Fractality of an MHD shell model for turbulent plasma driven by solar wind data: A review

2021 ◽  
Vol 214 ◽  
pp. 105524
Author(s):  
Víctor Muñoz ◽  
Macarena Domínguez ◽  
Giuseppina Nigro ◽  
Mario Riquelme ◽  
Vincenzo Carbone
Keyword(s):  
Solar Wind ◽  
Shell Model ◽  
Turbulent Plasma ◽  
Wind Data

Study of the fractality of magnetized plasma using an MHD shell model driven by solar wind data

2018 ◽  
Vol 25 (9) ◽  
pp. 092302 ◽  
Author(s):  
Macarena Domínguez ◽  
Giuseppina Nigro ◽  
Víctor Muñoz ◽  
Vincenzo Carbone
Keyword(s):  
Solar Wind ◽  
Shell Model ◽  
Magnetized Plasma ◽  
Wind Data ◽  
Model Driven

scholarly journals Prediction of SYM-H index during large storms by NARX neural network from IMF and solar wind data

2010 ◽  
Vol 28 (2) ◽  
pp. 381-393 ◽  
Author(s):  
L. Cai ◽  
S. Y. Ma ◽  
Y. L. Zhou
Keyword(s):  
Neural Network ◽  
Solar Wind ◽  
Correlation Coefficient ◽  
Characteristic Time ◽  
Ring Current ◽  
Wind Data ◽  
Distinct Advantage ◽  
Elman Network ◽  
H Index ◽  
Narx Neural Network

Abstract. Similar to the Dst index, the SYM-H index may also serve as an indicator of magnetic storm intensity, but having distinct advantage of higher time-resolution. In this study the NARX neural network has been used for the first time to predict SYM-H index from solar wind (SW) and IMF parameters. In total 73 time intervals of great storm events with IMF/SW data available from ACE satellite during 1998 to 2006 are used to establish the ANN model. Out of them, 67 are used to train the network and the other 6 samples for test. Additionally, the NARX prediction model is also validated using IMF/SW data from WIND satellite for 7 great storms during 1995–1997 and 2005, as well as for the July 2000 Bastille day storm and November 2001 superstorm using Geotail and OMNI data at 1 AU, respectively. Five interplanetary parameters of IMF Bz, By and total B components along with proton density and velocity of solar wind are used as the original external inputs of the neural network to predict the SYM-H index about one hour ahead. For the 6 test storms registered by ACE including two super-storms of min. SYM-H<−200 nT, the correlation coefficient between observed and NARX network predicted SYM-H is 0.95 as a whole, even as high as 0.95 and 0.98 with average relative variance of 13.2% and 7.4%, respectively, for the two super-storms. The prediction for the 7 storms with WIND data is also satisfactory, showing averaged correlation coefficient about 0.91 and RMSE of 14.2 nT. The newly developed NARX model shows much better capability than Elman network for SYM-H prediction, which can partly be attributed to a key feedback to the input layer from the output neuron with a suitable length (about 120 min). This feedback means that nearly real information of the ring current status is effectively directed to take part in the prediction of SYM-H index by ANN. The proper history length of the output-feedback may mainly reflect on average the characteristic time of ring current decay which involves various decay mechanisms with ion lifetimes from tens of minutes to tens of hours. The Elman network makes feedback from hidden layer to input only one step, which is of 5 min for SYM-H index in this work and thus insufficient to catch the characteristic time length.

Foreshock ULF fluctuations near the Moon: THEMIS observations

2021 ◽  
Author(s):  
Anna Salohub ◽  
Jana Šafránková ◽  
Zdeněk Němeček
Keyword(s):  
Solar Wind ◽  
Rest Frame ◽  
Low Frequency ◽  
Solar Wind Plasma ◽  
Turbulent Plasma ◽  
Bow Shock ◽  
Ulf Waves ◽  
The Moon ◽  
Shock Surface ◽  
The Earth

&lt;p&gt;The foreshock is a region filled with a turbulent plasma located upstream the Earth&amp;#8217;s bow shock where interplanetary magnetic field (IMF) lines are connected to the bow shock surface. In this region, ultra-low frequency (ULF) waves are generated due to the interaction of the solar wind plasma with particles reflected from the bow shock back into the solar wind. It is assumed that excited waves grow and they are convected through the solar wind/foreshock, thus the inner spacecraft (close to the bow shock) would observe larger wave amplitudes than the outer (far from the bow shock) spacecraft. The paper presents a statistical analysis of excited ULF fluctuations observed simultaneously by two closely separated THEMIS spacecraft orbiting the Moon under a nearly radial IMF. We found that ULF fluctuations (in the plasma rest frame) can be characterized as a mixture of transverse and compressional modes with different properties at both locations. We discuss the growth and/or damping of ULF waves during their propagation.&lt;/p&gt;

scholarly journals Forecasting GOES 15 >2 MeV Electron Fluxes From Solar Wind Data and Geomagnetic Indices

