Articles | Volume 27, issue 1
Nonlin. Processes Geophys., 27, 11–22, 2020
https://doi.org/10.5194/npg-27-11-2020
Nonlin. Processes Geophys., 27, 11–22, 2020
https://doi.org/10.5194/npg-27-11-2020

Research article 03 Feb 2020

Research article | 03 Feb 2020

Prediction and variation of the auroral oval boundary based on a deep learning model and space physical parameters

Yiyuan Han et al.

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Cited articles

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Boudouridis, A., Zesta, E., Lyons, R., Anderson, P. C., and Lummerzheim, D.: Effect of solar wind pressure pulses on the size and strength of the auroral oval, J. Geophys. Res.-Space, 108, https://doi.org/10.1029/2002JA009373, 2003. 
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Cattell, C., Dombeck, J., Keiling, A., Wygant, J., Bergmann, R., Hudson, M. K., and Parks, G.: Disclaimer: The following material is being kept online for archival purposes, available at: https://pwg.gsfc.nasa.gov/polar/data_products.shtml#instruments (last access: 15 June 2016), 1996. 
Cho, J. S., Lee, D. Y., Kim, K. C., and Lee, J. H.: Response of the poleward boundary of the nightside auroral oval to impacts of solar wind dynamic pressure enhancement, J. Astron. Space Sci., 27, 189–194, https://doi.org/10.5140/JASS.2010.27.3.189, 2010. 
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Short summary
We design a new non-linear model to construct an accurate relationship between auroral oval boundaries and 18 space physical parameters, and explore the influence of every single space physical parameter on auroral oval boundary in this paper. As a result, we found the combination of some space physical parameters can strengthen each other's influence on aurora oval boundary prediction, and this model can achieve the best performance when only partial space physical parameters are used as input.