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There is an established tradition at ESWW for different forecast centres to present a Live Space Weather Forecast, either before or after the morning plenary session. Attending these forecasts enables participants to gain insights into the forecasting process and understand the real-world impact of space weather on end-users. It provides an opportunity to reflect on how we, as a community, can enhance our communication of these complex concepts to end-users and the public. With different forecast centres presenting, attendees benefit from a variety of forecasting perspectives and methodologies tailored to different end-users. Additionally, it offers an excellent opportunity for forecast centres to showcase their expertise and highlight the communication channels they use, fostering a deeper understanding and collaboration within the space weather community.
Conveners: Magnus Wik; Andrew Dimmock
Co-Chairs: Lucilla Alfonsi, Lisa Baddeley (online), Andrew Dimmock
Moderator: David Boteler (NRCan, Canada)
Panellists:
- Peter Löfwenberg (Climate and Space Weather Officer, Swedish Armed Forces)
- Thomas Ulich (Head of Science, EISCAT)
- Yana Maneva (PECASUS for Aviation and SW operations at RWC Belgium)
- Sarah Schultz Beeck (Effects on GNSS in the Arctic, DTU)
- Magnar Gullikstad Johnsen (Leader for TGO and NOSWE within ISES)
As the Arctic undergoes technological expansion, it is emerging as a new frontier for space weather challenges. Space weather is becoming increasingly important to society due to many factors that impact technology and human activity. Although the scientific understanding and mitigation of space weather hazards are a global challenge, they are becoming increasingly crucial for end users in the Arctic regions.
Increasing human activity in high-latitude regions—driven by commercial operations, scientific activities, military operations and auroral tourism—has led to growing reliance on critical infrastructure. This makes the Arctic vulnerable to space weather hazards such as geomagnetically induced currents, HF radio disruptions, GPS inaccuracies, and space debris risks during rocket launches.
This round table will bring together key stakeholders to discuss the impacts of space weather in the Arctic and the challenges faced by end users and service providers. Participants will share their perspectives on operational risks, the need for scientific advancements, and the role of service providers in mitigating space weather hazards to ensure the resilience of current and future Arctic infrastructure and operations in this rapidly evolving technological landscape.
Dr. Sarah Beeck is a tenure-track researcher in the Geodesy and Earth Observation Division at DTU Space, the Technical University of Denmark. Her research centres on Arctic space weather, where she focuses on categorising the different impacts on GNSS to develop methods for local, reliable alerts for GNSS users across the region.
Sarah earned her PhD at DTU Space as part of the SWADO project, where she utilised a network of scintillation receivers along the coast of Greenland to study space weather in the Arctic. As part of her PhD and postdoctoral work, she has collaborated with research groups in other Arctic countries and visited research groups at the University of Bath and the University of Colorado Boulder.
Ground-based space weather monitoring, remote sensing of the LTI region, ionospheric vertical sounding, ionospheric long-term evolution / ionospheric climatology
Affiliation: EISCAT AB (formerly EISCAT Scientific Association), Kiruna, Sweden, and Sodankylä Geophysical Observatory, U Oulu, Sodankylä, Finland.
I work in the European Arctic since 1995, in the last 16 years responsible for observatory measurements of space weather on the ground, as well as incoherent scatter radars. Research interests include high-latitude atmosphere and ionosphere including climatology thereof.
1: main expertise, monitoring, ground-based observations
2: interest in space debris and space traffic (since about a year, with EISCAT)
3: not an export on GNSS and Aviation per se, but I have been working with HF forecasts (and am a glider pilot)
4: no
5: some experience with tourism, limited
6: I have been part of such discussions, but not an expert on mitigation
7: I have been part of such discussions, but not an expert on mitigation
Probably ok, depends a bit more on the topical range of the panel I am asked to participate in.
The structure of the heliospheric background solar wind is shaped by the interaction between slow and fast wind streams. These interactions give rise to stream interaction regions (SIRs) and co-rotating interaction regions (CIRs), which can lead to shocks, compression- and rarefaction regions—key contributors to minor and moderate geomagnetic activity.
A deep understanding of solar wind dynamics, along with the surrounding magnetic field and their origins, is essential for improving the accuracy of space weather predictions.
This session focuses on current research related to the origin, evolution, and space weather effects of slow and fast solar wind. Observations from recent missions like the Parker Solar Probe (PSP) and Solar Orbiter (SolO), along with long-standing missions such as the Solar Dynamics Observatory (SDO) and the Solar Terrestrial Relations Observatories (STEREO), provide valuable data to refine and expand our knowledge in this field.
We invite contributions exploring various topics, including the sources and acceleration mechanisms of slow and fast solar wind, stream interactions, and the magnetic and plasma structure at the source surface and in the inner heliosphere. Additionally, we welcome studies that integrate observational data with modeling to advance our understanding of solar and heliospheric physics in the context of space weather forecasting.
The solar wind is an uninterrupted flow of highly ionised plasma that streams from compact sources at or near the Sun and expands into the whole interplanetary space, being a major driver of space weather phenomena. Understanding the conditions that regulate the formation of the solar wind, its acceleration across the corona, and its transition to the heliospheric propagation regime is key to addressing many open questions in heliophysics. Physical links between observations of surface and coronal events with measurements made in-situ in the interplanetary medium are affected by the interplay between plasma flow and magnetic field, solar wind expansion and rotation, and steady and time-variable phenomena.
I will review on-going efforts in establishing connectivity across the corona and heliosphere, as well as recent advances in solar wind modelling and forecasting. I will address some of the main challenges related to the implementation and validation of connectivity and solar wind models, the delicate balance between physical accuracy and computational performance, as well as the pernicious issues that stem from the scarcity of observations made in between the two boundaries of the Sun–Earth system.
Results from data-driven global solar wind simulations that cover several solar activity cycles will be presented, highlighting relations of magnetic connectivity jumps with solar wind plasma signatures, their occurrence frequency and amplitudes at different epochs of the solar cycle, as well as the benefits of multiple of multiple points of observations (e.g L5 and off-ecliptic) for solar wind modelling.
Periodic density structures (PDSs) are a type of solar wind mesoscale structure characterised by quasi-periodic variations in the density of the solar wind ranging from a few minutes to a few hours. They are trains of advected density structures with radial length scales of LR =100-10,000 Mm. Analysis of case studies shows that PDSs can be compressed when embedded in a stream interaction region (SIR), leading to larger density variations and an increased impact on the magnetospheric and radiation belt dynamics. In this work we perform an extensive statistical study to identify PDSs embedded in SIRs as well as their corresponding frequency and radial length scale distributions. We used an extensive list of 186 SIRs and 1217 embedded PDS events from the entire Wind dataset (1995-2022), spanning more than two solar cycles, to investigate the frequency and radial length scales of PDSs. With the use of wavelet methods, we classified these PDSs as coherent or incoherent, based on the shared periodic behaviour between proton density and the alpha-to-proton ratio, and we derived the corresponding occurrence distributions. We found that 130 out of 186 SIR events have embedded coherent PDSs, which exhibit an increasing probability of occurrence with increasing frequency (up to ~3 mHz). Furthermore, the investigation of radial length scales of coherent PDSs in SIRs reveals significant compression compared to PDSs in the ambient solar wind, as the most probable LR values are 120-130 Mm and 160-190 Mm for the slow and fast compressed solar wind, respectively. The coherent PDS LR decreases with a rate of -0.74, while the corresponding amplitude increases with a rate of 0.74 with increasing solar wind proton density, both following a power law function. Our results indicate that coherent PDSs occur more often than not in SIRs. This is consistent with a picture in which PDSs are formed at the Sun, advected by the solar wind, and enhanced by their interaction with SIRs, while both their radial length scale and amplitude are controlled by the level of compression in the interaction region.
Comets are directly influenced by the solar wind and one of the most spectacular interactions is a tail disconnection event. This can occur when a solar wind structure such as a CME, a faster solar wind stream or a change in magnetic field polarity (Heliospheric Current Sheet crossing) causes the tail of the comet to be completely detached from the nucleus.
Using a combination of amateur observations and STEREO HI data, multiple disconnection events from different comets have been analysed using the HUXt solar wind model. This uses a reduced physics approach and can be used alongside data assimilation to provide solar wind conditions for bodies in the solar system. Comets cover a large range of latitudes and therefore can provide an insight into the solar wind conditions out of the ecliptic plane. The aim of this research is to test how effective the solar wind model is outside of the ecliptic plane, using comet tail disconnection events as in-situ probes over a wide range of locations which are currently unoccupied by spacecraft.
The increasing reliance on technology-driven transportation systems makes the sector highly vulnerable to space weather impacts. Intense solar storms can disrupt GNSS-based navigation, degrade HF and satellite communications, interfere with avionics, and even induce currents in railway infrastructure potentially leading to disruption or harm. Historical impacts, such as railway signalling anomalies in Sweden during a storm in July 1982 and the degradation in positional accuracy of GPS farming equipment for precision agriculture during the May 2024 Gannon storm, highlight the real-world impacts of geomagnetic disturbances on transportation systems. With the approaching solar maximum, understanding these vulnerabilities and developing mitigation strategies is more critical than ever.
This session aims to bring together academics and industry stakeholders to showcase the latest research in space weather impacts on transportation. We invite contributions that assess operational risks, historical case studies, forecasting advancements, and resilience strategies. The session will serve as a platform for interdisciplinary discussion and understanding. Many of these systems are interdependent, and disruptions are likely to occur simultaneously across the board, emphasising the need to foster collaborations between sectors.
This is a timely opportunity to highlight regional case studies and global challenges alike. Due to its high latitude, transportation systems in the Arctic, where reliable transport is essential for both local communities and expanding industries, may be more prone to geomagnetic disturbances even during less intense periods of solar activity. Therefore, this session is especially relevant to the theme for this year’s ESWW.
Understanding the radiation risks associated with space weather phenomena is becoming increasingly critical as aviation and space travel push beyond traditional boundaries. With the rise of commercial aviation at high altitudes, the emergence of suborbital tourism, and long-duration missions in low Earth orbit (LEO), assessing radiation exposure under varying space weather conditions is crucial for ensuring human safety. This study evaluates radiation dose scenarios across three key flight environments: commercial aviation at 12 km, suborbital flights at 88 km, and orbital missions at 400 km, such as those aboard the International Space Station.
Key parameters, including solar radiation intensity and the shielding effectiveness of Earth's atmosphere and spacecraft, are investigated using advanced methodologies, including Monte Carlo simulations. Specifically, the study simulates exposure under a range of geomagnetic storm conditions, from minor disturbances (G1-class) to severe solar events (G5-class), which can dramatically increase radiation levels.
By comparing these altitudes and storm intensities, the research highlights how both altitude and exposure duration influence radiation risks. It concludes with strategic recommendations, such as improved radiation shielding, operational adjustments during heightened solar activity, and in particular the adoption of advanced space weather forecasting technologies.
Geomagnetic storms, as a part of space weather phenomena, are known to degrade the performance of Global Navigation Satellite Systems (GNSS), which are increasingly relied upon in aviation operations. This study investigates whether such disturbances correlate with deviations in GNSS-derived aircraft positions, as broadcast in ADS-B surface messages and passively collected via a ground receiver network for Prague Airport (LKPR), with a focus on major geomagnetic storms during solar cycle 25—especially in May 2024.
Our approach combines a statistical analysis of ADS-B data collected from a multi-receiver ground network with detailed examination of selected aircraft trajectories on taxiways at Prague Airport. Surface-position ADS-B messages were selected due to their suitability for comparison with geodetically surveyed taxiway axes, allowing accurate quantification of GNSS position deviations. The study includes only aircraft confirmed to operate without SBAS corrections. Reference conditions were established using data from geomagnetically quiet periods, and where applicable, deviations were also compared across different phases of the solar cycle, including quiet periods, for the same aircraft.
Several space weather indicators – SYM/ASYM indices, AATR, and TEC, were evaluated using composite metrics. The results show a consistent pattern of increased GNSS positional deviations during disturbed periods, with the magnitude of observed deviations varying according to the specific indicator. These findings confirm the measurable influence of geomagnetic conditions on GNSS positioning integrity.
The findings support the feasibility of leveraging large-scale ADS-B surveillance data for both statistical correlation and targeted detection of GNSS performance degradation due to space weather effects. This approach provides a scalable, infrastructure-independent means for passive monitoring of space weather impacts on GNSS-based navigation integrity and operational awareness in aviation.
Track circuits were introduced in 1872 by Robinson in the United States and were quickly adopted around the world. The basic concept is to divide the railway line into blocks and only allow one train at a time into a specific block. Track circuits are used to detect the presence of a train and control the signals to prevent a following train from entering an occupied block of track. In the 1950s Swedish railways experienced problems with track circuit operation during geomagnetic disturbances. This prompted engineers and consultants working for SJ (the Swedish state railway) to undertake major investigations into modelling geomagnetic interference and measures to protect against geomagnetic disturbances. This work was published in Swedish but has recently been translated into English. This talk will describe the early geomagnetic effects on railways in Sweden, summarize the reports by Alm and Lejdström and Svensson published in 1956, and put their work into a modern context.
Railways rely on interdependent systems for power, navigation, communications, and signalling, many of which are at risk of disruption by space weather. Understanding how and to what extent space weather can impact these systems is crucial to maintaining the safe and punctual operation of railway networks.
Among the many examples of potential impacts, power supply failures would leave trains on electrified lines stranded and can disrupt signalling operations, while geomagnetically induced currents introduced into the AC-electrified overhead line equipment can affect a train’s on-board transformer. Disruption to GNSS can interfere with a high-speed train’s tilt control system, limiting its speed and leading to delays. Loss of service of GSM-R (Global System for Mobile Communications Railway), which is used for communications and is an integral part of the ETCS (European Train Control System), would disrupt operations. DC track circuit signalling systems on AC-electrified lines are susceptible to interference from geomagnetically induced currents, which can lead to delays or, in the worst case, collisions.
This presentation aims to provide an overview on our understanding of space weather impacts on railway systems, highlight recent work, and identify potential avenues for future study.
Progress in space weather research and forecasting depends on an accurate assessment of our current modeling capabilities. As space weather models become more complex and sophisticated, and play an increasingly important role in operational forecasting, the need for an improved validation infrastructure becomes clear. To enable meaningful model validation, this infrastructure must be based on comprehensive, reproducible, and consistent validation protocols. Developing these protocols requires community-wide initiatives to agree on essential physical properties, events or time periods, and metrics. Therefore, close collaboration between scientists, model developers, forecasters, and software engineers across space weather domains is needed.
In this session, we welcome contributions that highlight:
1) Progress in validating and verifying space weather and space science models;
2) Use of multi-spacecraft observations for model validation;
3) Development of software, tools, and repositories that facilitate open validation;
4) Applications of artificial intelligence and machine learning in space weather model validation;
5) Strategies for (near) real-time validation of space weather models and forecasts;
6) Community challenges and initiatives.
Recurring Coronal Holes (CHs) are long-lived structures in the solar corona that survive over multiple solar rotations. They are generating high speed solar wind streams that can have a recurring geoeffective impact. For our study we employ the Potential Field Source Surface (PFSS) and the Schatten Current Sheet (SCS) models incorporated in the coronal modelling domain of EUHFORIA (European Heliospheric Forecasting Information Asset). We evaluate the model efficiency in reconstructing the area of open field associated with 16 recurring CHs and we attempt to study their vertical structure and assess how it changes with each rotation. We determine the optimal parameter space for model initiation for each CH, compare the model output both to EUV and coronagraph white-light emissions, and assess the reconstructed heliospheric plasma and magnetic field conditions using in situ measurements from different positions throughout the inner heliosphere.
Coronal Holes (CHs) are the major source of origin of the fast solar wind, which is its most geo-effective component. Identifying the coronal hole configuration and the large-scale structures of the solar corona enables prediction of the solar wind at 1 AU, thus allowing validation of solar wind models in reference to 1 AU observations. In this work, we contribute to the WindTRUST project, which aims to fill the gaps in France’s ability to predict and protect against the most intense solar events, and we are dedicated to improving numerical simulations of the Sun-Earth system and its interaction with the Parker spiral. We focus on developing a solar wind model validation pipeline based on coronal hole EUV observations. Specifically, we use SDO/AIA images for 193˚A and
composite images (171˚A, 193˚A, & 211˚A), under different solar conditions within solar cycle 24. CHs are detected by identifying the darker regions, which correspond to the low-density and low-temperature regions of the solar corona. Therefore, based on the intensities in the EUV wavelengths, the threshold value corresponding to the segmentation of CH regions requires optimization.
Based on CH contour observations, we develop a machine learning algorithm to determine the optimized threshold value for a fixed time within solar cycle 24. To this end, we use the EZSEG algorithm and the opencv-python library to trace the CH contours, and the optimized threshold values are identified by matching the observed CH area on the solar disk at that time with the CH contour area detected using these two methods. The machine learning algorithm is trained using solar indices data and data from large-scale events such as solar flares and Coronal Mass Ejections during solar cycle 24. Once we identify the optimized threshold values for the CH contours in the
SDO/AIA images, we validate them using a diagnostic test with the CH contours produced from the Potential Field Source Surface (PFSS) model (non-MHD) and the WindPredict (WP) model (Polytropic and Alfv´en Wave) (MHD). Subsequently, we repeat the above systemic procedure to develop a comprehensive automatic validation tool, extending it to include solar cycles 23 and 25 using SoHO, STEREO, and SolO EUV images of the solar corona. Finally, we couple the machine learning model and the validation pipeline to develop an automation tool for solar wind predictions
at 1 AU.
Space weather forecasting needs fast and reliable prediction of the solar wind in the inner heliosphere. The complex physics of the corona, the turbulent heating and acceleration of the solar wind are thus often bypassed using empirical models driven by observations. In 2023, we introduced a novel technique based on white light coronagraph observations made by LASCO C2 to infer the state of the solar wind at 0.1 AU and propagate it to Earth orbit using an MHD model. This model, HelioCast, has been shown to be very efficient during solar minimum (2018), performing better than full MHD models of the corona for a much lower numerical cost. Yet, as the solar cycle rises, fast varying solar magnetic structures renders white light Carrington maps extracted from the coronagraph increasingly difficult to handle. In this work, we assess quantitatively the performance of this method in the rising solar cycle and compare it with other kind of input, for instance obtained through a PFSS extrapolation of the observed solar magnetic field. We test a hybrid approach combining the data from the coronagraph and magnetograms that show more consistent performances from 2018 to 2024. Finally, we assess the effect of these solar wind solutions on CME propagation.
Modern radio telescopes have the ability to probe the inner Heliosphere using propagation techniques such as Interplanetary Scintillation. Such approaches have huge promise for data validation, since they are able to survey such a large volume of the Heliosphere including measurements well out of the ecliptic and at a range of heights. The density of measurements is particularly high for the latest generation of radio telescopes. For example, The MWA and ASKAP, both situated in Western Australia are characterised by an enormous field of view, which provides an unprecedented number of lines of sight.
These observations therefore present a huge opportunity. however, this approach also comes with enormous challenges, particularly for a community that is used to working with in-situ measurements. The principle difference is that such observations are integrated along the line of sight. IPS also does not directly sense density or velocity, but some proxy for these quantities, which must then be scaled using empirical relationships.
I will describe some of the IPS datasets we have (which we hope to make available to the community in the near future), and how they can be used both to measure the background solar wind, and detect and characterise Stream Interaction Regions and CMEs. I will discuss some of the ways that these datasets may be used for validation; for example, by forward-modelling IPS and other observations from 3D simulations, or by using tomographic techniques.
Geomagnetic indices such as the Dst index are relevant for quantifying global geomagnetic storm impacts, yet their operational forecasting remains constrained by reliance on solar wind measurements from L1-based spacecrafts like ACE or DSCOVR. Current models depend on near-real-time L1 data to make the predictions, creating a fundamental limitation: the magnitude of the geomagnetic storm cannot be forecasted until solar wind disturbances reach ACE. This delay inherently restricts the forecast horizon, as extending predictions beyond the time it takes for storms to propagate from L1 to Earth risks excluding the critical initial phase of the disturbance.
We have evaluated the practical limits of forecasting the Dst index using L1 data. We quantify this constraint by analyzing recent intense storms (May and October 2024) with a neural network trained on ACE plasma and IMF parameters to forecast the Dst index 1 to 6 hours ahead. We observe that predictions initialized before a storm’s arrival at ACE fail to capture the onset and main phase of the storm. Our analysis reveals a reliable forecast horizon of 3 hours, beyond which predictive skill for the main phase degrades sharply. However, longer horizons (>6 hours) remain viable for forecasting the storm recovery phase.
During the May storm of 2024, at 2024-05-10 16:00:00 UTC the disturbance had not yet reached L1 (the first measurement of an active proton speed was at around 16:40). As such the Dst forecasting model predicted little to no variation in the index, since the situation, at that time, was similar to inactive time. However, the Dst will take values of +25, +66, -33, -131, -157 and -277 in the next 6 hours. This presents a critical limitation: a disturbance will start in the upcoming hours, but it has not yet reached ACE, therefeore it is unfeasible to forecast. At 2024-05-10 17:00:00 UTC the first few measurements of the disturbance have already reached L1 and the model starts to properly forecast the main phase of the storm. However, the model requires another hour of data to make a reliable forecast of the disturbance. A similar situation can be observed during the October 2024 storm, where for the longest time horizon the start of the main phase of the storm was not forecasted reliably.
In contrast, the forecasts of the model up to 6 hours ahead show a good accuracy with the Dst index during the recuperation times, as for those changes all the relevant information has already been measured by ACE.
The extreme space weather events of Solar Cycle 25 highlight the urgent need for a comprehensive, interdisciplinary approach to understanding solar-Earth interactions. This session aims to bring together experts from solar and heliospheric physics, as well as
magnetospheric, ionospheric, and atmospheric physics to investigate the formation, propagation, and impacts of solar storms. By studying the magnetic connectivity and dynamics of the source regions leading to solar flares, and eruptions accompanied by the solar energetic particle events, we seek to understand how solar activity influences interplanetary space and interacts with the planetary environment. The propagation of coronal mass ejections and their interactions within the heliosphere are crucial for assessing the extent of space weather disturbances. The session will also address the
broader space implications of these extreme events, as the impact of geomagnetically induced currents on engineering infrastructure remains an important topic for space weather mitigation strategies. We encourage you to submit abstracts on events covering all aspects of space weather, from the Sun to the Earth, and their impacts on other planetary
environments. We welcome modeling and observational studies. By fostering interdisciplinary collaboration, this session aims to improve our understanding of space weather as a system-wide phenomenon and strengthen links between research communities.
As Solar Cycle 25 reaches its peak of activity, Solar Orbiter is observing a substantial increase in solar flares, coronal mass ejections (CMEs), and solar energetic particles (SEPs). Specifically, the Energetic Particle Detector (EPD) on board Solar Orbiter has been tracking and characterizing the rise in SEP activity over the past five years. This paper focuses on the intensities of suprathermal and energetic particles from 2020 through 2025. Both electrons, ions, and 3He particles show a notable increase, which aligns closely with other solar phenomena. We compare the SEP flux observed during this cycle with the measurements from Cycles 23 and 24, as recorded by ACE. The results reveal that the flux levels in Cycle 25 are significantly higher than those of Cycle 24, and comparable to those observed during Cycle 23. This surge in solar activity is filling the heliosphere with high-energy SEP particles, which are influencing the entire solar system, including Earth.
Energy spectra of solar energetic particles provide valuable insights into particle acceleration processes. However, also transport effects have been found to potentially alter the spectra, especially in the case of solar energetic electron (SEE) events, which commonly show broken power-law shapes. We analyze the energy spectra of the 50 most intense SEE events measured by Solar Orbiter’s Energetic Particle Detector (EPD) between December 2020 and December 2022. These measurements provide new opportunities to understand the physics shaping SEE spectra due to EPD's unprecedented energy resolution and the spacecraft's varying distance to the Sun. We investigate the shape of SEE peak-intensity spectra by fitting them with various mathematical models. We find five different spectral shapes in our sample: a single power-law (3 events), a double power-law (8 events), a double power-law with exponential cutoff (1 event) and two types of triple power-laws, which have not been observed before: a knee-knee (KK, 10 events) and an ankle-knee (AK, 16 events) triple power-law. Surprisingly, no significant correlations with radial distance were identified, but the observed spectral shapes show an ordering with the longitudinal separation between the spacecraft and the associated solar flare.
A comparison of our results with different transport modeling studies suggest that the two breaks of the KK triple power-law spectra arise from distinct effects, Langmuir-wave generation and pitch-angle scattering respectively, while the AK triple-power law could be due to Langmuir-wave generation including part of the electron beam gaining energy as Langmuir waves are absorbed. We also find evidence for the double power-law events being formed by a merging of the first and second break of KK triple power-laws.
Systematic study of solar energetic particles (SEPs) provides the necessary basis to understand their origin, acceleration and propagation in the interplanetary space. It is believed that SEPs are produced following solar eruptive processes, such as solar flares and/or coronal mass ejections. SEPs can be accelerated to the GeV/n range, yet the bulk are with energies of about 100 MeV/n. In the case when SEPs are in the GeV range, they can induce an atmospheric cascade in the Earth’s, atmosphere which secondary particles can be registered by ground-based detectors, such as neutron monitors (NMs). This class of events is called ground-level enhancements (GLEs). A quite interesting event occurred on 11 May 2024. It was observed by NMs and particle detectors aboard spacecraft in near-Earth orbit. The event was observed during the deep phase of a significant Forbush decrease and one of the strongest geomagnetic storms. The disturbed interplanetary space and geomagnetic conditions make the analysis of this event particularly challenging. Here we present results from observations and analysis of this event focusing of NM data records. We derived the spectral and angular characteristics of the SEPs leading to this GLE. We modeled the particle propagation in the Earth’s magnetosphere and atmosphere. The solar protons spectra and pitch angle distributions were obtained in their dynamical development throughout the event. Several space weather impacts are discussed.
The fleet of spacecraft distributed in the inner heliosphere during May-June 2024 offered us the unique opportunity to analyze, over a wide range of heliolongitudes, the effects produced by the complex sunspot group formed by the NOAA active region (AR) 13664 (later numbered AR 13697).
The intense level of solar activity recorded from 2024 May 8 to 2024 June 21 led to unusually elevated energetic particle intensities observed by a number spacecraft at heliocentric distances $<$1 au including Solar Orbiter, Parker Solar Probe, STEREO-A, and near-Earth spacecraft.
The result was a long-time interval ($>$40 days) with elevated ($>$10 MeV) proton intensities observed over at least a heliolongitude span of $\sim$170$^{\circ}$.
Among this intense period of activity, a major solar energetic particle (SEP) stood out because of its large intensity, its association with both a fast ($\sim$1500 km s$^{-1}$) halo coronal mass ejection (CME) and an intense X-ray flare (with an estimated class X16.5) occurring at a longitude $\sim$170$^{\circ}$ with respect to Earth
The peculiar proton energy spectra measured near Earth (flattening over the energy range 30-80 MeV), the formation of an heliospheric energetic particle reservoir displaying similar particle intensities that extended over a longitude span of $\sim$170$^{\circ}$ and for a period of $\sim$2 weeks makes this event exceptional.
We propose several mechanisms to explain the spread of SEPs during this intense event and the formation of this energetic particle reservoir, including particle acceleration over a broad CME-driven shock, efficient particle transport across the interplanetary magnetic field, and particle reflection and redistribution from some distance beyond 1~au where prior CMEs merged.
Solar energetic particles (SEPs) are typically accelerated during solar eruptions and propagate along magnetic field lines in the inner heliosphere. These eruptions include shockwaves driven by coronal mass ejections (CMEs) and magnetic reconnection in solar flares. Since spacecraft rarely reach the acceleration regions close to the Sun, measured SEP intensities reflect a combination of acceleration, injection, and interplanetary transport.
This study characterizes the longitudinal spread of the SEP event on 28 May 2021 by fitting Gaussian curves at regular time intervals to particle intensities as a function of spacecraft position. This offers unprecedented detail compared to earlier studies, which were limited to only three observers, while we now benefit from a novel fleet: including Solar Orbiter, Parker Solar Probe (PSP), STEREO-A, Wind, and SOHO.
To support the interpretation of the evolving profiles, we use a 2D SEP transport model to infer the particle acceleration parameters.
Through this approach, we aim to better identify signatures associated with the two main acceleration phenomena: solar flares and CME-driven shockwaves and characterize transport effects.
The structure of the heliospheric background solar wind is shaped by the interaction between slow and fast wind streams. These interactions give rise to stream interaction regions (SIRs) and co-rotating interaction regions (CIRs), which can lead to shocks, compression- and rarefaction regions—key contributors to minor and moderate geomagnetic activity.
A deep understanding of solar wind dynamics, along with the surrounding magnetic field and their origins, is essential for improving the accuracy of space weather predictions.
This session focuses on current research related to the origin, evolution, and space weather effects of slow and fast solar wind. Observations from recent missions like the Parker Solar Probe (PSP) and Solar Orbiter (SolO), along with long-standing missions such as the Solar Dynamics Observatory (SDO) and the Solar Terrestrial Relations Observatories (STEREO), provide valuable data to refine and expand our knowledge in this field.
We invite contributions exploring various topics, including the sources and acceleration mechanisms of slow and fast solar wind, stream interactions, and the magnetic and plasma structure at the source surface and in the inner heliosphere. Additionally, we welcome studies that integrate observational data with modeling to advance our understanding of solar and heliospheric physics in the context of space weather forecasting.
The ambient solar corona and solar wind plays an essential role in space weather at Earth and throughout the solar system. The magnetic field is a key aspect of describing the solar wind ambient state, and solar wind properties are closely tied to magnetic structure. The field is most readily measured in the photosphere, so models must extrapolate this field out into the solar wind. We describe a continuously updated, time-evolving MHD model of the solar corona and inner heliosphere, extending from the upper chromosphere to 1 AU, run for a month of evolution during the time leading up to the April 8, 2024 total solar eclipse. The model assimilates HMI magnetograms into a surface flux transport (SFT) model, which is used to create full-Sun boundary conditions for the MHD model. The time-evolving model is highly dynamic, with many small-scale eruptions. We compare the model results with snapshots from potential field and steady-state MHD calculations using the same boundary conditions, including differences in the magnetic connectivity predicted by the models at L1 and other spacecraft locations.
Research Supported by NASA and NSF.
The validation of the 3D MHD model EUHFORIA (EUropean Heliospheric FORecasting Information Asset, Pomoell & Poedts, 2018) at near-Sun distances was made possible with the availability of solar wind data from the Parker Solar Probe (PSP) mission. We carried out solar wind simulations for the first ten perihelion encounters by PSP, each covering a period of approximately three weeks and spanning radial distances of 0.1 – 0.4 au. However, the modeled results show large discrepancy with the PSP data, and one of the possible reasons for this could be the simple coronal model used in EUHFORIA. The default coronal model of EUHFORIA uses the potential field source surface, Schatten current sheet model and Wang-Sheeley-Arge (WSA, Arge et al., 2003) description of the solar wind to provide the plasma and magnetic conditions at the inner boundary (0.1 AU) of EUHFORIA.
The recently developed global coronal model, COCONUT (The COolfluid COroNa UnsTructured, Perri et al., 2022) is an ideal-MHD model which has already provided some promising first modelling results (Kuźma et al. 2023). In this work, we employ the COCONUT coronal model together with the EUHFORIA heliospheric model. We then compare the solar wind modelling results from this set-up with the standard EUHFORIA coronal+heliospheric model setup, by evaluating their agreement with the in situ observations from PSP for its first ten close encounters.
In this work we incorporate Solar Orbiter’s Polarimetric and Helioseismic Imager (PHI)
Full Disc Telescope (FDT) observations into the Air Force Data Assimilative
Photospheric flux Transport (ADAPT) model to construct more complete global solar
photospheric maps. We feed these maps into the Wang-Sheeley-Arge (WSA) model to
reconstruct the solar corona and perform solar wind simulations for a period of two
months in 2024 at multi-spacecraft locations (Solar Orbiter, PSP, ACE, STEREO-A). We
assess the quality of our predictions, and compare our results when no FDT data have
been employed in order to understand how the addition of far side information affects
the open magnetic field topologies on the Sun, their connectivity with various spacecraft
of interest, the shape and structure of the heliospheric current sheet, as well as the
solar wind predictions at different points in the interplanetary space. Our results
demonstrate the value of incorporating far-side information in improving the heliospheric
modeling and forecasting globally, as well as the significance of 4pi continuous
monitoring of the Sun for more reliable space weather predictions overall.
The presence of energetic electrons in the heliosphere is associated with solar eruptions, but details of the acceleration and transport mechanisms are still unknown. We explore how electrons interact with shock waves under the assumptions of shock drift acceleration (SDA), diffusive shock acceleration (DSA), and stochastic shock drift acceleration (SSDA). Consideration of the shock wave parameter space, such as shock speed, shock obliquity, shock thickness, and plasma density upstream of the shock, helps determine electron spectra and their highest energies. With suitable simulation parameters, the model is able to accelerate thermal electrons to relativistic energies and, additionally, to produce an electron beam upstream of the shock wave, a requirement for the type II radio burst seen in radio observations associated with shock waves and particle acceleration.
This presentation delves into the results of the presented model in regards to electron acceleration and transport within shock waves, contributing to our understanding of solar and interplanetary phenomena and their practical applications in space weather forecasting.
Additionally, the model is developed to be an easy-to-use open source tool for understanding observations of high energy electron populations and the ensuing highly localized radio bursts, integration to other heliosphere plasma models through wrappers, and teaching modeling of particle acceleration in a high-performance computing setting.
This study has received funding from the European Union's Horizon Europe research and innovation programme under grant agreement No 101134999 (SOLER). The presentation reflects only the authors' view and the European Commission is not responsible for any use that may be made of the information it contains.
The Sunspot Number (SN; Clette and Lefèvre, 2016 ) and Group Number (GN; Chatzistergos et al., 2017) series are the only direct time series (1610- present) that trace the long-term variations of solar activity over the past centuries. These records are crucial not only for solar/stellar physics and space weather studies but also for assessing the Sun's influence on Earth's climate.
While modern observations provide better links with space weather effects, SN and GN remain the longest direct observations of solar activity, and thus, are an indispensable bridge linking past and present solar behavior.
In 2016, an international team led a major update of the existing SN/GN series. However, issues remain and a decade after the release of SN version 2.0, efforts to refine sunspot calibrations continue, leading to several new versions of GN (Clette et al., 2023). Current work is focused on updating the GN database (following Vaquero et al., 2016), culminating in the development of a new SN database for historical data and the subsequent reconstruction of GN and SN, paving the way for version 3.0.
This session welcomes presentations on all aspects of historical sunspot observations, including (but not limited to) analyses of characteristics of the sunspot series, performance of cross-calibration techniques, recovery and correction of historical sunspot records, and also comparisons of sunspot series with other solar activity indices. By exchanging ideas, through presentations and discussions, we can strengthen our collective effort to make both time series more accurate, understandable and accessible to the scientific community.
Understanding long-term solar activity is key for advancing our knowledge of the solar dynamo and improving space climate forecasting capabilities. In this contribution, we present a comprehensive revision of sunspot records from two key periods: the early telescopic era and the decades following the Maunder Minimum.
First, we reanalyze Christoph Scheiner’s observations from Rosa Ursina and Prodomus, correcting errors such as misinterpretations in current databases. Our new group counts clarify the shape of the solar cycle in the 1620s and reduce its maximum amplitude by 20%.
Second, we revise records by Johann Leonhard Rost and Sebastian Alischer, identifying that previous datasets often confused individual spots with groups. This led to overestimated activity levels during Solar Cycles −3 and −4. Our corrected data suggest a more gradual recovery from the Maunder Minimum, in agreement with proxy-based reconstructions (e.g., cosmogenic isotopes).
Parisian solar observations constitute the richest sunspot data set covering the Maunder minimum. Thirty years ago, sunspot latitudes were reconstructed by Elisabeth Nesme-Ribes, but these data have been lost. We present an extensive set of newly reconstructed sunspot parameters for both sunspot groups and individual sunspots. Based on the hand-written notes in the observational journals by Philippe de La Hire we carefully evaluated areas (both umbra and penumbra), latitudes, longitudes, as well as other parameters. We also estimated the differential rotation, and the fraction of sunspot groups obeying or violating Joy's law.
Within four centuries of instrumental sunspot observations, the Maunder Minimum is known as a unique period with extremely small solar cycles and enhanced hemispheric asymmetry of the reported sunspot groups, as well as the apparent loss of the solar coronal streamers. Sunspot group positions have been discussed mostly on the basis of the French and German observers’ records. Some researchers express doubts because of a possible selection bias for the sunspot group detections in this period that might have affected both of these aspects. This presentation aims at independently assessing these possibilities on the basis of the comprehensive analyses of the extensive records of the English sunspot observations. These English records allow us to confirm a concentration of the reported sunspot groups in the southern solar hemisphere, supporting the results of the above-mentioned previous studies. We have also computed the active day fraction, estimated the international sunspot number at that time on this basis, and found their solar cycle amplitudes extremely small, even in comparison with Solar Cycle 24 or the Dalton Minimum. Our result indicates that the Maunder Minimum was not a secular minimum but rather a grand minimum.
The reconstruction of the Sunspot Number (SN) series is a cornerstone for long-term solar activity studies and space climate research. In recent years, the exploitation of historical solar observations has proven essential for identifying inconsistencies, improving calibration, and extending the SN record. This presentation reviews recent advancements in the use of historical documents—such as early telescopic drawings, textual records, and neglected archival sources—to refine the SN series. We will discuss current efforts to reanalyze past observations of the solar disk, highlight outstanding tasks and challenges for the community, and show how these studies contribute to better solar activity reconstructions and predictions. Additionally, we will explore the impact of this work on science communication and education. Preliminary results from ongoing, unpublished research will also be presented to foster discussion and collaboration.
