This session focuses on the combined use of space weather missions, instrumentation, and models to observe, understand, and predict extreme events and their effects on the heliosphere, magnetosphere, ionosphere, thermosphere, auroral regions, and radiation belts. Continuous and timely measurements are essential to capture event initiation and evolution, quantify environmental impacts, and provide robust boundary conditions and validation data for physics‑based and data‑driven models.
We have welcomed contributions showcasing the capabilities of current and upcoming missions and instruments measuring plasma properties, electromagnetic fields, radiation, and atmospheric response. Particular emphasis is placed on how these observations enable near‑real‑time situational awareness, support data assimilation, and help close critical observational gaps during severe events through coordinated multi‑mission strategies and hosted payloads.
The session also addresses advances in space weather modelling and forecasting, including physics‑based modelling, machine‑learning approaches, and hybrid techniques. We promote contributions evaluating model performance during extreme events, identifying key physical and observational limitations, and proposing paths to improved predictive skill. Topics include uncertainty quantification, extreme‑event benchmarks, and translating model outputs into actionable information for operational and pre‑operational services.
By bringing together instrument developers, mission teams, modellers, and forecasters, this session aims to strengthen the connection between observations and models and to advance end‑to‑end capabilities for monitoring and forecasting extreme space weather.
Orals: Wed, 6 May, 10:45–12:30 | Room 0.94/95
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.
Between 10 and 14 May 2024, the Sun produced an extraordinary sequence of eruptions, the Mother’s Day event, culminating in the most intense geomagnetic storm in decades. This event was driven by at least ten interacting coronal mass ejections (CMEs), accompanied by solar flares and filament eruptions, forming highly complex heliospheric structures with exceptional geoeffectiveness. Such CME–CME interactions present significant challenges for operational space-weather forecasting.
This study addresses the complexity of modelling extreme events within operational space weather frameworks. A key difficulty lies in constraining CME and solar wind input parameters, especially for halo and partial-halo CMEs, where parameter sensitivity is heightened. Accurate representation of CME–CME interactions necessitates physics-based magnetohydrodynamic (MHD) modelling rather than empirical approaches.
We employ multi-point remote observations in white light and extreme ultraviolet to identify CME sources and derive their kinematic and geometric properties. These parameters drive three-dimensional MHD simulations of CME evolution and heliospheric propagation using the EUHFORIA model. Our best-performing simulation reproduced the storm’s arrival time within approximately two hours and estimated its peak intensity with ~70% accuracy. Crucially, the inclusion of all contributing CMEs was essential for achieving this level of predictive reliability.
Our findings underscore the need for improved observational infrastructure and enhanced modelling capabilities to address the inherent complexity of extreme space weather events. Advancing the speed and accuracy of MHD-based forecasting tools is critical for mitigating the impacts of future solar superstorms. We highlight how the Mother’s Day 2024 event serves as a benchmark for understanding the limitations of current models and a call for the urgent requirement for community-wide investment in both observational and computational resources.
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.
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.
How to cite: Biro, N., Zhao, L., Sokolov, I., Sawant, S., Corti, C., Sachdeva, N., Gombosi, T., Koban, G., Aronne, M., Juelfs, E., Nieves-Chinchilla, T., and Mays, M. L.: A Fully Automated CME Simulation Pipeline Developed by the CLEAR Space Weather Center of Excellence, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15258, https://doi.org/10.5194/egusphere-egu26-15258, 2026.
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.
How to cite: Koban, G., Zhao, L., Sokolov, I., Biro, N., Liu, W., Sawant, S., Gombosi, T., Liu, X., Sachdeva, N., Corti, C., Juelfs, E., Aronne, M., Whitman, K., Mays, L., and Nieves-Chinchilla, T.: Operational Testing of the CLEAR Space Weather Center of Excellence’s SEP Prediction Pipeline During the ARTEMIS-II Mission, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15262, https://doi.org/10.5194/egusphere-egu26-15262, 2026.
How to cite: Sivadas, N., Sibeck, D., Subramanyan, V., Walach, M.-T., Ozturk, D. S., Ferdousi, B., and Michotte de Welle, B.: Extreme Geomagnetic Storms may have a Larger Impact than we realize, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16136, https://doi.org/10.5194/egusphere-egu26-16136, 2026.
Track circuit signalling systems are widely utilised across the world, and their ability to accurately detect trains is crucial for the safe and reliable operation of a railway network. These systems use electric circuits to detect the presence or absence of trains in sections along a railway line, as such, they can be susceptible to interference from geomagnetically induced currents.
With input from a project advisory group of industry experts, we are developing a model to analyse the impact that space weather has on DC track circuits along realistic sections of the UK railway network. Recent improvements to the model include utilising the British Geological Survey Near Surface Electrical Resistivity Model of Great Britain for more accurate leakage to ground estimations. The latest results from the model for arterial routes in England and Scotland, including case studies of historical extreme events will be shown.
How to cite: Patterson, C., Wild, J., Beggan, C., Hübert, J., Richardson, G., and Boteler, D.: Could Space Weather Delay Your Train? Modelling the Impacts of Geomagnetically Induced Currents on Railway Signalling Systems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16735, https://doi.org/10.5194/egusphere-egu26-16735, 2026.
Energetic electron precipitation (EEP) from the inner magnetosphere is a key element of magnetosphere–ionosphere–thermosphere (MIT) coupling and a major driver of space weather impacts on the upper atmosphere. Controlled by wave–particle interactions such as whistler-mode chorus, hiss, and electromagnetic ion cyclotron (EMIC) waves, EEP contributes to auroral emissions, enhanced atmospheric ionization, and NOx production, with important consequences for atmospheric chemistry and dynamics. Robust quantification of EEP and its impacts is therefore essential for advancing space weather monitoring and modelling.
