Kathryn Whitman

Numerical models are essential for advancing fundamental research in heliophysics and for operational forecasting of space weather events. Open science can accelerate progress, but applying an open science approach to numerical models requires careful consideration; models are not data analysis software and often demand substantial domain expertise, experience, and computational resources to be used correctly. This commentary distills the recommendations from Corti et al. (2026), https://doi.org/10.22541/essoar.176824639.92354528/v2 and proposes a pathway for advancing heliophysics and space weather modeling through open science. Rather than focusing on open science policies and practices, we recommend concrete actions to make models usable by the broader community (open use), increase confidence and transparency throughout the Research‐to‐Operations‐to‐Research (R2O2R) pipeline (open validation), enable collaborative model improvement (open development), and strengthen partnerships (open collaboration). Together, these recommendations address several of the open science challenges experienced by contributing authors in developing, validating, using, and hosting advanced models, as identified during the 2024 Open Science Workshop held at College Park, USA. The recommendations also highlight the importance of a culture of shared responsibility, adherence to best practices, and proper recognition of prior work. The Heliophysics Open Modeling Environment will serve as a platform for modelers and model users to work together, facilitate community modeling, and increase the scientific return on modeling investment.

We present a community‐wide effort to develop a strategy and action plan to advance heliophysics and space weather modeling through open science. While open science has the potential to enhance the quality and pace of scientific discovery, its application to scientific modeling requires more careful consideration regarding open data and open software guidelines, as scientific models differ significantly from data analysis software. We gathered feedback from modeling teams worldwide through a living survey and discussion sessions at the 2024 Open Science Workshop in College Park, USA, and at the 2025 COSPAR ISWAT Working Meeting in Cape Canaveral, USA. We complement these findings with lessons learned from almost 25 years of experience at the Community Coordinated Modeling Center in enabling open use of models. We identify key roadblocks in current open science practices and guidelines and offer recommendations for future progress across four overlapping themes: open use of models and simulation results, open validation, open development, and open collaboration. An essential outcome of the discussion is the need for model developers and model users to speak with a united voice and promote the role of models in future open science efforts. We introduce a new cross‐domain community initiative called the Heliophysics Open Modeling Environment (HOME), which will be integrated as an overarching activity within COSPAR ISWAT. HOME will serve as a platform for modelers and model users to work together, facilitate community modeling, improve the scientific return on modeling investment, and advance understanding, modeling, and forecasting in heliophysics and space weather.

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.

Solar Energetic Particle (SEP) events can adversely affect space and ground‐based systems. Space weather effects associated with SEP events can impact communication and navigation systems, spacecraft electronics and operations, space power systems, crewed space missions, and commercial aircraft operations. With the increasing importance of accurate forecasting—particularly for upcoming human exploration missions such as Artemis—there is a clear need to transition SEP prediction models to operational readiness. Achieving this requires rigorous validation to ensure that the models perform reliably under real‐world conditions and can support critical mission decision‐making. In this work, we present the results of the validation of the different modules of the newly updated version of the ASPECS (Advanced Solar Particle Event Casting System) nowcasting (post‐eruption) part of the tool based on the SEPVAL 2023 (SEP Model Validation) sample. Emphasis is given on the comparison of the different inputs/catalogs and the resulting performance of the tool. Metrics such as the Probability of Detection (POD), the False Alarm Rate (FAR), the Percent Correct (PC), the Heidke Skill Score (HSS), and the True Skill Score (TSS) are generated for each of the different ASPECS modules. Comparisons between the predicted and observed peak proton fluxes and SEP time profiles at E 10 MeV and E 100 MeV are discussed.

Solar energetic particles (SEPs) can pose hazardous radiation risks to both humans and spacecraft electronics in space. Numerical modeling based on first principles offers valuable insights into the underlying physics of SEPs and provides synthetic observables for SEPs at any time and location in the inner heliosphere. In this work, we present a numerical scheme, which conserves the number of particles based on integral relations for Poisson brackets, to solve the kinetic equation for particle acceleration and transport processes. We implement this scheme within the Space Weather Modeling Framework, developed at the University of Michigan. In addition, we develop a new shock-capturing tool to study the coronal mass ejection-driven shock originating from the low solar corona. These methodological advancements are applied to conduct a comprehensive study of a historical SEP event on 2013 April 11. Observations from multiple spacecraft, including the Solar and Heliospheric Observatory, Solar Dynamics Observatory, Geostationary Operational Environmental Satellite, Advanced Composition Explorer near Earth, and STEREO-A/B, are used for model–data comparison and validation. We show synthetic observables, including extreme ultraviolet and white-light images, proton time–intensity profiles, and energy spectra, and discuss their differences and probable explanations compared to observations. Our simulation results demonstrate the application of the Poisson bracket scheme with a particle solver to simulating a historical SEP event. We also show the capability of extracting the complex shock surface using our shock-capturing tool and understand how the complex shock surface affects the particle acceleration process.

Multi-spacecraft observations of solar energetic particle (SEP) events not only enable a deeper understanding and development of particle acceleration and transport theories but also provide important constraints for model validation efforts. However, because of computational limitations, a given physics-based SEP model is usually best suited to capture a particular phase of an SEP event, rather than its whole development from onset through decay. For example, magnetohydrodynamic (MHD) models of the heliosphere often incorporate solar transients only at the outer boundary of their so-called coronal domain – usually set at a heliocentric distance of 20–30 R_⊙. This means that particle acceleration at coronal mass ejection (CME)-driven shocks is also computed from this boundary onwards, leading to simulated SEP event onsets that can be many hours later than observed, since shock waves can form much lower in the solar corona. In this work, we aim to improve the modelled onset of SEP events by inserting a “fixed source” of particle injection at the outer boundary of the coronal domain of the coupled WSA–Enlil 3D MHD model of the heliosphere. The SEP model that we employ for this effort is Solar Energetic Particle MODel (SEPMOD), a physics-based test-particle code based on a field line tracer and adiabatic invariant conservation. We apply our initial tests and results of SEPMOD’s fixed-source option to the 2021 October 9 SEP event, which was detected at five well-separated locations in the inner heliosphere – Parker Solar Probe, STEREO-A, Solar Orbiter, BepiColombo, and near-Earth spacecraft.