Progress in space weather research and forecasting depends on an accurate assessment of our current modeling capabilities. As space weather models become more complex and sophisticated, and play an increasingly important role in operational forecasting, the need for an improved validation infrastructure becomes clear. To enable meaningful model validation, this infrastructure must be based on comprehensive, reproducible, and consistent validation protocols. Developing these protocols requires community-wide initiatives to agree on essential physical properties, events or time periods, and metrics. Therefore, close collaboration between scientists, model developers, forecasters, and software engineers across space weather domains is needed.
In this session, we welcome contributions that highlight:
1) Progress in validating and verifying space weather and space science models;
2) Use of multi-spacecraft observations for model validation;
3) Development of software, tools, and repositories that facilitate open validation;
4) Applications of artificial intelligence and machine learning in space weather model validation;
5) Strategies for (near) real-time validation of space weather models and forecasts;
6) Community challenges and initiatives.
Electrons in Earth’s radiation belts exhibit significant variability in both space and time during geomagnetic storms, posing potential risks to satellites and astronauts. Physics-based models aim to describe the behavior of energetic electrons in the radiation belts but often face challenges due to uncertainties and inaccuracies, especially in the initial and boundary conditions. Data assimilation addresses these limitations by integrating satellite observations with model predictions, incorporating all available information to produce a more reliable reconstruction. This study evaluates the performance of the data-assimilative 3D Versatile Electron Radiation Belt code (VERB-3D) using data from three independent satellite missions: Arase and GOES for assimilation and Van Allen Probes for validation. The datasets were carefully cleaned and normalized to ensure compatibility. The results confirm that the model accurately reproduces radiation belt dynamics, highlighting the effectiveness of data assimilation techniques for space weather research and improving our understanding of the radiation belt environment.
The ionosphere and thermosphere are critical regions of Earth’s upper atmosphere, playing a significant role in technologies such as radio communication and Global Navigation Satellite Systems (GNSS). However, their variability, driven by solar and geomagnetic activity, as well as interactions with neutral molecules and the lower atmosphere, makes accurate prediction of their state particularly challenging. Reliable ionospheric and thermospheric models are essential to mitigate risks to terrestrial technologies, prompting significant investment in upper atmosphere nowcasting and forecasting. The Advanced Ensemble Networked Assimilation System (AENeAS) is being deployed at the UK Met Office as an operational model for nowcasting and forecasting the coupled ionosphere-thermosphere system. It is built on the Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIE-GCM) and enhanced through data assimilation of GNSS, ionosonde, and radio occultation observations, amongst others. This study presents a systematic validation of AENeAS under a wide range of geophysical conditions, with a particular focus on the design and application of robust validation methods. We evaluate model performance across different latitudes and under a range of solar and geomagnetic forcing scenarios, using independent observational datasets not assimilated into the model. Comparisons are also made with other established upper atmosphere models to benchmark AENeAS’s relative performance. Special attention is given to how different assimilated data types influence output accuracy, and how ensemble spread and bias evolve in time and space. We also examine the impact of key tuning parameters on model performance, with the aim of informing future operational improvements. This validation effort benchmarks AENeAS against established observational datasets, including ionosonde and GNSS TEC data, and against other upper atmosphere models, such as its background model TIE-GCM, the International Reference Ionosphere (IRI), and the Empirical Canadian High Arctic Ionospheric Model (E-CHAIM). This work provides a clear assessment of its capabilities and limitations, as well as representing a key step in strengthening confidence in operational upper atmosphere forecasting and guiding AENeAS’s continued development.
In this study, we present the results of a comprehensive assessment of thermosphere models under geomagnetic storm conditions, defined by a geomagnetic index ap ≥ 80. This work builds upon Bruinsma et al. (2024, DOI: 10.1051/swsc/2024027), which evaluated the performance of empirical and physics-based thermosphere models during storm periods. Utilizing models hosted at NASA's Community Coordinated Modeling Center (CCMC), we conduct an unbiased evaluation of their performance. Model simulations are analyzed across four storm phases: pre-storm, onset, recovery, and post-storm, relative to the time of peak ap. After applying a debiasing procedure based on the pre-storm phase, we compare the modeled neutral density data to high-fidelity observational datasets from TU Delft, derived from CHAMP, GOCE, GRACE, GRACE-FO, and SWARM-A satellites.
Key performance metrics, including mean density ratios, standard deviations, and correlation coefficients, are used to construct thermosphere model scorecards. These scorecards provide a valuable resource for users to identify the most suitable model for specific applications. The ultimate objective of this study is to establish a near-real-time scorecard for thermosphere model assessment at NASA/CCMC, employing consistent and standardized metrics.
The ionosphere is a significant error source in radio communication and modern GNSS satellite navigation. Variations in electron density, especially during the geomagnetic storm, can lead to signal distortion, fading, and transmission delays. Therefore, accurately predicting and forecasting ionospheric conditions is essential for mitigating these effects, which requires the use of advanced ionospheric models. Recent advancements in modeling techniques and satellite missions have significantly enhanced our understanding of the near-Earth space environment, leading to more accurate global ionospheric models. The NASA Community Coordinated Modeling Center (CCMC) hosts a variety of state-of-the-art ionospheric models developed by the aeronomy community, offering valuable resources for researchers and practitioners. At CCMC, we have initiated a model validation campaign to assess the model performance during geomagnetic storms across solar cycles 23 to 25. This study focuses on the 10-12 May 2024 Gannon storm event, assessing various ionospheric models, including empirical, physics-based, and data assimilation models hosted by CCMC, National Ocean and Atmospheric Administration, and National Cheng Kung University. Model validation is conducted using GNSS single-frequency precise point positioning (PPP). The use of the GNSS single-frequency PPP enables an assessment of the practical effectiveness of the ionospheric models developed by the aeronomy community. This study aims to present a novel approach to model validation through application-based methods.
The session focuses on the state-of-the-art understanding of the complex mechanisms ruling the Magnetosphere-Ionosphere-Thermosphere (M-I-T) coupling and how they translate into space weather impacts. Such an understanding is fundamental for the developing effective countermeasures against disruption, failure and deterioration of vulnerable technologies, including GNSS critical applications, HF/VHF/UHF radio communications and LEO satellite operations. It is essential to improve the prediction of both the underlying physical phenomena and how these are related to space weather impacts. This improved understanding is crucial for better forecasts, warnings, and mitigate measures for adverse space weather effects. Other crucial aspects of M-I-T coupling are the interhemispheric symmetric/asymmetric response to variable drivers, vertical coupling and coupling between different latitudinal regions which, if properly predicted, could support regional space weather modelling. This session seeks to encourage and foster dialogue between researchers studying the underlying physical phenomena and operators seeking to mitigate space weather impacts. As such, contributions are invited which address any aspect of M-I-T coupling and associated threats to systems at regional and global scales.
Auroral forms can provide information not only on the state of near-Earth space but also on conditions in the lower-thermosphere–ionosphere. The so-called dune aurora, consisting of brighter stripes forming a wave-like pattern in the dim, diffuse green aurora, has been hypothesised as being an optical signature revealing the presence of a large-scale atmospheric wave above or near the mesopause. However, only a few dune aurora events have been studied to date, leaving many open questions regarding the nature of this phenomenon. We carry out the first statistical analysis of dune aurora events by collecting citizen science observations of the dunes since 2000 using the Skywarden (https://taivaanvahti.fi) database of observations. From a total of 289 dune aurora observations made during 56 different events by citizen scientists from Northern Europe, North America, Australia, and New Zealand, we investigate the distribution of dune events as a function of location, month, local time, solar wind and interplanetary magnetic field conditions, and geomagnetic activity. We compare those distributions to that of all the aurora observations reported in Skywarden since 2000. We also estimate the duration of dune events based on the available observations, and we investigate a possible relationship between dune aurora and equivalent current patterns derived from ground-based magnetometer measurements. We present preliminary results and discuss their consistency with the hypothesis that the dunes reveal the presence of an atmospheric wave at about 100 km altitude.
The center of the South Atlantic Magnetic Anomaly (SAMA) is located in the southern part of Brazil, where enhanced energetic particle precipitation (EPP) can occur, particularly during strong geomagnetic storms. Consequently, the sporadic auroral E layer (Esa), a feature typically observed in ionograms from high latitude auroral regions, can also be detected at stations near the center of the SAMA, especially during the recovery phase of such storms. However, this study reports the detection of the Esa layer during the recovery phase of the May 10, 2024, geomagnetic storm in Belém (BLM, 1.45° S, 48.50° W), an equatorial magnetic station. This observation suggests that the EPP produced anomalous ionization levels that extended beyond the central region of the anomaly, reaching areas at the edge of the SAMA. Moreover, the presence of disturbed electric fields appeared to weaken the equatorial electrojet (EEJ), thereby inhibiting the formation of plasma irregularities typically observed in equatorial regions. As a result, unusually dense sporadic E layers were detected during the daytime, an unexpected phenomenon at equatorial magnetic latitudes. These findings are significant, as they indicate that equatorial regions in Brazil can temporarily exhibit auroral-like ionospheric behavior. Such occurrences have important implications for radio wave propagation and the performance of communication and navigation systems, particularly during geomagnetically active periods.
Geomagnetic storms during the declining phase of the solar cycle 24 were studied using the total electron content (TEC) data obtained from three geodetic receivers located in Portugal: Lisbon (Continental Portugal), Furnas (Azores) and Funchal (Madeira). Two of the receivers (Lisbon and Furnas) are located at about the same latitude (~39ºN) while the third receiver (Funchal, ~33ºN) is positioned quite well to the south.
In this work, we study TEC variations during geomagnetic storms (Dst <= -50 nT) observed between 2015 and 2019. We draw conclusions on the strength type of the ionospheric response, and on the geomagnetic and other conditions that might favour a significant ionospheric storm in the studied region. Special attention is given to the analysis of the longitudinal and latitudinal (dis)similarities in the ionospheric variations, observed at the three studied locations.
The main drivers of geomagnetic storms are interplanetary coronal mass ejections (ICMEs) and solar wind high-speed streams (HSSs) with stream interaction regions (SIRs). In this presentation, we show one example of both cases and study their effects on the high-latitude ionosphere-thermosphere (I-T) system. We use a multi-instrument approach with ground-based instruments (e.g., the EISCAT Svalbard and Tromso incoherent scatter radars, GNSS TEC receivers, and magnetometers) as well as satellites (e.g., TIMED, Swarm, and GRACE-FO). The 10-13 May 2024 ICME driven storm was a superstorm (minimum SYM-H -518 nT), while the HSS/SIR drove only a moderate storm on 14-20 March 2016 (minimum SYM-H -69 nT). Both storms caused a depletion in F-region electron density and total electron content (TEC), increase in the ion temperature, and enhanced neutral density at the low-Earth orbit (LEO). While the changes during the May 2024 superstorm were more drastic than during the March 2016 storm, the duration of the changes was longer in the latter one. In the polar cap, 60% of the TEC disappeared during the whole day on 11 May 2024 and the depletion lasted to some extent 3 days, while in March 2016 the depletion in TEC lasted 6 days and was 25-50%. During the superstorm, LEO densities were increased by 300-500%, but in the moderate storm by 100%. The processes behind the I-T changes stem from increased Joule heating in the ionosphere, which we estimate for both storms with a new method utilizing data from SuperDARN radars, Ampere project field-aligned currents and SuperMAG magnetometers.
Drop-in between 18.30-19.00
We’re delighted to introduce a new feature at this year’s ESWW: Plenaries Showcasing Parallel Sessions. These two special plenary sessions will highlight standout contributions from the parallel programme. Each session will feature two distinguished presentations, nominated by the conveners of the originating parallel session. This is an opportunity for selected presentations to gain enhanced visibility and recognition and to promote the originating parallel session to a broader audience.
By highlighting these talks in a plenary setting, we aim to celebrate excellence across the programme and encourage cross-disciplinary engagement. Presenting authors whose contributions are selected will be formally recognised by the Programme Committee and awarded a certificate of distinction.
Each session will include two talks, 25 minutes each including Q&A.
Solicited talk originating from parallel session OPS.
A geomagnetic storm of similar intensity to the historic Carrington event of 1859 would present a serious risk to ground-based technological systems, particularly high-voltage power transmission networks. In a previous study, Blake et al. (2021, Space Weather, doi:10.1029/2020SW002585) reconstructed the magnetic field variations observed at Colaba, India, during the Carrington storm, and provided global estimates of external magnetic field variations at the surface of the Earth.
Building on these results, we apply a first-principles modelling method to calculate the geoelectric field induced in the Fennoscandian region. This method incorporates a high-resolution, three-dimensional model of the Earth's subsurface electrical conductivity to simulate how the ground responds to geomagnetic disturbances.
To evaluate the severity of a potential Carrington-class storm, we compare these estimates to modelled geoelectric fields from the October 2003 geomagnetic storm (commonly known as the Halloween storm) - one of the most powerful space weather events of the last 100 years. The Halloween storm provides an ideal reference due to the availability of dense, high-quality magnetometer observations across Fennoscandia.
Our results show that the peak geoelectric fields induced by a Carrington-level storm in Fennoscandia could be approximately four to ten times greater than those induced during the Halloween event. These findings highlight the significant geoelectric hazard posed by extreme geomagnetic activity, emphasizing the vulnerability of high-latitude regions with critical infrastructure.
Solicited talk originating from parallel session SWR1.
We present a comprehensive catalogue of solar energetic electron (SEE) events, derived from joint observations by remote-sensing and in-situ instruments aboard the Solar Orbiter spacecraft. The Energetic Particle Detector (EPD) is used to characterise the properties of energetic electrons in situ and to estimate their injection times at the Sun. The timing, location, and intensity of associated X-ray flares is obtained using the Spectrometer/Telescope for Imaging X-rays (STIX), while the Extreme Ultraviolet Imager (EUI) provides complementary observations of the flare evolution and eruptive phenomena. The Solar Orbiter coronagraph (Metis) and heliospheric imager (SoloHI) are employed to characterise potentially associated coronal mass ejections (CMEs). Type III radio bursts detected by the Radio and Plasma Waves (RPW) instrument are used to connect the eruptive solar events to the SEE events observed in situ. We present the catalogue's contents, and the methodology employed to determine key parameters. Finally, we discuss statistical results from the first release of this catalogue.
There is an established tradition at ESWW for different forecast centres to present a Live Space Weather Forecast, either before or after the morning plenary session. Attending these forecasts enables participants to gain insights into the forecasting process and understand the real-world impact of space weather on end-users. It provides an opportunity to reflect on how we, as a community, can enhance our communication of these complex concepts to end-users and the public. With different forecast centres presenting, attendees benefit from a variety of forecasting perspectives and methodologies tailored to different end-users. Additionally, it offers an excellent opportunity for forecast centres to showcase their expertise and highlight the communication channels they use, fostering a deeper understanding and collaboration within the space weather community.
Space weather is largely driven by the drastic and sudden evolution of magnetic structures in the Sun. Sometimes such transients lead to the sudden release of magnetic energy in the form of radiation or mass ejections. In other cases, newly formed or emerging structures alter the equilibrium of a magnetic complex, triggering eruptions. While the study of the magnetic field in the solar atmosphere remains a significant challenge for observations and models, understanding these mechanisms is essential to improve our space weather prediction capabilities. Magnetic structures, such as flux ropes, filaments/prominences and coronal loops form as part of active regions and along polar inversion lines. These structures evolve dynamically across the layers of the solar atmosphere, from the photosphere to the corona, and their evolution can culminate in eruptive events. Many theories based on observations (from new instruments such as PHI, EUI, METIS on-board Solar Orbiter) or numerical simulations have been put forward to explain how they trigger space weather events. Moreover, such mechanisms in the solar corona are the only close and observable examples of several plasma processes (e.g. magnetic reconnection or magnetic confinement) that hold the key to a deeper understanding of plasma physics. In this session, we will host contributions that show the current state of the art of observation and modelling of the solar atmosphere that illustrate the role of these magnetic structures and how their evolution affects space weather and how they can be used to help to improve our forecasts.
Magnetic flux ropes are ubiquitous features observed in the low corona and propagating through the solar atmosphere. They are formed by combined action of the magnetic field of the Sun and its internal processes, observed remotely and in situ. Flux ropes are one of the main components of Coronal Mass Ejections, drivers of major geomagnetic storms, and they are therefore of high importance in space weather. One of the intrinsic properties of the magnetic flux rope structure is a twist which quantifies the rotation of the magnetic field lines around its axis. Twist is also one of the key input parameters for some of the most advanced heliospheric MHD models simulating propagation of CMEs. Despite its importance, since its estimation is not a simple task, in most studies by default, only an average value of this parameter is used.
The twist parameter has a significant impact on the CME modelling results, in particular on the CME speed and the intensity of its internal magnetic field. It is therefore important to understand what the optimal method for the estimation of twist is, and what are the impacts of its variations in the context of space weather forecasting.
In this work, we validate a straightforward method for deriving the twist parameter based on observations, for its use in the forecasting workflow. We consider the relationship between the twist and the ratio of axial length to minor radius of the flux rope and adapt it to be implemented based on a selected EUV (Extreme Ultraviolet) image. We apply the adapted method to 43 flux rope events observed simultaneously in the EUV data by one or more spacecraft. Each analysed event is associated with a CME, for which we estimated the kinematics employing a 3D reconstruction method based on the extended geometry considered for the FRi3D (Flux-Rope in 3D) CME model.
We study the influence of the projection effect on the estimation of the parameters of solar magnetic flux ropes and found its consistent impact on the estimation of the twist simultaneously observed by different spacecraft. The de-projected twist obtained for the selected events is found to be between 1.8 and 3.3 turns.
We also inspected a relationship between the twist and the associated CME speed and flare energy. It was found that the twist value follows a polynomic trend with respect to the CME speed, regardless the flare energy, for C-class and M-class events. However, for events associated with more energetic flares, such trend becomes more diffuse.
The inference of the photospheric magnetic field has, until recently, been limited to one view point: that from Earth. This is especially important when studying the long term evolution of active regions, where they are only close to disc centre for approximately a week. The Polarimetric and Helioseismic Imager (PHI) on board Solar Orbiter (SO) has made it a reality to extend the coverage of an active region in combination with Earth-bound assets, such as SDO/HMI. Due to the highly elliptical orbit, on 6 month intervals, SO/PHI observes the photospheric magnetic field over a wide range of viewpoints away from the Sun-Earth line.
In particular the High Resolution Telescope of PHI (SO/PHI-HRT) has observed active regions mostly when Solar Orbiter is near perihelia and separated from Earth by angles of 40-80+ degrees. This provides the possibility to significantly extend studies of flares and coronal mass ejections that monitor the formation, evolution, and destabilization of coronal structures. Cross-calibrations during Sun-Earth alignment show that SO/PHI-HRT and SDO/HMI are consistent with one another, enabling these types of extended studies.
From spring 2025, SO started to rise significantly above the ecliptic, providing full spectropolarimetric observations of the solar poles for the first time. Such information, of great relevance for solar dynamo studies as well as for coronal heating and solar wind models, will also be crucial for the quantitative constraint of the magnetic field in heliospheric models. Similarly, synoptic maps are widely used as boundary conditions to global models of the magnetic coronal field for space weather applications. The data from these new polar observations can be used for pole-filling in combined SO/PHI and SDO/HMI synoptic maps. Finally, SO/PHI is also the forerunner of the Photospheric Magnetic-field Imager (PMI) onboard the forthcoming L5 mission Vigil, which will provide some of the above applications as routine data products.
Our Sun is a highly dynamic star, exhibiting a broad range of activity from subtle dynamic events to powerful flares and large-scale coronal mass ejections (CMEs). CMEs are vast expulsions of magnetised plasma from the solar corona, while flares are intense bursts of electromagnetic radiation originating in the solar atmosphere. A flare and CME often occur concomitantly; the flare would then be considered “eruptive” and “confined”, respectively. Understanding the intricacies of large-scale solar eruptions and their links to any preceding activity is a central topic in modern heliophysics. Therefore, here, our goal is to determine whether small-scale transient activity within active regions (ARs) contains information about imminent major events. We focus on weak (microflares) to medium-scale (up to M-class) flaring activities. We have developed the BRightenings AnD Polarity Inversion Tracking (BRADPIT) method to monitor the temporal distribution of transient brightenings using extreme ultraviolet (EUV) data from the Atmospheric Imaging Assembly (AIA) instrument onboard the Solar Dynamics Observatory (SDO). BRADPIT compares the spatial distribution of the detected transient brightenings to the polarity inversion line (PIL) of the AR, derived from SDO Helioseismic and Magnetic Imager (HMI) line-of-sight magnetograms. Using a comparative analysis, we tested this methodology on an X-class flaring AR and a non-flaring one and obtained significant differences in the number of events, as well as the intensity and magnetic unsigned flux co-spatial with the detected brightenings in both cases. Through the comparative analysis of a flare productive and a flare quiescent AR, we seek insights into the pre-flare activity of ARs that we will then use on a statistically significant AR sample to assess their robustness and further validate our findings. This latter step will determine a potential predictive capability of the BRADPIT method, as well as how much it contributes to our understanding of solar dynamics.
Solar flares are primary drivers of space weather and play a crucial role in the Sun-Earth connection. The physical mechanisms underlying solar flare initiation remain a topic of intense research. It is widely accepted that flares result from the rapid release of magnetic energy stored in the stressed configurations of ARs. Several competing, and possibly concurrent, mechanisms have been proposed to explain this energy release, including magnetic reconnection, flux emergence, and shear-driven instabilities. Each of these processes contributes in a distinct way to the destabilization of the magnetic configuration, ultimately triggering the flare.
A central challenge in solar flare forecasting is the identification of reliable precursors. Over the past decades, feature-based machine learning approaches have been explored to tackle this task, relying on physically meaningful parameters extracted from solar magnetograms, particularly from instruments like the Helioseismic and Magnetic Imager onboard Solar Dynamics Observatory.
In this work, we propose a physics-informed extension to traditional feature-based machine learning. Our method constructs non-linear combinations of features guided by physical laws and dimensional analysis. This not only improves the machine learning model interpretability but also allows us to discern how many and which mechanisms govern flare initiation, through sparsity-enhancing and feature ranking methods.
We highlight the significance of the product of magnetic flux and electric current, a quantity related to magnetic helicity, as a powerful physics-informed feature, which is a well-investigated candidate for representing a significant portion of the energy budget stored in the active regions. Our findings suggest that this physics-informed strategy holds promise for uncovering novel descriptors of energy distribution in ARs, potentially improving the identification of flare precursors.
Understanding the causative mechanisms behind solar eruptions and solar energetic particle events is crucial for space weather forecasting. However, although numerous models have been proposed regarding the relationship between the magnetic fields of active regions and solar eruptions, the structure and parameters of the magnetic fields that govern these events remain unclear, which still hinders the accurate forecasting of space weather events. We have recently developed a new prediction scheme for large flares, known as the κ-scheme, based on MHD instability theory (Kusano et al. 2020, Science). The κ-scheme accurately predicts the onset of large flares and is capable of pinpointing the precise location of the flare epicenter for most events. This successful prediction suggests that the distribution of twist flux density and nonpotential field intensity within high free-energy regions (HiFER), including the polarity inversion line (PIL), is crucial in determining when, where, and how large a flare will occur. Nevertheless, the κ-scheme remains incomplete and cannot predict certain specific flares. In this presentation, we explore the reasons for the κ-scheme's limitations by analyzing the 3D magnetic field structures of various active regions using the latest nonlinear force-free magnetic field model. Furthermore, we statistically investigate the relationship between the 3D magnetic field structures of active regions and the formation of coronal mass ejections (CMEs) as well as the occurrence of solar energetic particle (SEP) events. As a result, we find that (1) the κ-scheme can be updated by considering the vertical magnetic field structure, thereby improving its predictive capability for flare occurrences, (2) the flare regions that produce CMEs can be classified more accurately by considering both the vertical distribution of the magnetic field decay rate and the ratio of direct current to return current across the PIL (Muhamad and Kusano 2025), and (3) there is a discernible relationship between the accumulated free energy of HiFER and the flares that generate SEP events. These findings suggest that analyzing the 3D magnetic field structures of active regions can provide vital information for predicting space weather events.
Kusano K, Iju T, Bamba Y, Inoue S. A physics-based method that can predict imminent large solar flares. Science. 369, 587–591 (2020).
Muhamad, J. and Kusano, K. Eruptivity of Flaring Active Regions Based on Electric Current Neutralization and Torus Instability Analysis, The Astrophysical Journal. 983, L28 (2025).
In recent decades, diverse catastrophic phenomena, from earthquakes and landslides to structural collapses and myocardial infarctions, have been framed as critical transitions in complex systems, marked by a sudden, irreversible shift from equilibrium to an unstable state. Here, we extend for the first time the Natural Time Analysis (NTA) framework to solar Active Regions (ARs) in order to identify reliable precursors of flare initiation. Using time series of magnetograms from Helioseismic and Magnetic Imager onboard the Solar Dynamics Observatory and the corresponding SHARP indices (total magnetic flux, magnetic shear angle, current helicity, etc.), we construct event sequences weighted by energy to probe the AR dynamic evolution. Case studies of M-flare-productive ARs reveal pronounced changes in the NTA parameters, most notably in current helicity, immediately before flare onset, whereas a control set of non-flaring regions shows no analogous critical signatures. These results validate NTA as a powerful tool for solar flare nowcasting and suggest that current helicity may serve as a key indicator of the approach to criticality. This novel application not only enriches our understanding of the physical mechanisms underlying flare triggering within the theory of critical phenomena but also offers a promising operational technique for short-term space-weather forecasting.
The extreme space weather events of Solar Cycle 25 highlight the urgent need for a comprehensive, interdisciplinary approach to understanding solar-Earth interactions. This session aims to bring together experts from solar and heliospheric physics, as well as
magnetospheric, ionospheric, and atmospheric physics to investigate the formation, propagation, and impacts of solar storms. By studying the magnetic connectivity and dynamics of the source regions leading to solar flares, and eruptions accompanied by the solar energetic particle events, we seek to understand how solar activity influences interplanetary space and interacts with the planetary environment. The propagation of coronal mass ejections and their interactions within the heliosphere are crucial for assessing the extent of space weather disturbances. The session will also address the
broader space implications of these extreme events, as the impact of geomagnetically induced currents on engineering infrastructure remains an important topic for space weather mitigation strategies. We encourage you to submit abstracts on events covering all aspects of space weather, from the Sun to the Earth, and their impacts on other planetary
environments. We welcome modeling and observational studies. By fostering interdisciplinary collaboration, this session aims to improve our understanding of space weather as a system-wide phenomenon and strengthen links between research communities.
Extreme geomagnetic storms have become more often over the recent years as we have reached and passed beyond the maximum levels of activity for the current solar cycle 25. Extreme storms are typically caused by arrivals of fast coronal mass ejections either linked to active regions and related flaring activity or driven by underlying filament or prominence eruptions. Real-time forecasting of extreme events presents great challenges for the on duty space weather forecasters. Some are related to delays in the available space weather data and the limited time for events analysis. A major challenge arises from the complexity of having multiple independent or inter-related space weather events one after the other as was the case for the May 2024 event. This work considers the complexity of the May 2024 event for space weather forecasting, including multiple daily Earth-directed CMEs which lifted off the solar surface particularly on May 08 and May 09. Analysing the intricate nature of this event we perform comparison between expected CME arrivals and related impacts based on isolated modelling of the individual CMEs, including the largest CME throughout the period, with forecasted arrival and impact predictions based on heliospheric modelling including all relevant CMEs. We include cross-model arrival time validation for selected CMEs throughout the event and compare them with the observed in situ solar wind data. Finally we conclude on the lessons learned from the analysis of this complex event and suggest possible ways forwards for future forecasting improvements.
Solar coronal mass ejections (CMEs) leave several signatures in the low corona as identified in EUV and X-ray images, such as intense flares, dimmings, EUV waves, etc., before they appear in white-light coronagraph images. Among them, coronal dimmings are arguably the most reliable indicator of the CME, but their predictive potential for space weather is not yet demonstrated beyond the general remark that dimming regions may represent locations of mass evacuation due to the CME, including the footprints of the flux rope that drives it. We statistically study how much of the properties of CMEs and their heliospheric consequences, i.e., interplanetary CMEs (ICMEs), may be learned from the nascent CMEs as captured in EUV images from SDO, STEREO and Solar Orbiter by correlating observed CME proxies with ICMEs in both directions. We also use AWSoM (Alfven-Wave Solar Model) simulations to understand the link between the solar origin and 1 AU consequences of CMEs of different magnitudes.
The importance of magnetic helicity in understanding Coronal Mass Ejections (CMEs) is well recognised. Many studies have supported the idea that a dominant helicity accumulation in active regions (AR) can be a reason for CME eruptions. This study investigates its potential for constraining input parameters in inner heliospheric CME propagation models. We propose a new method to constrain the magnetic flux of the spheromak CME model in the European Heliospheric Forecasting Information Asset (EUHFORIA) by utilising the CME's helicity content. This methodology provides a direct and quantitative inference of the helicity content of the CME. As a proof of concept, we analyse the CME observed on 10 March 2022 from NOAA AR 12962, observed in situ by both Solar Orbiter (SolO) and WIND, along with remote sensing observations. A helicity difference was observed in the pre- and post-eruptive phases of the source active region and was attributed to the corresponding CME. We relate the eruption-related magnetic helicity budget to the axial field of the spheromak model using the Graduated Cylindrical Shell CME forward-modelling technique. We identify axial strength as the magnetic field at the spheromak’s axis ($B_{spheromak}$). The toroidal magnetic flux is derived from $B_{spheromak}$ and the CME’s geometrical parameters. The EUHFORIA simulation results are compared with in situ magnetic field and plasma measurements from SolO at 0.43 AU and WIND at 0.99 AU to assess the method's reliability. The calculated magnetic flux from the CME’s helicity content shows that our method effectively reproduces magnetic field in situ measurements at 0.43 AU and 0.99 AU. The in situ data from SolO, approximately in the middle of the Sun-Earth line, were crucial for refining input parameters to improve the predictive accuracy at L1, highlighting the importance of studying multi-spacecraft observed events at different radial distances. The simulation results show that the peak magnetic field magnitude at $\sim$1 AU is underestimated by 19%. The power-law index with which the magnetic field magnitude is found to vary from 21.5 $R_{s}$ to 2 AU is estimated as -1.6.
During their propagation from the Sun, through the solar corona and into the inner heliosphere, the coronal mass ejections (CMEs) encounter variable solar wind. Some of the recent studies of the in situ observations of CMEs, by the rather closely positioned spacecraft, showed very different CME signatures. Such different in situ CME signatures may be due to the distorted CME shape which results from its interaction with the variable ambient solar wind.
In this study we present how the CME-solar wind interaction influences two rather different CMEs, modelled by the 3D MHD model EUHFORIA and the cone CME model. The selected halo CMEs were first detected in the SOHO/LASCO C2 field of view at 16:24 UT on 07 December 2020, and at 15:48 UT on 28 November 2021. Both CMEs arrived at Earth although their main propagation direction was somewhat southward from the Sun-Earth line (Valentino & Magdalenic, 2024). Here we demonstrate how the data-driven modelling can help us to understand often very complex in situ signatures of CME. Namely, the interaction of the CME and the variable solar wind can induce strong distortions of the CME and the CME-driven shock. This structured CMEs will then show very different in situ CME signatures at spacecraft separated even as close as 4 degrees. We also found that the CME arrival time can vary by several hours depending on which part of the distorted CME impacted the spacecraft. Consequently, the forecasted CME arrival at the spacecraft or at Earth can significantly vary if the CMEs propagation direction changes even by a few degrees.
Space weather predictions of the solar wind impacting Earth (including its transients) are usually first based on remote-sensing observations of the solar disc and corona, and eventually validated and/or refined with in-situ measurements taken at the Sun–Earth Lagrange L1 point, where real-time monitoring probes are located. However, this pipeline provides, on average, only a few tens of minutes of lead time, which decreases to ∼30 minutes or less for solar wind speeds of ∼800 km/s and above.
During Solar Cycle 25, the growing fleet of spacecraft operating in the inner heliosphere has spurred a wave of CME and, more generally, solar wind studies focussed on the use of inner probes as upstream monitors to improve prediction accuracy at 1 au. The central question driving these investigations is whether space weather forecasts are significantly improved by in-situ solar wind measurements taken upstream of L1. Additionally, if upstream measurements do enhance forecast accuracy, a key challenge becomes determining the ideal number and spatial distribution of probes required to provide sufficient and timely coverage.
In this presentation, we reflect upon the advantages of measuring the solar wind in situ upstream of L1, leading to improvements in both fundamental research of interplanetary physics and space weather predictions of the near-Earth environment. We present some examples characterised by fortuitous alignments of inner probes with 1 au assets (Earth and/or STEREO-A) and use these cases to evaluate the effectiveness of current models in assimilating upstream data to forecast solar wind conditions and CME impacts at 1 au.
Coronal mass ejections (CMEs) with a strong and sustained southward magnetic field component are the main drivers of strong geomagnetic activity at Earth. One of the greatest challenges in space weather is accurately forecasting their arrival and magnetic structure, essential towards mitigating their impact to both space and ground systems. As CMEs propagate from the Sun, they undergo many processes that affect their evolution, including interactions with other solar wind transients which increase the complexity of their structure. Thus, their exact internal structure can only be known through direct in situ spacecraft measurements.
Using magnetic field observations taken by Solar Orbiter whilst it was located far upstream of the Earth at 0.4 AU, we present the first real-time predictions of the magnetic structure of two CME events at Earth, launched on the 17th and 23rd March 2024 respectively, and the resulting geomagnetic impact. Despite the large heliocentric distance between measurement at Solar Orbiter and CME arrival at Earth, predictions of the in situ magnetic structure at Earth were remarkably similar to what was later observed. Combining the predicted CME arrival time at Earth produced by the ELEvo model with the predicted magnetic structure as an input to the Temerin and Li model, values of the minute resolution Dst index were produced, with prediction lead times of 15.3 and 7.1 hours, respectively. Qualitatively, our predictions replicated the observed geomagnetic response profiles well, though underestimated storm intensity during already disturbed conditions.
We also retrospectively investigate the events and conditions surrounding their propagation, evaluating the effect on our predictions of the CME magnetic structure and the resulting geomagnetic impact. Our approach demonstrates how future real-time upstream monitors could advance our ability to forecast space weather impacts with actionable lead times, whilst also identifying their limitations.
The session focuses on the state-of-the-art understanding of the complex mechanisms ruling the Magnetosphere-Ionosphere-Thermosphere (M-I-T) coupling and how they translate into space weather impacts. Such an understanding is fundamental for the developing effective countermeasures against disruption, failure and deterioration of vulnerable technologies, including GNSS critical applications, HF/VHF/UHF radio communications and LEO satellite operations. It is essential to improve the prediction of both the underlying physical phenomena and how these are related to space weather impacts. This improved understanding is crucial for better forecasts, warnings, and mitigate measures for adverse space weather effects. Other crucial aspects of M-I-T coupling are the interhemispheric symmetric/asymmetric response to variable drivers, vertical coupling and coupling between different latitudinal regions which, if properly predicted, could support regional space weather modelling. This session seeks to encourage and foster dialogue between researchers studying the underlying physical phenomena and operators seeking to mitigate space weather impacts. As such, contributions are invited which address any aspect of M-I-T coupling and associated threats to systems at regional and global scales.
During a solar flare, the fluxes in various lines and continua of the solar spectrum increase, leading to enhanced ionisation of the illuminated part of the Earth’s ionosphere and an increase in the total electron content (TEC). It has been previously shown that nearly 50% of X-class solar flares exhibit a second peak in warm coronal lines, such as Fe XV and Fe XVI, (called the ”EUV late phase”) the effect of which on the ionosphere remains largely unexplored. This study presents an analysis of the ionospheric response to 14 X-class flares with pronounced late phases. For the first time, empirical relationships between the increase in TEC and the solar flux enhancement during the impulsive and late phases of the flare are derived. Additionally, we demonstrate the influence of flare location on the intensity of geoeffective solar spectral lines and the ratio of the ionospheric responses to the impulsive and late phases of solar flares.
The bow shock current (BSC) plays an important role in supplying the magnetosphere with solar wind energy, in particular during times of low solar wind magnetosonic Mach numbers. Since the magnetic pile-up in the magnetosheath has to be maintained, the BSC cannot close locally, but must instead connect to magnetospheric current systems. However, the details of this closure remain poorly understood. For east-west interplanetary magnetic field (IMF) it has been hypothesised that the BSC partly closes to the high-latitude ionosphere, as field-aligned currents (FACs) on open field lines, but there is still no statistical evidence of this. In order to investigate this hypothesis, we use nine years of Defence Meteorological Satellite Program (DMSP) data to construct normalised FAC maps of the northern hemisphere polar cap. We sort them according to different IMF clock angles, IMF magnitudes and magnetosonic Mach numbers. By separating opposite polarity FACs, we show that, on average, a unipolar FAC exists in the dayside polar cap when the IMF $B_y\neq0$, regardless of the sign of the IMF $B_z$. This current flows out of (into) the ionosphere in the northern hemisphere for IMF $B_y>0$ ($<0$) and is thus of the correct polarity to connect to the north-south component of the BSC. Moreover, it is strongest when the BSC flows predominantly in the north-south direction. These results constitute the first statistical evidence in support of at least a partial closure of the BSC to the ionosphere during non-zero IMF $B_y$.
