The Large-scale Coronal Structure of the 2017 August 21 Great American Eclipse: An Assessment of Solar Surface Flux Transport Model Enabled Predictions and Observations

Abstract

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.Please consult the full DRO policy for further details. AbstractOn 2017 August 21, a total solar eclipse swept across the contiguous United States, providing excellent opportunities for diagnostics of the Sun's corona. The Sun's coronal structure is notoriously difficult to observe except during solar eclipses; thus, theoretical models must be relied upon for inferring the underlying magnetic structure of the Sun's outer atmosphere. These models are necessary for understanding the role of magnetic fields in the heating of the corona to a million degrees and the generation of severe space weather. Here we present a methodology for predicting the structure of the coronal field based on model forward runs of a solar surface flux transport model, whose predicted surface field is utilized to extrapolate future coronal magnetic field structures. This prescription was applied to the 2017 August 21 solar eclipse. A post-eclipse analysis shows good agreement between model simulated and observed coronal structures and their locations on the limb. We demonstrate that slow changes in the Sun's surface magnetic field distribution driven by long-term flux emergence and its evolution governs large-scale coronal structures with a (plausibly cycle-phase dependent) dynamical memory timescale on the order of a few solar rotations, opening up the possibility for large-scale, global corona predictions at least a month in advance.

Predicting the corona for the 21 August 2017 total solar eclipse

“…The model employs two key innovations: a physicsbased formulation that describes the heating of the corona over a broad range of conditions, and a novel approach for energizing the coronal magnetic field on global scales. A related prediction of this eclipse with a different model has also been made 19 , and is discussed below.…”

Images and Spectra of the 2017 Total Solar Eclipse Corona From Our Oregon Site

“…A composite of our images has been used to successfully compare with the predictions (Mikic et al, 2018) made preeclipse on the basis of the prior month of magnetograms of the solar disk from the HMI instrument on NASA's Solar Dynamics Observatory. Nandy et al (2018) used a solar-surface flux-transport model for predicting the coronal configuration.…”

Prediction of the Sun’s Coronal Magnetic Field and Forward-modeled Polarization Characteristics for the 2019 July 2 Total Solar Eclipse

“…Testing these theoretical modeling approaches through "true" predictions and constraining them through observations is best achieved at the time of solar eclipses when lunar occultation masks the bright solar disk revealing the Sun's faint coronal structure. In an earlier work, Nandy et al (2018) postulated a novel methodology for long-term coronal field prediction. Posteclipse assessment demonstrated that they successfully predicted all the major large-scale structures of the great American solar eclipse (2017 August 21) including a pseudostreamer that was not captured in an alternative prediction methodology (Mikić et al 2018).…”

“…Posteclipse assessment demonstrated that they successfully predicted all the major large-scale structures of the great American solar eclipse (2017 August 21) including a pseudostreamer that was not captured in an alternative prediction methodology (Mikić et al 2018). Nandy et al (2018) used a data-driven Predictive Solar Surface Flux Transport (PSSFT) model developed at the Center of Excellence in Space Sciences India (CESSI; to first predict the surface magnetic field distribution and then feed this into a potential field extrapolation model to obtain the prediction of the coronal magnetic field structure. The PSSFT model simulates the evolution of the surface magnetic field driven by the emergence of tilted active regions and redistribution of associated magnetic flux mediated via large-scale plasma flows and supergranular diffusion.…”

“…The CESSI PSSFT model used in Nandy et al (2018) is driven by observed sunspot data over century timescale and calibrated with polar flux observations spanning multiple solar cycles (Muñoz-Jaramillo et al 2012;. The slow evolution of the photospheric magnetic field aids in building up a long-term memory (due to mostly deterministic surface flux transport processes) in the PSSFT model.…”