Testing the Accuracy of Data-driven MHD Simulations of Active Region Evolution

Leake, J. E.; Linton, M. G.; Schuck, P. W.

doi:10.3847/1538-4357/aa6578

Cited by 29 publications

(54 citation statements)

References 25 publications

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“…From the viewpoint of applying data-driven models to actual observational data, the lower temporal cadence of the photospheric boundary data may cause additional issue. Leake et al (2017) pointed out that for rapidly evolving features such as emerging flux, undersampling of the dynamics generates large electric currents and incorrect coronal fields and energies. Moreover, we should be aware that the observational data inherently contain some uncertainties (e.g., noise and 180 • ambiguity).…”

Section: Summary and Discussionmentioning

confidence: 99%

Comparative Study of Data-driven Solar Coronal Field Models Using a Flux Emergence Simulation as a Ground-truth Data Set

Toriumi

Takasao

Cheung

et al. 2020

ApJ

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For a better understanding of magnetic field in the solar corona and dynamic activities such as flares and coronal mass ejections, it is crucial to measure the time-evolving coronal field and accurately estimate the magnetic energy. Recently, a new modeling technique called the data-driven coronal field model, in which the time evolution of magnetic field is driven by a sequence of photospheric magnetic and velocity field maps, has been developed and revealed the dynamics of flare-productive active regions. Here we report on the first qualitative and quantitative assessment of different datadriven models using a magnetic flux emergence simulation as a ground-truth (GT) data set. We compare the GT field with those reconstructed from the GT photospheric field by four data-driven algorithms. It is found that, at least, the flux rope structure is reproduced in all coronal field models. Quantitatively, however, the results show a certain degree of model dependence. In most cases, the magnetic energies and relative magnetic helicity are comparable to or at most twice of the GT values. The reproduced flux ropes have a sigmoidal shape (consistent with GT) of various sizes, a verticallystanding magnetic torus, or a packed structure. The observed discrepancies can be attributed to the highly non-force-free input photospheric field, from which the coronal field is reconstructed, and to the modeling constraints such as the treatment of background atmosphere, the bottom boundary setting, and the spatial resolution.

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Section: Summary and Discussionmentioning

confidence: 99%

Comparative Study of Data-driven Solar Coronal Field Models Using a Flux Emergence Simulation as a Ground-truth Data Set

Toriumi

Takasao

Cheung

et al. 2020

ApJ

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“…These fields are required for quantifying the transport of magnetic flux, energy and helicity from the solar interior through the photosphere to the chromosphere and corona (Liu and Schuck, 2012;Tziotziou, Georgoulis, and Liu, 2013;Kazachenko et al, 2015). They also form the essential input for data-driven modeling of the corona (Wiegelmann, Petrie, and Riley, 2015;Inoue, 2016;Leake, Linton, and Schuck, 2017), in which the fields are used to specify the boundary condition at the lower radial boundary of the simulations. Due to the sparsity of direct observations of many of the key quantities that characterize the coronal plasma -most prominently the magnetic field -data-driven simulations offer currently the most feasible and often the only way to quantify the dynamics of the corona.…”

Section: Introductionmentioning

confidence: 99%

“…The accurate estimation of the Poynting and helicity fluxes is of significant practical value when the photospheric velocity/electric fields are used to derive the photospheric boundary condition for data-driven coronal modeling, in which case the boundary condition controls the injection of these quantities to the simulation domain. Such data-driven models vary in complexity ranging from simple static (non-linear) force-free field models employing only the magnetic field as the boundary condition (see e.g., Wiegelmann and Sakurai, 2012) to full time-dependent MHD models employing fully consistent photospheric boundary conditions (e.g., Jiang et al, 2016, Leake, Linton, andSchuck, 2017). In terms of complexity there exists a model between these extremes called the Timedependent MagnetoFrictional method (which we henceforth abbreviate as the TMF method).…”

Section: Introductionmentioning

confidence: 99%

Optimization of Photospheric Electric Field Estimates for Accurate Retrieval of Total Magnetic Energy Injection