Changes in solar wind flow, typically associated with interplanetary coronal mass ejections (ICMEs) and high-speed streams (HSSs), directly impact the near-Earth space environment. These structures disturb the Earth’s magnetosphere and induce variability in the geomagnetic field. Rapid variations in the geomagnetic field, characterized by elevated values of dBH/dt, can lead to the generation of geomagnetically induced currents (GICs), which pose risks to technological systems, including power grids, pipelines, and railways. In this study, we investigate the correlation between the characteristics of different solar wind structures and ground-based magnetic field perturbations (dBH/dt), focusing on geomagnetic storms identified by Sym-H index values of ≤-50 nT from 1995 to 2024. 623 events were selected, comprising 308 ICME-driven and 315 HSS-driven storms. We examined the occurrence of dBH/dt spikes at latitudes between ±50° and ±90°, highlighting notable asymmetries in magnetic local time (MLT) distribution and spatial preferences depending on the solar wind driver. Additionally, we performed a solar cycle analysis of dBH/dt activity, revealing how the occurrence and intensity of magnetic field perturbations vary across different phases of the solar cycle. Our results also emphasize the relevance of mesoscale solar wind structures and periodic variations in solar wind velocity and IMF magnitude. This analysis supports the development of improved machine learning models by incorporating solar wind parameters to enhance ground-based dBH/dt forecasts.
The near-Earth space environment is strongly influenced by the solar wind and embedded interplanetary magnetic field. Therefore a thorough understanding of the upper atmosphere response during the passage of geoeffective solar wind transients, such as high-speed streams/stream interaction regions (HSS/SIR) and interplanetary coronal mass ejections (ICMEs) is crucial for accurate space weather predictions. A key mechanism in dissipating the solar wind energy in the upper atmosphere is auroral Joule heating, which causes thermal expansion of the thermosphere, increasing the thermospheric density and causing low Earth orbiting (LEO) satellites to experience more drag. Applying a novel method for determining the Joule heating using AMPERE, SuperMAG and SuperDARN data, we study the northern hemispheric Joule heating and global thermospheric neutral density enhancements at Swarm and GRACE satellites during 231 geomagnetic storms between 2014 and 2024 by using superposed epoch analysis. It is found that the Joule heating in the ionospheric E-region and thermospheric neutral density enhancements at the altitude of the Swarm and GRACE satellites (350 – 550 km) show characteristics which depend on the geomagnetic storm driver. The Joule heating has a fast increase at the beginning of the storm main phase when the storm is initiated by a HSS/SIR or by the sheath region of ICMEs. In comparison, a more gradual and longer lasting increase is found in storms driven by magnetic clouds within ICMEs. This is in line with previous results of the total field-aligned and ionospheric currents during storms (Pedersen et al., 2021, 2022). The thermospheric neutral density as referenced to 450 km altitude increases gradually during the storm main phase to about 120% of the quiet time density, and the enhancements are typically largest and longest-lasting for storms driven by magnetic clouds. This is likely because of the prolonged interval of increased Joule heating during magnetic cloud-driven storms.
Space weather and space climate have their origin in the Sun's magnetic field, which forms the continuously changing plasma environment in the heliosphere. Long-term observations of the Sun over the past few centuries have identified variations of the solar activity on different time scales, the most prominent ones being the 11-year sunspot cycle and the centennial Gleissberg cycle. Understanding and forecasting solar activity and the conditions in the heliosphere, including their effects to the Earth, is a major challenge in the field of heliophysics. The last decade has seen a lot of progress in solar activity modeling and in developing predictive capabilities, and there is a large diversity of forecasts using multiple methodologies. In addition, different communities and end-users have different needs about the cadence, lead time, and accuracy of the forecast parameters. This session aims to discuss the current capabilities and challenges in understanding and forecasting of long-term solar activity and related heliospheric and terrestrial effects for time scales of a few solar rotations onward. Possible forecast parameters include, e.g., sunspot numbers, total and spectral irradiance, open heliospheric flux, radio fluxes, galactic cosmic rays, extreme solar energetic particles, coronal holes, high-speed solar wind streams, coronal mass ejections, geomagnetic activity, GICs, magnetic storms, ionospheric parameters (foF2, etc), polar vortices, sudden stratospheric warmings, etc. We invite talks and posters from all these space weather and space climate domains, from the Sun to geospace, discussing their current understanding and long-term forecasting, new observations, theories and models, forecasting methodologies, and validation efforts.
Particular attention has recently been paid to the near solar subsurface layer, known as NSSL. On the one hand, because of the disruptive role of the magnetic field, whose properties are still far from fully understood in this region. Secondly, because of some unsuspected properties of solar rotation that have been also recently put in evidence, notably the reversal of the rotation gradient towards latitudes close to 60°. These two aspects, well identified by HMI's results on SDO, clearly highlight the role of subsurface magnetism. The NSSL could thus be divided into at least two layers (perhaps three) one of which, the leptocline (from Greek “letpos”, thin and “klino”, slope, by analogy with the tachocline “tachos”, speed), would be the seat of numerous solar phenomena. Also a link explaining some properties of the solar gravitational moment. Here, we highlights the role of this shallow and sharp rotational shear layer lying from the surface to around 8 Mm in depth. Indeed, this layer could be the seat of many solar physical processes leading to a new vision of the structure and dynamics of the Sun. In such a way, it is not impossible, that this zone will play a significant role in the shaping of the solar activity cycles.
The Sun’s variability is controlled by the progression and interaction of the magnetized systems that form the 22-year magnetic activity cycle (the “Hale Cycle”) as they march from their origin at ∼55° latitude to the equator, over ∼19 years. Indeed, the Extended Solar Cycle is the Cycle.
Over the past few years, we have developed a new paradigm built around the overlapping extended cycles into a robust tool for predicting solar activity and its terrestrial consequences: we demonstrate the predictability of solar flares, EUV irradiance, solar wind properties and composition, and geomagnetic activity. Odd and even cycles also behave differently.
We discuss how skillful our forecasts (set in late 2021) have been for Cycle 25, and what may be expected, activity-wise, for the remainder of the cycle.
The study of magnetic activity in the Sun's polar regions is essential for understanding the solar cycle. However, measuring polar magnetic fields presents challenges due to projection effects and their intrinsically weak magnetic field strength. Faculae, bright regions on the visible solar surface associated with increased magnetic activity, offer a valuable proxy for measuring polar fields.
This research aims to analyze the magnetic activity of the Sun's polar regions through the use of polar faculae.
A neural network model (U-Net) was employed to detect polar faculae in images from the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO). The model was trained on synthetic data, eliminating the need for manual labeling, and was used to analyze 14 years of data, from May 2010 to May 2024.
The U-Net model demonstrates superior performance and efficiency over existing methods, enabling automated large-scale studies. We found that polar faculae numbers exhibit cyclical behavior with distinct minima and maxima, showing similar patterns between poles but with notable temporal delays (South Pole: minimum early 2014, maximum late 2016; North Pole: minimum late 2014, maximum mid-2019). Polar faculae magnetic fields remain consistent in magnitude ($\sim\pm75\,G$) across both poles and throughout the solar cycle. A strong linear correlation was found between polar faculae count and the overall polar magnetic field strength. The spatio-temporal evolution reveals systematic migration of field polarity reversals from mid-latitudes toward the poles at rates of 3--8 m/s. During solar minimum, we observed a small relative increase in stronger-field faculae compared to solar maximum, suggesting either the coexistence of two magnetic distributions or subtle solar cycle dependence in faculae properties.
The evolution of the solar magnetic field is the key factor governing space weather drivers. Accurate forecasting of space weather requires precise modelling of the magnetic field's evolution on the solar surface using methods like Surface flux transport (SFT). Conventionally used SFT modelling techniques involve grid-based numerical schemes, making them computationally expensive. In this presentation, we present a novel, mesh-independent machine learning-based approach using Physics-Informed Neural Networks (PINNs) to simulate the temporal evolution of Bipolar Magnetic Regions (BMRs) on the solar photosphere. The ability of PINNs to solve advection-diffusion equations make it an efficient and accurate technique to simulate SFT equation. We employ this approach to study how nonlinear effects influence SFT models, with the broader goal of improving our understanding and constraints on solar dynamo processes. In particular, we focus on two mechanisms recently proposed to modulate solar cycle amplitudes: tilt quenching (TQ), representing a negative feedback between the cycle strength and the average tilt angle of active regions, and latitude quenching (LQ), indicating a positive relationship between cycle strength and the mean emergence latitude of active regions. Using PINNs within the SFT framework, we systematically examine the nonlinearities introduced by TQ, LQ, and their combined effects. Our study aims to clarify the distinct contributions of TQ and LQ to the solar dynamo. We find that the balance between LQ and TQ effects is closely linked to the ratio of meridional flow speed to magnetic diffusivity in the SFT models. Given that LQ is better constrained through observations, it may offer a valuable benchmark for refining solar dynamo models to achieve closer alignment with solar observations.
To improve solar 11 yr cycle forecast one must be able to take into account the rising/declining phase asymmetry of the cycle. In our Solar Predict 11 yr cycle forecasting tool based on a 4D-var assimilation technique coupled to a Babcock-Leighton mean field dynamo model - we use meridional circulation as the primary control parameter for determining the future cycle's length and amplitude. In order to assess how the meridional flow could be further used to take into account the cycle asymmetry in the 11yr cycle prediction, we have performed a suite of kinematic dynamo simulations incorporating several physically motivated and time-dependent meridional flow profiles. To quantify the asymmetry, we examine four diagnostic parameters: (1) the relationship between rise and decay times, (2) the correlation between cycle amplitude and rise time, (3) the correlation between cycle amplitude and rise rate, and (4) the correlation between cycle amplitude and the decay rate near the preceding minimum. We find that the rise–decay time asymmetry is highly sensitive to the temporal structure of the meridional flow. In all scenarios, a statistically significant positive correlation emerges between the cycle amplitude and rise rate, reaffirming this as a robust and consistent proxy for cycle strength. In contrast, correlations between the cycle amplitude and both the rise time and the decay rate near the preceding minimum are generally weak or statistically insignificant, particularly in cases involving nonlinear or feedback-modulated flows. These findings underscore the critical role of meridional circulation variability in shaping solar cycle asymmetry and emphasize the need for improved observational constraints on its spatiotemporal behavior.
As military operations increasingly depend on GNSS, satellite communications, HF radio, and surveillance assets, understanding and mitigating the impact of space weather has become essential for operational readiness. Especially the arctic regions have now become interesting as a result of the opening of new shipping routes and the security question of Greenland, and these are the regions most susceptible to space weather effects. However, most space weather services today are designed with civil users in mind, and there is a growing gap between scientific outputs and actionable military needs.
This panel forum will explore the specific requirements of the defense community, from NATO partners to national armed forces, for tailored space weather support. It will highlight existing practices (e.g., UK Met Office, USAF, JMG/STCE collaboration), identify operational needs in areas such as forecasting, system vulnerability assessment, and training, and discuss how to move from physical space environment monitoring to mission-relevant, impact-oriented products.
Key questions include:
- What are the most critical defense use cases affected by space weather?
- How can we translate forecasts into platform-specific operational risks?
- What infrastructure and standards are required to support autonomous, secure defense operations under space weather influence?
- What are the current gaps in space weather services as perceived by defence users?
The session encourages active participation to clarify shared needs, foster civil-military cooperation, and help shape future space weather services that are operationally relevant to defense actors.
Space weather services are essential for sectors like satellite operations, GNSS, aviation, energy, and crewed spaceflight, where accurate forecasts help prevent costly disruptions. As their use grows, these services must be validated for real-world operations, transparent in their methods, and trusted by users. This session explores the full validation process, from research to operational deployment, emphasizing how end users benefit every step of the way.
We will examine practical questions such as which frameworks and metrics best measure the quality and reliability of space weather services. We’ll look at ways to compare models and products across different operational centers and how to clearly communicate uncertainty and confidence to users. The session will cover key performance tools - like scoreboards, maturity indices, and Key Performance Indicators - that track forecast accuracy, latency, and false alarms. We will also focus on evaluating resilience during significant storm events and the role of user feedback in confirming a product’s effectiveness.
By uniting researchers, operators, and end users, this session aims to show how thorough validation builds trust, supports informed decision-making, and drives the development of flexible, user-driven space weather capabilities.
This TDM complements the P2 Mars - Moon Space Weather plenary session (Gina di Braccio et al), with a particular focus on the Lunar environment.
The Moon’s surface offers a unique vantage point to characterize the solar wind, solar energetic particles, magnetospheric environments and interaction processes with airless bodies surfaces, and provides access to in situ resources. It is also a designated exploration target for ongoing and future institutional and private / commercial lunar missions, that will need to operate and survive accounting for highly variable space environment conditions. In this context we propose to organise a discussion around some key questions such as (but not exhaustively) :
1- Which operational scenarios, missions and instruments could provide key space weather observations from the lunar surface and lunar orbit ?
2 – What could we learn from the May 2024 Solar storm observations in terms of impact to the lunar environments ?
3 - What synergies would be necessary between future space based lunar missions and Earth-or solar system based observations to maximise humans and systems safety in lunar exploration ?
4- How can the space weather community and investigations support and help mitigate the risk for human activities at the lunar surface ?
Over several decades the global neutron monitor (NM) network provides continuous records of cosmic ray (CR) variations. NMs have been extensively used as the main global multi-instrument tool for the analysis of a specific class of strong solar particle events, namely ground level enhancements (GLEs) in which solar ions are accelerated to quasi and relativistic energies, leading to sudden increases of count rates of particle detectors at the surface of the Earth. GLEs are particularly strong SEP events with imminent space weather effects. In addition, NMs are used for the registration of Forbush decreases and recently observed anisotropic CR enhancements (ACREs). 2024 was a particularly interesting year with three GLEs, a notable Forbush decrease and ACRE reported.
The purpose of this meeting is to discuss the applications of the global NM network for space-weather purposes, including alerts for GLEs, aircrew dose assessment and monitoring services. The session is devoted to recent works related to all aspects of space weather, focusing on the application of NMs for study transients and the related space weather phenomena. Special focus will be given to recent advances of modeling of transients, nowcasting of aircrew dose exposure, specifically during GLEs. The applications of the global NM network measurements and outputs within satellite-born instruments, as well as, other ground based detectors, which provide complementary information to NM records and can be used to unfold open issues as e.g. spectra evolution during GLEs are relevant to this session.
The structure of the heliospheric background solar wind is shaped by the interaction between slow and fast wind streams. These interactions give rise to stream interaction regions (SIRs) and co-rotating interaction regions (CIRs), which can lead to shocks, compression- and rarefaction regions—key contributors to minor and moderate geomagnetic activity.
A deep understanding of solar wind dynamics, along with the surrounding magnetic field and their origins, is essential for improving the accuracy of space weather predictions.
This session focuses on current research related to the origin, evolution, and space weather effects of slow and fast solar wind. Observations from recent missions like the Parker Solar Probe (PSP) and Solar Orbiter (SolO), along with long-standing missions such as the Solar Dynamics Observatory (SDO) and the Solar Terrestrial Relations Observatories (STEREO), provide valuable data to refine and expand our knowledge in this field.
We invite contributions exploring various topics, including the sources and acceleration mechanisms of slow and fast solar wind, stream interactions, and the magnetic and plasma structure at the source surface and in the inner heliosphere. Additionally, we welcome studies that integrate observational data with modeling to advance our understanding of solar and heliospheric physics in the context of space weather forecasting.
Turbulence in plasmas involves a complex cross-scale coupling of fields and distortions of particle velocity distributions, with the generation of non-thermal features. How the energy contained in the large-scale fluctuations cascades all the way down to the kinetic scales, and how such turbulence interacts with particles, remains one of the major unsolved problems in plasma physics. Moreover, solar wind turbulence is not homogeneous but is highly space-localized and the degree of non-homogeneity increases as the spatial/time scales decrease (intermittency).
Here, by means of new measurements by Solar Orbiter, the radial nature of the turbulent magnetic fluctuations around ion scales during the expansion of the wind, has been investigated. The ion scales appear to be characterized by the presence of non-compressive coherent structures, such as current sheets, vortex-like structures, and wave packets identified as ion cyclotron modes, responsible for solar wind intermittency and strongly related to the energy dissipation. Particle energization, temperature anisotropy, and strong deviation from Maxwellian, have been observed in and near coherent structures, both in in-situ data and numerical simulations. Furthermore, kinetic effects for both protons and alpha particles have been studied in presence of switchbacks, large deflections of the magnetic field which occur simultaneously with a sudden increase in the radial solar wind velocity. Very interestingly we observe a clear correlation between switchbacks and alpha particle temperature, but not with proton temperature, suggesting a role of magnetic field deflections in preferentially heating heavy ions. Finally, we investigate kinetic features in a multi-component turbulent plasma by means of Vlasov-Maxwell simulations.
Understanding the physical mechanisms that produce coherent structures and how they contribute to dissipation in collisionless plasma will provide key insights into the general problem of solar wind heating.
Co-rotating interaction regions (CIRs) are formed at the interface of the background slow solar wind and the fast solar wind emanating from coronal holes. Their high velocities and plasma pressures shape the heliosphere and are one of the main drivers of geomagnetic storms. The recently formed fleet of spacecraft in the heliosphere, including Parker Solar Probe, Solar Orbiter and BepiColombo, offer an unprecedented dense coverage of solar wind measurements between 0.1AU – 1.5AU. To take optimal advantage of the various spacecraft constellations, we developed a solar wind model based on the simultaneous in-situ measurements from the multiple spacecraft. The data points are being propagated with the in-situ measured speed away from the Sun. Upon encounter of slower data points, the modeled plasma parcels interact by inelastically colliding with each other. Based on the model results we investigate the evolution of solar wind characteristics and corotating interaction regions over distance. Due to the co-rotating nature of CIRs, the resulting 2D map produces a reasonable forecast at Earth or Mars, even at larger longitudinal separations between the single spacecraft. We present a statistical evaluation of P2D model results for historical data covering recent years with respect to different longitudinal, latitudinal and radial separation of the spacecraft as well as give an outlook to the application of P2D for space weather forecast up to Mars distance.
Magnetic reconnection is a fundamental process in astrophysical plasma, as it enables the dissipation of energy at kinetic scales as well as large-scale reconfiguration of the magnetic topology. In the solar wind, its quantitative role in plasma dynamics and particle energization remains an open question that is starting to come into focus as more missions now probe the inner heliosphere. In particular, the first encounters of the Parker Solar Probe (PSP) mission with the Sun have revealed that the Heliospheric Current Sheet (HCS) was often reconnecting close to the Sun, opening question about the impact of HCS reconnection on the nearby solar wind.
In this work, we first make a thorough catalog of all HCS crossings measured PSP (encounter 5 to the latest available) and find that 88\% of crossings present magnetic reconnection signatures. This statistically confirms that magnetic reconnection is prevalent in the near Sun HCS. We then quantify the level of turbulence within the HCS and find enhanced energy at kinetic scales compared to the nearby solar wind, usually devoid of magnetic switchbacks. We furthermore highlight the frequent observation of mirror mode instabilities within the structure of the HCS, hinting that this process plays a particular role in the energy dissipation. These mirror mode instabilities are also observed within HCS crossings observed by Solar Orbiter further in the heliosphere. We finally plan to study the evolution of the HCS structure through multi-spacecraft observation.
Collectively, these results show that the HCS may play an important role in the energization of the near Sun solar wind. We discuss the impact of these observations on our current understanding of HCS reconnection and solar wind turbulence.
The solar wind is a complex and dynamic plasma environment, populated by a variety of structures. Among these we find Coronal Mass Ejections (CMEs), interplanetary shocks, Corotating Interaction Regions (CIRs) and large-amplitude non-linear deflections of the magnetic field called switchbacks. They have been shown by Parker Solar Probe to be ubiquitous (Bale et al. 2019). These switchbacks, often grouped in extended intervals called patches, are thought to be linked to interchange magnetic reconnection in the low corona and may imprint distinct signatures in the ion Distribution Functions (DFs), for instance through variations in alpha particle abundance or temperature anisotropies.
To investigate these signatures down to kinetic-scales, we have developed a new algorithm for analyzing the 3D ion DFs measured by the Proton-Alpha Sensor (PAS) onboard Solar Orbiter. With its unique time resolution from 1 to 4 seconds (Louarn et al. 2020), PAS enables detailed probing of proton and alpha populations. Our method extracts these populations directly from the energy spectra with Gaussian Mixture Models and computes their moments (density, bulk velocity, temperature anisotropy) in the magnetic field-aligned frame.
This approach will be used to study the kinetic properties of ions within switchbacks and patches, in order to better understand their origin and evolution. It will also be applied to other solar wind structures such as CMEs, shocks, and CIRs, which will serve as additional test cases to benchmark the algorithm and evaluate its performance across diverse plasma environments.
Large-scale coronal structures, such as streamers and pseudostreamers, are considered potential sources of the slow solar wind, contributing to its structured nature and variability. However, due to the lack of high-resolution coronal observations, the processes driving the dynamics of these structures and their role in the slow wind are not yet fully understood. In this study, we analyzed a pseudostreamer observed using the Full Sun Imager (FSI), with its footpoints captured by the High Resolution Imager (HRIEUV), offering a resolution of approximately 300 km. We identified the presence of propagating disturbances (PDs) near the footpoints, extending to heights of 50–60 Mm in several open strands. The projected velocities of these PDs ranged from 27 to 250 km/s, with an average of 121 km/s. Additionally, we identified distinct periodicities with significant power, specifically, 8.6, 9.2, and 16.5 minutes in these PDs. We also obtained faint signatures of flows in the FSI imaging time series. These PDs may play a role in transporting plasma to the outer corona, potentially contributing to the solar wind.
The session focuses on the state-of-the-art understanding of the complex mechanisms ruling the Magnetosphere-Ionosphere-Thermosphere (M-I-T) coupling and how they translate into space weather impacts. Such an understanding is fundamental for the developing effective countermeasures against disruption, failure and deterioration of vulnerable technologies, including GNSS critical applications, HF/VHF/UHF radio communications and LEO satellite operations. It is essential to improve the prediction of both the underlying physical phenomena and how these are related to space weather impacts. This improved understanding is crucial for better forecasts, warnings, and mitigate measures for adverse space weather effects. Other crucial aspects of M-I-T coupling are the interhemispheric symmetric/asymmetric response to variable drivers, vertical coupling and coupling between different latitudinal regions which, if properly predicted, could support regional space weather modelling. This session seeks to encourage and foster dialogue between researchers studying the underlying physical phenomena and operators seeking to mitigate space weather impacts. As such, contributions are invited which address any aspect of M-I-T coupling and associated threats to systems at regional and global scales.
The Low Frequency Array (LOFAR) is one of the most advanced radio telescopes in the world. When radio waves from a distant astronomical source traverse the ionosphere, structures in this plasma affect the signal. The high temporal resolution available (~10 ms), the range of frequencies observed (10-90 MHz & 110-250 MHz) and the large number of receiving stations (currently 52 across Europe) mean that LOFAR can also observe the effects of the midlatitude and sub-auroral ionosphere at an unprecedented level of detail.
Results are presented from a statistical study using 2,810 hours of observations of Cassiopeia A from a LOFAR station located in the Netherlands (station CS032, located at 52.9o N; 6.9o E) from 28th June 2014 to 27th November 2016. Ionospheric structures were identified in 469 (~17 %) of these observations. A comparison with proxies for geomagnetic activity (the Kp index) and solar activity (the F10.7 cm solar radio flux) showed that geomagnetic or solar effects were not the primary driver of these ionospheric structures. Ionospheric structures were more common in summer and between ~21 LT – 02 LT. These patterns in season and local time showed similarities to the occurrence of lightning strikes. When ionospheric structures were present, the mean number of lightning strikes in a spatial region close to the LOFAR observations (51.9o – 56.5o N; 3.9o – 9.9o E) two hours prior to the LOFAR observations was (70±25) per hour. This was substantially larger than the mean value of (19±5) per hour when the ionospheric structures were absent. This suggests that upward propagating Atmospheric Gravity Waves (AGWs) launched by deep convection above thunderstorms could be a source of the ionospheric structures. Collectively, these observations suggest that LOFAR can be used to infer ionospheric signatures of vertical coupling processes in the mid-latitude atmosphere.
LOFAR is currently being upgraded to LOFAR 2.0, which will increase the sensitivity of the telescope, but it will also be more vulnerable to ionospheric variability. The Dynamic Ionospheric Notifications for Operations and Scheduling (DINOS) project is using ionospheric results to attempt to mitigate the effects upon LOFAR. These approaches include using ionosondes, magnetometers and HF Continuous Doppler Sounding Systems. A model based upon ionosonde observations is presented. Such a model could reduce the number of observations which later need to be discarded due to the ionospheric conditions, optimizing the usage of telescope time, and making the operations more sustainable by reducing the computational and storage resources required.
Sporadic-E, thin metallic ion layers in the lower ionosphere compressed via neutral wind shears or externally imposed electric fields, pose considerable challenges for High Frequency (HF) radio propagation modelling. As their name suggests, these layers can appear to be quasi-stochastic, requiring both an abundance of metallic ions and a mechanism through which to compress them into thin, dense layers; as such, modelling Sporadic-E has been a persistent and long-standing challenge in ionospheric modelling. With physics-based models now beginning to develop the capability to capture the processes that produce these structures, we will here revisit empirical modelling of Sporadic-E at high latitudes and examine the capability of existing measurements to adequately constrain an empirical model. Using Radio Occultation (RO) measurements of these Sporadic-E layers, we have constructed a probabilistic model of Sporadic-E, its altitude, and its intensity over high latitude regions using neural networks. This presentation will provide an overview of this model and examine its performance via independent validation against both other RO and ground-based observations. We will furthermore examine the modelled behaviour and use the model to understand the climatological dynamics of Sporadic-E at high latitudes, which due to the additional role of magnetospherically-driven electric fields, include considerable dependence on the orientation of the high latitude electric fields and thereby the orientation and intensity of the solar wind magnetic field. Further discussion will explore how to implement this model within the existing Empirical Canadian High Arctic Ionospheric Model (E-CHAIM) and will explore the interplay between magnetospheric driving and thermospheric tides in controlling the convergence necessary to form Sporadic-E layers at high latitudes.
Sudden Commencements (SCs) are rapid, near-impulsive changes of the geomagnetic field that are measured on the ground. SCs are caused by sudden increases of solar wind dynamic pressure (e.g., interplanetary shocks), that compress the Earth’s geomagnetic cavity (the magnetosphere). Such changes in the geomagnetic field, as measured on the surface of the Earth, result in the creation of geoelectric fields in the solid Earth and consequently Geomagnetically Induced Currents in grounded, conducting infrastructure. When large, these GICs present a hazard to the continuous, safe operation of infrastructure such as power networks - we must be able to accurately predict their magnitude.
The links between the properties of the incident solar wind structure, the observed magnetic field signature at a given location and the resulting GIC are complex. In this work we explore the correspondence between these factors, introducing an analytical model to mimic the physical origin of each constituent component of the ground magnetic field signature. We explore how the each component varies across the globe, and attempt to link these underlying properties to the causal solar wind structure. We test how different types of SC magnetic signature translate to GIC within a well-documented example power network using synthetic tests and numerical models. This ultimately allows us key insights into the types of solar wind structure that will be most “geoeffective” at a given location.
AGATA (Antarctic Geospace and Atmosphere ReseArch) is a Scientific Research Programme (SRP) of the Scientific Committee on Antarctic Research (SCAR). The AGATA SRP was officially approved during the SCAR Delegates Meeting in August 2024, with scientific activities commencing in January 2025. The programme is designed to address key open questions concerning the coupling between the different layers of the polar atmosphere and their interaction with geospace.
Specifically, AGATA seeks to investigate the following fundamental scientific questions:
• How are different atmospheric layers coupled in the polar regions?
• How does the high-latitude upper polar atmosphere respond to enhanced geomagnetic activity, including energy inputs from space?
• In what ways does the entire polar atmosphere influence short- and long-term climate variability?
To place these questions in a global context, AGATA adopts an interhemispheric approach, considering both the Antarctic and Arctic regions. By comparing atmospheric processes across the two poles, the programme aims to identify commonalities and distinctions that identify the broader role of the polar atmosphere in global-scale geophysical and climatic phenomena. Further details can be found on the official SCAR website: https://scar.org/science/research-programmes/agata.
AGATA has recently established its internal governance and started its core activities, including communicating to the wider community AGATA’s mission and goals. A significant component of the programme also focuses on capacity building initiatives aimed at training the next generation of polar researchers, with a strong emphasis on interdisciplinary collaboration and international engagement.
AGATA will run for a period of eight years, from 2025 to 2032. Within this timeframe, the programme is expected to play a prominent role in the scientific activities leading up to and during the next International Polar Year (2032–2033), a major milestone event for the global polar science community (https://ipy5.info).
This paper provides an overview of AGATA’s initial activities, highlighting opportunities for scientific collaboration, data sharing, and training. The programme is open to contributions from researchers, institutions, and networks interested in advancing our understanding of the polar atmosphere and its connections to geospace and climate.
Space weather and space climate have their origin in the Sun's magnetic field, which forms the continuously changing plasma environment in the heliosphere. Long-term observations of the Sun over the past few centuries have identified variations of the solar activity on different time scales, the most prominent ones being the 11-year sunspot cycle and the centennial Gleissberg cycle. Understanding and forecasting solar activity and the conditions in the heliosphere, including their effects to the Earth, is a major challenge in the field of heliophysics. The last decade has seen a lot of progress in solar activity modeling and in developing predictive capabilities, and there is a large diversity of forecasts using multiple methodologies. In addition, different communities and end-users have different needs about the cadence, lead time, and accuracy of the forecast parameters. This session aims to discuss the current capabilities and challenges in understanding and forecasting of long-term solar activity and related heliospheric and terrestrial effects for time scales of a few solar rotations onward. Possible forecast parameters include, e.g., sunspot numbers, total and spectral irradiance, open heliospheric flux, radio fluxes, galactic cosmic rays, extreme solar energetic particles, coronal holes, high-speed solar wind streams, coronal mass ejections, geomagnetic activity, GICs, magnetic storms, ionospheric parameters (foF2, etc), polar vortices, sudden stratospheric warmings, etc. We invite talks and posters from all these space weather and space climate domains, from the Sun to geospace, discussing their current understanding and long-term forecasting, new observations, theories and models, forecasting methodologies, and validation efforts.
The Solar system travels in the interstellar medium and may encounter so-called clouds, regions of the enhanced density of matter relative to the surrounding space. Such passages can in theory affect the size of the heliosphere around the Sun and lead to a significant change of the space climate around the Earth. As one of the changes, a reduced heliospheric size can make the solar modulation of galactic cosmic ray (GCR) intensity less effective. The energetic particle radiation significantly increases due to that, and it should be reflected in the enhanced production of cosmogenic nuclides in lunar regolith and rocks. The present study assesses how an encounter of an interstellar cloud in the past could affect the present content of cosmogenic nuclide $^{26}$Al with the lifetime of 1.0 Myr, one of the nuclides most suitable for such studies. For that, we made an assumption about how the heliospheric size affects the GCR modulation and calculated the nuclide content as a function of the GCR modulation. The case of the Local Leo Cold Cloud has been applied to the presented model, and it showed that although it is theoretically possible to identify a such transient in the lunar nuclide data, the uncertainties of the existing experimental data (old measurements of samples from Apollo missions) do not allow to resolve the event. As a result of this work, we present the limit of detection of transiting interstellar clouds with long-living lunar cosmogenic nuclides and call for new measurements of existing lunar samples with modern techniques, as this can significantly improve the sensitivity of the method and may potentially lead to new discoveries.
The magnesium II core-to-wing ratio has been measured on a daily basis since 1978. It is a widely used proxy for solar chromospheric activity, essential for satellite drag calculations as well as the model that is the NOAA Climate Data Record for solar spectral irradiance. In 2017, this measurement became available operationally from GOES-16/EXIS at three-second cadence with high signal-to-noise. While the Earth's atmosphere may not respond to ultraviolet irradiance changes on such short timescales, it does respond to the time-integrated irradiance variation. Using a once-a-day measurement as was available before GOES-16 introduces a systematic bias in the estimated facular brightening that gets worse as solar activity increases. Using data from solar cycle 24, we can estimate a correction factor for the daily magnesium II index for previous solar cycles. In this presentation, we will discuss the magnesium II index and provide details of the instrumentation as well as how to retrieve the data from the NOAA web page.
Solar flares are transient energetic events triggered by electromagnetic plasma instabilities arising within regions of the solar corona. These events are characterized by a broadband radiative emission and energetic particle release and, in synergy with other transient solar phenomena, play a key role in shaping space climate. Despite decades of observations, the statistical properties and physical mechanisms underlying flare occurrence remain incompletely understood, limiting our ability to develop robust predictive models.
In this study, we analyze the solar soft X-ray emission measured by the GOES mission from January 2002 to December 2024 to investigate the statistical properties of interevent times between flares. Consistent with previous work, we find that the interevent time distribution follows a power law behavior across timescales ranging from 10^2 to 10^5 minutes, indicative of scale invariance. More notably, we report for the first time that the interevent time distribution is also invariant under scale transformation, i.e., flare flux integral selection, an unexpected property that significantly narrows the class of viable physical models.
By analyzing the fluctuations in the GOES time series and comparing them with theoretical expectations from the literature, we find that the observed behavior is consistent with the statistical properties of magnetohydrodynamic turbulence. This provides strong evidence that the flare timing is not stochastic but governed by underlying physical processes that are, in principle, predictable.
Our results suggest that solar flares, while complex, may be predicted within a physically constrained framework.
Solar eruptive activity manifests in several forms, the most prominent and well-studied being solar flares, coronal mass ejections (CMEs), and solar energetic particle (SEP) events. However, the upper limits of intensity for these eruptive phenomena remain largely uncertain. To date, only extreme solar particle events (ESPEs) have been identified in cosmogenic isotope records preserved in datable natural archives. In contrast, no definitive evidence has been found for extreme solar flares or CMEs, and it remains unclear whether the Sun is capable of producing such events.
Only a handful of ESPEs have been detected in cosmogenic isotope data spanning the Holocene epoch, suggesting a occurrence rate of approximately once every 1,500 years. Meanwhile, astronomical observations from the Kepler satellite have revealed the presence of superflares on solar-like stars—events that are several orders of magnitude more energetic than those recorded on the Sun. A recent reanalysis of Kepler data indicates that such superflares may occur as frequently as once every 100 years for events with bolometric energy exceeding $10^{34}$ erg.
Both the observed occurrence rates of solar ESPEs and stellar superflares are subject to significant observational biases. In this presentation, we demonstrate that the apparent disagreement in their occurrence frequencies can be naturally explained by the probabilistic nature of solar eruptive events. Using observations from the GOES satellite series and a global network of ground-based neutron monitors, we investigate the conditional probability of observing SEP events given the detection of a solar flare, considering both SEP and flare intensities.
Finally, we propose an analytical model that accurately reproduces observed X-ray solar flare and SEP data and provides a realistic estimate of the ESPE occurrence rate. According to our model, the production of ESPEs does not necessarily require a superflare; rather, strong—but not extreme—solar flares (e.g., >X10-class) may generate ESPEs under highly favorable conditions.
Investigating the intricate relationship between galactic cosmic rays (GCR) and solar activity is fundamental to our understanding of the physical mechanisms governing particle transport within the heliosphere. It also provides critical insights into radiation exposure and associated risks for space missions. In this study, we present advancements in our predictive model for solar modulation, designed to capture key particle transport processes such as diffusion, drift, convection, and adiabatic cooling. This model computes the energy spectrum and temporal evolution of cosmic radiation in the inner heliosphere with high fidelity. To improve its accuracy, particularly in the low-energy range, we calibrated and validated the model using the latest cosmic-ray data from space-based instruments, including AMS-02 aboard the International Space Station and ACE spacecraft. We established a robust cross-correlation between the model’s free modulation parameters and the sunspot number (SSN), serving as a proxy for solar activity. To enhance accuracy, we applied advanced signal decomposition techniques to filter out short-term periodicities typically associated with transient solar events such as flares and coronal mass ejections (CMEs). This correlation enables a solar cycle- and species-independent generalization, paving the way for long-term forecasting of GCR flux based solely on the knowledge of SSN. The model not only reproduces observations accurately but also demonstrates significant potential for space radiation monitoring and forecasting.
The goal of this TDM is to present, to the European space weather community, the computer modelling roadmap produced by the ESA Space Weather Office . This strategic document, endorsed by international experts in the domain, will be used to plan the space weather modelling activities of ESA for the next years. This will be an opportunity for the community to provide feedback and to weight in the topics covered by the roadmap. The conveners will share the document before the ESWW so all the community will have the option to prepare their intervention in the TDM. Convener 1 will provide an introduction to the document with only a few slides. The conveners will then give the floor to two or three of the experts that endorsed the document to hear their opinions and to answer questions from the public. The conveners will provide context to the discussions. Additional live interaction is expected with the use of interactive smartphone apps gathering questions, suggestions, and keywords.
There is growing evidence for climate change to have a significant impact on ground-based observations of space weather and space climate, especially in high-latitude regions. The list of impacts includes long-term atmospheric changes affecting radio wave propagation, more extreme weather events that can disrupt observations, thawing of the permafrost affecting the stability of observatories, changes in cloud cover hindering optical observations, and more.
While some of these impacts have been documented, most remain largely unknown or are rarely mentioned. This TDM, which is organised by the E-SWAN Sustainability Working Group, aims at sharing some examples but also, and foremost, collecting testimonies from the audience. Our objective is to raise awareness of these impacts and pave the way for a document that could provide a global view of the numerous (and often unsuspected) consequences of climate change on scientific activities.
The format will be that of a panel forum, targeting all users of infrastructure, especially in high-latitude regions.
The reliability of navigation and positioning services in the Arctic is increasingly critical for maritime operations, aviation, infrastructure development, and emergency response. These applications are all vulnerable to space weather disturbances, which primarily affect the ionosphere and, consequently, the performance of GNSS-based technologies.
This Topical Discussion Meeting (TDM) aims to bring together researchers, industry stakeholders, and end users to explore the synergies between GNSS radio occultation (RO), GNSS reflectometry (GNSS-R), and ground-based GNSS observations. By integrating these complementary data sources, we aim to discuss how to enhance ionospheric monitoring and modeling capabilities in the Arctic region, where conventional observation networks are sparse and space weather effects can be severe.
