Tamas I. Gombosi

For solar energetic particles (SEPs), it is generally expected that observers magnetically closer to the eruption source region exhibit higher particle intensities than those poorly connected to the eruption site. However, the 2023 February 24 SEP event departs from this simple picture: Earth and STA, near 1 au, are nominally better connected to the source region, whereas Solar Orbiter (SolO), at 0.77 au but less favorably connected, observed SEP fluxes more than an order of magnitude higher. This difference cannot be simply explained by nominal magnetic connectivity or radial scaling of SEP fluxes alone. To investigate this behavior, we perform a global magnetohydrodynamic simulation of the associated coronal mass ejection (CME) using the Alfvén Wave Solar-atmosphere Model-Realtime (AWSoM-R). The simulation reveals that the CME flux rope originates close to a coronal streamer and as it propagates and expands, the CME-driven shock is effectively distorted, developing into two distinct flanks with different strengths. Although the three spacecraft are separated by only ≲32° in heliolongitude, their magnetic footpoints differ by ≳50° in longitude because of a nearby stream interaction region. Specifically, Earth and STA connect to a weaker shock region, while SolO connects to the shock nose with a higher compression ratio and more efficient particle acceleration. We further simulate SEPs using the Multiple-Field-Line Advection Model for Particle Acceleration coupled with AWSoM-R, obtaining results that reproduce the observed flux differences among the three spacecraft, demonstrating that this counterintuitive behavior results from their connections to different regions of the inhomogeneous CME-driven shock.

Solar energetic particle (SEP) events pose severe threats to spacecraft, astronaut safety, and aviation operations. Accurate SEP forecasting remains a critical challenge in space weather research as a result of their complex origins and highly variable propagation. In this work, we built SEPNET , an innovative multi‐task neural network that jointly predicts future solar eruptive events, including solar flares and coronal mass ejections (CMEs) and SEPs, incorporating long short‐term memory and transformer architectures that capture contextual dependencies. SEPNET is a machine learning framework for SEP prediction that utilizes an extensive set of predictors, including the properties of solar flares, CMEs, and space‐weather HMI active region patches (SHARP) magnetic field parameters. SEPNET is rigorously evaluated on the SEPVAL SEP data set (Whitman, 2025b, https://doi.org/10.5281/zenodo.15555244 ), which is used to evaluate the performance of current SEP prediction models. The performance of SEPNET is compared with classical machine learning methods and current state‐of‐the‐art pre‐eruptive SEP prediction models. The results show that SEPNET , particularly with SHARP parameters, achieves higher detection rates and skill scores while maintaining the suitability for real‐time space weather alert operations. Although class imbalance in the data leads to relatively high false alarm rates, SEPNET consistently outperforms reference methods and provides timely SEP forecasts, highlighting the capability of deep multi‐task learning for next‐generation space weather prediction.

The CLEAR Space Weather Center of Excellence's solar energetic particle (SEP) model, SOlar wind with FIeld lines and Energetic particles (SOFIE), was run and evaluated on‐site during the Space Weather Prediction Testbed (SWPT) exercise at the National Oceanic and Atmospheric Administration's Space Weather Prediction Center (NOAA/SWPC) in May 2025. As a physics‐based SEP model, SOFIE simulates the acceleration and transport of energetic particles by the coronal mass ejection (CME)‐driven shock in the solar corona and inner heliosphere, and has been validated against historical events. However, questions remain regarding whether a physics‐based model, traditionally considered computationally expensive, could meet operational needs. The SWPT exercise offered a valuable opportunity to evaluate SOFIE under simulated operational conditions. On‐site interactive feedback from SWPC forecasters, Space Radiation Analysis Group (SRAG) console operators, Community Coordinated Modeling Center (CCMC) personnel, and Moon‐to‐Mars Space Weather Analysis Office (M2M SWAO) analysts led to significant strategic improvements in the model configuration. The simulation grid was optimized by combining a coarser background grid with higher‐resolution regions along the CME path and toward Earth, reducing computational cost without compromising accuracy. In this work, we present the simulated operational performance of SOFIE and its capability to predict SEP fluxes significantly faster than real time. During the SWPT exercise, SOFIE completed a 4‐day SEP simulation within 5 hr using 1,000 central processing unit cores, although the earliest SEP forecast was obtained a few hours after CME onset. This marks a milestone in demonstrating SOFIE's operational usefulness and robustness to support future human space exploration.

