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Simulating the Solar Corona with Multiple Solar Photospheric Magnetic Maps during the 8 April 2024 Total Solar Eclipse

This study utilizes the Alfvén Wave Solar atmosphere Model (AWSoM) to simulate the solar corona during the 8 April 2024 total solar eclipse, demonstrating that variations in photospheric magnetic field maps from four different sources significantly alter coronal magnetic topology, heating rates, and high-temperature emission properties, thereby highlighting the critical need for comprehensive magnetic data to improve future solar models.

Original authors: Xianyu Liu, Weihao Liu, Ward B. Manchester IV, Daniel T. Welling, Gábor Tóth, Tamas I. Gombosi, Marc L. DeRosa, Luca Bertello, Alexei A. Pevtsov, Alexander A. Pevtsov, Kevin Reardon, Kathryn Wilbanks

Published 2026-07-22

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CC BY 4.0

Original authors: Xianyu Liu, Weihao Liu, Ward B. Manchester IV, Daniel T. Welling, Gábor Tóth, Tamas I. Gombosi, Marc L. DeRosa, Luca Bertello, Alexei A. Pevtsov, Alexander A. Pevtsov, Kevin Reardon, Kathryn Wilbanks, Amy Rewoldt, Lulu Zhao

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine the Sun as a giant, churning star with a magnetic personality that never sleeps. Deep inside, it's a ball of hot plasma, but what we see from Earth is just the surface, or "photosphere." Hidden beneath that surface is a complex web of magnetic fields, like invisible rubber bands stretching and twisting. These magnetic bands don't just sit there; they reach way out into space to form the "corona," the Sun's super-hot, glowing atmosphere that usually gets hidden by the Sun's blinding brightness. The only time we can see the corona's true, wispy shape with our own eyes is during a total solar eclipse, when the Moon perfectly blocks the Sun's face. Scientists love these moments because the corona acts like a weather report for space. Just as storms on Earth can knock out power lines, storms on the Sun can mess up our satellites, GPS, and even power grids. To predict these space storms, scientists build computer models that try to simulate how the Sun's magnetic fields behave. But here's the tricky part: to start the simulation, they need a perfect map of the Sun's magnetic surface. If the map is slightly off, the whole computer model might predict a calm day when a storm is actually coming.

This paper is like a detective story where four different teams of scientists tried to draw that same magnetic map of the Sun using different tools and methods. The researchers wanted to see: if we use four different maps of the Sun's magnetic field, do our computer simulations of the corona look the same? They picked a very special date, April 8, 2024, when a total solar eclipse crossed North America, giving them a real-life photo to compare their computer guesses against. They took four different magnetic maps—one from a network of ground-based telescopes, and three from a satellite called SDO—and fed them into a powerful computer model called AWSoM (which stands for Alfvén Wave Solar atmosphere Model). Think of the model as a virtual wind tunnel that simulates how the Sun's atmosphere heats up and flows. The team ran four separate simulations, one for each map, to see if the resulting "virtual corona" looked like the real eclipse photo.

The results were surprising and showed that the choice of map matters a lot. Even though all four maps showed the Sun's magnetic fields in roughly the same places, the tiny differences in how strong those fields were led to big changes in the computer models. When the researchers looked at the "virtual eclipse" pictures generated by the models, they saw that some models created giant loops of light (called helmet streamers) where the real photo showed something else, or they created "pseudostreamers" (a different kind of magnetic structure) that didn't match the others. One specific map, called the NSO-NRT-HMI map, produced a result that looked quite different from the other three. It showed a much stronger magnetic field at the Sun's poles, which completely changed how the magnetic "rubber bands" connected, creating a unique pattern of open magnetic paths that the other maps missed.

The study also looked at how the Sun's corona gets so hot. The models assume that energy travels up from the surface in waves (like ripples on a pond) to heat the gas. The researchers found that while the total amount of energy they put into the simulations was the same, the way that energy was used varied. In the high-temperature regions, the models showed big differences in how much heat was generated, depending on which map they used. Specifically, the "Differential Emission Measure" (a way of measuring how much hot gas is present) varied significantly in the hottest parts of the corona, with differences of over 40% in some areas. This suggests that the way we measure the Sun's magnetic field directly changes our understanding of how the corona is heated.

Ultimately, the paper suggests that there is no single "perfect" map yet. The differences between the maps come from how they were made: some use ground-based data, some use satellite data, and some use different math to fill in the gaps where we can't see the Sun's back side. The authors found that the 3D shape of the magnetic field is extremely sensitive to these small differences. While the models agreed on the general layout, the specific details of the magnetic loops and the heating in the hottest regions changed drastically based on the input map. This tells us that to get better predictions for space weather, we need better, more consistent maps of the Sun's magnetic surface. The paper doesn't claim to have solved the mystery of the corona, but it clearly shows that the quality of our starting map is the key to unlocking the secrets of the Sun's atmosphere.

Technical Summary: Simulating the Solar Corona with Multiple Solar Photospheric Magnetic Maps during the 8 April 2024 Total Solar Eclipse

Problem Statement
Modern global coronal models rely heavily on photospheric magnetic field maps as primary input data to constrain the three-dimensional (3D) magnetic field and drive coronal heating mechanisms. However, multiple approaches exist to generate these synoptic or synchronic maps, utilizing different observational instruments (e.g., GONG vs. SDO/HMI) and data processing methods (e.g., flux transport models vs. vector field inversions). While these maps often show similar large-scale polarity patterns, they can exhibit significant discrepancies in local magnetic flux distributions. The impact of these discrepancies on the resulting magnetohydrodynamic (MHD) simulations—specifically regarding coronal magnetic topology, open flux regions, and heating properties—remains unclear. This study addresses the question of how MHD simulation results vary when driven by magnetic maps from four distinct sources.

