arXiv subjects

Hyun-Jin Jeong

Publications and source records attributed to Hyun-Jin Jeong.

8 recordsLinked to original sources

arXiv2026

Magnetohydrodynamic coronal modelling with high-fidelity magnetograms

To resolve coronal structures in detail, it is essential to constrain the coronal model using high-fidelity magnetograms with minimal smoothing. In this paper, we improve the numerical stability of COCONUT to enable efficient coronal MHD simulations driven directly by photospheric magnetograms, including magnetic field strengths exceeding 1000 Gauss (G), and then use it to investigate the impact of small-scale magnetic field structures that are typically filtered out of photospheric magnetograms on the coronal simulation results. We constrain this model with two types of magnetograms: the high-fidelity HMI photospheric magnetogram with only minor preprocessing through Gaussian smoothing, which preserves magnetic field strengths reaching 1500 G, and one processed using 50th-order filtered spherical harmonics, where the magnetic field strength is limited to below 100 G. The results demonstrate that the COCONUT can reproduce the large-scale coronal structures revealed by pB and EUV observations. Compared with the coronal MHD simulation constrained by a conventionally filtered magnetogram, the simulation constrained by the high-fidelity magnetogram yields approximately $50\%$ more OMF and produces EUV images that are more consistent with the observations. Moreover, the results show that when viewed from low latitudes in certain EUV passbands, such as the 193~Å channel, open-field regions near the solar poles may appear as bright structures that are typically associated with closed-field regions. This finding highlights the need for solar polar-orbiting missions to overcome the limitations and ambiguities arising from the restricted view of the polar regions from the ecliptic plane and to provide more complete full-Sun observations.

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arXiv2026

MHD modeling of magnetic flux evolution around solar maximum by the coronal model COCONUT

In this paper, we simulate the magnetic flux evolution at different heliocentric distances during two solar-maximum Carrington rotations (CRs) using the time-evolving coronal magnetohydrodynamic (MHD) model COCONUT to investigate the ``open flux problem". The simulated open magnetic flux (OMF) near the solar surface is comparable to that derived from \textit{in situ} observations by PSP and WIND satellites, and is about 5 times larger than that derived from SDO coronal hole (CH) observations, and the variation in the simulated radial solar wind speed is consistent with the evolution of the OMF evaluated around the corresponding solar disk center. We find that the OMF is reduced by up to $45\%$ from 1.01~$R_s$ to 0.1~AU and increases with a higher-resolution mesh. The OMF decreases mainly within 3~$R_s$, where the closed magnetic flux drops more rapidly, from about $60\%$ of the total magnetic flux at 1.01~$R_s$ to about $4\%$ at 3~$R_s$. Moderate adjustment of the heating source term can effectively regulate the simulated OMF. Preprocessing the photospheric magnetograms with a potential field solver that removes many high-order spherical harmonic components reduces the OMF in the low corona, while having little impact beyond 3~$R_s$. Additionally, the ratio of the maximum to the minimum OMF can reach 1.4 during a single solar maximum CR. These findings highlight the necessity of considering higher grid resolution, more realistic heating mechanisms, and the time-evolving regime of coronal MHD modeling when further addressing the ``open flux problem".

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arXiv2026

COCONUT: Toward practical time-evolving Sun-to-Earth magnetohydrodynamic modeling