This TDM aims to connect forecasters with modellers, researchers, and end-users to begin bridging the gap of the O2R pipeline. There is a disconnect in communications between operational forecasters and researchers, especially those not already included in an R2O2R pipeline which has had a significant impact on model development and progress in the space weather forecasting field. What is interesting from a scientific point of view might not be relevant for a specific space weather impact/user need/use case. An open and transparent pipeline from scientific investigations to space weather impacts would make fund allocation more efficient for modellers, so they could focus resources/efforts on model capabilities most relevant to end-users needs. Some of the key questions to ask during this TDM include: What products/tools would make your life easier as a forecaster? What feedback from the forecasters to model developers would be most helpful to improve models/tools? What communication style and/or products are most helpful to end-users? By answering these questions and offering this platform, we aim to spark advancements in space weather forecasting and foster meaningful conversations that drive the future development of related products and tools.
The Sunspot Number (SN; Clette and Lefèvre, 2016 ) and Group Number (GN; Chatzistergos et al., 2017) series are the only direct time series (1610- present) that trace the long-term variations of solar activity over the past centuries. These records are crucial not only for solar/stellar physics and space weather studies but also for assessing the Sun's influence on Earth's climate.
While modern observations provide better links with space weather effects, SN and GN remain the longest direct observations of solar activity, and thus, are an indispensable bridge linking past and present solar behavior.
In 2016, an international team led a major update of the existing SN/GN series. However, issues remain and a decade after the release of SN version 2.0, efforts to refine sunspot calibrations continue, leading to several new versions of GN (Clette et al., 2023). Current work is focused on updating the GN database (following Vaquero et al., 2016), culminating in the development of a new SN database for historical data and the subsequent reconstruction of GN and SN, paving the way for version 3.0.
This session welcomes presentations on all aspects of historical sunspot observations, including (but not limited to) analyses of characteristics of the sunspot series, performance of cross-calibration techniques, recovery and correction of historical sunspot records, and also comparisons of sunspot series with other solar activity indices. By exchanging ideas, through presentations and discussions, we can strengthen our collective effort to make both time series more accurate, understandable and accessible to the scientific community.
The extraordinary importance of the sunspot number is given by the fact that it is the longest data series available for the study of the long term behaviour of the solar activity. This is also one of the sources of its weakness because of the calibrational challenge of the datasets recorded in the large number of different time intervals. A further drawback is that it is a dimensionless parameter without direct physical meaning, a simple inventory of observed features regardless of their importance. The recent progress in the upgrade of the Debrecen sunspot databases makes it possible to achieve our goal published earlier to establish a new activity index constructed from the amount of magnetic flux in emerging active regions. This will be a genuine physical index measured in Weber. The lecture overviews the necessary calibration procedures and the preliminary status of the addressed time series by comparing it to the sunspot numbers. The planned dataset may open the possibility for the empirical assessment of the global magnetic flux amount produced during a solar cycle.
Historical observations play an important role in understanding past solar activity and any changes that may have occurred with respect to the sun. The current state of most historical data is not well-suited for modern data analysis, including artificial intelligence and machine learning. Most data sets are also not well discoverable and are at risk of being lost. This leaves historical data often overlooked and underutilized. This presentation will focus on recent work (including citizen science projects) to identify critical historical data, digitize, create appropriate metadata, and make it ready for modern usage, such as AI/ML. Along with a discussion on how the community may play a role in helping to identify and facilitate access to at-risk historical archives.
Long-term reconstructions of sunspot number (SSN) and group sunspot number (GSN) often tacitly assume that the basic characteristics of solar activity remain unchanged even over long times, e.g., that the sunspots and sunspot groups now and, say, 100 or 500 years ago have the same relations and characteristics. However, this assumption needs examination, especially as the long-term homogeneity between sunspots and several other solar activity parameters has recently been challenged (Mursula et al., 2024).
Here we use long series of sunspot observations to study if and how the number of sunspots per group varies at different time scales. We find that the yearly mean number of sunspots per group varies closely in phase with the solar cycle over the whole 270-year time interval. The yearly sunspot/group ratios vary between about 3 and 10. Moreover, we find that number of sunspots per group depicts a very similar secular (Gleissberg) cyclicity as sunspots.
The ESA Vigil mission will be the first dedicated space weather mission positioned at the L5 Lagrange point, providing a unique vantage point for continuous monitoring of solar activity and interplanetary space. By complementing observations from Earth’s perspective, Vigil will enable improved early warning capabilities for space weather forecasting and operational decision-making. The mission’s six baseline instruments—four dedicated to remote sensing and two for in-situ measurements—will deliver high-quality, low-latency observations from the solar surface, through the corona and heliosphere, and in situ — to enhance both real-time space weather services and solar physics research.
Although primarily designed as an operational mission, Vigil will provide unprecedented high-cadence science data from a unique perspective that will transform our understanding of space weather, from the Sun’s magnetic field evolution at the surface to solar atmosphere processes that drive space weather events.
A critical aspect of mission readiness is engaging with both operational and scientific communities to refine data products, develop new analytical tools, and enhance Vigil’s impact. This session focuses on strategies for fully exploiting the unique opportunity that Vigil presents. We welcome contributions incorporating L5-oriented research, especially those that combine multiple datasets with other current and upcoming missions, as well as new models, tools, and analysis techniques.
JEDI is a next-generation high cadence, multi-thermal EUV Imager selected by NASA to fly on the European Space Weather Mission Vigil in a halo orbit around the Lagrange Point L5. JEDI will improve our understanding of space weather and enhance space weather operations capability by providing vital observations of earth-directed space weather events from the solar disk out 6 R⊙. JEDI will also answer fundamental questions about the Ground State of Space Weather...the Solar Wind.
With 10x greater throughput than EUI/FSI in occulted mode >1.4 R⊙, JEDI makes high cadence observations of the "Middle Corona” (West et al. 2023) out to 6 R⊙ standard and routine. This important, yet little-observed, middle corona, is the critical region of CME acceleration, flare reconnection and solar wind formation. With its large FOV, JEDI directly complements other Vigil instruments, providing overlapping FOVs that connect the observations from the Photospheric Magnetic Field Imager (PMI) with those from CCOR (Compact Coronagraph).
The JEDI instrument is comprised of two simple, reliable, low-risk/high-heritage telescopes, the Space Weather Operational Coronal Imager (SWOC) and the Enhanced Wide-angle Observations of the Corona (EWOC). SWOC takes 4 min cadence images of the full solar disk and extended corona out to 3.2 R⊙ on the Earthward limb in three narrow passbands corresponding to temperatures ranging from the chromosphere to the flaring corona. EWOC takes images of the full solar disk and extended corona in two passbands sensitive to chromospheric and coronal plasma out to 6 R⊙. EWOC’s game-changing FOV is enabled by a moveable occulter, proven by Solar Orbiter Extreme Ultraviolet Imager (EUI/FSI), allowing interleaved, low-scattered light images of the extended corona and on-disk coronal structures.
Combined observations from the Metis coronagraph and EUI/FSI on board Solar Orbiter, together with recent results, will be presented to focus on future scientific objectives of the JEDI (Joint EUV coronal Diagnostic Investigation) instrument on Vigil.
In particular, we discuss the potential of multi-band coronal observations
(Metis in visible light and UV, and FSI at 174 Å and 304 Å, both on-disk and in coronagraphic mode) to provide a unique opportunity to infer the physical parameters of the streamer belt, where the sources of the slow solar wind are located.
This is fundamental for studying the key processes that connect coronal sources of outflows with the heliosphere and for constraining coronal models characterizing the mechanisms responsible for plasma heating and acceleration.
Accurately predicting the evolution and impact of solar disturbances—such as solar energetic particles (SEPs), stream interaction regions, and coronal mass ejections (CMEs)—demands a precise reconstruction of the background solar wind and its intricate small-scale structures. These subtle features play a critical role in determining the timing, shape, and geoeffectiveness of space weather events. To meet this challenge, we developed a novel, multi-step methodology called the Reverse In Situ and MHD Approach (RIMAP). Our approach begins by analyzing high-resolution in-situ measurements at 1 AU (near Earth), using ballistic mapping to trace the equatorial solar wind back toward the Sun. We then stop at 0.1 AU, where we use state-of-the-art 2D magnetohydrodynamic simulations—powered by the PLUTO code—to let the system relax into equilibrium, preserving the wind's fine-scale structure with exceptional fidelity. This reconstructed solar wind background enables us to model CME propagation with unprecedented detail, also providing information on the magnetic connectivity with the Sun of our planet, and any spacecraft orbiting the ecliptic plane. RIMAP has revealed the potential to infer the original chemical composition of solar plasma, as well as reproduce magnetic switchbacks as driven by flow pulses in sheared magnetic fields. As a compelling demonstration, we are applying RIMAP to the September 5, 2022 CME event, aiming to constrain shock front structures and gain new insights into SEP propagation mechanisms, and combining in-situ data acquired by Solar Orbiter and Parker Solar Probe. A lower resolution version of RIMAP is currently running under the SWELTO (Space Weather Lab in Turin Observatory) project, demonstrating the capability to provide a daily adjournment on the shape of the Parker spiral of interplanetary magnetic fields, solar wind densities, and velocities. Our results showcase the transformative potential of RIMAP-based modeling in advancing heliophysics research and enhancing real-time space weather forecasting capabilities. The RIMAP model will find a perfect application in the prediction of the circumterrestrial environment with an advance of about 4.5 days starting from the in situ data acquired by the future Vigil mission at the Lagrangian point L5.
Vigil is the first space weather mission in ESA’s Space Safety program to position a spacecraft at the Lagrangian L5 point of the Sun-Earth system. Vigil will peer behind the solar limb (as seen from Earth) and monitor solar activity in quasi real-time, 4-5 days before it becomes visible from ground.
A key instrument onboard Vigil is the Photospheric Magnetic-field Imager (PMI), a full-disc vector magnetograph and tachograph, that builds heavily on the heritage from the SO/PHI instrument, the Polarimetric and Helioseismic Imager on Solar Orbiter. Following the established design principles of SO/PHI, PMI relies on a reflecting off-axis telescope design and samples the photospheric Fe I absorption line at 617.3nm with a tuneable filter system based on a solid state LiNbO3 Fabry-Perot etalon. The polarisation of the incoming light is modulated by liquid crystal variable retarders and a linear polarizer, and subsequently recorded by a 2k x 2k CMOS detector, synchronously to the modulation. An image stabilisation system based on a limb-sensor provides the necessary stability to obtain difference images at a noise level of 0.001 of the continuum intensity, needed to detect line-of-sight magnetic fields as weak as 5-10 G.
The recorded spectropolarimetric data are converted into physical quantities of the solar atmosphere in near real-time, by numerically inverting the polarized radiative transfer equation onboard. PMI will provide full-disc maps of the photospheric continuum intensity, the three components of the magnetic field vector and the line-of-sight component of the photospheric flow velocity at a cadence of 30 min, with a spatial resolution of about 2” (1.6 Mm on the Sun), and with an on-board latency of about 20 min.
In addition to standard data products meant to improve forecasts, PMI will also provide observations of significant scientific value, as we are learning from the analysis of the data recorded by the SO/PHI instrument, especially when combining these data with those taken by resources in Earth orbit. Such observations will include Dopplergrams recorded at a 1 min cadence, making them useful for helioseismology.
The extreme space weather events of Solar Cycle 25 highlight the urgent need for a comprehensive, interdisciplinary approach to understanding solar-Earth interactions. This session aims to bring together experts from solar and heliospheric physics, as well as
magnetospheric, ionospheric, and atmospheric physics to investigate the formation, propagation, and impacts of solar storms. By studying the magnetic connectivity and dynamics of the source regions leading to solar flares, and eruptions accompanied by the solar energetic particle events, we seek to understand how solar activity influences interplanetary space and interacts with the planetary environment. The propagation of coronal mass ejections and their interactions within the heliosphere are crucial for assessing the extent of space weather disturbances. The session will also address the
broader space implications of these extreme events, as the impact of geomagnetically induced currents on engineering infrastructure remains an important topic for space weather mitigation strategies. We encourage you to submit abstracts on events covering all aspects of space weather, from the Sun to the Earth, and their impacts on other planetary
environments. We welcome modeling and observational studies. By fostering interdisciplinary collaboration, this session aims to improve our understanding of space weather as a system-wide phenomenon and strengthen links between research communities.
On May 10th 2024, the first of at least five interplanetary coronal mass ejections (ICMEs) arrived at Earth and caused the strongest geomagnetic storm (Gannon storm) in over twenty years. The effect of this storm was global, however in this study the effect on the Swedish power grid is in focus. By using satellite data from Wind, ground magnetometer data from the IMAGE network and ground conductivity modeling over the region, we study this storm from the solar wind down to the induced geoelectric field in the ground. A large induced geoelectric field is the leading cause of space weather related disturbances in the power grid and during this geomagnetic storm a disturbance was reported in southern Sweden. The disturbance occurred at 22:29 UT May 10th in the connecting line to the SwePol power line, connecting Sweden and Poland. Our analysis show that there are two clearly separated signatures on the ground, before and after the disturbance, which reached the same level of dB/dt and geoelectric field but can be connected to different solar wind drivers. Further, this event is put into context by analyzing two previous disturbances in the same power line, confirmed to be caused by geomagnetically induced currents (GICs). Another aspect of this study is the effect of timing and magnetic local time (MLT) to determine if the impact could have been worse or fundamentally different had the ICME arrived at a different time. Due to the complex structure of the interplanetary magnetic field (IMF) caused by the multiple ICMEs, timing is even more important in order to understand the effects of the complicated, embedded structures traveling with the solar wind. Through this timing analysis, we can see that Sweden's MLT experienced the worst of the storm at the time of the disturbance, but if the ICME would have arrived ten hours before, the effect would have been much greater, with electric field values more severe than those with an expected occurrence once every 100 years (Lanabere et al, 2024). This emphasises the unique impact merged ICME structures can have on a given region. The results of this study also highlight the need for further development of other parameters that accurately correlate with GIC effects, since the time of the reported incident did not coincide with the peak geoelectric field throughout the storm.
Geomagnetic disturbances lead to the generation of geomagnetically induced currents (GICs) in technological systems like power grids. In some cases, mainly during intensive events, these GICs can irreversibly damage the transformers and even cause power grid blackouts.
At the same time, the proper numerical simulation of GICs is rather challenging task due to the many variables that affect the behaviour of GICs in the power grids. Examples are: a) both spatial and temporal behaviour of the inducing magnetic field varies for different events and regions; b) GICs are induced by geoelectric fields, that in turn, depends on the spatially and depth varying ground conductivity distribution; c) GICs are strongly dependent on power grid configuration. And, last but not least, in addition to the feasible models for the aforementioned variables one also needs effective numerical methods and computational codes that allows all of these issues to be taken into account.
In this presentation we show how these challenges were overcome or, at least, tackled by the New Zealand Solar Tsunamis project:
a) A new spatial source parametrisation was created by using the magnetic field data from both global INTERMAGNET network and New Zealand newly installed grid of magnetic field observations called MANA (Magnetometer Array for New Zealand Aotearoa). This model allows us to describe the behaviour of the inducing magnetic field above New Zealand with only 6 – 8 spatial modes;
b) Using the data collected during multiple magnetotelluric campaigns we created a new three-dimensional conductivity model for the whole of New Zealand;
c) A new power grid model was created by using the updated data provided by Transpower New Zealand Limited;
Using the multi-site transfer function approach we performed and validated the simulations of GICs during the strongest geomagnetic events in recent years, i.e., the "Gannon storm" of May 2024. The obtained results show the good or even near-perfect agreement with observed GICs and confirm that each and every aforementioned “ingredient” is necessary for the accurate simulation.
In previous work, we applied our modelling framework for geomagnetically induced currents (GICs) in the German high-voltage transmission grid to geomagnetic storms of Solar Cycle 25, including the May 2024 event. This analysis revealed significant GIC amplitudes (>20 A) across several substations.
This consequently raises the question of the likelihood of such an impact in the future and the relevant local indicators. An established approach in the space weather community is the derivation of so-called geoelectric hazard maps from a purely geophysical perspective. This typically involves a selection of historical events, usually based on geomagnetic disturbance peaks recorded by geomagnetic observatories, and a statistical evaluation of the corresponding induced geoelectric fields, calculated considering the local conductivity structure.
Here we present the first results of this approach and address its adaptation to the technical characteristics of the German transmission grid. Based on assumed GIC thresholds and induced voltages in the transmission lines, respectively, we identify GIC-effective geoelectric field scenarios. To further enhance the practical relevance, we discuss the extent to which these scenarios are recognizable in readily available measurements or derived products, e.g. geomagnetic indices, which could serve as local GIC indicators.
The session focuses on the state-of-the-art understanding of the complex mechanisms ruling the Magnetosphere-Ionosphere-Thermosphere (M-I-T) coupling and how they translate into space weather impacts. Such an understanding is fundamental for the developing effective countermeasures against disruption, failure and deterioration of vulnerable technologies, including GNSS critical applications, HF/VHF/UHF radio communications and LEO satellite operations. It is essential to improve the prediction of both the underlying physical phenomena and how these are related to space weather impacts. This improved understanding is crucial for better forecasts, warnings, and mitigate measures for adverse space weather effects. Other crucial aspects of M-I-T coupling are the interhemispheric symmetric/asymmetric response to variable drivers, vertical coupling and coupling between different latitudinal regions which, if properly predicted, could support regional space weather modelling. This session seeks to encourage and foster dialogue between researchers studying the underlying physical phenomena and operators seeking to mitigate space weather impacts. As such, contributions are invited which address any aspect of M-I-T coupling and associated threats to systems at regional and global scales.
Space weather events like solar flares cause enhanced absorption of radio waves in the ionosphere most notably in the lowest part of it, the D-region (ca. 60–100 km altitude range) which can weaken radio signals and can pose difficulties to radio communication at certain frequencies. There exist several methods to qualitatively or quantitatively assess the absorption in the layers of Earth’s ionosphere using the different data of the ionosondes. For example based on the received amplitudes of the echoes, the D-region absorption in the ionosphere can be quantified (Buzás et al. 2023). In the current study, we present another method to utilize the upper, higher-frequency part of the spectrum (practically 10–30 MHz) of the ionosonde measurements where usually there are no reflections from the emitted electromagnetic pulses. Basically the instrument “listens” to the background noise (either of terrestrial or extraterrestrial origin) received by the antenna system at these frequencies. In this mode of measurement, it is possible to extract information on the ionospheric absorption. Here, we aim to show our preliminary results. We analyzed ionosonde amplitude data recorded at Dourbes, Sopron and Athens stations both during quiet periods and periods with M- and X-class solar flare events in 2024. The seasonal and diurnal variation of some selected frequency bands are discussed, as well as the ionospheric response at different frequencies and in the integrated frequency values during the flare events. According to the results the changes in the integrated frequency seems to be a promising way to determine the ionospheric absorption changes caused by solar flares.
References:
Buzás, A., Kouba, D., Mielich, J., Burešová, D., Mošna, Z., Koucká Knížová, P., & Barta, V. (2023). Investigating the effect of large solar flares on the ionosphere based on novel Digisonde data comparing three different methods. Frontiers in Astronomy and Space Sciences, 10, 1201625.
Ionospheric indices play a crucial role in monitoring and understanding the dynamic behavior of the ionosphere. By examining the temporal and spatial variability of Total Electron Content (TEC) or electron density, we can detect and characterize ionospheric perturbations across various scales. Beyond their scientific value, these indices also help assess the potential impact of space weather on the availability, accuracy, and functionality of modern radio systems used for telecommunications, navigation, and remote sensing.
Several ionospheric proxies have proven effective in estimating the degree of ionospheric perturbations at medium and large scales. However, indices such as the Rate of TEC Index (ROTI), the Disturbance Ionosphere indeX Spatial Gradient (DIXSG), the Gradient Ionosphere indeX (GIX), and the Sudden Ionospheric Disturbance indeX (SIDX) primarily rely on ground-based GNSS measurements, which are limited to observation sites mostly located in populated regions. In contrast, space-based measurements offer global coverage. In this context, ESA’s Swarm satellites have been providing high-quality data and services for more than a decade, enriching our understanding of space weather phenomena and their impact on human activities. The Swarm Ionospheric Bubble Index (IBI) detects sub-kilometer plasma bubbles in magnetic field measurements, while the Ionospheric Plasma IRregularities Index (IPIR) uses Langmuir Probe measurements along satellite tracks to estimate electron density gradients at scales of up to 100 km. Additionally, the newly developed TEC Gradient Index (TEGIX) and Electron Density Gradient Index (NeGIX) complement Swarm’s data products, enabling the estimation of spatial horizontal gradients at scales of approximately 100 km. The relevance of Swarm data products is further enhanced by recent efforts to provide them with a near-real-time Fast Track latency.
In this work, we present a comparative analysis using some of the ground- and space-based indices mentioned above and examine their potential to characterize the perturbation degree of the ionosphere. By comparing their performance, we aim to determine how strongly the indices correlate with one another and whether one can substitute another. For example, Swarm products may be used to estimate ionospheric perturbations for GNSS operators and users over oceans or in regions with limited ground-based data availability. The analysis is applied to events of strongly perturbed geomagnetic conditions, such as the Mother’s Day storm of 2024 and the St. Patrick’s Day storm of 2015, as well as to quiet conditions with little indication of ionospheric gradients. Although our results show a strong correlation between the ground- and space-based indices for the selected periods and regions, their overall performance and applicability require further analysis, which will be discussed in more detail during this presentation.
Such complimentary studies are essential for advancing space weather science and improving monitoring and forecasting capabilities. They also help in developing effective mitigation strategies and enhancing the resilience of technological systems against the effects of space weather.
Variations in the ionospheric currents can cause rapid disturbances in the magnetic field at the ground level, so called dB/dt spikes, and Geomagnetically Induced Currents (GICs) that can harm human infrastructure. When investigating dB/dt spike occurrence and GIC risks, the focus has typically been on geomagnetic storms. However, recently it has been argued that it is the substorm phenomena which contains the crucial physics for spikes and GICs, and which instead should be in focus. Here we present results from a statistical investigation on the occurrence of spikes in substorms (“substorm spikiness”) as observed in the geomagnetic activity indices SME, SMU, and SML provided by the SuperMAG collaboration. We study the substorm spikiness for different years in the solar cycle and for different levels of geomagnetic disturbance according to the SMR ring current index, and we search for possible solar wind drivers. We investigate both the magnitude and the variability of various potential drivers and conclude that some of the more important drivers are the solar wind speed magnitude and its variability.
The solar wind causes a continuous modulation of the high-latitude ionosphere, namely in the electric field, thermosphere heating, plasma transport, thermospheric composition and circulation. The main goal of this study is to investigate the mechanisms that play a role in the response of the ionosphere-thermosphere system to the variations in the solar wind and to quantify the response time regarding to different ionospheric regions.
We use about twenty-five years of data provided by IGS Total Electron Content (TEC), and also EISCAT electron density and TEC, AISstorm atmospheric ionization rates generated by precipitating particles and Kan-Lee merging electric field Em obtained from OMNIWeb for our study. We chose Tromsø, Norway (69.58°N, 19.23°E) location for our analyses, as multiple data sources collocate there and can be paired to study the ionosphere conditions for long time periods. We investigate the response of the ionosphere to the solar wind variations during winter nighttime by using a lagged correlation method on IGS TEC and OMNI Em dataset, covering a 90 days period (1 January ± 45 days) between the years of 1998 and 2024. To identify the response of the different ionospheric regions and quantify their sources of the response, we select four EISCAT campaigns covering multiple days in the winter nighttime. EISCAT TEC is integrated and interpolated from the electron density profiles, for three separated regions in the ionosphere: 90-150 km (E-layer), 150-500 km (F-layer), and 90-500 km (both layers). We apply the same lagged correlation method between OMNI Em and EISCAT TEC for all campaigns.
Our analyses show that the correlation between twenty-five years of IGS TEC and OMNI Em data in the auroral oval peaks at about ≈120 minutes time-lag. We observe two different ionospheric responses from the EISCAT campaigns: 1) driven by the E-region auroral particle precipitation (>keV) with ≈45-90 minutes of lag, and 2) driven by the F-region soft particle precipitation (a few hundred eV) during substorms and polar cap patches/blobs convected to the Tromsø location with ≈90-135 minutes of lag. Comparing the response times observed in the IGS and EISCAT datasets, we conclude that both auroral particle precipitation and convection processes that are taking place in the F-region are strongly controlling the persistent ionospheric response caused by the solar wind variations and modulating the ionosphere.
There is an established tradition at ESWW for different forecast centres to present a Live Space Weather Forecast, either before or after the morning plenary session. Attending these forecasts enables participants to gain insights into the forecasting process and understand the real-world impact of space weather on end-users. It provides an opportunity to reflect on how we, as a community, can enhance our communication of these complex concepts to end-users and the public. With different forecast centres presenting, attendees benefit from a variety of forecasting perspectives and methodologies tailored to different end-users. Additionally, it offers an excellent opportunity for forecast centres to showcase their expertise and highlight the communication channels they use, fostering a deeper understanding and collaboration within the space weather community.
Be welcome at the European Space Weather and Space Climate Association General Assembly! We will present interesting news and plans for the future. E-SWAN was established to provide a formal, organisational and supporting structure for the three cornerstones of the European Space Weather and Space Climate community: the ESWW, the JSWSC and the International Space Weather and Space Climate Medals. It holds the mission!
Moreover, E-SWAN incorporates and supports much more activities than these, so exciting news from Committees and Working Groups developments will be emphasised.
Programme:
• 10:00 E-SWAN Achievements
• 10:05 Highlights from the WGs
• 10:15 Highlights from the PubCom
• 10:20 Highlights from the AwCom
• 10:25 Highlights from the EOCom
• 10:30 Financial report
• 10:35 Statutes and bylaws update presentation and approval vote announcement
• 10:40 Q&A, Closing, including selection of volunteers for the signatures
Holding the yearly General Assembly is a legal requirement for the current association's setup.
The ESA SWWT plenary session is the opportunity for the European space weather community and users to work together with ESA to plan the future space weather activities in Europe. This is a great opportunity to bring to the attention of ESA the needs and innovations that require urgent attention, but also to propose a long-term vision for the future of the community. In this plenary session ESA and the SWWT will introduce its members and will present their shared vision for the S2P Period 3 (2025-2028) and long term prospects. Attendees will have the opportunity to have their voice heard by all the community and by key stakeholders. ESA and the SWWT will show how ideas become a reality at ESA, and how each member of the space weather community can interact with ESA via the SWWT.
The SWWT Steering Board invites all participants of the ESWW to prepare questions and suggestions and to actively discuss with the panellists during the plenary session. We also invite all participants to continue the discussions with the ESA and SWWT members outside the plenary session. Do not miss this opportunity to have your voice heard!
Program:
10:45 - European space weather landscape: an ESA and SWWT perspective
10:55 - The members of the SWWT Steering Board and their contribution to ESA and European space weather.
11:00 - Opportunities in the ESA Space Safety Programme for 2026-2028 and beyond
ESA Space Weather Service Network (Alexi Glover)
Space segment evolution (Melanie Heil)
Dedicated smallsat missions (Stefan Kraft)
VSWMC and modelling activities (Jorge Amaya)
R&D opportunities in the ESA Technology Directory (Gregory Deprez)
11:25 - Q&A
On Wednesday during ESWW, following the E-SWAN General Assembly and the ESA SWWT, we will host a panel discussion on the Future of Space Weather in Europe. This panel will bring together leading experts who will explore the future direction, challenges and opportunities in space weather for Europe. They will also discuss how we can maintain and enhance collaboration within our global community. Don’t miss this opportunity to shape the future of space weather policy in Europe. We look forward to your participation.
Chairs: Michaela Brchnelova. Suzy Bingham.
Panellists:
- Juha-Pekka Luntama, European Space Agency Space Weather Office (ESA/ESOC)
- Stefaan Poedts, European Space Weather and Space Climate Association (E-SWAN)
- Thea Dethlefsen, Directorate-General for Defence Industry and Space (DG DEFIS)
- Mamoru Ishii, International Space Environment Service (ISES)
- Johan Köhler, Swedish National Space Agency (SNSA)
Space weather forecasting can rely on either physics-based or data-driven approaches. On the one hand, physics-based methodologies have deeper historical roots, with physical equations being studied and applied to model solar events and better understand unknown physical processes. On the other hand, data-driven approaches and, specifically, artificial intelligence (AI) algorithms process multi-modal data to identify patterns/correlations with no (or little) reference to physical models.
However, it has been recently explored the possibility to combine both approaches, by leveraging physics to inform the machine learning methods, and applying machine learning to better estimate key parameters in MHD deterministic equations.
This session aims to provide a platform for sharing and discussing research on data-driven and hybrid approaches combining physics-based and AI methodologies in space weather studies, with a focus on forecasting applications. Topics include predicting solar phenomena driving space weather, such as solar flares, coronal mass ejections (CMEs), and Solar Energetic Particles (SEPs), as well as modeling CME and SEP propagation to estimate arrival times at Earth, and predicting geomagnetic disturbances.
Additionally, submissions on space weather-related forecasting applications are encouraged, such as identifying and classifying active regions and detecting solar structures.
As AI techniques have reached a high level of maturity, and recent studies have demonstrated that combining AI with physics-based approaches holds great promise offering reliable tools for space weather forecasting, coupled with the fact that solar activity is currently at its peak (when eruptive phenomena are more frequent and intense) the topic of the proposed session is particularly timely.
Timely and accurate forecasting of interplanetary coronal mass ejections (ICMEs) is essential for mitigating their impact on space- and ground-based infrastructure. While significant advances have been made in predicting ICME arrival times and identifying their in situ signatures, integrating these steps into a continuous operational pipeline remains a challenge.
In this work, we present the next major development of the ARCANE framework by coupling it with arrival time forecasting and real-time magnetic flux rope reconstruction. The resulting automated pipeline combines:
(1) ELEvo - a drag-based model for predicting ICME arrival times
(2) ARCANE - a machine learning–based framework for automatic ICME detection in solar wind in situ data
(3) 3DCORE - a semi-empirical flux rope model that is now automatically triggered by ARCANE to perform real-time reconstruction of the ICME’s internal magnetic structure.
Here, we demonstrate the first fully automated pipeline capable of identifying the onset of an ICME’s magnetic obstacle in real-time and initiating immediate 3D modeling of its internal structure. By integrating detection and modeling into a unified system, we enable both improved nowcasting and short-term forecasting. We describe the technical implementation of this end-to-end framework, showcase initial results and discuss its potential for operational use in the future, combining physics-based models with AI for improved space weather forecasting.
In this talk, we will present our results in leveraging deep learning techniques for the automatic classification of solar active regions, for both the Mount Wilson and the McIntosh classification schemes. For this latter one, we consider a hierarchical multitask learning approach that mirrors the dependency structure inherent in the McIntosh system, which decomposes sunspot morphology into three components: the modified Zurich class (Z), penumbral class (p), and compactness class (c). We will present advanced model training techniques, including the teacher forcing method applied in the McIntosh classification. This method proves useful for enhancing training stability and convergence speed. It also mitigates error propagation by incorporating ground truth labels as input for subsequent tasks, with its influence gradually decreasing throughout the training process.
Radiation-belt enhancement events, during which electrons reach energies high enough to penetrate spacecraft shielding, pose serious hazards to satellites. Reliable forecasts of both the peak flux level and the duration above an operationally safe threshold would be invaluable to satellite operators. In this talk, I present an algorithm based on Gaussian Processes (GP) to produce probabilistic forecasts that (1) estimate how long fluxes will remain elevated above a certain threshold, and (2) predict the peak intensity of each event. Our GPs are trained on historical NOAA GOES > 2 MeV electron-flux data to learn the characteristic signatures of enhancement events. We then construct a Bayesian framework that yields full predictive distributions, delivering not only mean forecasts but also uncertainty bands essential for space-weather decision-making. We demonstrate our approach across a variety of past radiation-belt storms, highlighting its accuracy, reliability, and the operational value of its uncertainty quantification.
In recent years, studies have shown that it is possible to predict the geoeffectiveness of solar activity (L1 solar wind speed, geomagnetic indices) directly from EUV solar images using deep learning models (Upendran et al. 2020; Bernoux et al. 2022; Brown et al. 2022; Hu et al. 2022; Wang et al. 2025). These models, which are ultimately intended to be used operationally to provide early warnings of space weather events, are currently at the prototype or proof-of-concept stage and, although already more accurate than most other approaches, have numerous limitations. This is the case for the SERENADE model (Bernoux et al. 2022), which predicts the daily maximum of the Kp index a few days in advance from EUV images at 193A provided by the Atmospheric Imaging Assembly (AIA) instrument on the Solar Dynamics Observatory mission. Although the prediction performance for fast solar wind driven events was at least as good as the current state of the art, several weaknesses in the model were identified. The first weakness was that a latent vector was extracted from each image using a pre-trained GoogLeNet model instead of a model specific to solar images. This was addressed by Tahtouh et al. (under revision at JGR: MLC), who show that a Variational AutoEncoder results in a better structured latent space that correlates better with the physical properties of the Sun and leads to more stable and credible predictions.
However, the gain in accuracy with SERENADE was modest. We are exploring another avenue here, which lies in the training dataset used. So far, we've only used images from the SDOML dataset with its limited temporal coverage (2010 - 2020) during a weak solar cycle. We now generated our own ML-prepared dataset of SDO/AIA images from 2010 to mid-2025, adding 4.5 years of data during the rising and maximum phases of the current, much more active solar cycle. In addition, we use a dataset of SOHO/EIT images, also ML-prepared, which allows us to extend our dataset back to 1996, benefiting from nearly 30 years of data instead of the original 11. We analyze the benefits of this temporal extension and assess the extent to which our previous model may have been underestimated. Given that many studies also rely exclusively on the use of the SDOML dataset, our results are potentially generalizable to other models, and may indicate whether performance gains could be achieved without changing the architecture but simply increasing the database.
In addition, we take advantage of having two datasets from different instruments to perform a preliminary study of the extent to which training such a model with one dataset can produce usable results when used with data from another instrument (zero-shot learning), which would be of interest in preparing for the future when the SOHO and SDO missions have been discontinued.
Machine learning (ML) has shown promise in space weather applications, yet its
predictive power is often limited by the scarcity of rare event data and the lack of
physical constraints. In this study, we explore a physics-informed neural network
(PINN) approach that integrates the VERB-CS model with a neural network model
to estimate cold plasma electron density in the plasmasphere. The network is trained
using in-situ observations from the Van Allen Probes (RBSP) and ARASE missions,
along with geomagnetic indices. Our goal is to assess whether embedding key physical processes such as particle transport, refilling, and loss mechanisms into the ML framework enhances forecasting performance, particularly during geomagnetically disturbed conditions.
We evaluate model skill across a range of geomagnetic activity, with a focus on the formation and evolution of plasmaspheric plumes at elevated Kp indices. Preliminary results suggest that the PINN approach captures both large-scale structure and
fine-scale features more accurately than purely data-driven models. Incorporating
physical knowledge into the machine learning model demonstrates increased generalizability across different geophysical conditions, including rare or extreme events. This work highlights the potential of combining physics-based models with data-informed learning to advance predictive capability in the near-Earth space environment.
As agencies and private enterprises around the world look to embark on an era of enhanced exploration beyond low-Earth orbit, so the need to understand better the radiation environment in transit and at the destinations of these missions is likewise enhanced. The near-term target for many missions is the Moon, the long-term horizon is Mars. Strategies to raise heavy spacecraft (elements) include electric orbit raising, due to the improved fuel efficiency but this can result in components spending time in the heart of the Earth's radiation belts. The accelerated "new space" approach applied to many robotic missions compared with previous exploration and science missions may result in components which may be susceptible to the environment. Humans travelling beyond the magnetically shielded confines of the Earth's magnetosphere will be exposed to a very different radiation field. The combination of risks to humans and to spacecraft components along with the need for very high reliability places stringent requirements on such missions. Requirements include both climatological understanding of the average and extreme environments as well as space weather forecasts for issuing of alerts both prior to launch and during mission operations.
Ensuring safety in the space environment is critical as human space activities, such as the Artemis program, become increasingly ambitious. In particular, solar energetic particle (SEP) events, triggered by solar flares (SFs) and coronal mass ejections (CMEs), pose significant risks to human health and space systems. To address these risks, Fujitsu Limited and ISEE, Nagoya University, have been collaborating since February 2023 on space weather research focused on predicting particle radiation, primarily for lunar, Mars, and deep space exploration. To further advance this initiative, we launched a collaborative research project with JAXA on February 1, 2025 (#1).
Conventional SEP forecasting methods have primarily relied on proton flux measurements in geostationary orbit. Recognizing the limitations of this approach for lunar and deep-space missions, our research group is working to develop predictive models based on in-situ observations and accurate radiation impact forecasting. This project employs AI to address these challenges.
First, we are developing an explainable AI (XAI) classification model for SEP event prediction using in-situ radiation data from lunar orbit. Second, we are constructing an AI-based regression model to estimate equivalent radiation doses from predicted SEP proton flux/fluence, incorporating the energy spectrum. This will facilitate discussions with JAXA regarding practical considerations for future missions.
(1) We are identifying SFs that cause radiation enhancements in lunar orbit using NASA's LRO/CRaTER data accessed via the CRaTER Web interface (#2). This unique in-situ dataset covers events from July 2009 to the present. We have identified SEP-induced SFs that have not been reported so far, in addition to known events (#3). We are developing a forecasting model for these SFs and associated SEPs using Fujitsu's XAI, "Fujitsu Kozuchi XAI WideLearning" (cf, ESWW2024, CD5.2 Kato et al.). This model provides event probability and prediction reasoning, valuable for operational decision-making. We will discuss our system's potential and advanced pre-flare forecasting models using ISEE NLFFF data.
