Validation scheme for solar coronal models: Constraints from multi-perspective 
observations in EUV and white light

Wagner, A.; Asvestari, E.; Temmer, M.; Heinemann, S. G.; Pomoell, J.

doi:10.1051/0004-6361/202141552

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Validation scheme for solar coronal models: Constraints from multi-perspective observations in EUV and white light

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Heinemann, S. G.
Department Solar and Stellar Interiors, Max Planck Institute for Solar System Research, Max Planck Society;

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https://ui.adsabs.harvard.edu/abs/2022A&A...657A.117W
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Citation

Wagner, A., Asvestari, E., Temmer, M., Heinemann, S. G., & Pomoell, J. (2022). Validation scheme for solar coronal models: Constraints from multi-perspective observations in EUV and white light. Astronomy and Astrophysics, 657, A117. doi:10.1051/0004-6361/202141552.

Cite as: https://hdl.handle.net/21.11116/0000-000C-B159-8

Abstract

Context. In this paper, we present a validation scheme to investigate the quality of coronal magnetic field models, which is based on comparisons with observational data from multiple sources.
Aims: Many of these coronal models may use a range of initial parameters that produce a large number of physically reasonable field configurations. However, that does not mean that these results are reliable and comply with the observations. With an appropriate validation scheme, which is the aim of this work, the quality of a coronal model can be assessed.
Methods: The validation scheme was developed with the example of the EUropean Heliospheric FORecasting Information Asset (EUHFORIA) coronal model. For observational comparison, we used extreme ultraviolet and white-light data to detect coronal features on the surface (open magnetic field areas) and off-limb (streamer and loop) structures from multiple perspectives (Earth view and the Solar Terrestrial Relations Observatory - STEREO). The validation scheme can be applied to any coronal model that produces magnetic field line topology.
Results: We show its applicability by using the validation scheme on a large set of model configurations, which can be efficiently reduced to an ideal set of parameters that matches best with observational data.
Conclusions: We conclude that by using a combined empirical visual classification with a mathematical scheme of topology metrics, a very efficient and objective quality assessment for coronal models can be performed.