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Abstract Global solar photospheric magnetic maps play a critical role in solar and heliospheric physics research. Routine magnetograph measurements of the field occur only along the Sun–Earth line, leaving the far side of the Sun unobserved. Surface flux transport (SFT) models attempt to mitigate this by modeling the surface evolution of the field. While such models have long been established in the community (with several releasing public full-Sun maps), none are open source. The Open-source Flux Transport (OFT) model seeks to fill this gap by providing an open and user-extensible SFT model that also builds on the knowledge of previous models with updated numerical and data acquisition/assimilation methods along with additional user-defined features. In this first of a series of papers on OFT, we introduce its computational core: the High-performance Flux Transport (HipFT) code (https://github.com/predsci/hipft). HipFT implements advection, diffusion, and data assimilation in a modular design that supports a variety of flow models and options. It can compute multiple realizations in a single run across model parameters to create ensembles of maps for uncertainty quantification and is high-performance through the use of multi-CPU and multi-GPU parallelism. HipFT is designed to enable users to write extensions easily, enhancing its flexibility and adaptability. We describe HipFT’s model features, validations of its numerical methods, performance of its parallel and GPU-accelerated code implementation, analysis/postprocessing options, and example use cases. 

The Sun’s corona is its tenuous outer atmosphere of hot plasma, which is difficult to observe. Most models of the corona extrapolate its magnetic field from that measured on the photosphere (the Sun’s optical surface) over a full 27-day solar rotational period, providing a time-stationary approximation. We present a model of the corona that evolves continuously in time, by assimilating photospheric magnetic field observations as they become available. This approach reproduces dynamical features that do not appear in time-stationary models. We used the model to predict coronal structure during the total solar eclipse of 8 April 2024 near the maximum of the solar activity cycle. There is better agreement between the model predictions and eclipse observations in coronal regions located above recently assimilated photospheric data. 

Abstract A plethora of coronal models, from empirical to more complex magnetohydrodynamic (MHD) ones, are being used for reconstructing the coronal magnetic field topology and estimating the open magnetic flux. However, no individual solution fully agrees with coronal hole observations and in situ measurements of open flux at 1 au, as there is a strong deficit between the model and observations contributing to the known problem of the missing open flux. In this paper, we investigate the possible origin of the discrepancy between modeled and observed magnetic field topology by assessing the effect on the simulation output by the choice of the input boundary conditions and the simulation setup, including the choice of numerical schemes and the parameter initialization. In the frame of this work, we considered four potential field source surface-based models and one fully MHD model, different types of global magnetic field maps, and model initiation parameters. After assessing the model outputs using a variety of metrics, we conclude that they are highly comparable regardless of the differences set at initiation. When comparing all models to coronal hole boundaries extracted by extreme-ultraviolet filtergrams, we find that they do not compare well. This mismatch between observed and modeled regions of the open field is a candidate contributing to the open flux problem. 

Abstract To address Objective II of the National Space Weather Strategy and Action Plan “Develop and Disseminate Accurate and Timely Space Weather Characterization and Forecasts” and US Congress PROSWIFT Act 116–181, our team is developing a new set of open-source software that would ensure substantial improvements of Space Weather (SWx) predictions. On the one hand, our focus is on the development of data-driven solar wind models. On the other hand, each individual component of our software is designed to have accuracy higher than any existing SWx prediction tools with a dramatically improved performance. This is done by the application of new computational technologies and enhanced data sources. The development of such software paves way for improved SWx predictions accompanied with an appropriate uncertainty quantification. This makes it possible to forecast hazardous SWx effects on the space-borne and ground-based technological systems, and on human health. Our models include (1) a new, open-source solar magnetic flux model (OFT), which evolves information to the back side of the Sun and its poles, and updates the model flux with new observations using data assimilation methods; (2) a new potential field solver (POT3D) associated with the Wang–Sheeley–Arge coronal model, and (3) a new adaptive, 4-th order of accuracy solver (HelioCubed) for the Reynolds-averaged MHD equations implemented on mapped multiblock grids (cubed spheres). We describe the software and results obtained with it, including the application of machine learning to modeling coronal mass ejections, which makes it possible to improve SWx predictions by decreasing the time-of-arrival mismatch. The tests show that our software is formally more accurate and performs much faster than its predecessors used for SWx predictions. 