(2) We are constructing an AI-based regression model to predict SEP proton profiles (flux, fluence, and energy spectrum) using data from SOHO/COSTEP EPHINE and GOES/EPAD, HEAPD, and SGPS. We plan to assess the potential impact of these events on spacecraft and astronauts and will present recent progress.
Finally, we will discuss future prospects for an integrated forecasting system, incorporating JAXA's developing detector. We aim to establish space weather forecasting and real-time dose evaluation using data from instruments like Lunar-RICheS and PS-TEPC (#4), planned for Artemis. We will report on AI collaboration discussions with JAXA's such compact, high-performance radiation measurement and real-time dose assessment for lunar utilization in addition to LRO/CRaTER data.
Solar eruptive events can accelerate electrons that usually precede the arrival of the proton and ion components during solar energetic particle (SEP) events. These latter species have been much more studied in SEP radiation environment models than electrons. Solar energetic electron (SEE) populations are typically detected in the kinetic energy range from a few keV to a few MeV, with differential flux enhancements, above the background levels, of up to several orders of magnitude (depending on the instrument). SEE events are important for space weather safety considerations as they can have significant effects such as surface charging and surface erosion, particularly for interplanetary missions of long duration.
The study we present is embedded within the scope of the ESA’s FIRESPELL project. One of the objectives of this project is to extend the application of the SAPPHIRE-2S particle radiation model to electrons, in the energy range from 50 keV to 4 MeV, and for interplanetary missions travelling within 0.2 au to 10 au from the Sun.
By using data from IMP-8/CPME, ACE/EPAM and SOHO/COSTEP, a standardized dataset of electron events has been generated spanning from 1974 to 2017. From this dataset an event list of 401 electrons events was generated. Based on both the observed intensity-time profiles of the events at 1 au and the heliolongitude of the parent solar eruptive event, a quantitative classification system was used to divide the event list into 5 categories, each covering a statistically significant portion of the sample. From each category, up to two reference events were selected due to their archetypical constitution of the given class, with a total of 8 reference events. These reference events were modelled and fitted to the 1 au observational data using the particle transport model PARADISE as well as, in some cases, the MHD model EUHFORIA. Following the procedure developed during the SEPEM project (sepem.eu) and for the modelled events, we next derived the peak intensities and the event fluences for several observers located at other radial distances which share the same magnetic connection to the solar source as the near-Earth spacecraft.
We present the final results of this study, the parameters and simulations used for fitting observations as well as the resulting peak intensity and fluence heliocentric radial dependence for the studied range of electron energies; thus, offering insights into the SEEs transport in interplanetary space.
FIRESPELL is a project funded by ESA (Contract No. 4000142510/23/NL/CRS).
The CHerenkov Atmospheric Observation System (CHAOS) is a student-developed particle detector flown aboard the BEXUS 35 balloon mission as part of the REXUS/BEXUS programme to test alternative concepts in near-relativistic ion detection. The instrument combines energy-loss measurements (dE/dx–dE/dx) with a velocity threshold from an aerogel-based Cherenkov detector, enabling clean measurements of the energy spectra from a few 10-s MeV to above 1 GeV for protons and helium. During its successful flight on BEXUS 35 in 2024, CHAOS took data above the Regener–Pfotzer maximum and demonstrated its ability to resolve the charged particle spectrum in the stratosphere. This shows that such a compact instrument can characterise the energetic particle environment.
ESA's European Exploration Strategy (Explore 2040) establishes goals oriented to cover destinations from low-Earth orbit to the Moon and on to Mars. One characteristic of the strategy is to enhance European leadership in key areas leveraging European expertise as a pathway to European self-determination whilst remaining a preferred partner for international cooperation. Science and enabling technologies are central to this aim as being transversal to all destinations. Two flavours of scientific investigations are foreseen: exploration-enabled science, fundamental or applied research led by the broader scientific community but taking advantage of ESA's exploration infrastructure, and exploration-focused science driven by programmatic needs.
Radiation science is of interest from a fundamental science perspective whilst being a critical area of exploration-focused science which is divided into four high-level key scientific goals addressing: Environments & Effects, Local Resources, Habitation, and Crew Health & Performance.
Radiation is a key component of the local environment, it is therefore important to understand the physical processes generating the local radiation field, how these environments will affect human and robotic activities and vice versa. The local surface materials and proposed habitation will impact the radiation field characteristics and constitute important inputs into simulations. Radiation-associated health risks are one of the critical aspects which may impact astronaut crew health and performance. Physical, chemical and biological understanding of the related processes in the human body is therefore important as is research into possible countermeasures to mitigate related effects.
This presentation provides an overview of radiation science for exploration as foreseen within Explore 2040. This includes:
- the specification and forecasting of the primary radiation environment focussed on solar energetic particles and galactic cosmic rays;
- the moderation of the radiation field by planetary surfaces and the Martian atmosphere;
- transport of radiation through spacecraft shielding and the effectiveness of different materials in moderating the radiation field;
- computation of dose quantities including human-specific doses and simulations of radiation effects in human biology;
- assessment on the risks of such radiation to humans;
- possible countermeasures to mitigate the effects of radiation.
It will also include requirements for in-space radiation measurements and ground-based facilities and the extent to which they are addressed with current and planned capabilities. Finally, the presentation will include details on the ongoing formulation of an exploration-specific radiation science roadmap to coordinate, direct, and harmonise efforts in this area in Europe to make best use of the resources and significant expertise available.
The Norwegian Radiation Monitor (NORM) is a compact, single-detector particle telescope developed for measuring energetic electrons and protons in space. Its modular architecture allows for deployment across a variety of orbital platforms, including geostationary (GEO), low Earth (LEO), and highly elliptical orbits (HEO).
The first NORM unit is currently flying aboard the Arctic Satellite Broadband Mission (ASBM), which follows a three-apogee (TAP), 16-hour highly elliptical orbit. This trajectory enables repeated crossings through the outer radiation belt and the outer edge of the inner belt—making it well-suited for monitoring dynamic space weather conditions.
Importantly, data from NORM are collected after each passage over the Arctic region, ensuring regular and consistent updates of the monitored radiation environment.
NORM provides high-quality, high-resolution flux measurements of solar protons in the 10–100 MeV range and electrons in the 0.4–6 MeV range, including ultra-relativistic populations that are often under-resolved by other instruments. These capabilities make NORM particularly valuable for space weather applications:
Validation studies confirm the consistency of NORM’s measurements of trapped relativistic electrons and solar energetic protons, showing strong agreement with ground calibrations, numerical response models, and third-party particle flux datasets.
The ASBM/NORM dataset represents a valuable new asset for the space weather community, providing both scientific-grade measurements and operational utility. Its unique orbital profile, coupled with reliable high-energy electron detection capabilities, establishes NORM as a key contributor to global efforts in space environment monitoring and modeling.
Acknowledgments: This work has been implemented by NORM Exploitation and Utilization System (NEXUS) project supported by the EC-DEFIS/2024/OP/0012 contract between European Commission, Directorate-General for Defence Industry and Space and IDEAS-SPARC. The development of NORM has been funded by the Norwegian Space Agency and ESA/ESTEC. The European Commission, the Norwegian Space Agency and Space Norway together own the rights to the data.
To achieve sustainable human planetary exploration, world-wide space agencies are collaborating to advance crewed mission programs. The Moon and Mars, key targets of these missions, lack the thick atmosphere and strong geomagnetic shield such like the Earth. As a result, the intensity of Galactic Cosmic Rays (GCRs) in these environments can reach an order of magnitude higher than in the low Earth orbit. In the lunar environment, it is estimated that particles composed of GCR above 500 MeV account for approximately 70% of astronauts’ GCR dose during an extravehicular activity and over 80% during an intra-vehicular activity. On deep-space missions, astronauts are continuously exposed to GCR, significantly increasing their risks of central nervous system (CNS) damage, cataracts, and cancer. Among the various constituents of GCRs, protons are particularly important as they account for more than half of the GCR dose to astronauts, making proton measurement essential for radiation risk assessment.
The energy spectrum of GCR protons typically peaks between 200 and 1000 MeV/n, depending on solar activity. Since its intensity decreases with a simple power-law beyond the peak, measuring at least five points in the 200–2000 MeV/n range would provide a reasonable approximation of the GCR spectral shape. Up to now, most of radiation protection detectors portable for astronauts have been measured energy loss of particles to determine their kinetic energies, such as semiconductor or gas detectors. However, measuring high-energy particles up to 2000 MeV with these detectors requires large and complex systems, which are not suitable for portable use. For compact devices capable of GeV-scale energy measurements, a Cherenkov detector is one of optimum choices.
Our team—comprising Japan Aerospace Exploration Agency (JAXA), Tokyo University of Science (TUS), and RIKEN—is developing the Lunar-RICheS (Ring Imaging Cherenkov Spectrometer). Lunar-RICheS integrates two independent detector systems: a stacked semiconductor detector for low-energy measurements (–250 MeV), developed by JAXA, and a Ring Imaging Cherenkov detector for high-energy measurements (250–2000 MeV), developed by RIKEN and TUS. The high-energy measurement unit, called the Compact Cherenkov Counter (CCC), consists of a ring imaging Cherenkov detector combined with a position-sensitive strip detector (Double Sided Si-strip Detector, DSSD).
The ring imaging Cherenkov spectrometer is a detector that combines a crystal radiator with a 64-channel Multi-Anode Photomultiplier Tube (MAPMT), and determines particle energy based on the number of Cherenkov photons detected. A key advantage of the CCC is its ring imaging–based background suppression capability, which reduces the impact of secondary particles such as a delta ray and a spallation reaction fragments —one of the main challenges in high-energy particle measurements with a Cherenkov detector.
To date, we have conducted proof-of-principle experiments using high-energy proton and heavy-ion beams at accelerator facilities, as well as integration tests with the position-sensitive strip detector. In this time, we will report on the principles and current development status of the CCC.
The ESA Vigil mission will be the first dedicated space weather mission positioned at the L5 Lagrange point, providing a unique vantage point for continuous monitoring of solar activity and interplanetary space. By complementing observations from Earth’s perspective, Vigil will enable improved early warning capabilities for space weather forecasting and operational decision-making. The mission’s six baseline instruments—four dedicated to remote sensing and two for in-situ measurements—will deliver high-quality, low-latency observations from the solar surface, through the corona and heliosphere, and in situ — to enhance both real-time space weather services and solar physics research.
Although primarily designed as an operational mission, Vigil will provide unprecedented high-cadence science data from a unique perspective that will transform our understanding of space weather, from the Sun’s magnetic field evolution at the surface to solar atmosphere processes that drive space weather events.
A critical aspect of mission readiness is engaging with both operational and scientific communities to refine data products, develop new analytical tools, and enhance Vigil’s impact. This session focuses on strategies for fully exploiting the unique opportunity that Vigil presents. We welcome contributions incorporating L5-oriented research, especially those that combine multiple datasets with other current and upcoming missions, as well as new models, tools, and analysis techniques.
We will present our modeling effort to characterize and understand the solar corona and wind with the Wind-Predict-AW data driven 3D MHD model. In particular we focus on how multi-vantage points EUV emissions along with while light images can be used to constrain such coronal model with advanced heating mechanisms (waves, turbulence, radiation or conduction). We extend our analysis to include the coronographic mode of Vigil/JEDI and how the streamer and pseudo streamers extent and latitudinal width and distribution can be used to improve our 3D understanding of the dynamical coupling between the surface magnetic fields and the observed coronal (bright ARs, dark Coronal holes) and wind (fast vs slow streams) properties.
Coronal holes (CHs) are known to be sources of high-speed solar wind streams (HSSs), yet the physical mechanisms linking CH position and characteristics to solar wind (SW) behaviour remain unclear. Our results reveal that the latitude of CHs, especially smaller ones, combined with the heliographic latitude of the solar disk’s central point (B0 angle), plays a critical role in driving discrepancies in SW velocity across the heliosphere. To investigate this, we use archival data from STEREO-B, STEREO-A, and Earth to simulate an L5-L1 configuration, where L5 is a vantage point approximately 60 deg. behind Earth in its orbit (as proposed for the Vigil mission), and L1 is between Earth and the Sun where SW measurements are typically taken. We use these insights to develop a predictive algorithm for HSSs, beginning with an analysis of the separation angle and distances between L5 and L1. We then introduce a predictive indicator and empirical criteria based on CH properties and the B0 angle to adjust for changes in SW velocity at L1. Our results show that the L5 viewpoint demonstrates the capability to significantly improve the accuracy and lead times of HSS predictions, enhancing our understanding of the CH-HSS relationship and potentially improving space weather forecasting.
The ESA Vigil mission, to be launched in 2031, will enable unique observations of solar activity and space weather monitoring from the Sun-Earth Lagrange point L5, a gravitationally stable position 60° behind Earth in its orbit. It will capture Earth-bound coronal mass ejections, which will also be observed from the L1 and Earth vantage points. NASA’s twin-spacecraft STEREO mission (launched 2006) demonstrated the power of such stereoscopic solar observations: STEREO captured simultaneous images of the Sun from different angles, allowing to triangulate the positions of coronal loops and CME fronts, and thus to reveal their three-dimensional structure, which a single line-of-sight view cannot discern. Previously, we developed algorithmic and data-driven multi-instrument solar eruptive feature recognition and tracking methods and applied them to tracking coronal bright fronts and CMEs from the low corona out to 30 solar radii, using ground- (COSMO K-Coronagraph) and space-based (SDO/AIA, SOHO/LASCO C2 and C3) telescopic observations. In the current work, we extend our approach to study events simultaneously observed from L1/Earth and near the L5 point (by STEREO-A and STEREO-B instruments), approximating the expected geometric configuration between Vigil and the Earth/L1. In addition, we showcase the application of our automated feature tracking methods to the newly available Compact Coronagraph (CCOR) data, thereby preparing our methodology for future Vigil observations.
The ambient solar wind plays an important role as one of the driver of geoeffective space weather activity. The solar magnetic field is 'frozen-in' and carried outward by the solar wind plasma. As it frozen-in, it follows the parker spiral. Usually 4-5 sectors of opposite polarity are present in the Interplanetary medium. Sector boundaries are the regions where the magnetic field direction changes rapidly. As the Sun rotates its sector structure — large-scale magnetic regions — rotates with it, fluctuations in the interplanetary magnetic field (IMF) in the near-Earth environment. When these variations in IMF orientation encounter Earth's magnetosphere, they can trigger Geomagnetic storms. Besides this, Solar wind also exhibits the Periodic Density Structures (PDSs) that are quasi-periodic fluctuations in the solar wind's density. PDSs advect with the solar wind and have radial scales of 10-1000 Mm . The periodic variations in solar wind density and dynamic pressure can drive oscillations in the magnetosphere, influencing geomagnetic activity and space weather [Di Matteo et al 2024] . Therefore, understanding their spatial extent, and IMF characteristics is vital.
Aditya-L1 is India's first solar observatory at the Sun-Earth L1 point which was successfully inserted into its halo orbit on January 6, 2024. Currently we are analysing the data from Aditya’s L1 insitu MAG and ASPEX payloads. Our goal is to characterize magnetic field and study turbulence, as well as solar wind ion velocities and density distributions. The long term data collected will be used to significantly enhance our understanding of anisotropies, shocks, and radial scales of solar wind periodic density structures (PDSs). We're have developed the ASPEX pdata processing pipeline. In the ESWW, we will present the results on Solar Wind PDSs structures derived from the solar wind density, time data-series measured from Jan 2024 till May 2025 by ASPEX payload onboard AdtiyaL1 spacecraft. We hope these results and methodologies will greatly benefit the Vigil mission too.
Nevertheless, measurements from a single spacecraft limits our capability to determine density and magnetic structures only along the radial Sun-Earth line (Lx). In order to observe the entire 3D expanse and size scales perpendicular to the Sun–Earth line (Ly), we need interplanetary multispacecraft observations capable of coherently and continuously measuring the solar wind at spatial separation perpendicular to the radial direction. In this scenario, Vigil mission, at L5 point that is located ~ 60 deg azimuthally behind wrt L1 point will be very useful.
A dual-point in-situ observation strategy, leveraging density and magnetic field measurements from both L1 and L5, might enable us to assess dimensions of solar wind PDSs , magnetic structures as well as their temporal evolution and fluctuation at the IMF sector boundaries when they cross these two crucial Lagrange points.
This study deals with prominence eruptions captured by the Extreme Ultraviolet Imager/Full Sun Imager (EUI/FSI) on board the Solar Orbiter. We analyse a selection of 230 eruptions from the detailed catalogue available at https://www.sidc.be/EUI/solar-eruptions, focusing on events where prominences reach projected heights beyond 2 solar radii. The large field of view (up to fourteen solar radii) allows FSI to observe solar eruptions from the solar disc up to heights never before observed in EUV passbands. This makes the instrument uniquely suited for tracing the early phases of eruptions through the middle corona.
The large set of investigated events offers a wide perspective on the inherent variability within these phenomena. This statistical analysis aims at uncovering the various properties of these eruptions, such as speed and deflection, morphological features, 3D characteristics, and their interaction with neighbouring magnetic field structures. We present here the status of this statistical study with an overview of the observed prominence eruptions and their properties. We focus especially on the difficulties we experienced due to limitations in the data and how the Vigil mission can contribute to this type of study to overcome these obstacles.
Space weather is largely driven by the drastic and sudden evolution of magnetic structures in the Sun. Sometimes such transients lead to the sudden release of magnetic energy in the form of radiation or mass ejections. In other cases, newly formed or emerging structures alter the equilibrium of a magnetic complex, triggering eruptions. While the study of the magnetic field in the solar atmosphere remains a significant challenge for observations and models, understanding these mechanisms is essential to improve our space weather prediction capabilities. Magnetic structures, such as flux ropes, filaments/prominences and coronal loops form as part of active regions and along polar inversion lines. These structures evolve dynamically across the layers of the solar atmosphere, from the photosphere to the corona, and their evolution can culminate in eruptive events. Many theories based on observations (from new instruments such as PHI, EUI, METIS on-board Solar Orbiter) or numerical simulations have been put forward to explain how they trigger space weather events. Moreover, such mechanisms in the solar corona are the only close and observable examples of several plasma processes (e.g. magnetic reconnection or magnetic confinement) that hold the key to a deeper understanding of plasma physics. In this session, we will host contributions that show the current state of the art of observation and modelling of the solar atmosphere that illustrate the role of these magnetic structures and how their evolution affects space weather and how they can be used to help to improve our forecasts.
The ERC-AdG project Open SESAME (project No 101141362) aims to develop a time-evolving model for the entire solar atmosphere, including the chromosphere and transition region, based on a multifluid description. Currently, models are primarily steady, rely on a single-fluid description and include only the corona due to computational challenges. We plan to use time-evolving ion-neutral and ion-neutral-helium models. The multifluid approach will enable us to describe the intricate physics in the partially ionised chromosphere and quantify the transfer of momentum and energy between the atmospheric layers. The questions of where the solar wind originates and how solar flares and coronal mass ejections are driven have fundamental scientific importance and substantial socio-economic impact. Indeed, the solar atmospheric model is the crucial missing link in the Sun-to-Earth model chain to predict the arrival and effects of CMEs on Earth reliably.
Combining our implicit numerical solver with a high-order flux-reconstruction (FR) method makes this ambitious goal possible. The implicit solver avoids the numerical instabilities that lead to strict time-step limitations on explicit schemes. The high-order FR method enables high-fidelity simulations on very coarse grids, even in zones of high gradients. We introduced three critical innovations. First, we combine high-order FR with physics-based r-adaptive (moving) unstructured grids, redistributing grid points to regions with high gradients. Second, we (will) implement CPU-GPU algorithms for the new heterogeneous supercomputers advanced by HPC-Europa. Third, we implement AI-generated magnetograms to make the model respond to the time-varying photospheric magnetic field, which is crucial for understanding important solar plasma properties and processes.
Thus, we will develop a first-of-its-kind high-order GPU-enabled 3D time-accurate solver for multifluid plasmas. If successful, we will implement the most advanced dynamic data-driven solar atmosphere model in an operational environment. The project started on 1 September 2024, and we will present interesting results on time-dependent corona modelling, high-order flux-reconstruction simulations on moving grids, and AI-generated magnetograms that are better than those obtained with the solar flux transport model.
Solar eruptions are ubiquitous in the sun and play a significant role in space weather. With the advent of multi-view observations, we can gain a better understanding of the three-dimensional structure of these eruptive events and identify the various energetic processes involved. To fully grasp the physics behind these phenomena, it is essential to develop innovative simulations that complement these observations.
To this end, we have developed a numerical framework to model the evolution of active regions (ARs) using non-force-free magnetic field extrapolation, based on a magnetogram taken close to the onset of a flare, along with a stratified atmosphere. This presentation highlights the results of a solar eruption that occurred in NOAA AR 12241 on December 18, 2014.
Our simulation shows that a flux rope develops and rises self-consistently in the same direction as the observed eruption, without any arbitrary assumptions regarding the flux rope structure. With the aid of an algorithm that identifies and tracks the magnetic flux rope, we examine the dynamics of this structure and determine its kinematic properties. Additionally, we calculate synthetic extreme ultraviolet (EUV) emissions from different perspectives, allowing us to make direct comparisons with observations.
We also incorporate test particles into the model to identify particle acceleration sites and predict the location and shape of non-thermal emissions. Furthermore, we quantify the energy proportion that is transferred into heating, eruptions, and particle acceleration.
Our work not only deepens our understanding of the processes involved during a solar eruption but also clarifies energy distribution throughout the event. Ultimately, this represents a significant step forward in enhancing predictive capabilities for space weather, which is crucial for safeguarding technology and infrastructure on Earth.
Studying magnetic flux ropes is crucial for understanding the origin and evolution of Coronal Mass Ejections (CMEs), as these twisted magnetic structures often serve as the core configuration driving CMEs from the solar corona. On November 9, 2021, the Metis coronagraph (Antonucci et al. 2020) on-board ESA Solar Orbiter mission, observed a slow erupting flux rope, when the spacecraft was at 0.88 AU and near the inferior conjunction with the Sun, thus enabling combined observations with SOHO/LASCO-C2. The erupting structure seems to be associated with the Active Region (AR) 12895 (26°N, 28°E), which had a simple bipolar magnetic configuration, and exhibited gradual changes in the coronal loop morphology, though no flare was detected.
The eruption manifested as a faint, bubble-like structure, slightly brighter than the surrounding ambient corona, and was observed in both visible light (VL) and ultraviolet (UV) Lyman-alpha channels of Metis. The morphology – characterized by a diffuse bright front and a weaker core - suggests that what we observed was a slowly expanding, hollow flux-rope. Base-difference images enhanced the visibility of the front and the core regions, revealing a darker cavity. The whole structure was first detected in LASCO-C2 data, with the core located at a heliocentric distance of 3.5 $R_{\odot}$ around 18:00 UT and entered in the Metis field-of-view (> 5 $R_{\odot}$) shortly after midnight.
Kinematic analysis of the Metis images indicates a propagation velocity of about 160 km/s for the core, with a weak acceleration (< 10 km/s$^2$). The flux-rope expansion was tracked across LASCO and Metis FOVs, displaying an overall elliptical geometry of both front and internal cavity. The Metis VL and UV Lyman-α images have been analysed using the direct ratio technique (Bemporad 2022) to obtain 2D maps of electron temperature inside the expanding flux-rope. The thermodynamic evolution of expanding flux ropes is indeed interesting to study, since many studies based mainly on SOHO/UVCS data have demonstrated the existence of unidentified physical processes responsible for additional heating against adiabatic cooling. Our results show – as expected – a gradual cooling of the embedded plasma during expansion, and we are now quantifying the possible additional heating during the expansion.
These preliminary results offer insights into the dynamics and thermal evolution of CMEs, which can potentially modify their interplanetary evolution and therefore their eventual impact on Earth.
We present a comprehensive statistical study between type II radio bursts from the metric (m) to the dekameric–hectometric (DH) domain and their association with different solar and space weather phenomena, namely, solar flares, sunspot configurations, filament eruptions, coronal mass ejections, their interplanetary counterparts and shocks, in situ detected particles and geomagnetic storms. We analysed m-type II radio bursts from Radio Solar Telescope Network (RSTN) data that is freely available from four worldwide stations: Learmonth, Sanvito, Sagamore Hills and Palehua, and DH-type IIs from Wind/WAVES for solar cycle 24 (2009−2019). We perform the temporal and spatial association between the radio emission and the listed above activities, separately for the three sub-categories, m-only, m + DH and DH-only type IIs. A quantitative assessment on the occurrence rates is presented as a function of the strength of the specific solar and space weather phenomena: highest rates are obtained with solar events, whereas a much weaker relationship is found with their interplanetary counterparts. The outcomes could be used in revealing the occurrence of solar and space weather activities based on the ground-based radio perspective.
Solar active regions, accumulations of strong magnetic field, play a crucial role in driving space weather. Their evolution can influence the solar wind, and they can trigger eruptive phenomena. Active regions have long been studied from Earth’s vantage point using instruments such as the Helioseismic and Magnetic Imager (HMI) on the Solar Dynamics Observatory (SDO). While HMI allows for uninterrupted monitoring of the photospheric magnetic field as seen from Earth, the full evolution of active regions can usually not be captured due to their extended lifetimes. Helioseismic far-side imaging offers indirect detection of active regions beyond Earth’s view. However, this method remains limited by the lack of polarimetric information, which prevents a complete study of the magnetic flux evolution of the regions from emergence to their eventual decay.
The ESA/NASA Solar Orbiter mission addresses these limitations by observing the Sun from different vantage points. Among its suite of instruments, the Polarimetric and Helioseismic Imager (SO/PHI) is the first instrument to acquire polarimetric data of the solar photosphere from outside the Sun–Earth line. The unique heliocentric orbit of the spacecraft allows observing the Sun for extended periods each year at a longitudinal separation angle of more than 130º from the Sun-Earth line. During these periods, SO/PHI performs regular synoptic observations with its Full Disk Telescope.
The combination of SO/PHI’s direct observations of the solar far side with direct observations of the near-Earth side gives rise to new opportunities for the study of the evolution of the photospheric magnetic field, especially of active regions. Thus, the regions can be observed well beyond the very limited window available from a single viewpoint. An almost uninterrupted tracking enables studying the evolution of active regions over multiple solar rotations, which in some cases means over their full lifetime. In combination with the extreme ultraviolet instruments on board Solar Orbiter and SDO this offers a deeper insight into the interplay between the surface magnetic fields of the active regions and their structures and dynamics in the overlying corona. Furthermore, co-temporal observations of the photospheric magnetic field from opposite sides of the Sun allow a more frequent production of synoptic maps, which enhances coronal and space weather models. Ultimately, the dual-perspective has permitted, for the first time, the direct calibration of helioseismic far-side measurements. Such calibrations are crucial, especially when direct far-side observations are unavailable, as they provide a more complete view of the Sun’s full 360º magnetic field which significantly influences solar wind modelling.
Space weather forecasting can rely on either physics-based or data-driven approaches. On the one hand, physics-based methodologies have deeper historical roots, with physical equations being studied and applied to model solar events and better understand unknown physical processes. On the other hand, data-driven approaches and, specifically, artificial intelligence (AI) algorithms process multi-modal data to identify patterns/correlations with no (or little) reference to physical models.
However, it has been recently explored the possibility to combine both approaches, by leveraging physics to inform the machine learning methods, and applying machine learning to better estimate key parameters in MHD deterministic equations.
This session aims to provide a platform for sharing and discussing research on data-driven and hybrid approaches combining physics-based and AI methodologies in space weather studies, with a focus on forecasting applications. Topics include predicting solar phenomena driving space weather, such as solar flares, coronal mass ejections (CMEs), and Solar Energetic Particles (SEPs), as well as modeling CME and SEP propagation to estimate arrival times at Earth, and predicting geomagnetic disturbances.
Additionally, submissions on space weather-related forecasting applications are encouraged, such as identifying and classifying active regions and detecting solar structures.
As AI techniques have reached a high level of maturity, and recent studies have demonstrated that combining AI with physics-based approaches holds great promise offering reliable tools for space weather forecasting, coupled with the fact that solar activity is currently at its peak (when eruptive phenomena are more frequent and intense) the topic of the proposed session is particularly timely.
Operational solar flare forecasting requires computationally efficient and energy-optimal methods that maximize the use of available observational resources to deliver timely and reliable predictions. Synoptic full-disk observations from the Solar Dynamics Observatory (SDO) provide continuous monitoring of solar magnetic activity over more than one solar cycle, enabling detailed studies of solar variability and space weather impacts. The Space-weather HMI Active Region Patches (SHARP) vector magnetic field (VMF) maps and parameters, derived from the Helioseismic and Magnetic Imager (HMI), support investigations of active region evolution and flare triggering mechanisms. In this study, we use time series of SHARP VMF maps as input to a Disentangled Variational Autoencoder (VAE), a Disentangled Representation Learning (DRL) method that extracts low-dimensional features capturing the morphological and dynamic characteristics of active regions. These VAE-derived features exhibit temporal evolution patterns similar to, but not redundant with, certain SHARP parameters, indicating that their combination provides an enhanced representation of solar magnetic activity. We construct a joint dataset merging human-curated SHARP parameters with machine-learned VAE features, resulting in a high-fidelity input for flare forecasting. Our forecasting pipeline utilizes this dataset to produce binary (Flare vs. No-Flare, Alert vs. No-Alert) and multi-class probabilistic predictions. The pipeline employs a Long Short-Term Memory (LSTM) network to learn the temporal evolution of the features for several time windows, followed by logistic regression to estimate probabilities for strategically labeled event classes. This integrated approach highlights the value of combining physics-derived and machine-learned representations to improve the accuracy and robustness of solar flare forecasting models.
Solar coronal magnetic fields store the magnetic energy that drives solar eruptions, such as flares and coronal mass ejections, which significantly impact space weather. Nonlinear force-free fields (NLFFFs) are commonly used to model the 3D coronal magnetic fields. We develop a physics-informed neural operator (PINO) model that learns the solution operator mapping 2D photospheric vector magnetic fields to 3D NLFFFs. The model is trained using both physics losses derived from the NLFFF partial differential equations and data losses from target NLFFF solutions. We first validate our approach on an analytical NLFFF model. Subsequently, we train and evaluate the model using 2,327 numerically computed NLFFF samples from 211 active regions in the Institute for Space-Earth Environmental Research (ISEE) database. Our results show that the trained PINO model can reconstruct NLFFFs in under one second on a single consumer-grade GPU, enabling real-time reconstruction of 3D coronal magnetic fields. For 30 selected active regions, the AI-generated NLFFFs exhibit qualitative and quantitative similarity to the target NLFFFs. Additionally, the magnetic energy evolution of the AI-generated NLFFFs for active region AR 11158 appears similar to both the target NLFFFs and those obtained from existing methods. Our model could be integrated into physics-based space weather forecasting frameworks, such as the flare forecasting method proposed by Kusano et al. (2020).
The spatial extension of active regions of the Sun and their associated images can strongly vary from one case to the next. This inhomogeneity is problematic when studying solar flares with convolutionnal neural networks (CNNs) due to their fixed input size. Several processes can be performed to produce a database with homogeneous-sized data, such as coarse resizing, cropping, or padding of raw images. Unfortunately, key features can be lost or distorted beyond recognition during these processes. This can lead to a deterioration of the ability of CNNs to predict flares of different soft X-ray classes, especially those from active regions with structures of great complexity.
This study aims to implement and test a CNN architecture that retains features of characteristic scales as fine as the original resolution of the input images. To do this, we compare the performance of two convolutional neural network models for solar flare prediction: the first one is a traditional CNN with convolution layers, batch normalization layers, max-pooling layers, and resized input whereas the other implements a spatial pyramid pooling (SPP) layer instead of a max pooling layer before the flatten-layer and without any input resizing. The models are trained on the SHARP Line-of-sight magnetogram database from 2010-05 to 2021-08 and using only images within 45◦ of the central meridian of the Sun. We also study two cases of binary classification: in the first case, our model has to distinguish active regions producing flares in less than 24h of class ≥C1.0 from active regions producing flares in more than 24h or never; in the second case, it has to distinguish active regions producing flares in less than 24h of class ≥M1.0 from active regions producing flares in more than 24h or never, or flares in less than 24h but of class lower than M1.0.
Our model implementing an SPP layer predicts flares ≥C1.0 within 24 hours more accurately than the traditional CNN model with a better TSS and PR AUC. However, its performances degrade sharply when the images of active regions producing a C-class flare are classified as negative. This may be attributed to its success in identifying features that appear in active regions a few hours before the flare, independently of their soft X-ray class. Furthermore, the results of this study may be the first lead to the importance of image size and ratio for flare forecasting using deep-learning methods. Further studies on the impact of image size and ratio may uncover important features for flare-triggering mechanisms.
We investigate data-driven strategies for identifying and predicting geoeffective events using long-term space environment observations. The study explores different unsupervised learning approaches for detecting statistical anomalies in solar wind in-situ measurements and geomagnetic data, with the aim of enhancing our understanding of solar-terrestrial interaction. Such anomalies may correspond to precursors or signatures of geomagnetic disturbances [1], particularly when the Natural Time Analysis [2] is applied to ground-based geomagnetic indices, such as SYM-H, to refine storm onset definitions beyond conventional threshold-based approaches.
[1] Sabrina Guastavino et al, Forecasting Geoffective Events from Solar Wind Data and Evaluating the Most Predictive Features through Machine Learning Approaches, 2024 ApJ 971 94.
[2] Panayiotis A. Varotsos et al. Complexity measure in natural
time analysis identifying the accumulation of stresses before major earthquakes, Scientific Reports 14.1 (2024): 30828.
In this work, we employ an attention-based deep learning approach to predict flare occurrence from multivariate time series of SHARP magnetogram features. The model takes as input active region data over varying time windows and outputs probabilistic predictions for C+-, M+-, or X+-class flare events. To capture the temporal evolution of active regions, the architecture leverages self-attention mechanisms and learnable positional embeddings, enabling it to model dependencies across time even in the presence of missing data.
We investigate how the temporal extent of the input affects forecasting skill by evaluating the model on sequences of different lengths, ranging from short snapshots to full 24-hour histories. This comparative analysis aims to determine whether longer observation windows, which offer a broader view of the magnetic evolution, allow the model to recognise precursor patterns that may not be evident over shorter intervals.
To ensure relevance to operational settings, the model is designed to accommodate missing data through a masking mechanism that guides the attention layers to focus only on valid observations. This allows the system to maintain predictive capability even in the presence of incomplete or irregular time series — a frequent challenge in space weather monitoring.
Our approach aims to support robust and flexible flare classification, and represents a step toward developing real-time forecasting tools that are resilient to data quality variability.
This session welcomes submissions on topics not covered under the remaining sessions. These submissions can be on any topic as long as they relate to Space Weather and Space Climate.
Interaction of Coronal Mass Ejection (CMEs) with High-Speed Streams (HSSs) could alter their plasma and magnetic field properties. The properties of the interaction should be encoded in the in situ plasma and magnetic field observations. To characterise the properties of the interaction, we analyse the in situ signatures of 30 interplanetary coronal mass ejections (ICMEs) interacting with high-speed streams (HSS) at 1AU between 2010 and 2018. We analyse the ICME velocity profiles, duration of the sheath and magnetic obstacle (MO), and distortion of the MO, as well as search for the signatures of the reconnection exhausts. We find 21 events where ICME is in front of the HSS and 9 events where it is behind the HSS. Statistical analysis is performed for these two classes of interaction separately. We find that ICMEs interacting with HSS generally show distinct speed profiles for cases where HSS is in front or behind. HSS catching up to ICMEs tends to accelerate them from the back, whereas HSS in front of ICMEs do not significantly alter the typical speed expansion profiles but tends to inhibit the formation of sheath - 70 precent of such events does not show discernible sheath region. We find that the average magnetic field magnitude tends to be higher for cases where the ICME is in front of the HSS compared to when it is behind. Although we find reconnection exhaust signatures in about 30% events, we do not find significant evidence of the distortion of the internal magnetic structure. Our results indicate that interaction with HSS does not significantly influence the ICME internal magnetic structure, however, it may significantly influence its kinematics.
Forbush decreases (FDs) are one of the very common in-situ signatures of interplanetary coronal mass ejections (ICMEs) throughout the heliosphere. These short-term reductions in the galactic cosmic ray flux are measured by ground-based instruments at Earth and Mars, as well as various spacecraft throughout the heliosphere (most recently by Solar Orbiter). We recently developed an analytical model to explain CME-related FDs using an expansion-diffusion approach and utilized it to develop a best-fit procedure (ForbMod, Dumbovic et al., 2018, 2020, 2024). According to the model, the amplitude of the depression at a given point in the heliosphere depends on the initial CME properties as well as its evolutionary properties.
We develop a scheme that will allow us to analyze CME evolution using a set of CME-ICME-FD observations, as well as in situ measurements only, and design a graphical user interface to perform ICME and FD analysis throughout the heliosphere. We measure, catalogue and analyse ICMEs and related FDs using Helios, Ulysess, SOHO and Solar Orbiter spacecraft, as well as ground-based measurements from the South Pole neutron monitor at Earth and MSL/RAD at Mars. This research was partly funded by the European Space Agency (projects ForbMod and ForbMod2) and partly by European Union (project SPEARHEAD, No 101135044). B. H and M. H. acknowledges support from the German Federal Ministry for Economic Affairs and Energy, the German Space Agency (Deutsches Zentrum für Luft- und Raumfahrt e.V., DLR) under grant 50OC2302. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Health and Digital Executive Agency (HaDEA). Neither the European Union nor the granting authority can be held responsible for them.
