Advancing space weather forecasting for human space exploration requires not only advanced scientific models, but also demonstration of their operational readiness, validation in realistic environments, and sustained feedback between research and operations (R2O2R). The CLEAR Space Weather Center of Excellence (CLEAR center) focuses on developing and transitioning advanced solar energetic particle (SEP) forecasting capabilities into operationally viable, real-time systems to support future missions.

In this presentation, we will describe the research to operation activities conducted within the CLEAR center, with an emphasis on the past testbed-based exercise, operational co-development, and real-time implementation. The CLEAR center actively participated in the 2025 Space Weather Prediction Testbed Exercise in support of Human Space Exploration and the Artemis-II Mission, which provided a realistic operational context to assess model performance under constraints relevant to flight decision support, including latency, robustness, automation, interpretability, and uncertainty communication.

We will also report on the deployment of the CLEAR center’s physics-based, empirical, and machine-learning SEP models into an automated, near-real-time forecasting framework, designed to operate continuously during mission-critical periods. Particular attention is given to operational architecture, including data acquisition, computational optimization, automation and fail-safe design, enabling timely delivery of prediction products for Artemis launch windows. Feedback from operators and forecasters has directly informed pipeline design, product placement, delivery timing, and visualization - closing the O2R loop.

This work demonstrates how sustained engagement with operational partners accelerates the transition of SEP research into actionable forecasting capabilities. The CLEAR experience provides a concrete example of effective R2O2R pathways for next-generation space weather modeling in support of Moon and Mars exploration.

How to cite: Zhao, L., Gombosi, T., Sokolov, I., Chen, Y., Yu, Y., Whitman, K., Arge, C., Shane, A., Sachdeva, N., Richardson, I., Bruno, A., Liu, W., Koban, G., Biro, N., Sawant, S., Verma, V., Jin, K., Mays, L., Adamson, E., and Bain, H. and the The CLEAR Team: Operational Implementation of Real-Time SEP Forecasting: R2O2R Activities from the CLEAR Space Weather Center of Excellence, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15453, https://doi.org/10.5194/egusphere-egu26-15453, 2026.

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.

How to cite: Liu, W., Zhao, L., Sokolov, I., Whitman, K., Gombosi, T., Sachdeva, N., Adamson, E., Bain, H., Corti, C., Mays, M. L., Romano, M., Alden, C., Anastopulos, M., Aronne, M., Dahl, S., and Juelfs, E. and the CLEAR Team: Simulated Real-Time Testing of the Prototype Implementation of the SOFIE Model: The 2025 Space Weather Prediction Testbed Exercise, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15204, https://doi.org/10.5194/egusphere-egu26-15204, 2026.

How to cite: Maharana, A., Soni, S. L., Pal, S., and Poedts, S.: Mother’s Day 2024 Extreme Solar Event: Modelling and Learning How to Improve Space Weather Forecasting , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17502, https://doi.org/10.5194/egusphere-egu26-17502, 2026.

The G5 geomagnetic superstorm of May 2024 represents one of the most extreme space weather events in the space era and provides a critical testbed for assessing our preparedness for future severe storms. The event was driven by an exceptionally fast and energetic Coronal Mass Ejection (CME) that resulted from the cannibalization of multiple preceding eruptions, producing a complex plasma structure that reached Earth in less than two days. Such short warning times underscore the urgent need for robust and accurate forecasting frameworks to protect space- and ground-based technological infrastructures. In this study, we investigate the predictability of the May 2024 superstorm using an ensemble, physics-driven machine learning approach that combines remote-sensing coronal observations with in-situ solar wind measurements. Our results show that this hybrid framework would have successfully predicted the CME Sun–Earth travel time with high accuracy, achieving a timing precision of the order of one minute and an uncertainty of approximately three hours. A sensitivity analysis was conducted to assess the robustness of the model against uncertainties in the input parameters. The analysis demonstrates strong stability of the forecasting framework, with the mean predicted arrival time remaining within a few minutes of the observed value and a mean absolute error of about three hours when realistic input uncertainties are considered. Furthermore, benchmarking against classical drag-based models and purely data-driven approaches reveals that the proposed hybrid method significantly outperforms existing techniques during this extreme event.These results highlight the potential of physics-driven machine learning as a key component of next-generation space weather forecasting systems. Finally, this contribution also discusses possible future improvements in extreme CME propagation and addresses the challenges related to the validation of forecasting methodologies, with a particular focus on assessing prediction skill and robustness.

How to cite: Guastavino, S., Piana, M., Legnaro, E., and Massone, A. M.: Forecasting Extreme Space Weather Events with Physics-Driven Machine Learning: CME Arrival Prediction for the May 2024 Superstorm, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10034, https://doi.org/10.5194/egusphere-egu26-10034, 2026.

