Solar Surface Magnetic Field Simulation Enabled Prediction of the Large-Scale Coronal Structure of the 21 August 2017 Great American Eclipse: An Assessment of Model Predictions and Observations

TL;DR

This study demonstrates that long-term surface magnetic flux transport simulations can accurately predict the large-scale coronal structure during a solar eclipse, aiding space weather forecasting.

Contribution

The paper introduces a novel methodology combining flux transport simulations with magnetic field extrapolation to predict coronal structures during solar eclipses.

Findings

Model predictions closely match observed coronal structures.

Surface magnetic field evolution influences large-scale coronal features.

Potential for predicting solar corona months in advance.

Abstract

On 21 August 2017 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 generation of severe space weather. Here we present a methodology for predicting the structure of the coronal field based on long-term surface flux transport simulations whose output is utilized to extrapolate the coronal magnetic field structures. This prescription was applied to the 21 August 2017 solar eclipse. 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 evolution govern large-scale coronal structures with a (plausibly cycle-phase dependent) dynamical memory timescale on the order of few solar rotations -- opening up the possibility of large-scale, global corona predictions at least a month in advance.