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The physics of solar flares occurring on the Sun is highly complex and far from fully understood. However, observations show that solar eruptions are associated with the intense kilogauss fields of active regions, where free energies are stored with field-aligned electric currents. With the advent of high-quality data sources such as the Geostationary Operational Environmental Satellites (GOES) and Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI), recent works on solar flare forecasting have been focusing on data-driven methods. In particular, black box machine learning and deep learning models are increasingly being adopted in which underlying data structures are not modeled explicitly. If the active regions indeed follow the same laws of physics, similar patterns should be shared among them, reflected by the observations. Yet, these black box models currently used in the literature do not explicitly characterize the heterogeneous nature of the solar flare data within and between active regions. In this paper, we propose two finite mixture models designed to capture the heterogeneous patterns of active regions and their associated solar flare events. With extensive numerical studies, we demonstrate the usefulness of our proposed method for both resolving the sample imbalance issue and modeling the heterogeneity for rare energetic solar flare events. 

Abstract A potential field solution is widely used to extrapolate the coronal magnetic field above the Sun’s surface to a certain height. This model applies the current-free approximation and assumes that the magnetic field is entirely radial beyond the source surface height, which is defined as the radial distance from the center of the Sun. Even though the source surface is commonly specified at 2.5R_s(solar radii), previous studies have suggested that this value is not optimal in all cases. In this study, we propose a novel approach to specify the source surface height by comparing the areas of the open magnetic field regions from the potential field solution with predictions made by a magnetohydrodynamic model, in our case the Alfvén Wave Solar atmosphere Model. We find that the adjusted source surface height is significantly less than 2.5R_snear solar minimum and slightly larger than 2.5R_snear solar maximum. We also report that the adjusted source surface height can provide a better open flux agreement with the observations near the solar minimum, while the comparison near the solar maximum is slightly worse. 

Abstract We describe our first attempt to systematically simulate the solar wind during different phases of the last solar cycle with the Alfvén Wave Solar atmosphere Model (AWSoM) developed at the University of Michigan. Key to this study is the determination of the optimal values of one of the most important input parameters of the model, the Poynting flux parameter, which prescribes the energy flux passing through the chromospheric boundary of the model in the form of Alfvén wave turbulence. It is found that the optimal value of the Poynting flux parameter is correlated with the area of the open magnetic field regions with the Spearman’s correlation coefficient of 0.96 and anticorrelated with the average unsigned radial component of the magnetic field with the Spearman’s correlation coefficient of −0.91. Moreover, the Poynting flux in the open field regions is approximately constant in the last solar cycle, which needs to be validated with observations and can shed light on how Alfvén wave turbulence accelerates the solar wind during different phases of the solar cycle. Our results can also be used to set the Poynting flux parameter for real-time solar wind simulations with AWSoM. 

This perspective paper brings to light the need for comprehensive studies on the evolution of interplanetary coronal mass ejection (ICME) complexity during propagation. To date, few studies of ICME complexity exist. Here, we define ICME complexity and associated changes in complexity, describe recent works and their limitations, and outline key science questions that need to be tackled. Fundamental research on ICME complexity changes from the solar corona to 1 AU and beyond is critical to our physical understanding of the evolution and interaction of transients in the inner heliosphere. Furthermore, a comprehensive understanding of such changes is required to understand the space weather impact of ICMEs at different heliospheric locations and to improve on predictive space weather models. 

Abstract To simulate solar coronal mass ejections (CMEs) and predict their time of arrival and geomagnetic impact, it is important to accurately model the background solar wind conditions in which CMEs propagate. We use the Alfvén Wave Solar atmosphere Model (AWSoM) within the the Space Weather Modeling Framework to simulate solar maximum conditions during two Carrington rotations and produce solar wind background conditions comparable to the observations. We describe the inner boundary conditions for AWSoM using the ADAPT global magnetic maps and validate the simulated results with EUV observations in the low corona and measured plasma parameters at L1 as well as at the position of the Solar Terrestrial Relations Observatory spacecraft. This work complements our prior AWSoM validation study for solar minimum conditions and shows that during periods of higher magnetic activity, AWSoM can reproduce the solar plasma conditions (using properly adjusted photospheric Poynting flux) suitable for providing proper initial conditions for launching CMEs. 

MHD-based global space weather models have mostly been developed and maintained at academic institutions. While the ``free spirit'' approach of academia enables the rapid emergence and testing of new ideas and methods, the lack of long-term stability and support makes this arrangement very challenging. This paper describes a successful example of a university-based group, the Center of Space Environment Modeling (CSEM) at the University of Michigan that developed and maintained the Space Weather Modeling Framework (SWMF) and its core element, the BATS-R-US extended MHD code. It took a quarter of a century to develop this capability and reach its present level of maturity that makes it suitable for research use by the space physics community through the Community Coordinated Modeling Center (CCMC) as well as operational use by the NOAA Space Weather Prediction Center (SWPC).