Due to computational efficiency and numerical stability limitations, coronal simulations constrained by static magnetograms are typically performed first and then used to drive inner-heliosphere (IH) models. In this paper, we calculate the Sun-to-Earth coronal and wind evolutions using a single time-evolving MHD model, showing that implicit MHD models have the potential to meaningfully simplify and improve the overall Sun-to-Earth modelling pipeline. We extend the implicit time-evolving coronal MHD model COCONUT out to 1 AU, and utilise it to investigate solar coronal and wind evolutions around a solar maximum Carrington rotation (CR). We compare quasi-steady-state and time-evolving Sun-to-Earth simulations to evaluate the impact of the inner-boundary magnetic field evolution, which is neglected in steady-state simulations, on background plasma parameters. Comparisons with commonly used coupled Sun-to-Earth simulations are also conducted to further validate and assess the Sun-to-Earth model COCONUT. The results show that the time-evolving implicit MHD modelling approach yields noticeable differences compared to oversimplified steady-state simulations, and is efficient enough for practical applications. Modelling the solar corona and wind using a single MHD model simplifies the modelling pipeline and avoids uncertainties associated with coupling different coronal and IH models. The noticeable differences in the temporal evolution of plasma parameters at the L1 and L5 points highlight the need to use continuously evolving, synchronised magnetic field observations to improve global coronal and solar wind simulations, whereas the overall consistent evolutionary trend reveals the reliability of using L5 observations to forecast solar wind conditions near Earth about four days in advance.

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arXiv2026

COCONUT: A coronal model with an energy decomposition strategy

In this paper, we propose an energy decomposition method combined with an HLL Riemann solver that includes an additional dissipation term in the energy equation to improve the numerical stability of the fully implicit, time-evolving coronal model COCONUT and extend its applicability to solar-maximum phases. In MHD simulations that evolve conservative variables in time, the thermal pressure is typically computed by subtracting the magnetic and kinetic energies from the total energy. In low-beta (the ratio of thermal to magnetic pressure; $< 10^{-3}$) regions, discretization errors of magnetic energy can be comparable to the thermal pressure, potentially leading to negative thermal pressure and causing the simulation to crash. Therefore, we update the decomposed energy, excluding the magnetic energy, at each time step. It avoids subtracting a large magnetic energy from the total energy to obtain a very small thermal pressure in low-$β$ regions, thereby improving the numerical stability of MHD models. We validate the algorithm using a time-evolving solar-maximum Carrington rotation simulation in 2025, which the previous code failed to run to completion. We also perform quasi-steady-state coronal simulations and 2D benchmark tests to further assess the algorithm's performance. The simulation results show that the algorithm produces results nearly identical to those obtained using the traditional full energy equation during solar minimum, while significantly improving COCONUT's ability to simulate coronal evolution under strong magnetic fields, even including fields exceeding 100 Gauss with $β<10^{-3}$. This method provides a promising approach for performing quasi-realistic coronal simulations during solar maxima.

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arXiv2025

SIP-IFVM: An observation-based magnetohydrodynamic model of coronal mass ejection

Currently, achieving a balance between computational efficiency, accuracy, and numerical stability in CME simulations, particularly in the sub-Alfv{'e}nic coronal region, remains a significant challenge. This paper aims to address the challenge by integrating observational data and developing advanced numerical algorithms, focusing on reproducing large-scale CME evolutions that are consistent with observations in the coronal region. Based on the recently developed fully implicit thermodynamic MHD coronal model (Wang et al. 2025a), we further use an observation-based RBSL flux rope to trigger a CME event during CR 2111. Additionally, we improve the temporal accuracy using a 2nd-order accurate ESDIRK2 method, with the intermediate stage solutions computed by the 2nd-order accurate BDF2 pseudo-time marching method. To enhance the numerical stability of ESDIRK2, we apply approximate linearisation in the implicitly solved intermediate stages. Furthermore, we adjust the time-evolving magnetic field B1 to zero at the end of each physical time step to further validate the extended magnetic field decomposition approach proposed by (Wang et al. 2025a). It is noticed that the model successfully reproduces the CME evolution consistent with white-light coronagraph observations, enables faster-than-real-time CME propagation simulations from solar surface to 0.1 AU using only a few dozen CPU cores on approximately 1 million grid cells, and remains numerically stable in CME simulations involving low-\b{eta} regions. The simulation results show that this novel MHD coronal model, combined with an observation-based magnetic flux rope, is sufficiently numerically stable and computationally efficient to reproduce real CME events propagating through the sub-Alfv{é}nic coronal region. Thus, the observation-based CME model is well suited for practical applications in daily space weather forecasting.

