Combined Surface Flux Transport and Helioseismic Far-side Active Region Model (FARM)
Dan Yang, Stephan G. Heinemann, Robert H. Cameron, and Laurent Gizon

TL;DR
This paper introduces FARM, a novel method combining helioseismic far-side imaging with surface flux transport modeling to better estimate the Sun's magnetic field, enhancing space-weather predictions.
Contribution
The study presents a new approach integrating seismic far-side active region data into flux transport models, improving magnetic field estimations and space-weather modeling accuracy.
Findings
FARM improves agreement between modeled and observed open-field areas.
Seismic maps reflect the spatial structure of active regions.
Inclusion of far-side regions enhances space-weather prediction models.
Abstract
Maps of the magnetic field at the Sun's surface are commonly used as boundary conditions in space-weather modeling. However, continuous observations are only available from the Sun's Earth-facing side. One commonly used approach to mitigate the lack of far-side information is to apply a surface flux transport (SFT) model to model the evolution of the magnetic field as the Sun rotates. Helioseismology can image active regions on the far side using acoustic oscillations, and hence has the potential to improve the modeled surface magnetic field. In this study, we propose a novel approach for estimating magnetic fields of active regions on the Sun's far side based on seismic measurements, and then include them into a SFT model. To calibrate seismic signal to magnetic field, we apply our SFT model to line-of-sight magnetograms from SDO/HMI to obtain reference maps of global magnetic fields.…
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