Journal cover Journal topic
Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 1.699 IF 1.699
  • IF 5-year value: 1.559 IF 5-year
    1.559
  • CiteScore value: 1.61 CiteScore
    1.61
  • SNIP value: 0.884 SNIP 0.884
  • IPP value: 1.49 IPP 1.49
  • SJR value: 0.648 SJR 0.648
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 52 Scimago H
    index 52
  • h5-index value: 21 h5-index 21
Discussion papers
https://doi.org/10.5194/npg-2019-28
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-2019-28
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 05 Jun 2019

Submitted as: research article | 05 Jun 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Nonlinear Processes in Geophysics (NPG).

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

Yiyuan Han1, Bing Han1, Zejun Hu2, Xinbo Gao1, Lixia Zhang1, Huigen Yang2, and Bin Li2 Yiyuan Han et al.
  • 1School of Electronic Engineering, Xidian University, Xi'an 710071, China
  • 2SOA Key Laboratory for Polar Science, Polar Research Institute of China, Shanghai 200136, China

Abstract. The auroral oval boundary represents important physical process with implications for the ionosphere and magnetosphere. An automatic auroral oval boundary prediction method based on deep learning in this paper are applied to study the variation of auroral oval boundary, associated with different space physical parameters. We construct an auroral oval boundary dataset to train our proposed model, which consists of 184416 auroral oval boundary points extracted from 3842 UVI images captured by Ultraviolet Imager of the Polar satellite and its corresponding 18 space physical parameters selected from OMNI dataset during December 1996 to March 1997. Furthermore, several statistical experiments and correlation analysis experiment are performed based on our dataset to explore the relationship between space physical parameters and the location of auroral oval boundary. The experiment results show that the prediction model based on deep learning method could estimate auroral oval boundary efficiently, and different space physical parameters have different effects on auroral oval boundary, especially interplanetary magnetic field (IMF), geomagnetic indexes and solar wind parameters.

Yiyuan Han et al.
Interactive discussion
Status: open (extended)
Status: open (extended)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
[Subscribe to comment alert] Printer-friendly Version - Printer-friendly version Supplement - Supplement
Yiyuan Han et al.
Yiyuan Han et al.
Viewed  
Total article views: 210 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
164 41 5 210 3 3
  • HTML: 164
  • PDF: 41
  • XML: 5
  • Total: 210
  • BibTeX: 3
  • EndNote: 3
Views and downloads (calculated since 05 Jun 2019)
Cumulative views and downloads (calculated since 05 Jun 2019)
Viewed (geographical distribution)  
Total article views: 141 (including HTML, PDF, and XML) Thereof 139 with geography defined and 2 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Cited  
Saved  
No saved metrics found.
Discussed  
No discussed metrics found.
Latest update: 19 Aug 2019
Publications Copernicus
Download
Short summary
We design a new nonlinear model to construct the 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.
We design a new nonlinear model to construct the accurate relationship between auroral oval...
Citation