Due to geomagnetic dipole tilt, the solar zenith angles and the resultant ionospheric conductivities at the same geomagnetic (GM) latitude and local time are highest in the tilt direction, which are the North American sector in the northern hemisphere and the Australian-New Zealand sector in the southern hemisphere. As a result, the geomagnetic disturbances at observatories in the tilt direction should be higher than those at others during the same level of ionospheric and magnetospheric activity. This unevenness affects the AE and Kp stations used to calculate the geomagnetic Kp and AE indices. We consider this effect for very high values of AU > 1200 nT and Kp ≥ 9-. Past statistics shows that occurrence frequencies of the very high AU> 1200 nT and Kp≥9- depend on UT, depending on the geographic latitudes of postnoon stations. Since G-scale is defined by Kp, the result indicates that some G4 geomagnetic storms occurring at 09-15 UT may be as severe as G5 storms, and that the May 2024 space weather event is the most severe one occurring at 09-15 UT since 1978.
Geomagnetic storms are phenomena that pose a hazard to electronic devices on the earth’s surface. Accurate knowledge of the disturbance conditions of the Earth’s magnetic field is crucial to mitigate potential adverse effects. Here we present a comparison of the Dst and SYM-H global geomagnetic indices with local disturbance data from six stations in southern Europe over the period 1981-2021. It is known that the longitudinal distribution of the stations used in the derivation of both indices and their normalization process can lead to some loss of information or/and deviations from the local data, so we wanted to study if these indices were good representations of the magnetic disturbance in these latitudes. It was found that, on average, both indices were positively deviating from the local data below +10nT for the whole period, with the deviation in calm being below +6nT but increasing up to ∼+30nT for medium intensity storms and up to ∼+50nT for the most intense storms, with this last value varying up to ∼+80nT for the highest latitude station. In this analysis, a positive deviation implies an overestimation of the disturbance by the index with respect to the local data. Overall, SYM-H provided a better quantification of the disturbance during moderate to intense storms than Dst. However, negative peaks in deviation were recorded much larger in magnitude in the 1-minute data (SYM-H) than in the 1-hour data (Dst), implying rapid variations in the local data not recorded by the global indices. Thus, not only Dst and SYM-H are positively deviated with respect to the local disturbance, they also fail to record rapid negative drop peaks. This indicates that the quantification of the severity of magnetic storms requires a more comprehensive analysis than the description given by the geomagnetic indices.
Space weather can adversely affect the operation of satellites in Earth orbit and consequently exacerbate the problem of debris generation in space. The associated effects are more probable around the peak of a solar cycle. As we approach the peak of the 25th solar cycle, we leverage on our institute’s cross-program capabilities in model-driven assessment and prediction of LEO objects’ aerodynamic drag, to investigate the deleterious impact of space weather-enhanced atmospheric drag. In this work, we performed detailed analysis of long- and short-term drag impact on selected [catalogued] LEO objects and thus provide space situational awareness (SSA) in the current solar cycle regime. We also predict their rate of orbit decay ahead of the solar cycle peak using a novel ephemeris data-assisted calibration (EDAC) simulation approach and subsequently support the outcome with data obtained in the post prediction regime.
Space weather is largely driven by the drastic and sudden evolution of magnetic structures in the Sun. Sometimes such transients lead to the sudden release of magnetic energy in the form of radiation or mass ejections. In other cases, newly formed or emerging structures alter the equilibrium of a magnetic complex, triggering eruptions. While the study of the magnetic field in the solar atmosphere remains a significant challenge for observations and models, understanding these mechanisms is essential to improve our space weather prediction capabilities. Magnetic structures, such as flux ropes, filaments/prominences and coronal loops form as part of active regions and along polar inversion lines. These structures evolve dynamically across the layers of the solar atmosphere, from the photosphere to the corona, and their evolution can culminate in eruptive events. Many theories based on observations (from new instruments such as PHI, EUI, METIS on-board Solar Orbiter) or numerical simulations have been put forward to explain how they trigger space weather events. Moreover, such mechanisms in the solar corona are the only close and observable examples of several plasma processes (e.g. magnetic reconnection or magnetic confinement) that hold the key to a deeper understanding of plasma physics. In this session, we will host contributions that show the current state of the art of observation and modelling of the solar atmosphere that illustrate the role of these magnetic structures and how their evolution affects space weather and how they can be used to help to improve our forecasts.
Coronal Mass Ejections (CMEs) are the primary drivers of space weather phenomena.
Once a CME reaches Earth the severity of the geomagnetic response is dependent on CME properties such as speed, dynamic pressure, and the specific magnetic configuration of the CME. CMEs can be modelled with a bright front, dark cavity and core. This core is associated with a flux rope in CME models.
We present a case study of the formation and subsequent expansion of a flux rope prior to its eruption. This flux rope was observed in extreme-ultraviolet (EUV) on the 28th February 2024.
In addition to being observable in EUV images, the formation and evolution of the rope appeared to be associated with radio emissions that indicate that the energisation of electrons was taking place. The emission mechanism for the radio has been determined to be fundamental plasma emission and this emission mechanism allows for electron density to be calculated; we found that the electrons generating the radio emission range in density from 0.3e9-2.4e9 cm^-3.
It was also found that the EUV expansion pattern matched the expansion pattern the radio emission, supporting the interpretation what the EUV emission and the radio emission are both originating in the same structure.
This study shows that the value of using complementary EUV and radio data to probe the timescales over which a pre-eruptive structure forms, and how the radio emissions can be used to probe important flux properties such as plasma density.
Physics-based modelling of the large-scale dynamics caused by space-weather relevant Coronal Mass Ejections (CMEs) is conventionally carried out employing either a coronal or heliospheric approach. In the former, the dynamics all the way from the low corona to the heliosphere is modeled, while in the latter the simulation is started at heliocentric distances where the solar wind is characterised by super-Alfvénic outflow. Recently, we have successfully demonstrated an alternative to this paradigm, where the middle corona is included in the magnetohydrodynamic (MHD) model, encompassing a spatial domain starting at ~ 5 solar radii and extending out to the heliosphere. In this work, we present our on-going effort to exploit our time-dependent fully data-driven low-coronal model to provide key magnetic parameters of eruptions that are launched into the middle corona model. Combining the two methods provides a unique capability to efficiently perform data-driven dynamic modelling of CMEs from the low corona to the heliosphere. In this work, we present results of our modelling approach using multi-spacecraft observations to assess the performance of the model.
The solar storms of May 2024 were a series of powerful solar flares and coronal mass ejections (CMEs) that occurred between 10 and 13 May 2024, followed by a few strong solar flares over the next few days during solar cycle 25. As these eruptions propagated through the corona, they generated multiple solar type II radio bursts, indicating the presence of shock waves.
This study aims to analyse the characteristics of a series of type II radio bursts associated with a CME that occurred on May 14th, with a focus on the coronal conditions during the event and findings on the likely location where the radio bursts are generated.
We utilised data from multiple sources, including satellite observations from the Solar Ultraviolet Imager (SUVI) onboard the Geostationary Operational Environmental Satellite (GOES) and the Large Angle and Spectrometric Coronagraph (LASCO) instrument onboard the Solar and Heliospheric Observatory (SOHO), along with ground-based radio observations between 10−240 MHz from the Irish Low-Frequency Array (I-LOFAR) in Birr. We also employed several electron-density models to estimate the radial distances of the radio sources and incorporated the Potential Field Source Surface (PFSS) and magnetohydrodynamic (MHD) models to examine the coronal plasma conditions.
We identified four type II bursts in the I-LOFAR radio dynamic spectrum over ∼15 minutes, exhibiting features such as band splitting, herringbones, and fragmentation. The characteristics of the type II bands and the shock speed were estimated. The shocks' speeds range between ~ 443−2075 km/s, with drift rates ranging from ~-361 to -78 kHz/s. The shocks’ strength range between MA ≈ 1.56−3.47, implying that the shocks were super-Alfvénic. The first type II burst was triggered ∼18 minutes after the CME launch. The bursts appear to have been generated at different heights in the corona, with the first two occurring within the SUVI field of view and the third and fourth within the gap between the SUVI and LASCO C2 instruments. From kinematics analysis and modelling results, we inferred that the type II bursts were likely produced at the CME’s bottom flank, where open magnetic field lines and relatively low Alfvén speeds facilitated shock formation.
This multi-instrument study provides new insights into the generation of type II radio bursts and their relationship with CME-driven shocks. The findings highlight the role of coronal conditions, particularly the magnetic field configuration and the Alfvén speed distribution, in determining the heights and locations where these bursts originate. Our results reinforce the importance of continuous, multi-wavelength observations for understanding shock dynamics and improving constraints on coronal models.
Modelling solar eruptions is crucial to understand their triggers and how they might impact Earth's magnetic environment. Thus, magnetic field simulations of the low solar corona are of great relevance for space weather forecasting. In particular, simulations that are driven by the observed magnetic field at the photosphere have proven to be a powerful tool to model the energy build up and destabilization of magnetic flux ropes (MFRs), leading to their eruption. However, especially for models that do not evolve on physical timescales, it is important to understand the effect of the photospheric driving on the MFR system. For example, at which point in the simulation does the system become unstable and what is the influence of the magnetogram evolution on the MFR once it has already reached an unstable state? To investigate this, we use a time-dependent data-driven magnetofrictional simulation to model a flux rope eruption of active region AR12473, and systematically perform so-called relaxation runs, i.e., simulations where the driving is stopped at different points in time. The simulations are then continued without driving using both the magnetofrictional model (MFM), and a magnetohydrodynamic (MHD) model for comparison. The flux ropes are extracted with GUITAR (Graphical User Interface for Tracking and Analysing flux Ropes) and analysed in terms of their stability and evolution of characteristic quantities. We find that even between relaxation simulations with eruptive MFRs, the MFR properties can vary greatly depending on the chosen relaxation time for both MFM and MHD. Furthermore, the earliest relaxation time that yields an eruptive MFR is different between MFM and MHD, as is the conclusion on the triggering instability.
Solar flares result from the rapid conversion of stored magnetic energy within the Sun's corona. These energy releases are associated with coronal magnetic loops, which are rooted in dense photospheric plasma and are passively transported by surface advection. Their emissions cover a wide range of wavelengths, with soft X-rays being the primary diagnostic for the past fifty years. Despite the efforts of multiple authors, we are still far from a complete theory, capable of explaining the observed statistical and individual properties of flares. Here, we exploit the availability of stable and long-term soft x-ray measurements from NASA's GOES mission to build a new solar flare catalogue, with a novel approach to linking sympathetic events. Furthermore, for the most energetic events since 2010, we have also provided a method to identify the origin of the observed flare and eventual link to the photospheric active region by exploiting the array of instruments onboard NASA's Solar Dynamic Observatory. Our catalogue provides a robust resource for studying space weather events and training machine learning models to develop a reliable early warning system for the onset of eruptive events in the solar atmosphere.
The METIS Coronagraph onboard Solar Orbiter observes simultaneously in the Visible (VL) band between 580 and 640 nm and the Ultraviolet (UV) band at 121.6 nm. It also observes at a high spatial and temporal resolution, thus allowing a comprehensive characterisation of solar events.
In particular, the Metis team is creating a database of solar eruptive events observed in both the VL and UV channels. The CME Catalogue now has more than three years' worth of data, from November 2020 to December 2023 and is actively being updated.
An important subset of these events could be geoeffective and could therefore be linked with various space weather phenomena. Here, we describe the work being done in identifying these events from the Metis CME catalogue. The first step is to identify events that can be potentially geoeffective. Then, the identification is refined by applying the triangulation method, where the Coronal Mass Ejections (CMEs) are fitted using the Graduated Cylindrical Shell (GCS) model. This results in accurate measurement of the position, speed and direction of propagation of these events. For this purpose, along with the VL Channel data from Metis, white light coronagraph data from LASCO C2 and STEREO COR2A were also used.
Furthermore, we employ physics-based empirical models like the Drag-Based Model (DBM) and the Drag-Based Ensemble Model (DBEM), as well as time-dependent 3D Magnetohydrodynamic models like WSA-Enlil and EUHFORIA to predict the times of arrival at Earth. In this work, I will present an overview of the Metis observed geoeffective events identified so far from the catalogue while comparing the predicted results with the measurements of the conditions of the space environment surrounding Earth. Finally, based on these comparisons, we plan to assess the relative forecasting capabilities of the propagation models employed.
The inner magnetosphere hosts a dynamic range of plasma populations including the relativistic radiation belts, the ring current and cold plasmaspheric ions. These populations are tightly coupled via a range of micro-, meso- and macro-scale processes, driving a complex interplay of acceleration, transport and loss. For example, chorus waves are generated by injected plasma sheet electrons and then accelerate 100’s keV electrons to relativistic energies to form the radiation belts, with this acceleration being most efficient in regions of low plasma density. In turn, precipitation of radiation belt particles into the atmosphere balances ionospheric outflows of cold plasma into the inner magnetosphere. Further research into these and other cross-scale couplings is essential to develop the capability to reliably forecast inner magnetospheric dynamics and associated space weather risks and impacts. This session calls for observational, modelling and theoretical studies related to the inner magnetospheres, as well as review papers and mission concepts as well as comparative studies with other magnetospheres. We invite observational contributions from current missions such as Arase, Themis, MMS and GPS, from ground-based facilities such as EISCAT, SuperDARN and VLF receivers, and from historical datasets such as from the Van Allen Probes, Cluster and climatological studies involving even earlier solar cycles. We invite numerical contributions spanning Fokker Planck simulations, kinetic simulations of wave-particle interactions, and of the global magnetosphere and its couplings to the ionosphere and solar wind, as well as novel machine learning approaches and solutions.
Inner magnetospheric dynamics and coupling processes are highly dynamic, in part due to the interaction of electrons with a variety of electromagnetic waves. Here we present a novel combination of observations made in Fennoscandia that reveal energetic electron precipitation into the D-region ionosphere due to these coupling processes. We identify various magnetospheric phenomena, including substorms and a variety of EMIC waves. Using the European Incoherent Scatter (EISCAT) UHF radar located in Tromsø, Norway, we investigate how the electron density (Ne) profile changes when energetic electron precipitation occurs as a result of inner magnetospheric processes. Additionally, we monitor the lower D-region close to EISCAT using Sodankylä Geophysical Observatory’s (SGO) Very Low Frequency (VLF) receiver observations at Kilpisjärvi (KIL), Finland, of 16.4 kHz transmissions from the Noviken facility (call sign JXN) in Norway.
We examine a remarkable day in March 2023 focussing on (i) a seven-hour dayside train of EMIC waves, (ii) an Interval of Pulsations of Diminishing Period (IPDP) at dusk, and (iii) several substorms. The train of EMIC waves is very clearly seen using the SGO search coil magnetometer (SCM) at Abisko (Sweden) located on the JXN-KIL VLF path, ~130 km from the UHF radar. The processes we are describing in this 24-hour period highlight dynamic magnetospheric processes, loss mechanisms from the radiation belts, and provide insight into the space weather impact on the Earth’s lower thermosphere and ionosphere.
In this study, we investigate the temporal responses of trapped relativistic electron fluxes in the heart of the outer radiation belt during three geomagnetic storm periods. We relate satellite observations of relativistic electron fluxes to the variations in electron precipitation made using VLF subionospheric propagation techniques, which are sensitive to D-region ionisation levels. Such comparisons are used to highlight the differences in inner magnetospheric dynamical and coupling processes driven by weak, moderate, and strong geomagnetic storms. In space, we use 2 MeV electron flux measurements made by the GPS satellite, NS41, at L=4.5; in order to differentiate between geomagnetic storms, we use the newly defined Ca index; while to monitor the D-region, we use AARDDVARK VLF narrow-band transmitter observations on subionospheric paths that respond to ionisation changes at L=4.5. The geomagnetic storms investigated are 02 Sept - 09 September 2012 (weak); 26 August - 07 September 2015 (moderate); and 17 March - 31 March 2015 (strong). We show that key processes, such as substorms and chorus waves, dominate at different times as part of the geomagnetic storm evolution, depending on storm severity. Understanding the interplay between these couplings is key in developing the capability to reliably model and forecast inner magnetospheric dynamics and its associated space weather impact.
Radial diffusion, driven by ultra-low frequency (ULF) waves, is a key process contributing to the acceleration and loss of electrons in the outer radiation belt by contributing to their inner or outer transport. Ground magnetometers give us continuous observations of such ULF waves but their usefulness is limited by the models used to transform ground measurements into their progenitor fields in space. A typical assumption is that of a dipole field, and so measurements can only be reliably performed during the day. We have chosen a series of magnetometer stations at different sets of geomagnetic latitudes, with stations in each set separated by several hours in longitude, and compared their Pc5 ULF power measurements, and the resulting calculated radial diffusion coefficients, from 11 years of their data. By comparing the mismatch of their results when they were in different time sectors, and for different values of Kp, we were able to assess the median deviation of measurements conducted before dawn or after dusk with those on the day side. This will be useful for projects requiring a longitudinally limited array of magnetometers or for parts of the world where such coverage is limited, such as FARBES (Forecast of Actionable Radiation Belt Scenarios).
Electromagnetic ion cyclotron (EMIC) waves are generated in the equatorial regions of the inner magnetosphere. These waves propagate to the middle or low latitudes in the ionosphere through the ionospheric duct, detected as Pc1 waves by ground-based magnetometers. To figure out how the Pc1 wave power attenuates during propagation in space and on the ground, we investigated a magnetically conjugate Pc1 wave event occurring around 07:00 Universal Time (UT) on January 8, 2018, using data obtained from the Arase satellite and the four PWING ground-based magnetometers located at Zhigansk (ZGN), Magadan (MGD), Moshiri (MSR), and Gakona (GAK). The EMIC waves observed at the Arase satellite were in the helium (He+) band with frequencies of 0.35-0.55 Hz, and the conjugated Pc1 waves were also simultaneously detected within a similar frequency range at the different PWING ground stations. According to polarization analysis of the Pc1 waves, we found that the polarization angles pointed toward the magnetic footprint of the satellite, suggesting that the observed Pc1 wave on the ground and EMIC waves at the Arase satellite were magnetically conjugated. To determine the propagation effect from the source region to each observation point, we calculated wave power attenuation in decibels (dB) relative to the wave power obtained at ZGN. We found that along the ionospheric wave duct, the wave power attenuation factors were 8.22, 5.34, and 4.71 dB/1000 km for the ZGN-MGD, ZGN-MSR, and ZGN-GAK intervals, respectively. Along the magnetic field lines in the magnetosphere, the power attenuation factor was 0.37 dB/1000 km, which can be considered the minimum value. From the results, we suggest that the wave power of Pc1 waves attenuates much less when they propagate in the magnetosphere along magnetic field lines than when they propagate in the ionosphere through ionospheric wave ducts. For future work, we plan to investigate more magnetically conjugate Pc1 wave events using various datasets, including those from geosynchronous orbit satellites and LEO satellites, to statistically examine how the power attenuation changes under different geomagnetic condition.
Paulikas and Blake first approached the relationship between relativistic electron flux and solar wind speed in late 1979 and found a linear correlation when considering averages of solar wind speed and geosynchronous relativistic outer radiation belts electron flux for three different time intervals (1 day, 27 days, and 180 days). Many years later, Reeves et al., 2011 expanded the analyses using 20 years of data from Los Alamos National Laboratory (LANL) geosynchronous energetic particles and concluded that instead of a linear correlation, the relativistic electrons at the geosynchronous orbit follow a triangular probability distribution. In this distribution, the highest electron flux at geosynchronous orbit is independent of the solar wind speed; otherwise, the lower limit does show a solar wind velocity dependence. These works brought several implications for the outer radiation belt studies in geosynchronous orbit. Many important advances came after to explain how solar wind speed can drive the Earth's magnetosphere. In this work we analyzed the inner portion of the outer radiation belts using the Van Allen Probes era measurements to investigate the region deeper than the geosynchronous orbit, i.e., below L = 5.5, using electron flux measurements from 1 MeV to 5 MeV together with ULF wave measurements. Solar wind speed data are provided by the ACE satellite. We compared the relationship of the three distinct energy channels (1.8 MeV, 3.4 MeV, and 5.2 MeV) of relativistic electron fluxes observed in three distinct regions of the outer radiation belt, L = 3.5, 4, and 5. We found that whatever the L-shell or energy range, the electron flux follows a triangular distribution function. Also, we found that in periods of most HSS events, the electron fluxes are consistently higher. Then we discuss the contribution of the ULF waves under these conditions. These results contribute to a better understanding of the solar wind driving the outer radiation belts' variation in the inner magnetosphere.
Public talk by Alice Wallner and Klaus Nielsen.
Moderator Gabriella Stenberg-Wieser
We’re delighted to introduce a new feature at this year’s ESWW: Plenaries Showcasing Parallel Sessions. These two special plenary sessions will highlight standout contributions from the parallel programme. Each session will feature two distinguished presentations, nominated by the conveners of the originating parallel session. This is an opportunity for selected presentations to gain enhanced visibility and recognition and to promote the originating parallel session to a broader audience.
By highlighting these talks in a plenary setting, we aim to celebrate excellence across the programme and encourage cross-disciplinary engagement. Presenting authors whose contributions are selected will be formally recognised by the Programme Committee and awarded a certificate of distinction.
Each session will include two talks, 25 minutes each including Q&A.
Solicited talk originating from parallel session SWR2.
The first severe geomagnetic storm of Solar Cycle 25 occurred on 23-24 April 2023, with Dst index of -213 nT. Utilizing the state-of-the-art observational and modeling techniques, we studied the Sun-to-Earth evolution of the coronal mass ejection (CME) which was launched from the Sun on 21st April 2023 and triggered this severe geomagnetic storm. Multi-wavelength and multi-vantage point remote sensing observations of this event were analysed to constrain the near-Sun CME properties, which serve as input for the interplanetary flux rope simulator (INFROS) model to predict the magnetic vectors of the ICME at 1 AU at two different spacecraft i.e. Wind and STEREO-A. Further, we integrated INFROS with the Drag-Based Model (DBM) and empirical Dst prediction models, to present a comprehensive space weather modeling framework to estimate the intensity of the associated geomagnetic storm. The validation of our INFROS based space weather modeling framework for this event shows a strong correlation between the observed and predicted SYM/H profiles, thereby suggesting that this framework could be used for forecasting the intensity of geomagnetic storms. The propagation characteristics of this CME was also studied which highlights the importance of heliospheric imaging and the availability of observations from several vantage points to estimate the true direction of propagation and hence the arrival time accurately. Further, the event provided a unique opportunity to analyse the space weather impact of this geomagnetic storm, in particular, the variation of the horizontal component of the geomagnetic field corresponding to the passage of the different ICME structures over the Indian and American longitude sectors. In my presentation, I will discuss the results of the analysis of the geoeffective event of April 2023. I will also discuss the limitations and challenges associated with modeling such events and the steps required to improve space weather forecasting.
Solicited talk originating from parallel session CD4.
Like other components of modern technological infrastructure, aviation safety and efficiency are vulnerable to the effects of space weather events, as they depend on navigation and communication systems that can be disrupted by solar and geomagnetic activity. While these effects can be identified and better understood through substantiated research, it must also be assessed whether these disturbances have an actual impact on work processes in aviation. For that reason, the German Aerospace Center (DLR) Ionosphere Monitoring and Prediction Center (IMPC) conducted an online user survey from 13th March to 28th April 2025 to gain insight how industry professionals perceive space weather threats. The results suggest that, although a significant proportion of participants are familiar with the concept of space weather, understanding of its specific impacts and associated risks is limited. The findings further reveal a perceived deficiency in communication between aviation stakeholders and space weather agencies. Consequently, this lack of communication hinders the ability of participants to identify and effectively utilize space weather services that are already available. We present and discuss the survey findings and present our recommendations to achieve and improve application-oriented space weather services for aviation.
There is an established tradition at ESWW for different forecast centres to present a Live Space Weather Forecast, either before or after the morning plenary session. Attending these forecasts enables participants to gain insights into the forecasting process and understand the real-world impact of space weather on end-users. It provides an opportunity to reflect on how we, as a community, can enhance our communication of these complex concepts to end-users and the public. With different forecast centres presenting, attendees benefit from a variety of forecasting perspectives and methodologies tailored to different end-users. Additionally, it offers an excellent opportunity for forecast centres to showcase their expertise and highlight the communication channels they use, fostering a deeper understanding and collaboration within the space weather community.
Extreme space weather events can severely impact critical infrastructure, from power grids and pipelines to GNSS, aviation, and satellite systems. To reduce risks, it is essential to establish an effective bridge between operational space weather forecasting centers and end-users, one that relies not only on scientific expertise but also on robust systems, service infrastructure, and clear communication channels. This session invites contributions that explore how space weather services are developed, implemented, and delivered to support real-world decision-making. Topics of interest include the design and operation of systems that link forecasting centers to end-users, such as data delivery chains, alert mechanisms, and operational resilience protocols. We also welcome insights into how dissemination standards and procedures have evolved, including tailored alert protocols for sectors like civil aviation, satellite operations, energy, and GNSS. Presentations may address user experiences with accessing and applying space weather information via web portals, APIs, or customized platforms, as well as approaches to enable two-way communication: ranging from impact reporting and feedback loops to co-development of tools and services. Case studies are encouraged that show how coordination between forecasters and users has led to timely mitigation actions during major space weather events. We also encourage reflections on lessons learned from past storms, highlighting how experiences have shaped operational workflows, tools, and partnerships. The overarching aim is to understand how space weather services can evolve to be more integrated, interoperable, and responsive, ensuring end-users receive information that is timely, relevant, and actionable in high-impact scenarios.
On May 10th 2024, the arrival of a series of CMEs led to the largest geomagnetic storm since 2003 and resulted in auroras observed at mid latitudes worldwide as well reported impacts across a range of sectors. This presentation offers a case study of the storm from the perspective of an operational forecaster at the Met Office Space Weather Operations Centre (MOSWOC) over the duration of the event. This will present a timeline of the event, shining a light on the challenges the MOSWOC advisors face in a real-world forecasting situation. We shall show the evolution from the initial analysis using our existing observational and forecast model capability, through to how we used this to inform and engage with key customers and stakeholders across a range of sectors, and how we were able to effectively communicate the risks and potential impacts of the event.
The International Civil Aviation Organization (ICAO) space weather service provides space weather alerts and forecasts for the aviation industry. It is comprised of four global centres: ACFJ (Australia, Canada, France, and Japan), PECASUS (Finland, UK, Italy, Belgium, Austria, Netherlands, Germany, Poland, and Cyprus), SWPC (USA), and CRC (China and Russia). The responsibility for the production of advisories is rotated every 2 weeks between centres, such that each centre is the on-duty centre for 2 out of every 8 weeks. Each centre has to abide by strict guidelines for the space weather advisories that are disseminated. ACFJ have been providing operational space weather information service alongside the other global Space Weather centres since 2019.
Within ACFJ, GNSS advisories are issued by France, represented by the SPECTRA consortium composed of ESSP, CLS and Météo-France, whenever total electron content (TEC) and scintillation data reach critical thresholds as specified by ICAO. The role of ESSP is to monitor TEC and scintillation data provided by all contributing ACFJ partners to create the advisories. Global scintillation data is provided by CLS using various networks of GNSS geodetic receivers, whereas global TEC data is supplied by the Polytechnic University of Catalonia (UPC). TEC and scintillation data are also observed and modelled by Natural Resources Canada (Canada) and National Institute of Information and Communications Technology (Japan) who provide information continuously to ESSP with alerts when their data passes the ICAO moderate and severe thresholds. Once an event is confirmed, an advisory is generated and sent to Météo-France which then disseminates it to airspace users.
In this presentation, we provide an overview of the GNSS advisory production and dissemination process, including how decisions are made regarding the validation of these advisories, and the performance of ACFJ with regard to GNSS advisories. An example of the advisory validation process, particularly during more extreme space weather periods, is provided. Finally, we outline future evolutions to further improve the service and the provision of GNSS advisories, including the harmonisation of GNSS advisories between global warning centres and the implementation of polygons to improve the spatial resolution of the advisories disseminated.
The Solar-Terrestrial Center of Excellence (STCE) plays an essential role in the Pan-European Consortium for Aviation Space weather User Services (PECASUS), providing specialized space weather services to the International Civil Aviation Organization (ICAO). The role of STCE is to act as a central data hub for the consortium and to provide 24/7 monitoring of the space weather conditions, generating advisory content once the ICAO requirements for issuing of space weather advisories are met. The goal of this work is to present the latest developments and updates of our service, aligned with the new ICAO requirements for the service provision. We discuss the ongoing transition to events area selection based on multi-point polygons instead of using pre-defined latitudinal and longitudinal bands. We present the envisioned advisory generation workflow and show examples of the future ICAO-compliant advisories, which would become in effect starting from November 2025. In addition, we describe our latest updates in the internal alerting system, including detection and visualisation of GNSS events (amplitude or phase scintillations, as well as vertical total electron content/VTEC enhancements).
In Defensie, we have started with a systematic study of space weather (SW) effects on the most SW-vulnerable defence systems to determine the associated SW risks when these systems are used in operations. We will first shortly present this initiative, including its goals and main challenges. An important part of this initiative is creating a bridge between the existing academic knowledge and what the different users actually need to know and hear.
Our top priority for now are radio systems and radars, as these are directly influenced by multiple space weather phenomena, depending on their type. Here, we will discuss some of our first results in this area: what the statistics of the (risk-relevant) SW-related radio disturbances are, what these SW radio disturbances actually look like to operators and how these compare to the theoretical predictions that we have available, such as the D-region absorption model for HF radio. We will speculate over the reasons behind these differences and touch on their implications for operations. Furthermore, we will also outline the challenges in translating such findings into a format that is both understandable and usable for the end-users.
From the operational perspective, planning - and thus, forecasting - is also essential. For this reason, we also present the challenges related to the forecasting of SW radio impacts in such a way that the information is helpful for the operators, and where we find that our understanding, modelling and communication still lacks.
Finally, we will mention our ongoing and planned efforts to help close some of these gaps and where further collaboration with other parties, including open academia, can be possible and profitable.
The Community Coordinated Modeling Center (CCMC) is starting a new space weather training tailored for different user groups with both custom in-person and online options, available on a web based platform for the benefit of the entire community. The training will be based on the U.S. ARSET (Applied Remote Sensing Training) Program’s (Earth Science) significant experience. Training material will be developed in co-production between subject matter experts and users, such that participants build practical skills in their area of need. Training will be designed to meet users at their level, from introductory to advanced. Training topics will be formulated based on user needs and we will start with the SWAG (Space Weather Advisory Group) user survey. CCMC subject matter experts will be instructors in addition to guest expert instructors from around the world. In the long term, the program will host community-developed training materials, including those from the M2M space weather forecasting bootcamps, and leverage the new ISWAT Team O4-01 User-focused space weather training network.
Space weather forecasting can rely on either physics-based or data-driven approaches. On the one hand, physics-based methodologies have deeper historical roots, with physical equations being studied and applied to model solar events and better understand unknown physical processes. On the other hand, data-driven approaches and, specifically, artificial intelligence (AI) algorithms process multi-modal data to identify patterns/correlations with no (or little) reference to physical models.
However, it has been recently explored the possibility to combine both approaches, by leveraging physics to inform the machine learning methods, and applying machine learning to better estimate key parameters in MHD deterministic equations.
This session aims to provide a platform for sharing and discussing research on data-driven and hybrid approaches combining physics-based and AI methodologies in space weather studies, with a focus on forecasting applications. Topics include predicting solar phenomena driving space weather, such as solar flares, coronal mass ejections (CMEs), and Solar Energetic Particles (SEPs), as well as modeling CME and SEP propagation to estimate arrival times at Earth, and predicting geomagnetic disturbances.
Additionally, submissions on space weather-related forecasting applications are encouraged, such as identifying and classifying active regions and detecting solar structures.
As AI techniques have reached a high level of maturity, and recent studies have demonstrated that combining AI with physics-based approaches holds great promise offering reliable tools for space weather forecasting, coupled with the fact that solar activity is currently at its peak (when eruptive phenomena are more frequent and intense) the topic of the proposed session is particularly timely.
Forecasting and understanding space weather remains a fundamental challenge due to the inherently multi-scale nature of plasma dynamics in the Sun-Earth connection. Traditional first-principles models like fully kinetic Particle-in-Cell (PIC) simulations are highly accurate but computationally prohibitive for operational or ensemble forecasting. In this work, we present a novel hybrid modelling strategy that bridges this gap by combining the rigor of physics-based simulation with the efficiency of machine-learned surrogates.
We focus on turbulent magnetosheath, a key region controlling solar wind energy transfer into the magnetosphere. We develop and validate a neural surrogate model which consists of physics based equations for ions and a neural surrogate for modelling pressure response of the electrons. This neural surrogate component which represents electron pressure dependence on lower order moments is trained on fully kinetic PIC simulations performed with ECsim (Energy Conserving Semi-Implicit PIC code). This represents the first instance of online (a posteriori) coupling and testing between a physics-based kinetic ion model and data-driven fluid electron closure that retains dynamic consistency implemented in efficient hybrid PIC code Menura that is ported on a GPU.
Our results demonstrate that the surrogate model generalizes across multiple ECsim runs. As a result, it contributes to the broader agenda of physics-informed AI, showcasing hybrid physics-AI methodologies for space weather. This parametrization of unresolved scales provides a surrogate that can capture complex plasma behavior, opening possibilities for computationally efficient high fidelity models, which is necessary for future operational viability of such space weather forecasting frameworks.
A series of PCA-based models were previously developed to forecast the total electron content (TEC) variations caused by space weather. The earlier versions used linear regression models to build a forecast, which later was replaced by neural networks (NN). Such models were tested on the TEC data obtained for a European mid-latitudinal region (Iberian Peninsula).
In this work we present a next generation of such models that now have several branches. First of all, we tested different NN algorithms (e.g., LSTM and CNN) to find a better one that suits our approach to model specific ionospheric parameters. We investigated the applicability of such approach to forecast other ionospheric parameters, like NmF2. Also, we compared the performance of the model using data obtained at locations that are almost opposite in longitude (European vs American).
As human space activities, such as the Artemis program, become increasingly ambitious, ensuring safety in the space environment has become more critical than ever. In response, Fujitsu Ltd. and Nagoya University are conducting joint research on space weather for future lunar and deep-space exploration. A key focus of this research is solar energetic particle (SEP) events, which are mostly triggered by solar flares (SFs) and coronal mass ejections (CMEs), and can pose significant risks to human health and space systems.
In our previous work, we built a binary classification model using Fujitsu’s explainable AI, Wide Learning (WL), to predict whether SFs are associated with SEP events (ESWW2024, CD5.2 Kato et al.). In this study, we extended our model to a three-class classification model with WL, which classifies SFs as follows: Class 0, not associated with SEP events (flux < 10 pfu); Class 1, associated with SEP events below the S2 threshold (10 pfu ≤ flux < 100 pfu); and Class 2, associated with SEP events at or above the S2 threshold (flux ≥ 100 pfu), based on the NOAA S-scale (≥ 10 MeV particles). The purpose of this extension is twofold: to enhance the detection of higher-risk events and to identify the characteristics of these events.
We created a data catalogue from X-, M-, and C-class SFs observed during Solar Cycle 24 and 25 (up to the end of June 2024). The catalogue consists of 57 features derived from GOES/XRS X-ray data, SDO/HMI magnetic field data, the κ-scheme, a physics-based flare prediction scheme developed by Kusano et al. (2020) that is based on three-dimensional extrapolated magnetic fields of solar active regions, and other data. Due to the class imbalance between positive (Class 1 and 2) and negative (Class 0) samples, we created two types of datasets from the catalogue: (a) an imbalanced dataset (positive:negative = 1:30), and (b) a balanced dataset (positive:negative = 1:2).
Our three-class model yielded true skill statistic (TSS) of (a) 0.54 ± 0.12 and (b) 0.57 ± 0.11, while the binary model achieved (a) 0.79 ± 0.11 and (b) 0.76 ± 0.09, which are comparable to those reported for a similar binary model. We also performed a leave-one-out analysis for Class 2 SEP events and identified possible reasons for the correct/incorrect predictions. In addition, analysis of the decision rationales extracted from WL revealed that the X-ray integrated flux was a key feature in the classification, particularly for Class 2. The free energy within high free-energy regions (HiFER) was also identified as an important contributor to the classification. This suggests that incorporating physics-based features, such as the free energy in HiFER, could significantly improve the predictability, especially for pre-flare SEP prediction, compared to utilizing solely the directly observed data. We will discuss the potential for developing a step-by-step SEP forecasting system from pre-flare to SEP onset, as well as the prospects for creating new physics-based predictors based on insights from our WL-based model.
The largest solar flares, of class X and above, are associated with strong energetic particle acceleration. The reconnection process thought to be responsible for solar flares can be mimicked with so-called cellular automata. In particular, sandpile models have proven to well reproduce solar flare statistics (Charbonneau et al. 2001) and have recently been shown to be consistent with MHD simulations of the low plasma-beta regions of the solar atmosphere (Lamarre et al. in review).
To begin, we describe our flare prediction pipeline, coupling the GOES X-ray flux to a new sandpile model with strong predictive capabilities (Strugarek et al. 2014) and improved solar-like statistical properties (Lamarre et al. in prep). Then, we report on integrating machine learning to our pipeline to speed up the assimilation process and render our approach closer to a real-time prediction tool. Finally, we give an update on the skill scores of this technique, from its initial promising results (Thibeault et al. 2022), to a new larger and more realistic sample of solar flare events.
Geomagnetic storms are large disruptions of the magnetosphere. These events can interfere with satellites, communication systems, and power grids, causing significant technological and economic damage. Current forecasting models utilise L1 satellite data, constraining lead time to a few hours', often insufficient for effective mitigation. Accurate long-lead forecasts would help protect infrastructure and ensure operational continuity.
We investigate how to extend the lead times of geomagnetic storm forecasts by using solar data. Associated spatial and propagation uncertainties of solar data are captured with a solar-wind ensemble of the computationally efficient one-dimensional HUXt numerical model, rather than a 3D-MHD based model. HUXt allows us to simulate significantly more solar wind profiles than 3D-MHD models. This work builds on a previous study on binary classification of geomagnetic storms, to now providing regression based forecasts targeting the open-ended Hp30 global geomagnetic index, which offers higher temporal resolution (30 minutes) compared to the more commonly used Kp index (3 hours), enabling finer-scale forecast evaluation.
