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Nonlinear mean-field dynamo and prediction of solar activity

Part of: 50 years of Mean Field Electrodynamics

Published online by Cambridge University Press:  14 June 2018

N. Safiullin Open the ORCID record for N. Safiullin [Opens in a new window] ,
N. Kleeorin Open the ORCID record for N. Kleeorin [Opens in a new window] ,
S. Porshnev ,
I. Rogachevskii Open the ORCID record for I. Rogachevskii [Opens in a new window]  and
A. Ruzmaikin
N. Safiullin
Affiliation:
Department of Information Technology and Automation, Ural Federal University, 19 Mira str., 620002 Ekaterinburg, Russia
N. Kleeorin
Affiliation:
Department of Mechanical Engineering, Ben-Gurion University of the Negev, P. O. Box 653, 84105 Beer-Sheva, Israel Nordita, KTH Royal Institute of Technology and Stockholm University, Roslagstullsbacken 23, 10691 Stockholm, Sweden
S. Porshnev
Affiliation:
Department of Information Technology and Automation, Ural Federal University, 19 Mira str., 620002 Ekaterinburg, Russia
I. Rogachevskii*
Affiliation:
Department of Mechanical Engineering, Ben-Gurion University of the Negev, P. O. Box 653, 84105 Beer-Sheva, Israel Nordita, KTH Royal Institute of Technology and Stockholm University, Roslagstullsbacken 23, 10691 Stockholm, Sweden
A. Ruzmaikin
Affiliation:
Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, USA
*
†Email address for correspondence: [email protected]
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Abstract

We apply a nonlinear mean-field dynamo model which includes a budget equation for the dynamics of Wolf numbers to predict solar activity. This dynamo model takes into account the algebraic and dynamic nonlinearities of the $\unicode[STIX]{x1D6FC}$ effect, where the equation for the dynamic nonlinearity is derived from the conservation law for the magnetic helicity. The budget equation for the evolution of the Wolf number is based on a formation mechanism of sunspots related to the negative effective magnetic pressure instability. This instability redistributes the magnetic flux produced by the mean-field dynamo. To predict solar activity on the time scale of one month we use a method based on a combination of the numerical solution of the nonlinear mean-field dynamo equations and the artificial neural network. A comparison of the results of the prediction of the solar activity with the observed Wolf numbers demonstrates a good agreement between the forecast and observations.

Keywords

astrophysical plasmasplasma nonlinear phenomenaspace plasma physics
Type
Research Article
Information
Journal of Plasma Physics , Volume 84 , Issue 3 , June 2018 , 735840306
Copyright
© Cambridge University Press 2018 

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