.1029/1999GL900009, 1999.
Rumelhart, D. E.: Learning representations by back-propagating errors,
Nature, 323, 533–536, https://doi.org/10.1016/B978-1-4832-1446-7.50035-2, 1986.
Sigernes, F., Dyrland, M., Brekke, P., Chernouss, S., Lorentzen, D. A.,
Oksavik, K., and Sterling, D. C.: Two methods to forecast auroral displays, J.
Space Weather Spac., 1, A03, https://doi.org/10.1051/swsc/2011003, 2011.
Starkov, G. V.: Statistical dependences between the magnetic activity
indices, Geomagn. Aeronomy
+, 34, 101–101, 1994a.
Starkov, G. V.: Mathematical model of the auroral boundaries, Geomagn.
Aeronomy
+, 34, 331–336, 1994b.
Vennerstrøm, S., Friis-Christensen, E., Troshichev, O. A., and Andersen,
V. G.: Comparison between the polar cap index, PC, and the auroral
electrojet indices AE, AL, and AU, J. Geophys. Res.-Space, 96, 101–113,
https://doi.org/10.1029/90JA01975, 1991.
Xing, Z. Yang, H., Han, D., and Wu, Z.: Dayside poleward moving auroral forms
and ionospheric convection under stable interplanetary magnetic field (IMF)
conditions, Sci. China Technol. Sc., 56, 910–916, https://doi.org/10.1007/s11431-013-5164-y, 2013.
Yang, Q., Hu, Z., Han, D., Hu, H., and Xiao, M.: Modeling and prediction of
ultraviolet auroral oval boundaries base on IMF/solar wind and geomagnetic
parameters, Chinese J. Geophys.-Ch., 59, 426–439, https://doi.org/10.6038/cjg20160203,
2016.
Yu, D. and Deng, L.: Deep learning and its applications to signal and
information processing [exploratory dsp], IEEE Signal Proc. Mag., 28,
145–154, https://doi.org/10.1109/MSP.2010.939038, 2011.
Zhang, Y. and Paxton, L. J.: An empirical Kp-dependent global auroral model
based on TIMED/GUVI FUV data, J. Atmos. Sol.-Terr. Phy., 70, 1231–1242, https://doi.org/10.1016/j.jastp.2008.03.008, 2008.