The HUXt solar-wind ensemble is processed through a series of regression based models trained on individual solar wind profiles, giving us an array of Hp30 forecasts. The ensemble forecasts are aggregated to produce a final prediction, with uncertainty estimated from the ensemble spread and the historical correlation between observed solar wind (OMNI) and the simulated profiles. Using 30 years of historical data, model performance is analysed over a variety of lead-times from 1 to 36 hours, and performance over a variety of storm intensities is assessed.
In this work, we evaluate multiple machine learning architectures and input variables for regression based forecasting, and focus particularly on reducing the Mean Absolute Error (MAE), and R-squared of our forecasts. Overall, we show the predictive capabilities of coupling computationally fast numerical modelling of the solar wind with machine learning algorithms to increase the lead time of Hp30 forecasting.
The extreme space weather events of Solar Cycle 25 highlight the urgent need for a comprehensive, interdisciplinary approach to understanding solar-Earth interactions. This session aims to bring together experts from solar and heliospheric physics, as well as
magnetospheric, ionospheric, and atmospheric physics to investigate the formation, propagation, and impacts of solar storms. By studying the magnetic connectivity and dynamics of the source regions leading to solar flares, and eruptions accompanied by the solar energetic particle events, we seek to understand how solar activity influences interplanetary space and interacts with the planetary environment. The propagation of coronal mass ejections and their interactions within the heliosphere are crucial for assessing the extent of space weather disturbances. The session will also address the
broader space implications of these extreme events, as the impact of geomagnetically induced currents on engineering infrastructure remains an important topic for space weather mitigation strategies. We encourage you to submit abstracts on events covering all aspects of space weather, from the Sun to the Earth, and their impacts on other planetary
environments. We welcome modeling and observational studies. By fostering interdisciplinary collaboration, this session aims to improve our understanding of space weather as a system-wide phenomenon and strengthen links between research communities.
Large geomagnetic field variations during geomagnetic storms induce geoelectric currents in the ground which flow through ground-based technology, such as power grids and pipelines. In recent years BGS have been working on improving measurements and models to help quantify the risk of space weather.
We have upgraded our long-term geoelectric field monitoring equipment at the 3 UK observatories, which have now been providing data for over a decade. We have also collected long-period magnetotelluric measurements at 53 new sites, which have been used to improve our geoelectric field models. Additional variometer sites have also been installed to improve the coverage of geomagnetic field measurements across the UK.
With these new data we have been able to upgrade our models and understanding of geomagnetically induced currents (GIC) in the power grid, identifying areas with increased GIC hazard. We have also been able to update our models of pipe-to-soil potential (PSP) in the high-pressure gas transmission system, improving our understanding of the potential for enhanced corrosion and possible safety implications of increased PSP during space weather events.
The Soil Moisture and Ocean Salinity (SMOS) mission was launched in 2009 and has been operational since commissioning in the first half of 2010. The Sun signal appears in most of the brightness temperature images collected by the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) payload. As such, the removal of this signal has always been a top priority for the success of the mission. Since MIRAS operates in the L-band at 1.4 GHz and produces full polarimetric images with an integration time of 1.2 s, the retrieved Sun signal is well fit to search for Solar Radio Burst (SRB) events in near real time (within 3 hours from acquisition). These events can be characterized both in terms of solar flux intensity and Degree of Circular Polarization (DoCP), providing unique and relevant information about the possible impact on technology systems such as GNSS signal and the originating emission mechanisms. The L-Band SRB database compiled by the SMOS detection algorithm, including events detected in the XXV solar cycle, is discussed and compared to the National Oceanic and Atmospheric Administration (NOAA) bulletins. Comparison is made concerning the L-band SRB database; data are further cross-correlated with the Solar flare list. Lastly, SRB occurred during the main storms from 2024: The Gannon/Mother’s Day storm and the October storm, are analysed using both the solar flux intensity and the associated Degree of Circular Polarization (DoCP) measured by SMOS. Data is compared to external data sources to provide a wider picture of events, thanks to the unique information captured by SMOS. L-Band solar flux and DoCP data by the SMOS mission are compared with solar flux data collected at different frequencies, focusing on SRB events onset, peak, and offset times. SRB collected by the SMOS mission provides a valuable resource for space weather applications, enabling a reliable and effective monitoring of the Sun at L-band almost in near real time.
RISER – Radio Investigations for Space Environment Research – is a £3.7M NERC-funded Large Environment 5-Year Project addressing the full chain of space weather phenomena from the Sun to the Earth. It investigates how regular radio observations taken using the LOw Frequency ARray (LOFAR) can be used for continuous, accurate tracking of inner-heliospheric and ionospheric plasma structures, combined with magnetospheric modelling, and incorporated into space weather forecast models leading to more precise and advanced forecasts of space weather and its impacts.
Since March 2025 the LOFAR-UK Rawlings Array has been used full time for RISER measurements, recording over 100 observations per day of interplanetary scintillation (IPS), distributed across the inner heliosphere, which are subsequently analysed to produce estimates of velocity and ‘g-level’, a measure of the strength of scintillation, related to density. These analyses are joined with the extant ISEE, Japan, IPS data available in near real time, supplemented by Murchison Widefield Array (MWA, western Australia) observations, thus covering different times and Earth longitudes, to input into tomographic reconstructions to visualise and track conditions throughout the inner heliosphere. Such a high number of observations offers the possibility of increasing the resolution of these tomographic reconstructions for greater accuracy in the visualisation, tracking, and resulting predictions.
The reconstructions are also used as input into an Enlil solar wind model variant under test at the Met Office. An important recent activity has been to build a workflow to automate the process of feeding the IPS data into the tomography code and then coupling the resultant tomographic reconstructions to the Enlil code. This is highly important for efficient exploitation of the IPS observations, but also key for the use of IPS observations in any upgraded near real time operational solar wind forecast system in the future.
This period has encompassed increased levels of activity with CMEs and high-speed streams affecting the magnetosphere-ionosphere system, occasionally pushing the auroral oval southwards to more middle latitudes. A vital component of the RISER project is to link the heliospheric conditions at Earth via magnetospheric modelling to conditions in the ionosphere. Observations of the strong radio source Cassiopeia A are taken overnight by the LOFAR-UK Rawlings Array, along with single-frequency all-sky imaging data (since May) to measure broadband ionospheric scintillation and refractive effects. The frequency dependence of scintillation observations makes them sensitive to ionospheric structures occurring over different spatial scales, while refractive effects are the result of larger-scale structure. When combined with independent observations (such as from satellites), this enables the reconstruction of ionospheric structures forming and occurring during the diverse space weather conditions occurring over this time.
During the geomagnetic storm of October 10, 2024, GNSS-based augmentation services such as EGNOS APV-I (Approach with Vertical Guidance) and LPV (Localizer Performance with Vertical Guidance) were reported to experience a significant disruption. These services were unavailable for nearly one hour over southern Iberia and showed reduced availability across other parts of Europe. This reduction in service availability may have affected the feasibility of satellite-based precision approach procedures at certain airports, highlighting the potential vulnerability of aviation operations to space weather events. In this study, we present a comprehensive analysis of the sequence of events that led to this degradation. We trace the origin of the disturbance from solar activity precursors and their propagation through the interplanetary medium, to their impact on Earth's geomagnetic field. Furthermore, we examine how the resulting perturbations affected GNSS signal integrity and continuity, ultimately leading to the interruption of critical navigation services.
Severe geomagnetic storms have a significant impact on ionospheric and geomagnetic dynamics, particularly in equatorial regions such as Thailand. These disturbances often manifest as modifications to the Equatorial Ionization Anomaly (EIA) and the development or suppression of Equatorial Plasma Bubbles (EPBs). These effects are primarily driven by storm-time electric fields and associated changes in ionospheric currents, which result from interactions between the energetic solar wind and Earth's magnetosphere, through the polar and higher latitude regions.
The intense geomagnetic storm that occurred between 10–12 May 2024, during the solar maximum of Solar Cycle 25, had a profound effect on the characteristics of the equatorial ionosphere and geomagnetic field [1–2]. This study investigates the storm’s impact using observations from the ionospheric monitoring network around Thailand, jointly operated by King Mongkut’s Institute of Technology Ladkrabang (KMITL), the Space Technology Research Center, Geo-Informatics and Space Technology Development Agency (GISTDA), and the National Institute of Information and Communications Technology (NICT). We analyze data from GNSS receivers, magnetometers, and ionosondes to assess the influences of storm-time interplanetary magnetic field (IMF) and interplanetary electric field (IEF) variations on local equatorial magnetic fields and ionospheric parameters.
We utilize Singular Spectrum Analysis (SSA) to decompose ionospheric Total Electron Content (TEC) and geomagnetic variations into elementary components. Using Pearson correlation analysis, we evaluate correlations between these SSA-reconstructed components and interplanetary geophysical parameters, as well as other ionospheric parameters such as the critical frequency of the F2 layer (foF2). Variations in correlation coefficients between the reconstructed field components and interplanetary parameters help to distinguish the sources and effects of these disturbances. The results clearly reveal distinct contributions to geomagnetic field variations from both interplanetary and ionospheric current systems. We also compare the impacts of the two most intense geomagnetic storms in Solar Cycles 24 and 25, namely the storms of March 2015 [3], and May 2024. The findings indicate that storm-time electric fields, such as prompt penetration electric fields (PPEF) and disturbance dynamo electric fields (DDEF), strongly influenced the equatorial electric field, which is indirectly inferred from geomagnetic field variations, leading to significant fluctuations in TEC.
Acknowledgement
This work is partially supported by King Mongkut’s Institute of Technology Ladkrabang and the NSRF via the Program Management Unit for the Human Resources and Institutional Development, Research and Innovation (Grant no. B41G680028).
References
[1] Myint, L.M., Perwitasari, S., Nishioka, M., Saito, S., Kaewthongrach, R., Supnithi, P. (2025). Analysis of ionospheric and geomagnetic field changes in Thailand during the May 2024 geomagnetic storm. Advances in Space Research. https://doi.org/10.1016/j.asr.2025.01.071
[2] Karan, D.K., Martinis, C.R., Daniell, R.E., Eastes, R.W., Wang, W., McClintock, W.E., et al. (2024). GOLD observations of the merging of the southern crest of the equatorial ionization anomaly and aurora during the 10 and 11 May 2024 Mother’s Day super geomagnetic storm. Geophysical Research Letters, 51(15), e2024GL110632. https://doi.org/10.1029/2024GL110632
[3] Wu, C.C., Liou, K., Lepping, R.P., et al. (2016). The first super geomagnetic storm of Solar Cycle 24: “The St. Patrick’s Day event (17 March 2015)”. Earth, Planets and Space, 68, 151. https://doi.org/10.1186/s40623-016-0525-y
The geomagnetic storm that began on May 10$^{th}$, 2024 — commonly referred to as the “Mother’s Day” or “Gannon” storm — was the strongest in decades, producing global auroral displays and major space weather impacts.
This study presents a detailed analysis of GNSS signal scintillation in the Arctic (50°–85°N, 160°W–40°E), combining multi-instrument datasets to examine both physical drivers and user-level impacts.
We analyze phase and amplitude scintillation indices from dense high-latitude GNSS receiver networks, supported by ionospheric convection patterns from the Super Dual Auroral Radar Network (SuperDARN) and current estimates from multiple ground magnetometer arrays.
Positioning performance is assessed for real-time kinematic (RTK) GNSS services in Tromsø (~70°N), with results showing that high-accuracy positioning was severely degraded — becoming practically unusable for up to 37 consecutive hours.
Spatial and temporal patterns of scintillation are mapped across the region, with clear links to the auroral oval, the tongue of ionization, and mesoscale current systems.
Scintillation was observed within both the eastward and westward electrojets, though it was not a constant feature. Activity in the eastward electrojet showed a slight preference for the poleward edge of the current system. Additionally, periods of strong field-aligned currents were sometimes associated with enhanced scintillation, suggesting complex electrodynamic coupling. As part of ongoing research-to-operations efforts, this analysis integrates GNSS data with supporting observations to contextualize the role of plasma structuring and total electron content (TEC) gradients. These findings inform the development of operational products such as real-time TEC and scintillation impact maps, regional ionospheric classification layers, and prototype GNSS risk indices to support PNT and communication users in high-latitude environments.
This work highlights the value of a multi-sensor approach in understanding and mitigating GNSS vulnerabilities during space weather events. It also demonstrates the potential for using real-time data from ground-based sources to improve Arctic ionospheric situational awareness. By linking scintillation behavior to electrodynamic drivers and user impacts, this study provides one of the most complete overviews of Arctic ionospheric conditions during the May 2024 storm and supports the design of future Arctic monitoring and early warning systems.
The inner magnetosphere hosts a dynamic range of plasma populations including the relativistic radiation belts, the ring current and cold plasmaspheric ions. These populations are tightly coupled via a range of micro-, meso- and macro-scale processes, driving a complex interplay of acceleration, transport and loss. For example, chorus waves are generated by injected plasma sheet electrons and then accelerate 100’s keV electrons to relativistic energies to form the radiation belts, with this acceleration being most efficient in regions of low plasma density. In turn, precipitation of radiation belt particles into the atmosphere balances ionospheric outflows of cold plasma into the inner magnetosphere. Further research into these and other cross-scale couplings is essential to develop the capability to reliably forecast inner magnetospheric dynamics and associated space weather risks and impacts. This session calls for observational, modelling and theoretical studies related to the inner magnetospheres, as well as review papers and mission concepts as well as comparative studies with other magnetospheres. We invite observational contributions from current missions such as Arase, Themis, MMS and GPS, from ground-based facilities such as EISCAT, SuperDARN and VLF receivers, and from historical datasets such as from the Van Allen Probes, Cluster and climatological studies involving even earlier solar cycles. We invite numerical contributions spanning Fokker Planck simulations, kinetic simulations of wave-particle interactions, and of the global magnetosphere and its couplings to the ionosphere and solar wind, as well as novel machine learning approaches and solutions.
Motivated by the need for improved radiation environment modeling, this study investigates the drivers behind sub-relativistic electron flux variations in the inner magnetosphere. We utilize electron flux measurements between 1 to 500 keV from the Hope and MagEIS instruments on board the RBSP satellites and the FEEPS instruments on board the MMS spacecraft. along with solar wind parameters and geomagnetic indices obtained from the OmniWeb2 and SuperMag data services. We calculate the correlation coefficients between these parameters and electron flux, and we also use mutual information analysis to reveal non-linear relationships within the data. Our results confirm that substorm activity is a crucial driver of the source electron population (10–100 keV), however, for seed electrons (100–400 keV), we find they are influenced not only by substorm events, but also from enhanced convection/inward diffusion. By introducing time lags, we capture a delayed response of electron flux to changes in geospace conditions, and we identify specific time lag periods where the correlation coefficients and mutual information values are maximum. This analysis also reveals the primary driver of the seed electrons, as well as the characteristic time delays of inward and outward diffusion. Overall, this work contributes to our broader understanding of the sub-relativistic electron dynamics in the outer belt and forms the basis for future research.
We investigate operational anomalies on GOES spacecraft that are attributed to shallow internal charging driven by enhanced flux of 100 - 300 keV electrons. This population is not traditionally associated with either surface charging (dominated by electrons with energies 10s of keV) or deep dielectric charging (typically linked to MeV-range electrons). Using data from 2017-2021 during the recent solar minimum, we examine solar wind conditions and geomagnetic indices associated with these anomalies. All anomalies are divided into clustered anomalies (separated by less than two days) and background anomalies (separated by longer intervals). Clustered anomalies exhibit a strong dependence on magnetic local time (MLT), peaking prior to local noon, and are associated with elevated Kp values. In contrast, background anomalies are uniformly distributed in MLT and have a distinct Kp dependence. Superposed epoch analysis reveals that clustered anomalies predominantly occur during high-speed solar wind streams (HSS), under conditions of elevated solar wind velocity, reduced proton number density, and weakly southward IMF Bz. The observed MLT and Kp dependencies for clustered anomalies suggest a charging process involving dielectric materials with relaxation timescales shorter than one day, while background anomalies may reflect longer-term accumulation. To demonstrate the modeling capabilities, we employ the Comprehensive Inner Magnetosphere Ionosphere (CIMI) model to simulate the 100 - 300 keV electron fluxes at GEO. The model reproduces the observed MLT distributions and flux magnitudes with reasonable accuracy, supporting the identification of this electron population as a key contributor to shallow charging phenomena. This study highlights the role of the intermediate-energy electron population in spacecraft anomaly occurrence at GEO, particularly during periods of HSSs. Our findings call for a reevaluation of hazard assessments for spacecraft charging to incorporate this important electron energy range.
Fifty years after the Burton equation proposal for the coupling between solar wind and magnetosphere, as quantified by the Dst index, this work proposes a missing term for the injection of energy from the solar wind into the ring current. This term is associated with Alfvén waves and allows us to explain the linear trend observed in the recovery phase of Dst or SYM-H indices during the passage of high speed streams from coronal holes. Using interplanetary and geomagnetic measurements, we select four case studies, validate our approach, and compute the parameters involved in the proposed theoretical expression for the injection of energy from the solar wind into the ring current due to Alfvénic fluctuations. Our results demonstrate that the linear trend in the recovery phase of geomagnetic storms associated with high speed streams, when compared to the exponential or hyperbolic trend, is not a different behavior of the magnetosphere, but the result of an injection of energy due to Alfvén waves, which was neglected in previous models.
During 10-19 May 2024, the largest storm in the past 20 years took place, characterized with minimum Dst index of -412 nT. In the storm main phase, dropouts of >700 keV relativistic electrons are observed by Arase and NOAA 18 satellites throughout the outer radiation belts. During the storm recovery phase, the fluxes of relativistic electrons locally increase by about more than two orders of magnitude at L > 2. To understand the mechanisms accounting for the relativistic electron dynamics during this storm, we perform simulations by using the Versatile Electron Radiation Belt (VERB) code. In these simulations, the measurements from Geostationary Operational Environmental Satellite (GOES) are used to set up the outer boundary. Magnetopause shadowing effect is included by using the location of magnetopause, which results in significant dropouts of relativistic electrons at L* > 4 during storm main phase. Due to the rapid erosion of the plasmapause (down to the inner belt), the local accelerations outside the plasmapause during the recovery phase are reproduced by including local diffusions driven by chorus waves, following by the appearance of slot region and remnant belt inside the plasmapause. We perform simulations using different radial diffusion coefficients and the density-dependent local diffusion coefficients. Our results show that only with realistic background plasma density, the simulations can reproduce the enhancement of multi-MeV electrons. We also investigate the decay of radiation belt electrons caused by hiss waves and dropout from the magnetopause shadowing effect.
The radiation-belt electron flux exhibits dramatic variations across a range of spatial and temporal scales, including global‐scale radial transport, mesoscale injections, and local‐scale wave‐particle interactions. Long-term variability has been successfully captured by solving the Fokker Planck diffusion equation (e.g., BAS-RBM), incorporating radial, pitch-angle and energy diffusion and imposed upon semi-empirical Tsyganenko magnetic models. However, during geomagnetic storms, non-diffusive processes become significant which can lead to significantly degraded forecasts. Enhancements in the partial ring current and induced electric fields, and associated magnetic field distortions can lead to violation of the third adiabatic invariant and rapid outward radial transport. Dropouts in the electron flux, across several orders of magnitude, are often observed in the outer radiation belt during the early phases of geomagnetic storms, hampering accurate modelling of the subsequent few days where forecasts are needed the most. These losses can occur either by precipitating into atmosphere or by escaping through magnetopause. In this study, we employ global magnetohydrodynamic and test-particle (MHD-TP) simulations to investigate the dropout mechanisms. By introducing an ensemble of test particles into the global MHD fields and tracking their trajectories, we aim to distinguish the relative contributions of magnetopause shadowing and wave–particle interactions in producing the observed rapid changes in electron flux.
Energetic particle precipitation (EPP) into the atmosphere can influence the chemical composition from the upper stratosphere to the lower thermosphere. The impact of precipitated relativistic electrons from the radiation belt on atmospheric chemistry and dynamics remain unresolved. In this study, we use the VERB-4D code to simulate radiation belt electron dynamics during geomagnetic storms, incorporating wave-particle interactions with chorus, hiss, and EMIC waves. Special attention is given to MeV electrons due to their rapid scattering into the atmosphere. We compute the global distribution of MeV electron precipitation and investigate the relative contributions of Earth’s dipole versus non-dipole magnetic field structures. Model results are compared against Low Earth Orbit (LEO) satellite observations for validation. The simulated MeV precipitating fluxes are then used to estimate ionization rates in the upper atmosphere , providing insight into their role in atmospheric processes.
High-energy solar energetic particle (SEP) events pose significant risks to space-borne and ground-based technologies and human spaceflight. Accurate detection and model validation of these events—particularly above ~100 MeV—remain complex tasks, hampered by instrumental limitations, event variability, background variations, and signal contamination. This topical discussion meeting will bring together researchers and stakeholders to discuss the current state of SEP model predictions at high energies, the cross-calibration of instruments, the standardization of event lists, and the integration of new data sources. The latter includes high-resolution satellite measurements and ground-level enhancement (GLE) observations. Emphasis will be placed on recent methodological advances, machine learning applications, and coordinated international efforts to improve consistency across datasets. The goal is to identify gaps, refine validation criteria, and enhance the reliability of high-energy SEP catalogs, paving the way for improved space weather forecasting.
This TDM is co-convened by SPEARHEAD (GA No 101135044) and SOLER (GA No 101134999) Horizon Europe projects.
Setting the Scenery (5 min)
Speaker: George Vasalos (NOA)
Opening remarks and setting the scene
Overview of TDM goals
Panelists | Pillar Presentations (15 min each)
Each pillar lead presents key insights, challenges, and next steps.
High Energy Data — Prof. Bernd Heber (Confirmed)
Validation for Operations — TBA
Machine Learning Additions — Dr Mirko Stumpo
Interactive Discussion
Moderator: George Vasalos (NOA)
Open discussion with input from all participants
Guided by : https://forms.gle/y4aBWBEccpKphqKP8
The moderator selects key questions (time permitting) and the panelists respond based on their expertise.
Panelist answers followed by group discussion and cross-feedback with the audience
Closing Remarks
Summary of key discussion points
One of the key aspects of the mission of the World Meteorological Organization (WMO), to facilitate worldwide cooperation on monitoring and predicting changes in weather, climate, water and other environmental conditions, is to promote globally coordinated observations and enable global exchange of observation data.
The WMO framework for these activities is called the WMO Integrated Global Observing System (WIGOS), as it deals with all relevant observation data, including those for Space Weather services.
An effort is currently undertaken to update the “Vision for the WMO Integrated Global Observing System in 2050” (see previous version at https://library.wmo.int/viewer/57028). The timeline works towards an approval through the WMO Infrastructure Commission in 2026 and Congress in 2027, with currently an ideal time window for the community to contribute to and help shape this Vision.
This document intends to present a likely scenario of how user requirements for observational data may evolve over the next 25 years, and an ambitious, but technically and economically feasible vision for an integrated observing system that will meet them
It includes both space- and surface-based instrumentation, involves public as well as private and commercial sector actors and addresses are relevant application areas, including Space Weather.
A first draft of the updated document will be presented and feedback and input from the Space Weather community is requested. Apart from inputs on the technical content we also, and above all, welcome any suggestions that can help to make this document more useful for the Space Weather community.
Questions and consideration of relevance to the Vision include: the expected evolution of requirements for Space Weather applications, technological evolution and innovation (such as H/W, Data, AI), observing systems plans and capabilities (surface and Space), expected evolution of public/private sectors in this field, evolution of the importance of Earth and Space Weather Observations to society.
This is an opportunity to align WMO’s vision with that of the Space Weather community, to address any related concerns, and to assure that the document can be leveraged by the community.
Space Weather observations are the backbone of all space weather services and science. Currently many new missions are in development that will provide crucial additional data to improve space weather forecasting, such as ESA’s Vigil mission going to L5. In a community discussion we would like to discuss and get feedback on further improvements: What other key observation points should be targeted by upcoming missions to achieve clear advances in forecasting lead times or accuracies? What is the low latency data stream that we currently do not have, but if we did, would provide a substantial advancement for space weather forecasting? We will introduce a few new concepts under discussion and open the floor for community ideas and needs.
In the 12 years since its initial establishment, the ESA Space Weather Service Network has developed into a well-established platform enabling many aspects of R2O2R with associated processes in place to demonstrate and test new capabilities with service end users in the loop. The current Space Weather Service Network constitutes an important step towards European operational services, providing a coordinated framework for development, facilitating engagement between the European space weather expert community and end users, demonstrating capability and providing a clear path between early-stage concept and pre-operational implementation.
This TDM will reflect on some of the key successes of the SWESNET project initiated in 2021, bringing together the SSCC and all five Expert Service Centres into a single project for the first time and underpinning a substantial development in service capabilities. With end user needs developing in areas such as arctic exploitation and cis-lunar safety along with funding for the next period of the Space Safety Programme set to be decided at the upcoming ESA Council at Ministerial level in November, discussion will then focus on high priority next steps and how the network will continue to build on the successes of the SWESNET project, strengthening the network’s R2O2R approach and improving user access to targeted information, whilst also preparing the community for evolution as new operational activities begin.
Space weather forecasting can rely on either physics-based or data-driven approaches. On the one hand, physics-based methodologies have deeper historical roots, with physical equations being studied and applied to model solar events and better understand unknown physical processes. On the other hand, data-driven approaches and, specifically, artificial intelligence (AI) algorithms process multi-modal data to identify patterns/correlations with no (or little) reference to physical models.
However, it has been recently explored the possibility to combine both approaches, by leveraging physics to inform the machine learning methods, and applying machine learning to better estimate key parameters in MHD deterministic equations.
This session aims to provide a platform for sharing and discussing research on data-driven and hybrid approaches combining physics-based and AI methodologies in space weather studies, with a focus on forecasting applications. Topics include predicting solar phenomena driving space weather, such as solar flares, coronal mass ejections (CMEs), and Solar Energetic Particles (SEPs), as well as modeling CME and SEP propagation to estimate arrival times at Earth, and predicting geomagnetic disturbances.
Additionally, submissions on space weather-related forecasting applications are encouraged, such as identifying and classifying active regions and detecting solar structures.
As AI techniques have reached a high level of maturity, and recent studies have demonstrated that combining AI with physics-based approaches holds great promise offering reliable tools for space weather forecasting, coupled with the fact that solar activity is currently at its peak (when eruptive phenomena are more frequent and intense) the topic of the proposed session is particularly timely.
Accurate three-dimensional (3D) characterization of coronal mass ejections (CMEs) is essential for modelling their propagation through interplanetary space and forecasting their arrival time at Earth. However, forecasting accuracy, assessed through platforms such as the Community Coordinated Modeling Center (CCMC) CME Scoreboard, has shown minimal improvement over the past decade, with persistent mean absolute errors around 13 hours. Several studies underscore fundamental issues in model inputs, notably uncertainties in CME parameter characterisation from coronagraph data as well as lack of knowledge of the solar wind conditions through which the CME propagates.
Current operational forecasting typically employs simplified morphological models such as the cone or Graduated Cylindrical Shell models, often relying on subjective manual fitting methods that underestimate true uncertainties, introduce user biases, and complicate statistically robust ensemble creation. Recent analyses reveal substantial variability in derived parameters depending on user input or viewpoint availability.
To address these challenges, we introduce a 3D CME cone model fitting framework using Bayesian inference, significantly reducing subjectivity by rigorously quantifying both observational and model uncertainties in the parameter space. Unlike traditional methods yielding single best-fit solutions, Bayesian inference provides a comprehensive posterior distribution for CME parameters, rigorously quantifying uncertainties and parameter correlations.
Using our framework, we investigate how parameter uncertainties and correlations change when expanding from a single viewpoint to two viewpoints. Using our posterior distribution, we model ensembles of CMEs using the HUXt model and compare the forecasted time of arrival distributions to more ad-hoc methods that do not account for parameter correlations. Additionally, the posterior distributions could offer informed priors crucial for data assimilation methods incorporating heliospheric imager (HI)-like observations, particularly valuable once missions such as Vigil become operational.
By making our framework's open-source code available, we aim to promote the broader adoption of uncertainty-aware CME characterization, addressing critical gaps identified in contemporary forecasting methodologies.
Timely and accurate solar flare forecasting is vital for minimizing the adverse effects of space weather on Earth and in space environments. We present a deep learning framework that integrates multi-modal solar observations—line-of-sight (LoS) magnetograms, continuum intensity images, and EUV observations (171Å, 193Å, and 304Å from SDO/AIA)—alongside physical parameters derived from SHARP vector magnetograms, specifically total unsigned magnetic flux and current helicity. The flare sample consists of several hundred events (GOES class C5.0 and above) spanning the period from 2010 to 2019, while the flare-quiet sequences are drawn from the broader 2010–2024 interval. For each event, the model receives a 36-minute sequence of observations. In the case of flaring events, this sequence begins two hours prior to the flare peak, allowing the model to learn pre-eruptive conditions in active regions. A deep convolutional autoencoder is used to extract spatial features from the multi-channel inputs, which are then passed to a recurrent deep learning model (LSTM) to capture temporal dependencies. Results show that combining imaging data with contextual physical parameters substantially enhances predictive performance. These findings demonstrate the potential of this approach for future integration into operational solar flare forecasting frameworks.
Understanding how the thermosphere responds to solar activity remains a critical challenge for space situational awareness, with growing relevance as atmospheric heating poses increasing risks to an expanding population of spacecraft and space debris.
We analyze several months of high-resolution orbit decay data from seven satellites spanning altitudes of ~470-810 km, integrated with continuous solar wind and radiation observations. Using a data-driven framework, we estimate atmospheric response times at these altitudes using a signal alignment technique. Subsequently, we model orbital decay from continuous solar driven observations, using the estimated response time as lead time. To support interpretation, we incorporate event catalogs and statistics of extreme events, in combination with explainable artificial intelligence.
We find that the atmospheric response time appears uniform across altitudes under strong geomagnetic activity. Additionally, monthly patterns in decay rate variability, while more pronounced at lower altitudes, are consistent between the selected spacecraft. We discuss the ranges within which different parameters (e.g., solar wind plasma temperature, electric field) exhibit stronger correlations to decay.
By statistically characterizing the variability of orbital decay across altitudes and time scales, this study aims to deliver new insights into solar-thermospheric coupling and contribute to operational decision-making.
The key challenges in low Earth orbit (LEO) with space operations are tracking and catalogue maintenance for resident space objects (RSOs) including lethal non trackable (LNT) objects, collision and conjunction analysis, manoeuvre planning, re-entry prediction, etc. All these aspects are deeply dependent on drag, which, in turn, has its major source of uncertainty in the thermospheric density. The economy emerging around LEO operations has recently grown exponentially, and so have the associated risks and opportunities, pushing forward research and innovation in the field. Currently, we don’t have a baseline for drag modelling, hence one of our aims is to develop a globally recognized benchmark for drag and thermospheric modelling, allowing consistent operations and reliable decision-making.
With regard to this, we are developing the next generation probabilistic drag modeling framework. Its most significant parts are the forecasting of space weather drivers (typically $F_{10}$ and $k_p$), dynamic modeling of the thermospheric density, and the associated algorithms necessary to integrate the models with orbit determination and prediction. In this talk, we will focus on the development of a reduced order probabilistic emulator (ROPE) for physics-based models of thermospheric density.
To perform dynamical modelling, we reduce the dimensionality of the system either by principal component analysis (PCA) or via nonlinear methods (autoencoders). The thermospheric density system would otherwise be unmanageable with current computational resources due to the large number of degrees of freedom of the original system.
We are building upon previous work with Dynamic Mode Decomposition (DMD) with a framework for identifying nonlinear dynamics (SINDY) and neural networks (NN) both for dimensionality reduction and dynamic modelling (LSTM, GRU and transformer architectures). We can highlight some of the differences and features between these two classes of models.
On the one hand, SINDY offers interpretability and improved accuracy over DMD. Improved accuracy during geomagnetic storms and timely responsiveness of the system are crucial for operational purposes, and both have been achieved within our framework. SINDY also admits a continuous time representation of the dynamics. On the other hand, NNs offer a significantly more accurate alternative than SINDY although they currently lack a continuous time representation. Our aim is to converge toward an optimal set of models to be used for tracking, cataloguing and other uses, and that also admit a continuous time representation.
Solar flares pose risks to infrastructure both on Earth and in space, from induced currents in power grids to satellite damages. Most operational flare forecasting models treat the problem as a binary classification task (flare vs. non-flare) based on a fixed prediction horizon, e.g., 24 hours.
We recast solar-flare forecasting as a continuous time-to-event problem, without the necessity of a fixed prediction boundary, and bring survival-analysis techniques – widely adopted in medicine and reliability engineering – into the realm of solar flare prediction. Using time series of photospheric vector-magnetogram features, we compare several survival models, including Cox proportional- and non-proportional-hazard networks, random survival forests, neural additive models, and universal function approximators derived from the Kolmogorov-Arnold representation theorem.
Our calculations show that these models not only predict when a flare is likely to occur, but also yield time-dependent feature importances, revealing the contributions of individual magnetic parameters to imminent flare risks. We analyze the advantages and disadvantages of each model to find the best suited method in the application on solar flare forecasting.
By providing time-continuous risk estimates and interpretable drivers of flare imminence, survival-analysis frameworks continue to provide a promising avenue towards real-time, probabilistic space-weather forecasting.
The aurora is one of the most captivating natural phenomena, serving as both a visual spectacle and a powerful tool for engaging the public in space weather science. Its beauty sparks curiosity, providing an accessible entry point for discussions on solar activity, geomagnetic storms, and their broader impacts on Earth. This session will explore how the aurora is used as a bridge between complex scientific concepts and public understanding.
We invite scientists, educators, and outreach professionals to share their projects, experiences, and strategies for using the aurora to communicate space weather. Topics may include innovative educational initiatives, public engagement programs, citizen science projects, and the role of artistic and cultural interpretations in science communication. How can we leverage the public’s fascination with the aurora to increase awareness of space weather’s relevance to modern society? What methods have been most effective in turning curiosity into deeper understanding? With Sweden offering frequent and spectacular auroral displays, this session provides a unique opportunity to discuss how this natural wonder can be used to inspire and educate diverse audiences. By exchanging ideas, we can strengthen our collective efforts to make space weather more accessible, inspiring, and impactful for diverse audiences.
It is 1 September 1859, in the morning. Carrington, a solar physicist sees a flash on the sun. A day later, the sky is on fire. The telegraph crackles and sparks. What happened?
What people saw was the result of a massive solar storm slamming into Earth. That was then. Now, anno 2025, this could happen again. Solar storms occur with clock-like regularity on the sun. Although the sun is some 150 million km away, these storms do sweep across the planet where we live, where thousands satellites orbit, planes fly from one side to the other side, electricity runs through enormous cables, where navigation systems show the way.
One of the missions of the STCE, the Belgian Space Weather Centre is to provide info on space weather and space weather impacts such that non-scientists understand. Polar lights and historical solar storms are a rewarding topic to introduce the topic, especially when it is combined with story telling. We will present one of our public lectures.
The aurora is one of the most mesmerizing natural phenomena, but for individuals with blindness or visual impairment, experiencing its beauty through traditional means is not possible. The ATOS project (A Touch of Space Weather) is dedicated to making space weather science accessible to students with disabilities, particularly those with visual impairments. As part of this initiative, we have developed a sonification approach that translates the aurora’s visual features into sound, offering a new way for blind and visually impaired students, especially high-schoolers, to imagine and engage with this captivating phenomenon.
This oral presentation will introduce the concept of aurora sonification, highlighting the methods used to convert the visual patterns of auroras into auditory representations. The focus will be on how sound can capture key aspects of the aurora’s characteristics, such as its colors, intensity, and movement, through specific auditory cues like pitch, rhythm, and volume. We will demonstrate this sonification technique and discuss how we use this sonification in engaging with students, including those with blindness and visual impairment.
The consequences of space weather are often viewed as potentially catastrophic and civilization threatening, yet they are very often much less dramatic, but significant, nevertheless. We believe that it’s important for the public to understand how we deal with space weather events, without recurring to Carrington scenarios and scaremongering.
In Italy, public awareness of space weather remains limited. Many people are still unaware that solar activity can have tangible effects on our environment—both on Earth and in the surrounding space.
Motivated by the emotional impact of the auroral displays seen in Italy over the past two years—which have sparked renewed public interest in solar activity—our team launched a monthly live broadcast series on YouTube and Facebook titled Il Lato Oscuro del Sole (The Dark Side of the Sun). This initiative is part of the Italian project Sorvegliati Spaziali, a science communication effort by INAF (National Institute for Astrophysics) focused on planetary defense.
The goal of Il lato oscuro del Sole is to make the complex and often overlooked topic of space weather more accessible and relatable to the Italian public.
With this product, we aim to channel the same sense of wonder to spark curiosity and foster engagement with space weather topics, even in regions where auroras are rarely seen.
Each session explores how solar activity and geomagnetic phenomena impact everyday technologies and systems, from satellite communications and aviation to power grids and GPS, starting with actual stories of people.
A few days before each show, we ask the public a question related to their personal experience with the topic. During the live stream, we open the show by reading and commenting on the responses we have received. This approach boosts audience engagement and clearly shows how relatable space weather is.
This presentation will share our experience in designing and delivering these broadcasts, including topic selection, audience engagement strategies, and the use of visuals and analogies to simplify complex ideas. We will also present data on audience reach, engagement, and feedback, illustrating the growing public interest in space weather when it is communicated in this relatable and consistent format.
