Coronal Hole Detection and Open Magnetic Flux
J. A. Linker, S. G. Heinemann, M. Temmer, M. J. Owens, R. M. Caplan,, C. N. Arge, E. Asvestari, V. Delouille, C. Downs, S. J. Hofmeister, I. C., Jebaraj, M. Madjarska, R. Pinto, J. Pomoell, E. Samara, C. Scolini, and B., Vrsnak

TL;DR
This study compares different coronal hole detection methods to quantify uncertainties in open magnetic flux estimates, revealing significant underestimation in observations and minor overestimation in simulations, impacting our understanding of solar magnetic flux.
Contribution
The paper systematically evaluates multiple detection techniques and magnetic data sources, quantifying uncertainties and biases in coronal hole open flux measurements.
Findings
Uncertainty in open flux estimates is about 26%, rising to 45% with data source variability.
Observed open flux is underestimated by 2.2-4 times compared to in-situ measurements.
Detection methods overestimate area and flux in simulated coronal holes, but errors are relatively small.
Abstract
Many scientists use coronal hole (CH) detections to infer open magnetic flux. Detection techniques differ in the areas that they assign as open, and may obtain different values for the open magnetic flux. We characterize the uncertainties of these methods, by applying six different detection methods to deduce the area and open flux of a near-disk center CH observed on 9/19/2010, and applying a single method to five different EUV filtergrams for this CH. Open flux was calculated using five different magnetic maps. The standard deviation (interpreted as the uncertainty) in the open flux estimate for this CH was about 26%. However, including the variability of different magnetic data sources, this uncertainty almost doubles to 45%. We use two of the methods to characterize the area and open flux for all CHs in this time period. We find that the open flux is greatly underestimated compared…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