Extreme space weather events pose a significant risk to modern society, but their inherent rarity limits our ability to evaluate forecasting performance and societal resilience during the most severe geomagnetic storms. In this talk, we use the Gorgon global magnetohydrodynamic (MHD) model to simulate the severe geomagnetic storms of May 2024 and October 2024 and compare the results. We evaluate key global MHD simulation benchmarks, including magnetopause location, cross-polar cap potential (CPCP), and geomagnetic indices derived from modelled ground magnetic field disturbances. The simulated CPCP and Kp indices show good agreement with observations, successfully reproducing both the sudden storm commencement and overall storm intensity. We then investigate the influence of the ring current on the magnetospheric system through coupling with a kinetic inner magnetosphere model, assessing its contributions to magnetospheric structure and boundaries, and ground magnetic perturbations.

How to cite: Desai, R. and Tong, Y.: Gorgon magnetospheric simulations of the May 2024 and October 2024 geomagnetic storms, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14758, https://doi.org/10.5194/egusphere-egu26-14758, 2026.

The geomagnetic storm in May 2024 represents the most extreme space weather event over the past twenty years, providing a unique opportunity to investigate energetic particle dynamics under exceptionally strong solar wind driving conditions. Among the various particle populations affected by such extreme storms, outer radiation belt electrons are of particular interest because they respond rapidly to storm-time magnetospheric reconfigurations and pose significant hazards to satellites operating in near-Earth space.

Observations from the Arase satellite reveal that MeV radiation belt electron fluxes dropped by several orders of magnitude during the main phase of the May 2024 superstorm. This extreme dropout coincided with intense magnetopause compression to a minimum standoff distance of L~3.7, estimated using the Space Weather Modeling Framework, and enhanced ultra-low-frequency (ULF) Pc5 wave activity derived from SuperMAG. To investigate the physical mechanisms controlling this extreme electron loss, we performed simulations using the Versatile near‐Earth environment of Radiations Belts and ring current (VERB) model, systematically examining the roles of magnetopause shadowing, radial diffusion, and local wave-driven scattering.

We find that commonly used empirical radial diffusion models, parameterized by Kp, fail to reproduce the observed electron flux profiles during this event. Instead, accurately capturing the extreme electron dropout requires that enhanced radial diffusion be properly timed relative to magnetopause compression and storm-time wave activity. In addition, stronger plasmaspheric hiss scattering is necessary to reproduce losses at lower L*. These results demonstrate that extreme radiation belt electron dropout during superstorms is controlled by the coupled timing of magnetopause compression, radial diffusion, and local scattering processes, emphasizing the importance of physically timed transport and loss representations in extreme storm modeling.

How to cite: Lyu, X., Wang, D., Haas, B., Shprits, Y., Sun, Y., Hanzelka, M., Miyoshi, Y., Katsavrias, C., and Aminalragia-Giamini, S.: Understanding Extreme Radiation Belt Electron Dropout During the May 2024 Superstorm, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7628, https://doi.org/10.5194/egusphere-egu26-7628, 2026.

Machine learning (ML) approaches are increasingly used in heliophysics to represent complex, coupled processes with greater computational efficiency than traditional physics-based models. As the number of data-driven models continues to grow, there is a need for frameworks that support their systematic integration and evaluation across multiple regions of the Sun–Earth system. The Artificial Intelligence Modeling Framework for Advancing Heliophysics Research (AIMFAHR) addresses this need by providing a modular, community-oriented environment for combining ML models into a unified geospace modeling capability.

Here we present initial efforts from the AIMFAHR model configuration and its application to storm-time geospace dynamics. The initial framework incorporates models spanning the magnetosheath, cusp regions, auroral precipitation, field-aligned currents (FACs), ionospheric electrodynamics, thermospheric density, and ground magnetic perturbations. Model behavior is examined during three geomagnetic storm events (4 January 2023, 6 May 2023, and 11 May 2024), selected as reference cases by the ML-based Geospace Environment Modeling (MLGEM) resource group at the Geospace Environment Modeling (GEM) workshop for the MLGEM Challenge Storm study.

AIMFAHR reproduces key features of storm-time responses across domains, including variations in dayside magnetic reconnection geometry and rates, cusp motion and ion energy dispersion, global auroral boundary evolution and spectral variability, enhanced FAC systems and ionospheric potentials, increased Joule heating and thermospheric density, and intensified ground magnetic disturbances. These results demonstrate the ability of an integrated, machine-learning-based framework to capture coherent system-level responses to solar wind driving. Ongoing AIMFAHR development focuses on enhancing the coupling between model components, transfer of learned representations across domains, quantification of predictive uncertainty, and transition toward operational space-weather applications.

How to cite: Valluri, S. G., Coughlan, M., Connor, H., Michotte de Welle, B. A., Cucho Padin, G., Murphy, K., Halford, A., Blandin, M., Bard, C., Uddin, J., Berndt, E., and Schultz, C.: Artificial Intelligence Modeling Framework for Advancing Heliophysics Research (AIMFAHR), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14939, https://doi.org/10.5194/egusphere-egu26-14939, 2026.