Space Weather ◽  
2020 ◽  
Vol 18 (8) ◽  
Author(s):  
C. Forsyth ◽  
C. E. J. Watt ◽  
M. K. Mooney ◽  
I. J. Rae ◽  
S. D. Walton ◽  
...  
Keyword(s):  
Solar Wind ◽  
Wind Data ◽  
Geomagnetic Indices

scholarly journals Solar Models in Light of New High Metallicity Measurements from Solar Wind Data

2017 ◽  
Vol 839 (1) ◽  
pp. 55 ◽  
Author(s):  
Sunny Vagnozzi ◽  
Katherine Freese ◽  
Thomas H. Zurbuchen
Keyword(s):  
Solar Wind ◽  
Wind Data ◽  
High Metallicity

scholarly journals Study of the fractality in a magnetohydrodynamic shell model forced by solar wind fluctuations

2020 ◽  
Vol 27 (2) ◽  
pp. 175-185 ◽  
Author(s):  
Macarena Domínguez ◽  
Giuseppina Nigro ◽  
Víctor Muñoz ◽  
Vincenzo Carbone ◽  
Mario Riquelme
Keyword(s):  
Solar Wind ◽  
Fractal Dimension ◽  
Energy Dissipation ◽  
Shell Model ◽  
Langevin Equation ◽  
Dissipation Rate ◽  
Solar Maximum ◽  
Magnetic Energy ◽  
Energy Dissipation Rate ◽  
The Magnetic Field

Abstract. The description of the relationship between interplanetary plasma and geomagnetic activity requires complex models. Drastically reducing the ambition of describing this detailed complex interaction and, if we are interested only in the fractality properties of the time series of its characteristic parameters, a magnetohydrodynamic (MHD) shell model forced using solar wind data might provide a possible novel approach. In this paper we study the relation between the activity of the magnetic energy dissipation rate obtained in one such model, which may describe geomagnetic activity, and the fractal dimension of the forcing. In different shell model simulations, the forcing is provided by the solution of a Langevin equation where a white noise is implemented. This forcing, however, has been shown to be unsuitable for describing the solar wind action on the model. Thus, we propose to consider the fluctuations of the product between the velocity and the magnetic field solar wind data as the noise in the Langevin equation, the solution of which provides the forcing in the magnetic field equation. We compare the fractal dimension of the magnetic energy dissipation rate obtained, of the magnetic forcing term, and of the fluctuations of v⋅bz, with the activity of the magnetic energy dissipation rate. We examine the dependence of these fractal dimensions on the solar cycle. We show that all measures of activity have a peak near solar maximum. Moreover, both the fractal dimension computed for the fluctuations of v⋅bz time series and the fractal dimension of the magnetic forcing have a minimum near solar maximum. This suggests that the complexity of the noise term in the Langevin equation may have a strong effect on the activity of the magnetic energy dissipation rate.

A third catalogue of high-speed plasma streams in the solar wind - data for 1978?1982

Solar Physics ◽  
1989 ◽  
Vol 120 (1) ◽  
pp. 145-152 ◽  
Author(s):  
B. A. Lindblad ◽  
H. Lundstedt ◽  
B. Larsson
Keyword(s):  
Solar Wind ◽  
High Speed ◽  
Wind Data

scholarly journals Ring current influence on auroral electrojet predictions

1999 ◽  
Vol 17 (10) ◽  
pp. 1268-1275 ◽  
Author(s):  
H. Gleisner ◽  
H. Lundstedt
Keyword(s):  
Solar Wind ◽  
Ring Current ◽  
Geomagnetic Storms ◽  
Wind Data ◽  
Common Source ◽  
Neural Net ◽  
Operational Forecasting ◽  
Storms And Substorms ◽  
Highly Correlated ◽  
Strong Control

Abstract. Geomagnetic storms and substorms develop under strong control of the solar wind. This is demonstrated by the fact that the geomagnetic activity indices Dst and AE can be predicted from the solar wind alone. A consequence of the strong control by a common source is that substorm and storm indices tend to be highly correlated. However, a part of this correlation is likely to be an effect of internal magnetospheric processes, such as a ring-current modulation of the solar wind-AE relation. The present work extends previous studies of nonlinear AE predictions from the solar wind. It is examined whether the AE predictions are modulated by the Dst index.This is accomplished by comparing neural network predictions from Dst and the solar wind, with predictions from the solar wind alone. Two conclusions are reached: (1) with an optimal set of solar-wind data available, the AE predictions are not markedly improved by the Dst input, but (2) the AE predictions are improved by Dst if less than, or other than, the optimum solar-wind data are available to the net. It appears that the solar wind-AE relation described by an optimized neural net is not significantly modified by the magnetosphere's Dst state. When the solar wind alone is used to predict AE, the correlation between predicted and observed AE is 0.86, while the prediction residual is nearly uncorrelated to Dst. Further, the finding that Dst can partly compensate for missing information on the solar wind, is of potential importance in operational forecasting where gaps in the stream of real time solar-wind data are a common occurrence.Key words. Magnetospheric physics (solar wind · magnetosphere interactions; storms and substorms)

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