Magnetic connectivity from the Sun to the Earth with MHD models. II. Impact of input magnetic maps
Year: 2026
Type: article
Abstract: Magnetic connectivity is a method used to understand the connection between the Sun and the satellites orbiting around it. It is especially useful for Earth, as it helps us anticipate the propagation and geoeffectiveness of space weather events. Connectivity estimation needs to be fast (within 24 hours), and thus usually relies on semi-empirical models. In the first paper of this study, we established that optimised magnetohydrodynamics (MHD) models can also be used to derive magnetic connectivity, yielding good results, especially for complex configurations. However, the modelling is not the only factor that can impact the connectivity estimation. Models' results, both semi-empirical and MHD, depend heavily on the input data used to drive the simulation. The impact of the chosen input magnetic map has been studied for coronal and heliospheric models independently, but rarely together, and with a specific focus on magnetic connectivity. This knowledge is crucial to reducing the uncertainty associated with space weather forecasts. In order to quantify the impact of input magnetic maps on our connectivity estimation, we used 20 different maps by four providers, with various processing treatments. We selected the period of July 2019, which corresponds to Carrington rotation (CR) number 2219, because it had already been studied with COCONUT in a previous paper. It corresponds to a period of minimum activity, meaning minimal interference from transient structures, but with a high-speed stream and several polarity switches, making it interesting and challenging to model. We have compared our results with in situ data from ACE and developed several visualisation techniques to validate connectivity not only for a specific date but also for a full CR. The choice of the input magnetic map drastically affects the connectivity estimates, with global scores going from 28% global agreement to 63% in the most extreme case. The HMI and GONG maps consistently give the best connectivity estimations, with more than 50% correct results of the full CR. For this specific period, the GONG-ADAPT maps yield the least reliable results, with a score of below 35%. This is not surprising, as we have shown before that COCONUT required parameter adjustments for these maps. However, WSO maps that show poor coronal agreement actually provide good connectivity estimates, showing that the heliospheric current sheet (HCS) location is in the end the most crucial parameter for connectivity (compared to open field areas locations or streamer shape). We have confirmed with this study that minimum of activity cases are actually challenging for magnetic connectivity, due to the proximity of the HCS with Earth's position. Consistent data over a full CR are to be favoured compared to daily updates (which proved more unstable in terms of connectivity), or even high-resolution.
Source: Astronomy and Astrophysics
Authors: B. Perri, S. Kennis, B. Schmieder, S. Poedts
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,865
Funders: National Science Foundation, National Aeronautics and Space Administration, Vlaams Supercomputer Centrum, European Commission, Agence Nationale de la Recherche, Fonds Wetenschappelijk Onderzoek, KU Leuven, Vlaamse regering, National Oceanic and Atmospheric Administration, Stanford Bio-X, Air Force Research Laboratory
Awards: G1138639576, G1394250481, A new-generation high-order Flux Reconstruction solver to model aerothermodynamics and transition for hypersonic vehicles, New theory and applications with the regularized Kappa-distribution, G2348175834, Understanding the solar radio emission processes from the Sun to EarthAcronym: UnderRadioSun, G4507733766, G4543786493, Improving the reliability of solar wind predictions for Earth, G6180803798, G6513226023, G7080048889, G8301932736, G8863810249, G949029963
OpenAlex ID: W7214270208