Under the Space Safety Programme (S2P) and as part of the European Space Agency's D3S (Distributed Space Weather Sensor System), ESA's Space Weather Office has conducted the mission studies and pre-developments of a small satellite mission constellation that shall monitor the Auroral Oval for operational space weather applications. The observation of the Sun's activity and its interaction with the Earth through the monitoring of the Aurora is considered to become a core element of future Space Weather (SWE) monitoring systems, through the observation of the corresponding Auroral emissions and of the underlying particle and geo-magnetic state conditions. The foreseen demonstration mission (Aurora-D) follows a novel approach initially using a single small satellite focused on Auroral Oval imaging, followed by a constellation mission (Aurora-C) of SmallSats in a later period, enabling continuous (24/7) monitoring of the Auroral oval from a MEO orbit that is expected to be accessible and affordable only by a micro-launcher. 

The core instrumentation of Aurora consists of the Auroral Optical Spectral Imager (AOSI) covering several emission lines emitted in the visible spectral range, and the Auroral UV Imager (AUI) that will address two bands in the far UV spectral range. Furthermore, a modular instrument combining several radiation monitors and magnetometers (RadMag) is baselined as a secondary payload to monitor magnetic field dynamics and the radiation environment. The instruments are based on recent developments employing new technologies that will be deployed to space for the first time. A constellation of four satellites in MEO orbit is envisaged on the long-term. The Aurora-D demonstrator mission, is now under development to pave the way for a future operational constellation. We will present the mission objectives, observational concept and the measurements that are expected to be provided by the instruments. We will also give an outlook towards the products that could be developed in the future.

How to cite: Kraft, S.: Development and mission objectives of ESA's Aurora mission, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21923, https://doi.org/10.5194/egusphere-egu26-21923, 2026.

This work presents the concept of the SAWA mission, a Polish nanosatellite project designed to strengthen space weather monitoring. SAWA is developed in line with the European Space Agency’s Space Situational Awareness (SSA) and Space Safety (S2P) programmes and forms part of the Distributed Space Weather Sensor System (D3S). The mission aims to investigate magnetosphere–ionosphere–thermosphere (MIT) interactions using both in-situ observations and remote sensing techniques. Measured parameters include the geomagnetic field, plasma wave spectra, plasma density, thermospheric neutral density, and atomic oxygen density. An optional payload for energetic particle monitoring is under consideration. The mission has completed the analysis and feasibility stages (Phase 0/A) and is expected to become an important component of the European space weather awareness system in the coming years.

How to cite: Jujeczko, P., Knapkiewicz, P., Rothkaehl, H., Wawrzaszek, R., Belyayev, S., and Morawski, M. and the SAWA team: SAWA: A Small Space Weather Mission for Investigations of Magnetosphere-Ionosphere-Thermosphere Coupling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10317, https://doi.org/10.5194/egusphere-egu26-10317, 2026.

Space missions in Earth orbit are frequently exposed to intense and highly variable energetic electron environments. In the outer radiation belt, electrons with energies from ~100 keV to 10 MeV can reach extreme intensities, posing serious risks to spacecraft. Characterizing and forecasting this vast and dynamic environment relies on two complementary strategies: (i) deploying radiation monitors on as many spacecraft as possible, and (ii) applying advanced physics-based space weather models within data assimilation frameworks, where measurements provide boundary conditions. A critical observational input to these models is directionally resolved energy spectra. As electrons are magnetically trapped, directional measurements at a single location can be used to infer their distribution across the radiation belts.

In this context, the 3D Energetic Electron Spectrometer (3DEES) has been designed as a compact, science-class instrument optimized for measuring angle-resolved electron spectra from 0.1 – 10 MeV within Earth’s radiation belts. In addition, it enables quantification of proton fluxes in the 2.5 – 50 MeV energy range.

On 5 December 2024, a demonstrator model of the instrument (capable of simultaneous measurements from six directions) was launched aboard PROBA-3, a non-spinning spacecraft. The mission operates in a highly elliptical orbit with an apogee of 60,530 km, a perigee of 600 km, an inclination of 59°, and an orbital period of 19.7 hours. With these orbital parameters, the satellite is covering parts of the inner belt, outer belt and mostly the border of the magnetosphere. Since 29 July 2025, 3DEES is operated on a regular basis, performing measurements in the radiation belts every orbit when the satellite is at an altitude < 40000 km.

The presentation will provide a brief overview of the 3DEES instrument onboard PROBA-3, present initial results on electron pitch-angle distributions, and illustrate how the instrument captures radiation belt dynamics, such as sudden drop-outs, subsequent flux enhancements and steady decays. It will show that the 3DEES dataset constitutes a valuable new data source for the space weather community, delivering high-quality measurements.

How to cite: Benck, S. and Borisov, S.: The 3D Energetic Electron Spectrometer (3DEES): measuring pitch angle distributions of energetic electrons on a non-spinning spacecraft, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4066, https://doi.org/10.5194/egusphere-egu26-4066, 2026.