2017

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Estimates of the photospheric magnetic, electric and plasma velocity fields are essential for studying the dynamics of the solar atmosphere, for example through the derivative quantities of Poynting and relative helicity flux and by using of the fields to obtain the lower boundary condition for datadriven coronal simulations. In this paper we study the performance of a data processing and electric field inversion approach that requires only high-resolution and high-cadence line-of-sight or vector magnetograms-which we obtain from Helioseismic and Magnetic Imager (HMI) onboard Solar Dynamics Observatory (SDO). The approach does not require any photospheric velocity estimates, and the lacking velocity information is compensated using ad hoc assumptions. We show that the free parameters of these assumptions can be optimized to reproduce the time evolution of the total magnetic energy injection through the photosphere in NOAA AR 11158, when compared to the recent state-of-the-art estimates for this active region. However, we find that the relative magnetic helicity injection is reproduced poorly reaching at best a modest underestimation. We discuss also the effect of some of the data processing details on the results, including the masking of the noise-dominated pixels and the tracking method of the active region, both of which have not received much attention in the literature so far. In most cases the effect of these details is small, but when the optimization of the free parameters of the ad hoc assumptions is considered a consistent use of the noise mask is required. The results found in this paper imply that the data processing and electric field inversion approach that uses only the photospheric magnetic field information offers a flexible and straightforward way to obtain photospheric magnetic and electric field estimates suitable for practical applications such as coronal modeling studies.

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“…New simultaneous multiwavelength, multi-height observations will directly offer the crucial information. Simulations for the volume crossing the solar surface (e.g., Amari et al 2004;Abbett 2007;Fan 2009;Toriumi & Yokoyama 2012;Leake et al 2017) will help determine theorybased vertical gradients. We will improve our MHD model in the future, by incorporating these sophisticated models and new observations in order to enhance our understanding on the energy build-up and conversion processes in AR systems.…”

mentioning

confidence: 99%

Magnetohydrodynamic Simulations for Solar Active Regions using Time-series Data of Surface Plasma Flow and Electric Field Inferred from Helioseismic Magnetic Imager Vector Magnetic Field Measurements

Hayashi

Feng

Xiong

et al. 2019

ApJL

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Temporal evolution of magnetic structures of the solar active region (AR) NOAA AR 11158, is simulated with our magnetohydrodynamic (MHD) simulation models using time-dependent solar-surface electric field or plasma flow data. Using the Solar Dynamics Observatory/Helioseismic Magnetic Imager vector magnetogram data, the solarsurface boundary electric field maps are derived with our recently developed algorithm to reproduce the temporal evolution of solar-surface vector magnetic field as observed. The plasma motion velocity maps are calculated through the Differential Affine Velocity Estimator for Vector Magnetograms. In both data-driven models, the simulated evolutionary magnetic field structures at strong-field low-beta regions appear near force-free state, as the current helicity density (J B B 2 · ) are roughly constant along each field line. Although the magnetic energy simulated with the newly developed plasma-velocity-driven model is about 10% of that by the electric-field driven model, the plasma-velocity-driven model can maintain the frozen-in condition, and evolution of current and free energy generated by the solar-surface plasma motions can be spatially and temporally traced. The present MHD simulation models for AR system can be a step toward better, more realistic data-driven evolutionary modeling, in particular, establishing boundary treatments for introducing the time-dependent observation data in a physically and mathematically consistent manner.

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Testing the Accuracy of Data-driven MHD Simulations of Active Region Evolution

Cited by 29 publications

References 25 publications

Comparative Study of Data-driven Solar Coronal Field Models Using a Flux Emergence Simulation as a Ground-truth Data Set

Comparative Study of Data-driven Solar Coronal Field Models Using a Flux Emergence Simulation as a Ground-truth Data Set

Optimization of Photospheric Electric Field Estimates for Accurate Retrieval of Total Magnetic Energy Injection

Magnetohydrodynamic Simulations for Solar Active Regions using Time-series Data of Surface Plasma Flow and Electric Field Inferred from Helioseismic Magnetic Imager Vector Magnetic Field Measurements

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