Time‐dependent MHD modeling of the global solar corona for year 2007: Driven by daily‐updated magnetic field synoptic data

Yang, Liping; Feng, Xueshang; Xiang, Changqing; Liu, Yang; Zhao, Xue; Wu, S. T.

doi:10.1029/2011ja017494

Cited by 56 publications

(60 citation statements)

References 117 publications

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“…With the radial magnetic field B r given by synoptic maps, we use the PNC method plus the plasma mass flux limit to prescribe the eight variables on the solar surface. This method was described [ Hayashi , ; Yang et al , ; Feng et al , ] in detail. However, it needs further treatment when applied to our situation because

\frac{\partial B_{r}}{∂t}

is no longer zero in our simulation.…”

Section: Data Preprocessing and Boundary Conditionsmentioning

confidence: 99%

“…Considering E t =−( v × B ) t , we have

\frac{\partial E_{θ}}{∂t} = B_{ϕ} \frac{\partial v_{r}}{∂t} + v_{r} \frac{\partial B_{ϕ}}{∂t} - B_{r} \frac{\partial v_{ϕ}}{∂t} - v_{ϕ} \frac{\partial B_{r}}{∂t}

and

\frac{\partial E_{ϕ}}{∂t} = B_{r} \frac{\partial v_{θ}}{∂t} + v_{θ} \frac{\partial B_{r}}{∂t} - B_{θ} \frac{\partial v_{r}}{∂t} - v_{r} \frac{\partial B_{θ}}{∂t},

from which

\frac{\partial v_{θ}}{∂t}

and

\frac{\partial v_{ϕ}}{∂t}

can be derived and applied to the PNC method instead of v ϕ B r − v r B ϕ =0 and v θ B r − v r B θ =0 [ Yang et al , ]. Then we apply the limit of the plasma mass flux escaping from the solar surface and the invariant entropy to the model's inner boundary and obtain another two independent constraints on the derivatives of MHD variables [ Hayashi , ; Yang et al , ; Feng et al , ].…”

Section: Data Preprocessing and Boundary Conditionsmentioning

confidence: 99%

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Data‐driven modeling of the solar wind from 1 R_s to 1 AU

Feng

Xiang

2015

JGR Space Physics

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We present here a time‐dependent three‐dimensional magnetohydrodynamic (MHD) solar wind simulation from the solar surface to the Earth's orbit driven by time‐varying line‐of‐sight solar magnetic field data. The simulation is based on the three‐dimensional (3‐D) solar‐interplanetary (SIP) adaptive mesh refinement (AMR) space‐time conservation element and solution element (CESE) MHD (SIP‐AMR‐CESE MHD) model. In this simulation, we first achieve the initial solar wind background with the time‐relaxation method by inputting a potential field obtained from the synoptic photospheric magnetic field and then generate the time‐evolving solar wind by advancing the initial 3‐D solar wind background with continuously varying photospheric magnetic field. The model updates the inner boundary conditions by using the projected normal characteristic method, inputting the high‐cadence photospheric magnetic field data corrected by solar differential rotation, and limiting the mass flux escaping from the solar photosphere. We investigate the solar wind evolution from 1 July to 11 August 2008 with the model driven by the consecutive synoptic maps from the Global Oscillation Network Group. We compare the numerical results with the previous studies on the solar wind, the solar coronal observations from the Extreme ultraviolet Imaging Telescope board on Solar and Heliospheric Observatory, and the measurements from OMNI at 1 astronomical unit (AU). Comparisons show that the present data‐driven MHD model's results have overall good agreement with the large‐scale dynamical coronal and interplanetary structures, including the sizes and distributions of the coronal holes, the positions and shapes of the streamer belts, the heliocentric distances of the Alfvénic surface, and the transitions of the solar wind speeds. However, the model fails to capture the small‐sized equatorial holes, and the modeled solar wind near 1 AU has a somewhat higher density and weaker magnetic field strength than observed. Perhaps better preprocessing of high‐cadence observed photospheric magnetic field (particularly 3‐D global measurements), combined with plasma measurements and higher resolution grids, will enable the data‐driven model to more accurately capture the time‐dependent changes of the ambient solar wind for further improvements. In addition, other measures may also be needed when the model is employed in the period of high solar activity.

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\frac{\partial B_{r}}{∂t}

is no longer zero in our simulation.…”

Section: Data Preprocessing and Boundary Conditionsmentioning

confidence: 99%

“…Considering E t =−( v × B ) t , we have

\frac{\partial E_{θ}}{∂t} = B_{ϕ} \frac{\partial v_{r}}{∂t} + v_{r} \frac{\partial B_{ϕ}}{∂t} - B_{r} \frac{\partial v_{ϕ}}{∂t} - v_{ϕ} \frac{\partial B_{r}}{∂t}

and

\frac{\partial E_{ϕ}}{∂t} = B_{r} \frac{\partial v_{θ}}{∂t} + v_{θ} \frac{\partial B_{r}}{∂t} - B_{θ} \frac{\partial v_{r}}{∂t} - v_{r} \frac{\partial B_{θ}}{∂t},

from which

\frac{\partial v_{θ}}{∂t}

and

\frac{\partial v_{ϕ}}{∂t}

Section: Data Preprocessing and Boundary Conditionsmentioning

confidence: 99%

Data‐driven modeling of the solar wind from 1 R_s to 1 AU

Feng

Xiang

2015

JGR Space Physics

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“…We call the resulting E ⊥ the inductive solution for the electric field, and denote it by E ⊥0 . This solution has been used to drive coronal models in numerous studies (e.g., Mikić et al 1999;Amari et al 2003;Mackay et al 2011;Cheung & DeRosa 2012;Gibb et al 2014;Yang et al 2012;Feng et al 2015). Unfortunately, there is good reason to believe that the non-inductive potential Ψ is non-negligible on the real solar surface.…”