The Sun moves with respect to the local interstellar medium (LISM) and modifies its properties to heliocentric distances as large as 1 pc. The solar wind (SW) is affected by penetration of the LISM neutral particles, especially H and He atoms. Charge exchange between the LISM atoms and SW ions creates pickup ions (PUIs) and secondary neutral atoms that can propagate deep into the LISM. Neutral atoms measured at 1 au can provide us with valuable information on the properties of pristine LISM. New Horizons provides us with unique measurements of pickup ions in the SW region where they are thermodynamically dominant. Voyager 1 and 2 spacecraft perform in-situ measurements of the LISM perturbed by the presence of the heliosphere and relate them to the unperturbed region. The Interstellar Boundary Explorer (IBEX) makes it possible identify the 3-D structure of the heliospheric interface. We outline the main challenges in the physics of the SW–LISM interaction. The physical processes that require a focused attention of the heliospheric community are discussed from the theoretical perspective and space missions necessary for their investigation. We emphasize the importance of data-driven simulations, which are necessary for the interpretation and explanation of spacecraft data. 

Abstract Flux-rope-based magnetohydrodynamic modeling of coronal mass ejections (CMEs) is a promising tool for prediction of the CME arrival time and magnetic field at Earth. In this work, we introduce a constant-turn flux rope model and use it to simulate the 2012 July 12 16:48 CME in the inner heliosphere. We constrain the initial parameters of this CME using the graduated cylindrical shell (GCS) model and the reconnected flux in post-eruption arcades. We correctly reproduce all the magnetic field components of the CME at Earth, with an arrival time error of approximately 1 hr. We further estimate the average subjective uncertainties in the GCS fittings by comparing the GCS parameters of 56 CMEs reported in multiple studies and catalogs. We determined that the GCS estimates of the CME latitude, longitude, tilt, and speed have average uncertainties of 5.°74, 11.°23, 24.°71, and 11.4%, respectively. Using these, we have created 77 ensemble members for the 2012 July 12 CME. We found that 55% of our ensemble members correctly reproduce the sign of the magnetic field components at Earth. We also determined that the uncertainties in GCS fitting can widen the CME arrival time prediction window to about 12 hr for the 2012 July 12 CME. On investigating the forecast accuracy introduced by the uncertainties in individual GCS parameters, we conclude that the half-angle and aspect ratio have little impact on the predicted magnetic field of the 2012 July 12 CME, whereas the uncertainties in longitude and tilt can introduce relatively large spread in the magnetic field predicted at Earth. 

Abstract Coronal holes are recognized as the primary sources of heliospheric open magnetic flux (OMF). However, a noticeable gap exists between in situ measured OMF and that derived from remote-sensing observations of the Sun. In this study, we investigate the OMF evolution and its connection to solar structures throughout 2014, with special emphasis on the period from September to October, where a sudden and significant OMF increase was reported. By deriving the OMF evolution at 1 au, modeling it at the source surface, and analyzing solar photospheric data, we provide a comprehensive analysis of the observed phenomenon. First, we establish a strong correlation between the OMF increase and the solar magnetic field derived from a potential-field source-surface model (cc_Pearson= 0.94). Moreover, we find a good correlation between the OMF and the open flux derived from solar coronal holes (cc_Pearson= 0.88), although the coronal holes only contain 14%–32% of the Sun’s total open flux. However, we note that while the OMF evolution correlates with coronal hole open flux, there is no correlation with the coronal hole area evolution (cc_Pearson= 0.0). The temporal increase in OMF correlates with the vanishing remnant magnetic field at the southern pole, caused by poleward flux circulations from the decay of numerous active regions months earlier. Additionally, our analysis suggests a potential link between the OMF enhancement and the concurrent emergence of the largest active region in solar cycle 24. In conclusion, our study provides insights into the strong increase in OMF observed during 2014 September–October. 

Abstract The arrival time prediction of coronal mass ejections (CMEs) is an area of active research. Many methods with varying levels of complexity have been developed to predict CME arrival. However, the mean absolute error (MAE) of predictions remains above 12 hr, even with the increasing complexity of methods. In this work we develop a new method for CME arrival time prediction that uses magnetohydrodynamic simulations involving data-constrained flux-rope-based CMEs, which are introduced in a data-driven solar wind background. We found that for six CMEs studied in this work the MAE in arrival time was ∼8 hr. We further improved our arrival time predictions by using ensemble modeling and comparing the ensemble solutions with STEREO-A and STEREO-B heliospheric imager data. This was done by using our simulations to create synthetic J-maps. A machine-learning (ML) method called the lasso regression was used for this comparison. Using this approach, we could reduce the MAE to ∼4 hr. Another ML method based on the neural networks (NNs) made it possible to reduce the MAE to ∼5 hr for the cases when HI data from both STEREO-A and STEREO-B were available. NNs are capable of providing similar MAE when only the STEREO-A data are used. Our methods also resulted in very encouraging values of standard deviation (precision) of arrival time. The methods discussed in this paper demonstrate significant improvements in the CME arrival time predictions. Our work highlights the importance of using ML techniques in combination with data-constrained magnetohydrodynamic modeling to improve space weather predictions.