The CLEAR Space Weather Center of Excellence's solar energetic particle (SEP) prediction model, SOlar wind with FIeld lines and Energetic particles (SOFIE), was run and evaluated on-site during the Space Weather Prediction Testbed (SWPT) exercise at the National Oceanic and Atmospheric Administration's Space Weather Prediction Center (NOAA/SWPC) in May 2025.As a physics-based SEP simulation and prediction model, SOFIE simulates the acceleration and transport of energetic particles by the coronal mass ejection (CME)-driven shock in the solar corona and inner heliosphere, and has been validated against historical events. However, questions remain regarding whether a physics-based model, traditionally considered computationally expensive, could meet operational needs.The SWPT exercise offered a valuable opportunity to evaluate SOFIE's performance under simulated real-time conditions. On-site interactive feedback during the SWPT exercise from SWPC forecasters, Space Radiation Analysis Group (SRAG) console operators, Community Coordinated Modeling Center (CCMC) personnel, and Moon-to-Mars Space Weather Analysis Office (M2M SWAO) analysts led to significant strategic improvements in the model configuration. The simulation resolution was optimized by combining a coarser background grid with higher-resolution regions along the CME path and toward Earth, reducing computational cost without compromising accuracy.In this work, we present the operational performance of SOFIE and its capability to predict SEP fluxes significantly faster than real time. During the SWPT exercise, SOFIE could complete a 4-day SEP simulation within 5 hours using 1,000 CPU cores, although the earliest SEP forecast was obtained a few hours after CME onset. This marks a critical milestone in demonstrating SOFIE's operational usefulness and robustness to support future human space exploration.

The presented work revisits the Statistical Injection of Condensed Helicity (STITCH) model by Antiochos et al. 2013; Mackay et al. 2014; Dahlin et al. 2022. The model includes a statistical description of the small-scale circulation motions in the solar photosphere and accounts for their effect on the magnetic helicity in the solar corona.The statistical effect of small-scale circulation motions may be quantified following well-known analogy between the density of the magnetic moment of microscopic currents in magnetic media, on one hand, and the angular momentum density, on the other hand. Within the framework of magnetostatics the magnetic moment density of the microscopic currents (referred to as magnetization) is a statistical quantity characterizing the magnetized medium. Its gradient in non-uniform medium results in macroscopic magnetization current. Similarly, the angular momentum density may be involved as the statistical characteristic of the small-scale horizontal motions in the photosphere. In this application only the vertical component of the angular momentum density, ζ, matters, which is ideologically close to the parameter used in the STITCH model.Analogously to the magnetization current in magnetostatics, the horizontal gradient in ζ would result in large-scale horizontal motion. Indeed, for a uniform isotropic turbulence, the chaotic small-scale and high-frequency velocity would cancel in average. However, with any horizontal gradient in ζ the larger rotational velocity of a stronger nearby vortex is not fully balanced by the opposite rotation of a smaller vortex, thus resulting in the averaged larger-scale velocity.This velocity is perpendicular to the horizontal gradient of vertical magnetic field, BR or, equivalently, it is aligned with the level contours, BR=const, of the vertical field. Such motion drags the footpoints of the field lines of the coronal magnetic field, thus resulting in generation and accumulation of the magnetic helicity. The average velocity field parameterized in terms of gradients in ζ may be used as the boundary condition for an analytical or numerical model of the solar corona. Particularly, it is implemented in the SWMF code of the University of Michigan and used to pump helicity and the magnetic free energy of the active region to bring it to the threshold of eruptionAssuming that the sign of ζ is the same as that of projection of the solar angular velocity vector on the radial direction, it should be mostly positive in the northern hemisphere and mostly negative in the southern hemisphere. The rate of magnetic helicity production appears to be proportional to the negative of ζ. Hence, the described mechanism may result in the magnetic helicity in the solar corona such that the negative magnetic helicity dominates in the northern hemisphere and the positive magnetic helicity dominates in the southern hemisphere. The latter conclusion agrees with the so called “hemisphere rule” as confirmed by statistical analysis of observations.