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arXiv2024

Magnetogram-to-Magnetogram: Generative Forecasting of Solar Evolution

Investigating the solar magnetic field is crucial to understand the physical processes in the solar interior as well as their effects on the interplanetary environment. We introduce a novel method to predict the evolution of the solar line-of-sight (LoS) magnetogram using image-to-image translation with Denoising Diffusion Probabilistic Models (DDPMs). Our approach combines "computer science metrics" for image quality and "physics metrics" for physical accuracy to evaluate model performance. The results indicate that DDPMs are effective in maintaining the structural integrity, the dynamic range of solar magnetic fields, the magnetic flux and other physical features such as the size of the active regions, surpassing traditional persistence models, also in flaring situation. We aim to use deep learning not only for visualisation but as an integrative and interactive tool for telescopes, enhancing our understanding of unexpected physical events like solar flares. Future studies will aim to integrate more diverse solar data to refine the accuracy and applicability of our generative model.

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arXiv2022

Improved AI-generated Solar Farside Magnetograms by STEREO and SDO Data Sets and Their Release

Here we greatly improve Artificial Intelligence (AI)-generated solar farside magnetograms using data sets of Solar Terrestrial Relations Observatory (STEREO) and Solar Dynamics Observatory (SDO). We modify our previous deep learning model and configuration of input data sets to generate more realistic magnetograms than before. First, our model, which is called Pix2PixCC, uses updated objective functions which include correlation coefficients (CCs) between the real and generated data. Second, we construct input data sets of our model: solar farside STEREO extreme ultraviolet (EUV) observations together with nearest frontside SDO data pairs of EUV observations and magnetograms. We expect that the frontside data pairs provide the historic information of magnetic field polarity distributions. We demonstrate that magnetic field distributions generated by our model are more consistent with the real ones than before in view of several metrics. The averaged pixel-to-pixel CC for full disk, active regions, and quiet regions between real and AI-generated magnetograms with 8 by 8 binning are 0.88, 0.91, and 0.70, respectively. Total unsigned magnetic flux and net magnetic flux of the AI-generated magnetograms are consistent with those of real ones for test data sets. It is interesting to note that our farside magnetograms produce consistent polar field strengths and magnetic field polarities with those of nearby frontside ones for solar cycle 24 and 25. Now we can monitor the temporal evolution of active regions using solar farside magnetograms by the model together with the frontside ones. Our AI-generated Solar Farside Magnetograms (AISFMs) are now publicly available at Korean Data Center (KDC) for SDO.

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arXiv2020

Solar Coronal Magnetic Field Extrapolation from Synchronic Data with AI-generated Farside

Solar magnetic fields play a key role in understanding the nature of the coronal phenomena. Global coronal magnetic fields are usually extrapolated from photospheric fields, for which farside data is taken when it was at the frontside, about two weeks earlier. For the first time we have constructed the extrapolations of global magnetic fields using frontside and artificial intelligence (AI)-generated farside magnetic fields at a near-real time basis. We generate the farside magnetograms from three channel farside observations of Solar Terrestrial Relations Observatory (STEREO) Ahead (A) and Behind (B) by our deep learning model trained with frontside Solar Dynamics Observatory extreme ultraviolet images and magnetograms. For frontside testing data sets, we demonstrate that the generated magnetic field distributions are consistent with the real ones; not only active regions (ARs), but also quiet regions of the Sun. We make global magnetic field synchronic maps in which conventional farside data are replaced by farside ones generated by our model. The synchronic maps show much better not only the appearance of ARs but also the disappearance of others on the solar surface than before. We use these synchronized magnetic data to extrapolate the global coronal fields using Potential Field Source Surface (PFSS) model. We show that our results are much more consistent with coronal observations than those of the conventional method in view of solar active regions and coronal holes. We present several positive prospects of our new methodology for the study of solar corona, heliosphere, and space weather.

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