Methodology
The authors performed four steady-state MHD simulations of the solar corona for the 8 April 2024 Total Solar Eclipse (TSE) using the Alfvén Wave Solar atmosphere Model (AWSoM) within the Space Weather Modeling Framework (SWMF). The simulations extended from the upper chromosphere (1 $R_s$ ) to 24 $R_s$ .

Input Data: Four photospheric radial magnetic field ( $B_r$ $B_{r}$ ) maps were utilized, all produced one day prior to the eclipse:
- ADAPT-GONG: Based on GONG line-of-sight (LOS) magnetograms assimilated into a flux transport model.
- HipFT-HMI: Based on SDO/HMI LOS magnetograms processed via the High-performance Flux Transport (HipFT) model.
- LM-ESFAM-HMI: Based on SDO/HMI LOS magnetograms processed via the Lockheed Martin Evolving Surface Flux Assimilation Model.
- NSO-NRT-HMI: Based on SDO/HMI near-real-time vector magnetograms (processed via a non-standard pipeline to resolve azimuth ambiguity).
Preprocessing: To minimize resolution artifacts, the original maps (varying in grid types and resolutions) were remapped onto a uniform $360 \times 180$ grid ( $1^\circ \times 1^\circ$ ). Despite this, the total unsigned magnetic flux ( $\Phi_B$ ) varied significantly, with a maximum-to-minimum ratio of approximately 1.86.
Heating Calibration: To isolate the effects of magnetic topology from total energy injection, the authors adjusted the Poynting flux ratio $(S_A/B)_\odot$ for each map. This ensured that the total Alfvén wave energy injection rate ( $P_I$ ) at the inner boundary was identical ( $\approx 3.09 \times 10^{28}$ erg s $^{-1}$ ) across all four models, despite the differing $\Phi_B$ .
Simulation Setup: The models used adaptive mesh refinement (AMR) to resolve small-scale structures in the low corona. Synthetic white-light images were generated by integrating Thomson-scattered signals along the line of sight (LOS) from Earth.

Key Results

Magnetic Topology and Synthetic Images: The synthetic white-light images revealed significant discrepancies among the four models, driven by distinct 3D magnetic field connectivities.
- Local Flux Sensitivity: In the upper-left quadrant of the synthetic images, Models A, H, and L produced helmet streamers, whereas Model N (NSO-NRT-HMI) produced a pseudostreamer. This difference arose because the NSO-NRT-HMI map contained a significantly stronger magnetic pole (P4) in the high-latitude region, which broke the direct connectivity between poles P1 and P2 found in the other models.
- Bottom Quadrant: Similarly, Model N exhibited a large region of weak white-light intensity due to an expanding open flux region originating from a stronger positive pole (P6), whereas the other models showed helmet streamers anchored to different pole configurations.
- Conclusion: Even when maps share similar global polarity patterns, local enhancements in magnetic flux can fundamentally alter 3D magnetic connectivity and the resulting observable structures.
Open Magnetic Flux:
- Models A, H, and L exhibited similar open flux distributions dominated by an equatorial dipole field, with the outer boundary ( $24 R_s$ ) showing a heliospheric current sheet typical of solar maximum.
- Model N displayed a distinct topology dominated by an axial dipole, with a continuous current sheet around the Sun. It contained two large-scale unique open flux regions (near $\lambda = \pm 50^\circ$ ) that accounted for 36% of its total open flux.
- The study found that during solar maximum, the polarity of open flux is sensitive to the global photospheric flux distribution rather than just the polarity near the poles.
Coronal Heating and Emission:
- Dissipation Rates: Although the total energy injection ( $P_I$ ) was identical, the volume-integrated total heat dissipation rate ( $P_D$ ) varied by approximately 18% across the models (ranging from $1.47 \times 10^{28}$ to $1.73 \times 10^{28}$ erg s $^{-1}$ ).
- Differential Emission Measure (DEM): The DEM distributions were relatively consistent (within ~15%) for typical quiet Sun temperatures ( $5.8 < \log T_e < 6.2$ ). However, significant discrepancies (>40%) were observed in high-temperature regions ( $\log T_e > 6.4$ , $T_e > 2.5$ MK), which are associated with active regions.

Significance and Claims
The paper underscores the critical importance of comprehensive and accurate photospheric magnetic field data for improving solar coronal models. The authors claim that:

Topology Sensitivity: The 3D magnetic field topology is highly sensitive to the local distribution of magnetic flux in the input map. Small differences in flux magnitude at specific locations can lead to entirely different magnetic structures (e.g., streamers vs. pseudostreamers) and open flux configurations.
Heating Sensitivity: Coronal heating in high-temperature regions is particularly sensitive to the choice of magnetic map, suggesting that the propagation and dissipation of Alfvén waves in active regions depend heavily on the underlying magnetic field resolution and strength.
Data Diversity: The discrepancies among the maps stem from differences in instrumentation (LOS vs. vector) and data assimilation methods (flux transport models vs. direct inversion). The authors note that the lack of simultaneous, full-disk, multi-angle observations necessitates these diverse processing methods, which in turn introduces uncertainty into model predictions.

The study concludes that while global models can reproduce general features, the specific details of coronal structure and heating are contingent upon the specific photospheric magnetic map used, highlighting the need for future missions capable of providing simultaneous, full-disk, and multi-angle observations to reduce these uncertainties.

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