The Center for All-Clear Solar Energetic Particle Forecast (CLEAR) project aims to transform space weather forecasting by delivering robust, quantifiable predictions of solar energetic particles (SEP) up to 24 hours in advance, enabling reliable identification of hazardous conditions and safe “all-clear” periods for astronauts, aviation, and satellite operations. The Solar Wind With Field Lines and Energetic Particles (SOFIE) model is the physics-based model in CLEAR, consisting of the Alfvén Wave Solar atmosphere Model–Realtime (AWSoM-R), which models the background solar wind, the Eruptive Event Generator based on the Gibson–Low flux rope (EEG-GL) tool to determine the input parameters for Coronal Mass Ejections (CME), and the Multiple Field Line particle Advection Model for Particle Acceleration (M-FLAMPA) module that handles the transport and acceleration of SEPs.We present an automatic version of CME information retrieval and simulation initiation integrated into SOFIE. We utilize the Space Weather Database Of Notifications, Knowledge, Information (DONKI) system available at the Community Coordinated Simulation Center (CCMC) to retrieve new CME detections, then use the CME time, speed, and source location information, alongside with the corresponding National Solar Observatory Global Oscillation Network Group (NSO GONG) magnetogram, as inputs into an algorithm detecting Regions of Interest (ROI). The ROI detection algorithm then, based on the magnetogram, finds the candidate Active Regions (AR) closest to the source location of the CME, and calculates the flux-weighted centroid position of the positive and negative polarities. The polarity coordinates are then passed on to EEG-GL for calculation of the CME input parameters. Once the parameters are determined by EEG-GL, SOFIE will launch an integrated CME and SEP simulation and provide forecasts. Only CMEs whose speed exceeds 800 km/s and half width exceeds 20 degrees will be simulated. The automated CME simulation pipeline is a crucial component of the automatic SOFIE pipeline, enabling routine physics-based simulations of SEPs for mission support and operational readiness. The pipeline is to be tested operationally during the Artemis launch scheduled between February and April, and to be used in upcoming missions.

A long-standing challenge to both basic space science and space weather capabilities is understanding what determines the amount of energy that goes into the CME (mass motions) versus the flare (plasma heating) in a solar eruptive event. It is also unclear what role thermodynamics plays in shaping CME dynamics. Although there have been many detailed observational studies of this issue using particular events, there have been very few quantitative theoretical studies due to the difficulty in keeping an accurate track of the energy released by the explosive reconnection during the flare impulsive phase. In this work, we conduct MHD simulations of the CME using an energy-conservative numerical scheme, which ensures energy conservation and accurate tracking of the energy release channels. The simulations are based on the Alfvén Wave Solar Model-Realtime (AWSoM-R). We used a simple photospheric magnetic field map composed of two dipoles. We then used the statistical injection of the condensed helicity model to generate CME eruptions. First, we analyzed the evolution of the magnetic, thermal, and kinetic energies during the CME eruption and found that the fraction of the released magnetic energy converted to thermal energy can be \approx 40 \%. We noticed that using the energy conservation scheme results in significantly more energy being converted to kinetic energy. The thermal energy leads to extremely high temperatures in the flare current sheet. We analyzed the evolution of density, electron temperature, and ion temperature in the flare current sheet. We found that extremely high temperatures hinder reconnection, resulting in a long-lasting current sheet. A second CME eruption is triggered due to this current sheet. Our results show that energy conservation and full thermodynamics play a key role in affecting the CME eruption process. We discuss the implications of our results for understanding energy release in a solar eruption and for interpreting observations of CMEs/eruptive flares.

