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-2019-2
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-2019-2
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 20 May 2019

Research article | 20 May 2019

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

Compacting the Description of a Time-Dependent Multivariable System and Its Time-Dependent Multivariable Driver by Reducing the System and Driver State Vectors to Aggregate Scalars: The Earth’s Solar-Wind-Driven Magnetosphere

Joseph E. Borovsky1 and Adnane Osmane2 Joseph E. Borovsky and Adnane Osmane
  • 1Center for Space Plasma Physics, Space Science Institute, Boulder, Colorado, USA
  • 2Department of Physics, Universityof Helsinki, Helsinki, Finland

Abstract. Using the solar-wind-driven magnetosphere-ionosphere-thermosphere system, a methodology is developed to reduce a state-vector description of a time-dependent driven system to a composite scalar picture of the activity in the system. The technique uses canonical correlation analysis to reduce the time-dependent system and driver state vectors to time-dependent system and driver scalars, with the scalars describing the response in the system that is most-closely related to the driver. This reduced description has advantages: low noise, high prediction efficiency, linearity in the described system response to the driver, and compactness. The methodology identifies independent modes of reaction of a system to its driver. The analysis of the magnetospheric system is demonstrated. Using autocorrelation analysis, Jensen-Shannon complexity analysis, and permutation-entropy analysis the properties of the derived aggregate scalars are assessed. This state-vector-reduction technique may be useful for other multivariable systems driven by multiple inputs.

Joseph E. Borovsky and Adnane Osmane
Interactive discussion
Status: open (until 15 Jul 2019)
Status: open (until 15 Jul 2019)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
[Subscribe to comment alert] Printer-friendly Version - Printer-friendly version Supplement - Supplement
Joseph E. Borovsky and Adnane Osmane
Data sets

The 1991-2007 data set of hourly values of S(1), S(2), S(3), D(1), D(2), and D(3) J. E. Borovsky https://doi.org/10.5281/zenodo.1560686

The 1991-2007 data set of hourly values of S(1), S(2), S(3), D(1), D(2), and D(3) J. E. Borovsky https://doi.org/10.17605/OSF.IO/QYTNJ

Joseph E. Borovsky and Adnane Osmane
Viewed  
Total article views: 140 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
113 22 5 140 1 1
  • HTML: 113
  • PDF: 22
  • XML: 5
  • Total: 140
  • BibTeX: 1
  • EndNote: 1
Views and downloads (calculated since 20 May 2019)
Cumulative views and downloads (calculated since 20 May 2019)
Viewed (geographical distribution)  
Total article views: 110 (including HTML, PDF, and XML) Thereof 108 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: 26 Jun 2019
Publications Copernicus
Download
Short summary
A methodology is developed to simplify the mathematical description of activity in a time-dependent driven system. The method describes the response in the system that is most-closely related to the driver. This reduced description has advantages: low noise, high prediction efficiency, linearity in the described system response to the driver, and compactness. The analysis of the Earth’s magnetospheric system is demonstrated.
A methodology is developed to simplify the mathematical description of activity in a...
Citation