Regression-based forecast model of induced geoelectric field-Reference-Cited by-同舟云学术

Regression-based forecast model of induced geoelectric field

Published:2017-01 Issue:1 Volume:15 Page:180-191
ISSN:1542-7390
Container-title:Space Weather
language:en
Short-container-title:Space Weather

Author:

Lotz S. I.¹^ORCID,Heyns M. J.¹²,Cilliers P. J.¹³^ORCID

Affiliation:

1. Space Science Directorate; South African National Space Agency; Hermanus South Africa

2. National Astrophysics and Space Science Programme, Department of Mathematics and Applied Mathematics; University of Cape Town; Cape Town South Africa

3. Department of Electrical Engineering; University of Cape Town; Cape Town South Africa

Funder

National Research Foundation of South Africa

Publisher

American Geophysical Union (AGU)

Subject

Atmospheric Science

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1002/2016SW001518/fullpdf

Reference30 articles.

1. Ionospheric disturbance magnetic field continuation from the ground to the ionosphere using spherical elementary current systems;Amm;Earth, Planets Space,1999

2. Improvement in the prediction of solar wind conditions using near-real time solar magnetic field updates;Arge;J. Geophys. Res.,2000

3. The complex-image method for calculating the magnetic and electric fields at the surface of the Earth by the auroral electrojet;Boteler;Geophys. J. Int.,1998

4. A statistical comparison of solar wind propagation delays derived from multispacecraft techniques;Case;J. Geophys. Res.,2012

5. Analysis of geomagnetic hourly ranges;Danskin;Space Weather,2015

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