In this study, we present recent advances in lifetime models of energetic electrons developed to quantify EEP driven by whistler-mode chorus waves. Using these models, we perform numerical simulations to calculate precipitating electron fluxes and associated ionization rates. The results demonstrate that additional scattering mechanisms, beyond those included in current state-of-the-art chorus and hiss models, are required to accurately estimate EEP and its atmospheric effects.
These developments build on collaborative efforts of our ISSI team “Precipitation of Energetic Particles from the Magnetosphere and Their Effects on the Atmosphere,” with coordinated activities within ISWAT. Within this framework, we reviewed precipitation mechanisms affecting radiation belt and ring current electrons, assessed potential missing processes, and examined EMIC wave–electron resonance, including constraints on minimum affected energies. Storm-time space weather impacts, including those during the extreme geomagnetic event of 10–15 May 2024, were also discussed.
Finally, we place these observational and modelling efforts in the context of the ESA Study the Energetic Electron Precipitation (SEEP) project, developed in response to the ESA call for a New Earth Observation Mission Idea (NEOMI). SEEP aims to provide new observational constraints on EEP and its atmospheric effects, enabling improved model validation and supporting future space weather monitoring capabilities.
How to cite: Wang, D., Shprits, Y., Drozdov, A., Yue, C., Haas, B., Grishina, A., Sinnhuber, M., Hanzelka, M., Lyu, X., Feng, H., Jia, J., and Nesse, H.: Energetic Electron Precipitation and Atmospheric Impacts: Implications for Space Weather Monitoring, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18564, https://doi.org/10.5194/egusphere-egu26-18564, 2026.
In the present study, the extreme space weather events in March 1989, November 2003 and May 2024, causing the most severe geomagnetic storms that occurred in the satellite era, are investigated from the point of view of the associated hazard, described in terms of the surface geoelectric field, which in turn can result in geomagnetically induced currents. The surface electric field at the scale of Romania, produced by the variable magnetic field of geomagnetic storms, is determined on the basis of Earth’s magnetic field records from the national geomagnetic observatory and information on the electrical conductivity of the underground. We show that the amplitude of geoelectric field depends on the morphology rather than the amplitude of the geomagnetic disturbance, and is significantly higher in case of March 1989 than in case of the other two events. The maps of the amplitude of the geoelectric field vector on the Romanian territory are presented, constituting the geoelectric hazard map at the country scale.
How to cite: Dobrica, V., Stefan, C., and Demetrescu, C.: Extreme space weather events in March 1989, November 2003 and May 2024 and their associated hazard over Romanian territory, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18674, https://doi.org/10.5194/egusphere-egu26-18674, 2026.
Modelling the Earth’s radiation belts is a key tool in the modern space physics research arsenal. With current state-of-the-art radiation belt models, such as the British Antarctic Survey Radiation Belt Model (BAS-RBM), we can investigate wave-particle interactions and long-term radiation belt behaviour, as well as provide short-term forecasting of radiation belt fluxes. The quality of these model outputs can only ever be as good as the inputs, however.
As with any Fokker-Planck based radiation belt model, the BAS-RBM is driven and moderated by two key inputs: the initial conditions, and the boundary conditions. For both inputs, we require full knowledge of the radiation belt conditions, over the whole simulation space (initial) and over slices at the edges of the simulation space (boundary). Deriving these from in-situ satellite data gives us the best chance at reproducing real-world events and providing accurate predictions, however satellites are notoriously localised, proving only trace measurements throughout the simulation space. One of the biggest limitations of satellite measurements for these purposes is the lack of full electron equatorial pitch-angle distribution (PAD) measurements. Even with satellites such as RBSP and Arase, which purport to provide full PADs, any time they are off-equator we get only partial equatorial PADs. This necessitates some form of “filling” to allow for full pitch-angle distributions in simulation space. Traditionally this is done using regressive models, such as the Shi et al. (2016) PAD model; these models are limited, however.
We present a novel technique for deriving electron pitch-angle distributions using white-box machine-learning, allowing for the generation of full PADs from partial data, derived from electron measurements from the RBSP satellites. We demonstrate the benefits that this model has over traditional approaches, and the impacts that such “realistic” models have on the outputs of the BAS-RBM. Finally, we investigate the potential utility of this model in other areas of radiation belt science.
How to cite: Hendry, A., Glauert, S., and Meredith, N.: Bridging the data gap: Decision tree models for complete pitch-angle distributions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20171, https://doi.org/10.5194/egusphere-egu26-20171, 2026.
Operational space weather forecasting relies on the timely interpretation of complex, multi-domain information, yet the physical and procedural links between successive processes are often weakly captured and rarely exploited in a systematic way. This is particularly critical for Coronal Mass Ejections (CMEs), where uncertainties in detection, characterization, and propagation strongly affect forecast quality.
We present an event-chain framework developed at the Solar Influences Data Analysis Center (SIDC) that links CME observations, derived products, and model outputs across the full Sun–Earth system. The framework consolidates CME detections from SIDC catalogues and automated tools such as CACTUS with heliospheric propagation models including EUHFORIA and drag-based models, and integrates them into a unified forecasting interface used in daily operations.
How to cite: Shukhobodskaia, D., de Patoul, J., Rodriguez, L., Verstringe, F., and Vinoelst, L.: From CME Detection to Forecast: An Event-Chain Approach to Operational Space Weather Forecasting at SIDC, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21932, https://doi.org/10.5194/egusphere-egu26-21932, 2026.