Section: Introductionmentioning

confidence: 99%

“…If we assume that the plasma obeys the ideal Ohm's law E = −v × B then E ⊥ can, in principle, be computed from vector observations of B and of the plasma velocity v. In practice, however, such observations are not routinely available for the full solar surface. Many authors have therefore opted to estimate E ⊥ purely by inverting Equation (1), typically using only a time sequence of B r (θ, φ, t) data (Mikić et al 1999;Wu et al 2006;Mackay et al 2011;Cheung & DeRosa 2012;Yang et al 2012). In this case, only the radial component of (1) is used, ∂ t B r = −e r · ∇ × E ⊥ .…”

Section: Introductionmentioning

confidence: 99%

Sparse Reconstruction of Electric Fields from Radial Magnetic Data

Yeates

2017

ApJ

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The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders. Preprint typeset using L A T E X style AASTeX6 v. ABSTRACTAccurate estimates of the horizontal electric field on the Sun's visible surface are important not only for estimating the Poynting flux of magnetic energy into the corona but also for driving time-dependent magnetohydrodynamic models of the corona. In this paper, a method is developed for estimating the horizontal field from a sequence of radial-component magnetic field maps. This problem of inverting Faraday's law has no unique solution. Unfortunately, the simplest solution (a divergence-free electric field) is not realistically localized in regions of non-zero magnetic field, as would be expected from Ohm's law. Our new method generates instead a localized solution, using a basis pursuit algorithm to find a sparse solution for the electric field. The method is shown to perform well on test cases where the input magnetic maps are flux balanced, in both Cartesian and spherical geometries. However, we show that if the input maps have a significant imbalance of flux -usually arising from data assimilation -then it is not possible to find a localized, realistic, electric field solution. This is the main obstacle to driving coronal models from time sequences of solar surface magnetic maps.

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“…Wu et al . [] for Cartesian coordinates and spherical coordinates [ Wang et al ., ; Yang et al ., ], respectively. The boundary conditions for the lateral and top sides are set by the nonreflective boundary conditions.…”

Section: The Descriptions Of the Magnetohydrodynamic Modelsmentioning

confidence: 99%

A data‐constrained three‐dimensional magnetohydrodynamic simulation model for a coronal mass ejection initiation

Zhou

Jiang

et al. 2016

JGR Space Physics

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In this study, we present a three‐dimensional magnetohydrodynamic model based on an observed eruptive twisted flux rope (sigmoid) deduced from solar vector magnetograms. This model is a combination of our two very well tested MHD models: (i) data‐driven 3‐D magnetohydrodynamic (MHD) active region evolution (MHD‐DARE) model for the reconstruction of the observed flux rope and (ii) 3‐D MHD global coronal‐heliosphere evolution (MHD‐GCHE) model to track the propagation of the observed flux rope. The 6 September 2011, AR11283, event is used to test this model. First, the formation of the flux rope (sigmoid) from AR11283 is reproduced by the MHD‐DARE model with input from the measured vector magnetograms given by Solar Dynamics Observatory/Helioseismic and Magnetic Imager. Second, these results are used as the initial boundary condition for our MHD‐GCHE model for the initiation of a coronal mass ejection (CME) as observed. The model output indicates that the flux rope resulting from MHD‐DARE produces the physical properties of a CME, and the morphology resembles the observations made by STEREO/COR‐1.

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Time‐dependent MHD modeling of the global solar corona for year 2007: Driven by daily‐updated magnetic field synoptic data

Cited by 56 publications

References 117 publications

Data‐driven modeling of the solar wind from 1 R_s to 1 AU

Data‐driven modeling of the solar wind from 1 R_s to 1 AU

Sparse Reconstruction of Electric Fields from Radial Magnetic Data

A data‐constrained three‐dimensional magnetohydrodynamic simulation model for a coronal mass ejection initiation

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Time‐dependent MHD modeling of the global solar corona for year 2007: Driven by daily‐updated magnetic field synoptic data

Cited by 56 publications

References 117 publications

Data‐driven modeling of the solar wind from 1 Rs to 1 AU

Data‐driven modeling of the solar wind from 1 Rs to 1 AU

Sparse Reconstruction of Electric Fields from Radial Magnetic Data

A data‐constrained three‐dimensional magnetohydrodynamic simulation model for a coronal mass ejection initiation

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Product

Resources

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Data‐driven modeling of the solar wind from 1 R_s to 1 AU

Data‐driven modeling of the solar wind from 1 R_s to 1 AU