A CRITICAL ASSESSMENT OF NONLINEAR FORCE-FREE FIELD MODELING OF THE SOLAR CORONA FOR ACTIVE REGION 10953

By M. DeRosa, C. Schrijver, G. Barnes, K. Leka, B. Lites, Markus J. Aschwanden, Tahar Amari, A. Canou, J. McTiernan, S. Régnier and 9 more. 2009

313 citations recorded by the source corpus.

Nonlinear force-free field (NLFFF) models are thought to be viable tools for investigating the structure, dynamics, and evolution of the coronae of solar active regions. In a series of NLFFF modeling studies, we have found that NLFFF models are successful in application to analytic test cases, and relatively successful when applied to numerically constructed Sun-like test cases, but they are less successful in application to real solar data. Different NLFFF models have been found to have markedly different field line configurations and to provide widely varying estimates of the magnetic free energy in the coronal volume, when applied to solar data. NLFFF models require consistent, force-free vector magnetic boundary data. However, vector magnetogram observations sampling the photosphere, which is dynamic and contains significant Lorentz and buoyancy forces, do not satisfy this requirement, thus creating several major problems for force-free coronal modeling efforts. In this paper, we discuss NLFFF modeling of NOAA Active Region 10953 using Hinode/SOT–SP, Hinode/XRT, STEREO/SECCHI–EUVI, and SOHO/MDI observations, and in the process illustrate three such issues we judge to be critical to the success of NLFFF modeling: (1) vector magnetic field data covering larger areas are needed so that more electric currents associated with the full active regions of interest are measured, (2) the modeling algorithms need a way to accommodate the various uncertainties in the boundary data, and (3) a more realistic physical model is needed to approximate the photosphere-to-corona interface in order to better transform the forced photospheric magnetograms into adequate approximations of nearly force-free fields at the base of the corona. We make recommendations for future modeling efforts to overcome these as yet unsolved problems.

Subfields: Astronomy and Astrophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/eac8a6e1d0eb5acf07b3df3fd1d3c1438e53be2e

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In Astronomy and Astrophysics

  1. T. Wiegelmann
  2. G. Aulanier
  3. P. Démoulin
  4. C. Schrijver
  5. Brigitte Schmieder
  6. J. Thalmann
  7. T. Sakurai
  8. Yang Liu

In Molecular Biology

  1. T. Wiegelmann
  2. T. Sakurai
  3. J. Thalmann
  4. J. Todd Hoeksema
  5. C. Schrijver
  6. Xudong Sun
  7. P. Démoulin
  8. Yang Liu

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