Solar phenomena such as flares, coronal mass ejections (CMEs), and solar energetic particles (SEPs) are actively monitored and assessed for space weather hazards. In recent years, machine learning has demonstrated considerable success in solar flare forecasting. Accurate SEP forecasting remains challenging in space weather monitoring due to the complexity of SEP event origins and propagation. We introduce SEPNET, an innovative multi-task neural network that integrates forecasting of solar flares and CME summary statistics into the SEP prediction model, leveraging their shared dependence on space-weather HMI active region patches (SHARP) magnetic field parameters. SEPNET incorporates long short-term memory and transformer architectures to capture contextual dependencies. The performance of SEPNET is evaluated on the state-of-the-art SEPVAL SEP dataset and compared with classical machine learning methods and current state-of-the-art pre-eruptive SEP prediction models. The results show that SEPNET achieves higher detection rates and skill scores while being suitable for real-time space weather alert operations.

Accurate real-time and forecast of the space radiation environment caused by solar energetic particles (SEPs) is essential in supporting the human and robotic exploration activities in space. We implemented an automated and end-to-end pipeline based on the Solar Wind With Field Lines and Energetic Particles (SOFIE) model developed at the University of Michigan. SOFIE is a framework coupling several physics-based models that simulates the ambient solar wind, coronal mass ejections (CMEs), and SEPs. The ambient solar wind and the propagation of the CME is modeled using the Alfvén Wave Solar atmosphere Model–Realtime (AWSoM-R) model, a three-dimensional extended magnetohydrodynamic model that self-consistently accounts for Alfvén wave–driven heating and solar wind acceleration. The CME is generated by putting a Gibson–Low flux rope on the source region using the Eruptive Event Generator (EEGGL). The SEP acceleration and transport are modeled by the Multiple Field Line Particle Advection Model for Particle Acceleration (M-FLAMPA). We have implemented the SOFIE pipeline in which the ambient solar wind will be running continuously, ingesting hourly updated photospheric magnetic field observations to maintain an up-to-date solar wind solution in the heliosphere. When a CME is detected, the pipeline will launch a branched integrated CME and SEP simulation, in which the arrival of the Interplanetary Coronal Mass Ejection (ICME) and the complete SEP profiles at the energies of interest to the operation will be forecasted within a few hours of simulation time. The SOFIE pipeline is now fully automatic without human intervention. Model outputs and forecast products, including real-time solar wind conditions in the heliosphere, the forecasted arrival of the ICME and the proton fluxes will be made publicly available through the CLEAR website (https://solarwind.engin.umich.edu/). We will test the readiness and robustness of the pipeline and evaluate its performance during the Artemis-II mission.

Magnetic reconnection in coronal current sheet(s) is widely believed to be the main energy release process powering solar eruptive events, such as flares, coronal mass ejections (CME), and coronal jets. Modeling this process and determining the channels for the energy release, mass motions and heating, has long been a major goal in space science. We present results from a two-fluid MHD simulation of an eruptive flare/CME using a newly developed Strategic Capability, SCEPTER, which is based on the well-validated and widely used Space Weather Modeling Framework. SCEPTER incorporates two major advances in numerical capability. First, we use the STITCH formalism for the energy buildup, so that we start with a potential-field minimum-energy state and slowly form a sheared filament channel over a polarity inversion line as is observed on the Sun. Second, we use a new formulation of the plasma energetics that is explicitly energy conserving while calculating separate electron and ion temperatures and separate parallel and perpendicular pressures, as desired. For this first simulation with our new model, we opted for the non-adiabatic heating to go solely into the protons and for an isotropic pressure. We discuss the resulting energetics of the reconnection and, in particular, the plasma heating in the reconnecting current sheets, mass acceleration, and shock formation. We also discuss the implications of our results for understanding solar eruptions, in general. This work was supported by the NASA Living With a Star Program.