Mytherrella is an interactive art installation which creates a dynamic environment collocating scientific data and mythological storytelling to transport the public into an imaginary world where all-sky scientific images of aurora take plenary part in the show through distortion into imagination, fears and myths of older populations from the North. The deep learning core generates real time video streams aided by a custom StyleGAN approach exploiting a model trained on a large set of all-sky auroral images recorded in Kiruna available from the public scientific domain. By combining the scientific dataset with a small number of alternative-style artistic images, the real data is subject to divergent effects that reinforces the mythological narrative. The installation allows live interaction with the AI generated, not- and un- real, “mythologically” altered all-sky-like auroral images although still within the bounds of the learned scientific based feature. Mytherrella was developed during an artistic residency in the Institute of Space Science Măgurele, Romania, organized by Qolony, Bucharest.
The aurora borealis and aurora australis have been sources of inspiration, interest, and mystery to humans since ancient times. Due to recent advances in technology and space weather awareness, this “once in a lifetime” phenomenon has gained a wider reach. Major geomagnetic storms of 2023 and 2024 provided an initial experience with space weather effects for people who had not seen aurora before. Curiosity prompted many of them to search for more information on the aurora. In fact, the May 2024 Gannon Storm boosted Google searches of “northern lights” to an all-time high. Many publications on space weather are not geared towards the general public and tend to be written in professional language with heavy use of jargon. Some of these resources may not be accessible outside of academia or space weather operations. As a result, mass media, social media, websites, and blogs became the primary sources of space weather news and space weather science. Information shared through these sources are not always written in collaboration with space weather professionals, and it is not uncommon to see space weather presented in inaccurate or even catastrophizing ways. This can increase disappointment, mistrust in science, and even fear of space weather events. “Space Weather Unplugged” was started as a grassroots project by aurora chasers with the aim to address existing gaps in space weather education for the general public. It is run by three space weather enthusiasts with space weather education from Millersville University and a passion for science communication. The project is unique in its focus on recent events, which are traced from their origin on the Sun to their impacts on Earth. Short, informal educational videos in the format of conversation between session hosts and a brief review of relevant scientific information are streamed live on YouTube and posted on a weekly basis. Learner feedback occurs both formally with posted surveys, as well as informally with questions posted in live chat and comments for the posts/recorded sessions. Additional reading in relevant scientific publications is suggested for learners. In addition to weekly short videos, the group hosts monthly featured talks by academic and operational space weather professionals. These presentations focus on topics of interest in greater detail, such as the impacts of space weather events on important industries like precision farming and power grid operations. The general public’s increased interest in aurora provides a perfect opportunity for broader space weather and science communication. We will provide an overview of this effort and share our experience with this outreach mechanism, as well as analysis of audience engagement.
The inner magnetosphere hosts a dynamic range of plasma populations including the relativistic radiation belts, the ring current and cold plasmaspheric ions. These populations are tightly coupled via a range of micro-, meso- and macro-scale processes, driving a complex interplay of acceleration, transport and loss. For example, chorus waves are generated by injected plasma sheet electrons and then accelerate 100’s keV electrons to relativistic energies to form the radiation belts, with this acceleration being most efficient in regions of low plasma density. In turn, precipitation of radiation belt particles into the atmosphere balances ionospheric outflows of cold plasma into the inner magnetosphere. Further research into these and other cross-scale couplings is essential to develop the capability to reliably forecast inner magnetospheric dynamics and associated space weather risks and impacts. This session calls for observational, modelling and theoretical studies related to the inner magnetospheres, as well as review papers and mission concepts as well as comparative studies with other magnetospheres. We invite observational contributions from current missions such as Arase, Themis, MMS and GPS, from ground-based facilities such as EISCAT, SuperDARN and VLF receivers, and from historical datasets such as from the Van Allen Probes, Cluster and climatological studies involving even earlier solar cycles. We invite numerical contributions spanning Fokker Planck simulations, kinetic simulations of wave-particle interactions, and of the global magnetosphere and its couplings to the ionosphere and solar wind, as well as novel machine learning approaches and solutions.
This study investigates chorus wave activity and relativistic electron dynamics in the radiation belts during the High-Speed Stream (HSS) event of July 7, 2016. Electron flux measurements from the REPT instrument and magnetic field data from EMFISIS aboard the Van Allen Probes were analyzed, along with solar wind and interplanetary magnetic field (IMF) parameters from the DSCOVR satellite. The interaction between fast and slow solar wind streams within the stream interaction region (SIR) led to a strong southward IMF Bz component, which favored intense chorus wave activity. High-amplitude chorus bursts were detected in both the lower (0.1–0.5 fce) and upper (0.5–0.9 fce) bands. Rising-tone elements and nonlinear wave signatures were observed, revealing coherent wave–particle interactions. Oblique and parallel chorus wave modes coexisted, suggesting a competition between acceleration and loss processes driving variability in the outer radiation belt relativistic electron flux. Local plasma conditions (ωpe/Ωce < 5) supported nonlinear wave growth. The observed pitch-angle scattering led to spatially localized enhancements and losses of relativistic electrons, particularly at L-shells greater than 5. Other wave modes—such as electromagnetic ion cyclotron (EMIC) and ultra-low frequency (ULF) waves—were also investigated. The results indicate that chorus waves likely played a significant role in the localized loss of relativistic electrons. However, the global flux depletion also involved contributions from EMIC and ULF wave activity as well as magnetopause shadowing, highlighting a combined loss process driven by the HSS-related solar wind structure. These findings reinforce the importance of identifying the dominant wave–particle interactions and external drivers responsible for radiation belt variability during high-speed stream events.
We describe a large database of natural electromagnetic emissions of lower band whistler mode chorus and exohiss within the Earth's magnetosphere. It is based on more than 50 milion selected survey measurements of the magnetic fluctuations, recorded between 2001 and 2020 by the two NASA Van Allen Probes and four ESA Cluster spacecraft. The database provides a comprehensive view of amplitudes of these important electromagnetic emissions in the audible frequency range. We carefully condition the data to minimize the influence of instrumental artefacts. We also remove all data points which may be contaminated by instrumental noise using a newly developed method to define detection thresholds as a function of frequency, time, and instrument settings. The database can serve as a valuable resource for studying space weather, magnetospheric physics, and radiation belt dynamics.
Chorus waves play a significant role in the dynamic evolution of energetic electrons in the inner magnetosphere. Thus, understanding the spatial and temporal dynamics of these electrons requires global distributions of chorus waves, which are not usually possible to obtain from a single satellite mission. In this study, we use 11 years of data from both the Van Allen Probes mission and the Arase satellite to create a global model for the magnetic intensity of chorus waves. The statistical model is based on data with latitudinal coverage up to 40 degrees, providing good coverage over all magnetic local times (MLT) and at high L-shells. This results in a model with excellent spatial and temporal continuity. The model is generated for both Upper-Band Chorus (UBC; 0.5fce < f < fce) and Lower-Band Chorus (LBC; 0.05fce < f < 0.5fce) waves, where fce is the equatorial electron gyro-frequency. These models are parameterized by the Kp index for geomagnetic activity and are functions of L-shell, magnetic latitude (λ), and MLT. Our model is well-suited for inclusion in quasi-linear diffusion calculations of electron scattering rates and particle simulations in the inner magnetosphere.
Whistler-mode waves are commonly observed in magnetized plasma environments, such as Earth’s inner magnetosphere, where wave-particle interactions play a significant role in radiation belt electron dynamics during geomagnetically active periods. Several mechanisms have been proposed for the generation of whistler-mode waves within the dense plasmasphere and plasmaspheric plumes, including local generation by anisotropic electron injection, penetration of chorus emissions from the outer zone, and nonlinear wave growth. This study investigates the generation and propagation characteristics of whistler-mode emissions during a series of plume-crossing events observed by the Van Allen Probes during an ICME-driven geomagnetic storm from April 9 to 12, 2015. The relative positioning of the probes enabled a comparative analysis of wave activity in regions with different plasma densities. Energy flux analyses from both spacecraft, simultaneously located inside and outside plume structures, indicate that the propagation of chorus waves across the plume boundary often precedes the appearance of large-amplitude plume hiss emissions. Rising-tone chorus-like waves are also observed within the plume, suggesting a contribution from nonlinear growth processes. Notably, changes in the direction of wave energy propagation are observed between plume and plasmathrough regions, with inward-directed flux in the plasmathrough between two high-density regions potentially facilitating energy transfer into the plasmasphere. Additionally, the occurrence of exohiss near the plumes shows a correlation with anisotropic electron populations, indicating local generation. These findings contribute to a deeper understanding of the relative role of different generation mechanisms and propagation of whistler-mode waves under varying plasma conditions.
We report an observation of long-lasting echo trains of lightning-generated whistlers recorded by the WBD instruments on the Cluster spacecraft near the plasmapause on 23 April 2002 during an interval of quasiperiodic emissions. The whistler traces exhibit spectral discontinuities, which split each of them into two branches around 3.6 kHz, with lower-frequency components being stronger and arriving about 2 seconds later than higher-frequency ones. This highly unusual structure is not seen in similar whistler trains recorded just 25 minutes earlier. Ray tracing analysis suggests that this spectral splitting arises from propagation through two discrete field-aligned ducts separated by about 1.5 Earth radii. The intensification of the lower-frequency part is attributed to wave-particle interactions at the equator. These findings provide indirect evidence of fine-scale ducting structures near the plasmapause boundary.
Space weather and space climate have their origin in the Sun's magnetic field, which forms the continuously changing plasma environment in the heliosphere. Long-term observations of the Sun over the past few centuries have identified variations of the solar activity on different time scales, the most prominent ones being the 11-year sunspot cycle and the centennial Gleissberg cycle. Understanding and forecasting solar activity and the conditions in the heliosphere, including their effects to the Earth, is a major challenge in the field of heliophysics. The last decade has seen a lot of progress in solar activity modeling and in developing predictive capabilities, and there is a large diversity of forecasts using multiple methodologies. In addition, different communities and end-users have different needs about the cadence, lead time, and accuracy of the forecast parameters. This session aims to discuss the current capabilities and challenges in understanding and forecasting of long-term solar activity and related heliospheric and terrestrial effects for time scales of a few solar rotations onward. Possible forecast parameters include, e.g., sunspot numbers, total and spectral irradiance, open heliospheric flux, radio fluxes, galactic cosmic rays, extreme solar energetic particles, coronal holes, high-speed solar wind streams, coronal mass ejections, geomagnetic activity, GICs, magnetic storms, ionospheric parameters (foF2, etc), polar vortices, sudden stratospheric warmings, etc. We invite talks and posters from all these space weather and space climate domains, from the Sun to geospace, discussing their current understanding and long-term forecasting, new observations, theories and models, forecasting methodologies, and validation efforts.
SAPPHIRE-2S is a novel model developed over several years which concerns Solar Energetic Particle (SEP) particle radiation and is the extension of the Solar Accumulated and Peak Proton and Heavy Ion Radiation Environment (SAPPHIRE) model. SAPPHIRE-2S is the first publicly available SEP climate specification model offering as base output solar particle flux time-series. The time-series outputs are spectrally and statistically coherent across all solar particles species included in the model, protons, electrons, helium and heavy ions(Z=[3,92]), and across the large energy ranges the model covers, 40keV-900 MeV/nuc for protons helium and heavy ions, and 40keV-5MeV for electrons. SAPPHIRE-2S can provide outputs for timespans ranging from ~1 month to arbitrarily large periods of several decades, for both solar active and solar quiet conditions, and this includes the capability to define severe SEP event environments beyond what has been observed in the space age. The model allows for probabilistic estimations of SEP radiation at any confidence level for the base output of time-series as well as any synoptic output, such as cumulative fluence and peak flux, which is directly derived from the time-series. Thus, SAPPHIRE-2S is an SEP climate model which can create short and long term scenarios of SEP radiation which are used for mission specification while the time-series output allows for the analysis of radiation effects, such as total dose, single events and detector interference, on a previously unavailable level of detail. Furthermore, the model allows the direct and detailed coupling with magnetospheric shielding for missions which will fly partially or entirely inside the Earth’s radiation belts wherein SEP intensities are attenuated. Finally, SAPPHIRE-2S has been coupled with state-of-the-art physics-based model simulations for particle propagation and transport, such as PARADISE, so as to be able to provide outputs for helioradial distances other than 1 AU, allowing for heliospheric SEP climate probabilistic modelling covering the region from the orbit of Mercury at 0.4AU out to the Kronian orbit region at 9.5 AU. The model builds on many years of work to process and cross-calibrate underlying data which led to the creation of the ESA proton and helium Reference Dataset (RDS) and it also incorporates newly curated cleaned and cross-calibrated data concerning low energy (E<5 MeV) solar protons, helium and heavy ions, as well as solar electrons from the ACE, IMP8, and SOHO missions for all SEP events from 1974 to 2017.
The intensity and energy spectrum of energetic charged radiation in the heliosphere are significantly influenced by solar activity. This phenomenon is known as solar modulation of cosmic rays.
As interplanetary travel becomes a reality, missions in low-earth orbit become longer and more frequent. In order to accurately assess the radiation hazard experienced by astronauts during space missions, there is an emergent need for accurately depicting the space radiation environment and predicting the cosmic-ray flux in the heliosphere.
Using a new effective and predictive model of solar modulation which incorporates fundamental physics processes of particle transport, we were able to compute the solar modulated cosmic-ray flux near Earth as it evolves with the solar activity cycle.
Empowered by this model we will present our estimates and predictions of the dose imparted onto humans in space by the cosmic-ray flux of all elemental nuclei up to nickel, contributing to the full picture required to assess the radiation environment during space travel.
We will also present our studies on the impact of the geomagnetic field and its shielding effect on the total dose experienced by astronauts as they orbit Earth on board of the International Space Station.
We will present a method for developing transfer functions in order to improve the accuracy of dose estimates near planetary magnetic fields.
By providing a robust framework for understanding cosmic ray variations and their implications for space travel, our research contributes to advancing the safety and effectiveness of space exploration endeavours.
In recent years, our star has significantly increased its activity. This has been reflected in a larger number of geomagnetic storms, manifesting in the geomagnetic field perturbation and the formation of strong ground electric fields (GEFs). One of the most important consequences of exceptionally high levels of GEF is the occurrence of geomagnetically induced currents (GICs), which are particularly dangerous for electrical infrastructure and the increasing number of grid failures. One of the interesting events was the May 2024 storm, the most powerful geomagnetic storm in twenty years.
In our study we analaze various parameters related to solar, heliospheric, and geomagnetic activity during the solar cycles 24 and 25, using different machine learning techniques. Moreover, we compute the GEF in the Poland region for these periods using a 1-D layered conductivity Earth model. Based on these parameters, we investigate the anomalies in transmission lines in Poland. Our findings indicate an increase in the anomalies during the appearance of geo-effective events, indicating a potential coupling between them.
Annual carbon isotope (δ13C) data obtained from the Pafuri Baobab trees from north-eastern Southern Africa for the period 1200 AD – 2000 AD were used to investigate the presence and variability of the Hale, Schwabe and Gleissberg solar periodicities during the Wolf (1280 – 1340 AD), Spörer (1388 – 1550 AD), Maunder (1621 – 1715 AD) and Dalton (1790 – 1820 AD) solar minima. Spectral analysis using Morlet wavelets, Lomb-Scargle and Maximum Entropy techniques of the proxy rainfall record of north-eastern South Africa based on annual carbon isotope (δ13C) data show clear evidence of the presence of characteristic solar periodicities. Solar periodicities that were identified above the 95% confidence level include the ~ 11-year Schwabe as well as the ~ 22-year Hale cycles. In this presentation it will be shown that considerable variation in the power of the 11-year Schwabe, the 22-year Hale as well as the 80-year Gleissberg cycles exist in the δ13C proxy summer rainfall data for Southern Africa during the respective solar sunspot minima. The findings in this investigation provide clear evidence of the influence of solar activity on the climate of the Southern Hemisphere. In contrast, most previous solar-climate interaction results were based on Northern Hemisphere data sets.
We study magnetic storms during the 120-year time interval (1903-2023), which covers the whole Modern Maximum (MM, the latest Gleissberg cycle) of solar activity. Storms are mainly driven by coronal mass ejections (CME) and high-speed solar wind streams with related stream interaction regions (HSS/SIR). CME occurrence closely follows sunspots, the emergence of new strong magnetic flux, while HSS/SIR occurrence depends on the global structure of solar corona, in particular coronal holes, which is determined by the evolution of solar active regions (plages/faculae). Studying different types of storms during the last century can yield information on the occurrence of CMEs and HSSs both in the growth phase and in the decay phase of the Modern Maximum and, thereby, on the evolution of the Sun during this period of exceptional activity.
We find that the CME storms were relatively more frequent in the MM growth phase than in the decay phase. This is in agreement with the recent finding of the change in the mutual relation of sunspots and plages so that sunspots are relatively more frequent in the growth than the decay phase of the MM. We discuss these results in view of better understanding the solar centennial evolution and the long-term occurrence of CME and HSS/SIR storms at the Earth. We also note on the implications of these findings on the stellar evolution of the Sun and Sun-like stars.
APL1, APL2, OPS,CD1,CD6,CD7,CD8,CD9,SWR3,P2
There is a growing demand in the space weather community for the analysis of large datasets, due to availability of data from space missions and numerical simulations. To keep pace, the community is using off-the-shelf algorithms, adapting models from computer science. These models offer valuable opportunities to improve space weather services, e.g. forecasting capabilities. Other important applications include automatic detection and segmentation of areas of interest, where machine learning algorithms provide greater flexibility and efficiency compared to traditional methods. Furthermore, such algorithms can be implemented in ground-based facilities and spacecraft for on-board automation and maximizing the retrieval of scientifically interesting data. Given the volume of publications of AI in space weather, the community should come up with better practices for standardization of data and methods to facilitate unbiased comparisons between the models. Importantly, transitioning AI from research prototypes to tools used by space weather centers requires trust, uncertainty quantification, validation and explainability. In this TDM, we raise the following questions: 1.) What are the fundamental challenges of producing trustworthy forecasting of space weather events using AI? 2.) What are the best practices for automatic detection and annotation of space weather events? 3.) Can AI not only detect events but also assess their importance and trigger high-resolution data capture for selective downlink.
As part of the Space Safety Programme, ESA’s Space Weather Office is preparing a small satellite mission for monitoring the Auroral Oval (AO) for operational space weather applications. This mission will be part of ESA’s Distributed Space Weather Sensors System (D3S), which has the purpose of monitoring the interaction of the Earth with the Sun and to assess and measure the actual conditions in the proximity of the Earth. Monitoring of the aurora is an important element of enhanced space weather nowcasting and forecasting capability since it enables the observation of the impact of the solar wind and Coronal Mass Ejections (CMEs) on Earth’s magnetosphere and upper atmosphere. The impacts may trigger geomagnetic storms and sub-storms when hitting the Earth. Auroral emissions (optical, far-UV and X-ray) are a direct manifestation of physical processes occurring when the magnetosphere responds to the solar wind and CME plasma streams. It is planned to launch a demonstration mission in the time frame 2030/2031 that shall pave the way towards an operational system consisting of a constellation of 4 satellites that would be launched about 2-3 years later.
The topical discussion will focus on the following questions:
• Which potential new services and products can be established by using the observational data of the mission?
• Which possible enhancements of existing services can be envisaged?
• Which developments would have to be carried out to enable future service and product provisions.
• What improvements in terms of the expected data quality or mission requirements are desirable for the identified services or product.
• Which observational capabilities are desirable in terms of monitoring specific features or events in different space weather conditions (e.g. substorm monitoring, global mapping capabilities, pointing strategy).
It is essential to have global cooperation in ground-based observation for space weather phenomena because no one can cover the observational area all over the world by oneself.
Many organizations have been discussing the establishment of international cooperative relationships, data sharing, and standardization of formats. However, there have been few successful examples.
GION, Global Ionosonde Observation/operation network has been established on January 2025 for promoting communications among ionosonde operators and data users. This action comes from the discussion in the International Space Weather Coordination Forum held on November 17, 2023 at WMO headquarter, Geneve.
In TDM, the aim of GION will be introduced and have talks from some members but spend more time for discussion about what we can and what we should.
This TDM will focus on a lively discussion of current innovation models and their application to the Space Weather domain building on real case studies and lessons learned.
The TDM builds on the outcomes of the panel event held during ESWW2024 titled “Towards Space Weather Operational Governance in Europe: Lessons Learned from Natural Hazard Management”. Among the key takeaways highlighted by the panel of experts was the importance of finding a middle ground between open and directed innovation and the clear need for boosting commercial dynamics.
Building on these conclusions, this year ESA has launched several initiatives: an Innovation Journey, starting with an ESA Hack (Hackathon) offering a structured two-day programme to drive innovation and develop proof-of-concepts targeting the domain of space weather services for Spacecraft Operations; and an enabling study geared towards assessing market readiness and opportunities for commercial downstream services in other maturing domains.
This TDM will discuss these initiatives along with other innovation models being successfully deployed in different sectors to accelerate the use of space data in downstream contexts. It will assess how such models may further contribute to the development of relevant, tailored space weather services along with service adoption by end-users. In this context, the Innovation Journey winning team will be invited to present their experience and their planned next steps. Discussions will then focus on assessing the value of such innovation-oriented initiatives and considering what role they may play in supporting development of the space weather service landscape in Europe.
Extreme space weather events can severely impact critical infrastructure, from power grids and pipelines to GNSS, aviation, and satellite systems. To reduce risks, it is essential to establish an effective bridge between operational space weather forecasting centers and end-users, one that relies not only on scientific expertise but also on robust systems, service infrastructure, and clear communication channels. This session invites contributions that explore how space weather services are developed, implemented, and delivered to support real-world decision-making. Topics of interest include the design and operation of systems that link forecasting centers to end-users, such as data delivery chains, alert mechanisms, and operational resilience protocols. We also welcome insights into how dissemination standards and procedures have evolved, including tailored alert protocols for sectors like civil aviation, satellite operations, energy, and GNSS. Presentations may address user experiences with accessing and applying space weather information via web portals, APIs, or customized platforms, as well as approaches to enable two-way communication: ranging from impact reporting and feedback loops to co-development of tools and services. Case studies are encouraged that show how coordination between forecasters and users has led to timely mitigation actions during major space weather events. We also encourage reflections on lessons learned from past storms, highlighting how experiences have shaped operational workflows, tools, and partnerships. The overarching aim is to understand how space weather services can evolve to be more integrated, interoperable, and responsive, ensuring end-users receive information that is timely, relevant, and actionable in high-impact scenarios.
To enhance preparedness for space weather (SWE) events, impact or event-based alerts have been implemented for delivering tailored SWE information via email, SMS and prototype dashboards to four of the ESA SWE service user domains covering ‘Aviation’, ‘downstream Global Navigation Satellite System (GNSS) services’, ‘Power Systems Operations’ and ‘Satellite Operations’. Since April 2022, over 280 messages were issued for aviation, >50 for GNSS, 6 for power systems operations, and >45 for satellite operations, following, where available, domain-specific thresholds and user-agreed protocols.
This presentation illustrates how SWE alerting and dashboard curation have evolved through continuous interaction with the end-users within the frame of coordinated user-support campaigns. Feedback meetings with the end-users are organised to define the content and format of the tailored bulletins, and thresholds for triggering warnings and alerts during SWE events.
For each user domain, tailored dashboards compile relevant SWE data and information for operators, serving as platforms to test new concepts and functionalities, such as dedicated summary tables that provide impact or event-based overviews (nowcast/forecast) of current activity. Using past events with increased space weather (SWE) activity as examples, we demonstrate how tailored bulletins can be delivered across different user domains, showcasing the efficient, responsive, and user-focused integration of SWE services.
We present the new Solar Wind Scoreboard, which is hosted by NASA’s Community Coordinated Modeling Center (CCMC) and developed with the community as part of the COSPAR ISWAT initiative. The Solar Wind Scoreboard will serve the space weather and science community as a hub for real-time solar wind predictions at Earth, Mars, and other locations of interest. It will allow users to view the ensemble of community-contributed models and compare their performance during extreme space weather events. Our overarching objective is to build an open platform that allows us to identify science models that show potential to improve operational services. In this presentation, we will share our progress from the COSPAR ISWAT Workshop in Cape Canaveral, FL, USA, focusing on the open information architecture, including metadata standards, automated prediction submissions, and front-end development. Additionally, we will discuss how the Solar Wind Scoreboard integrates with existing CCMC Scoreboards and feeds into the new Geospace Scoreboard. We will share lessons learned from running models like AWSoM (University of Michigan) and ICARUS (KU Leuven) in real-time, and how we integrate their results into the Scoreboard. Finally, we will outline future plans and how we envision broader community engagement in line with open science principles.
Machine learning (ML) has shown promise in solar flare forecasting, yet major challenges remain in moving beyond single studies often published without the associated code and datasets. Are forecast models truly reproducible, deployable, operable, updatable, and monitorable? It has been nearly a decade since the influential work of Bobra & Couvidat (2015), where Support Vector Machine (SVM) was used to predict solar flares using features derived from solar magnetic field observations, what progress has been made? While the field has made strides in improving flare forecasting, little to no progress has been made in ensuring the reproducibility, deployment, and sustained operation of ML models. As models become increasingly complex, how can we guarantee that their results are reproducible and transparent? How do we ensure that models remain accurate as new data becomes available and do not degrade over time? The traditional approach of publishing research papers—often without the accompanying code, data, or reproducibility frameworks—is no longer sufficient. To truly advance the field, we must move beyond current academic practices and adopt best practices from ML and meteorology, which have well-established methodologies for real-time prediction systems. This includes:
Reproducibility: Establishing standardized benchmarks, dataset and model versioning, and open-source implementations.
Deployment: Models must be easily deployed from zero to a running deployment with as little human intervention as possible.
Operation: Ensuring once deployed models can easily be run in research or operational environments and they are robust to missing data, latency, and changing solar cycle conditions.
Updates: Implementing retraining strategies to include new data and prevent model degradation over time.
Monitoring: Developing frameworks for continuous evaluation, explainability, and reliability of forecasts.
We must leverage platforms like Hugging Face, Kaggle, Comet, Neptune, WandB and open source solutions like MLFlow to facilitate transparent and collaborative development. The space weather community must also engage MLops, automated retraining pipelines, and robust monitoring tools to transition ML-based forecasting from one off publications to an operational reality.
This session invites submission focusing on the use or implementation of any of the above aspects and interdisciplinary approaches to move ML-based space weather forecasting from promise to practice.
We present an ongoing effort dedicated to the development of reproducible and operationally viable artificial intelligence models for solar flare forecasting. Our approach leverages the extensive archive of multi-wavelength solar images captured by the Solar Dynamics Observatory (SDO) using the Atmospheric Imaging Assembly (AIA). Specifically, we employ a self-supervised learning strategy, utilizing a masked autoencoder task to pre-train a transformer-based architecture. This pre-training phase enables the model to learn robust and meaningful representations of diverse solar activity patterns from unlabeled data. Subsequently, we transfer the knowledge acquired during pre-training to a downstream task focused on segmenting sunspots in images of the photosphere obtained from the SDO's Helioseismic and Magnetic Imager (HMI). This work aims to bridge the gap between fundamental research and practical deployment by systematically addressing key challenges related to the generalization of the models to unseen solar conditions , ensuring their scalability to handle large volumes of solar data and optimizing their performance for deployment in resource-limited operational environments. The ultimate goal of this research is to provide the way for future real-time AI applications in space weather operations, which will give important advance notice for reducing the effects of solar flares on critical infrastructure and human activities.
The CORonal mass ejection, solar eNERgetic particle and flare forecaSTing from phOtospheric sigNaturEs (CORNERSTONE) project focuses on the prediction of intense solar events through the application of machine learning (ML) techniques to real observational data. This domain poses significant challenges to reproducibility, primarily due to the heterogeneous nature of the data and the complexity of applying ML methods in a physically meaningful way. Real-world solar data are typically acquired by diverse teams of specialists - from electronics and engineering to physics - and usually originate from in-flight instruments. As a result, these data are subject to a variety of issues not directly related to the measurements themselves, but to the conditions and limitations under which data acquisition occurs. Moreover, from a methodological perspective, while ML algorithms are widely accessible, their rigorous and interpretable use in the context of solar physics requires careful data preprocessing. In particular, ensuring that models are trained on stratified and statistically consistent distributions is critical for producing reliable results, especially when moving from offline experimentation to real-time operational forecasting. The CORNERSTONE project aims to address these issues by developing methodologies and tools that enable reproducible and verifiable ML-based research. It provides high-quality criteria for data stratification, the construction of curated datasets for benchmarking, and a framework for testing multiple ML algorithms under reproducible conditions. The ultimate objective of CORNERSTONE is the development of an open-source Python library that integrates data preprocessing, data verification, model implementation, and validation tools. This library will facilitate reproducibility by enabling researchers to both verify their own models and independently assess those developed by others, fostering transparency and collaboration in the solar forecasting research community.
Segmentation and characterization of solar coronal structures are essential for advancing our understanding of the solar atmosphere and accurately identifying key regions such as active regions and coronal holes which are precursors to phenomena like solar flares and coronal mass ejections (CMEs). In parallel, it is crucial to incorporate onboard such artificial intelligence (AI) algorithms into future space missions to automate tasks, overcome challenges such as communication delays when ground-based human intervention is required, intelligently activate instruments, discard non-essential data to conserve storage and resources, and prioritize critical data for downlink.
In this study, we investigate various complementary approaches to automate this process. First, we employ a previously presented deep learning-based U-Net architecture tailored for segmenting and characterizing solar coronal structures. Since this framework is notoriously heavy to be deployed even for testing, we explore ways to address this problem more efficiently without compromising performance. To this end, we first reformulate the task as a detection problem and explore the widely used You Only Look Once (YOLO) model, whose nano edition requires 95% fewer trainable parameters than U-Net. We also propose an even more lightweight Convolutional Neural Network (CNN) capable of achieving comparable accuracy. Additionally, we design a framework that combines basic computer vision techniques with traditional machine learning methods to segment and characterize solar coronal structures. This approach relies on a carefully selected set of hand-crafted features and unsupervised clustering methods such as K-means and t-SNE, facilitating the distinction of coronal features like active regions, coronal holes, and bright spots. All methods are evaluated and compared against each other and against state-of-the-art techniques using metrics such as the Dice score and Intersection over Union (IoU). Furthermore, we assess them in terms of trainable parameters, number of operations, and inference time across various hardware configurations.
The method that best balances segmentation and detection performance with computational efficiency will be selected for integration into a prototype designed to support future space exploration missions. Employing such a framework onboard can automate solar activity monitoring and facilitate solar phenomena prediction and observance such as solar flares and coronal mass ejections (CMEs). This work is part of the project AutomaticS in spAce exPloration “ASAP” funded by the European Union under HORIZON Research and Innovation Action (GA no.101082633).
We develop two artificial intelligence-based models (Models A and B) to predict time-evolving photospheric magnetic fields with an adjustable timestep, ranging from a few seconds to one solar rotation ahead or behind. Model A predicts future magnetic field data using three consecutive radial magnetic field datasets with a 12-hour cadence. Model B reconstructs evolving magnetic fields over the past solar rotation using two sets of three consecutive datasets—one from the present and one from the previous solar rotation—each with a 12-hour cadence. To train and evaluate our models, we use a Pix2PixCC-based architecture and datasets of SDO/HMI vector magnetograms during the solar maximum periods of 2012–2016 and 2021–2023. Models A and B successfully generate magnetic field data corresponding to the input prediction timestep. Based on several evaluation metrics applied to the model outputs, Model A outperforms the persistence model and yields results comparable to the classical surface flux transport model, and Model B shows improved performance over both. We also compare the prediction results using data from NOAA AR 12673 and 13664, which produced intense solar flares and geomagnetic disturbances. Notably, our models can predict emerging magnetic flux several days in advance if their evolution is already captured in the input data. Model B can reconstruct radial magnetic flux that smoothly overlaps in regions near the Sun’s east and west limbs. Finally, we discuss the potential applications of our models in space weather forecasting—particularly in forecasting extreme solar flares—and in revisiting past solar events near the solar limb.
Many objects in the solar system lack an intrinsic magnetic field, for example some planets, moons, comets, and other small bodies. The interaction of these objects with the solar wind is fundamentally different to the interaction of the solar wind with magnetized bodies such as the Earth. This session will focus on space weather effects on the surface, atmosphere and space environment of such bodies.
Topics include, but are not limited to, the response to a varying space weather and the effects of extreme solar events on the surface, atmosphere, and plasma environment. We also invite abstracts discussing the influence of the space environment on spacecraft operation and scientific instrument performance close to these bodies.
We welcome abstracts addressing these topics using a variety of methods, including laboratory experiments, numerical modelling and observations.
The Rosetta mission followed comet 67P over heliospheric distances ranging from 1.25 to 3.6 AU. A come tis essentially a gas cloud embedded in the solar wind. When gas molecules are ionized they are picked up by the solar wind stream, the solar wind is “mass loaded”. The initial reaction of the solar wind to mass loading is to be deflected in the direction opposite to the solar wind electric field. During the two year mission several coronal mass ejections (CME) and coronating interaction regions (CIR) affected the comet environment. We have looked through the data from the Rosetta Plasma Consortium for the most extreme space weather events including all identified CIR:s and CME:s. We have re-analysed the data in terms of the observed solar wind flow direction in the comet - solar wind - electric field reference system. We find that the solar wind events are significantly affected by the comet environment. Solar wind flow directions for the extreme events often deviate from the expected deflection of the flow in the direction opposite to the solar wind electric field direction. We present our current understanding of how a disturbed solar wind reacts to the comet environment. This provides a general picture of how space weather events evolve in the presence of a back ground neutral gas providing mass loading of the solar wind.
A chain of CME events, occured on September 2014, led to strong perturbations in the interplanetary magnetic field and remarkable enhancements in the energetic particle fluxes measured at different heliospheric distances. We conducted a multi-spacecraft and multi-parameter analysis of such intense events, using observations from a fleet of spacecraft distributed in the inner Solar System, such as STEREO B, MESSENGER , Mars Express, SOHO. A characterization of the interaction between Coronal Mass Ejections and the inner planets of the Solar System is presented, focusing on some of the main effects on each planetary environment. Besides, we applied a numerical simulation to reconstruct the magnetic connection from Mercury, Earth and Mars to the solar corona in the dates when the CME events occurred, in order to enrich the interpretation of the data providing a more general theoretical frame. The aim of the work we present is to offer a detailed cause-and-effect framework for studying space weather events in the Solar System, allowing us to obtain a more global picture of the related phenomena in action.
Waves in the cometary plasma environment occur at almost every activity level of a comet, both far away from the Sun and near its perihelion. They play an important role in the thermalization of the cometary pick-up ions and in the redistribution of energy. Upstream of the nucleus and for several thousands of kilometers downstream the gyrating motion of both the solar wind (SW) plasma and the cometary ions creates highly anisotropic ion velocity distribution functions. These anisotropies can result in a variety of different wave phenomena. In this study we use the 3D hybrid particle simulation code Amitis to simulate a comet magnetosphere at approximately Mars' distance, with an outgassing rate of $Q \approx 10^{27}$ s$^{-1}$.
We find large-scale wave features that reach from far upstream of the comet nucleus to downstream of the bow shock.
The wave signatures are most apparent in the +E hemisphere and near the quasi-parallel bow shock, whereas in the -E hemisphere and at the quasi-parallel bow shock the magnetic field pile-up dominates. In the innermost part of the comet magnetosphere, where the cometary ions are far more dominant than the solar wind ions, the waves are absent. Peaks in the magnetic field strength and the solar wind density are out of phase, while the cometary ion density is approximately correlated with the magnetic field. These characteristics are consistent with slow magnetosonic waves.
The waves create enhancements in the SW density, which in turn result in an increased dynamic pressure. These dynamic pressure enhancements are commonly referred to as magnetosheath jets. We investigate the possibility that the waves contribute to the formation of jets in the cometary magnetosphere.
The Lunar Surface, while exposed to different plasma and radiation regimes along its orbit around Earth, is also encountering time varying conditions when transitioning from quiet solar activity to solar storms, leading to CMEs crossing, EUV and X-rays fluxes changes or perturbed magnetospheric conditions. In addition, lunar surface topography and illumination changes lead to a variety of interactions between the surface and the local plasma, whereby local slopes, regolith coverage and shadowing effects strongly determine surface and volume potential gradients, as well as near surface electrons, ions, dust densities and temperatures.
In the current context of designing and implementing missions to the Moon, ESA requires to specify lunar surface conditions and their variabilities to both constrain design choices regarding environmental risk and allow for smooth operations of surface assets, vehicles and exploration systems, such as landers, rovers, astronauts and eventually lunar habitats.
In this context, the current study presents an assessment of different terrain models, when subject to varying illumination and plasma environments, including the occurrence of Space Weather events. Potential maps as well as plasma and dust populations densities and temperatures, and their variability range, will be presented for the sites of interest. In addition, implication for performing lunar science measurements
locally will be discussed.
The inner magnetosphere hosts a dynamic range of plasma populations including the relativistic radiation belts, the ring current and cold plasmaspheric ions. These populations are tightly coupled via a range of micro-, meso- and macro-scale processes, driving a complex interplay of acceleration, transport and loss. For example, chorus waves are generated by injected plasma sheet electrons and then accelerate 100’s keV electrons to relativistic energies to form the radiation belts, with this acceleration being most efficient in regions of low plasma density. In turn, precipitation of radiation belt particles into the atmosphere balances ionospheric outflows of cold plasma into the inner magnetosphere. Further research into these and other cross-scale couplings is essential to develop the capability to reliably forecast inner magnetospheric dynamics and associated space weather risks and impacts. This session calls for observational, modelling and theoretical studies related to the inner magnetospheres, as well as review papers and mission concepts as well as comparative studies with other magnetospheres. We invite observational contributions from current missions such as Arase, Themis, MMS and GPS, from ground-based facilities such as EISCAT, SuperDARN and VLF receivers, and from historical datasets such as from the Van Allen Probes, Cluster and climatological studies involving even earlier solar cycles. We invite numerical contributions spanning Fokker Planck simulations, kinetic simulations of wave-particle interactions, and of the global magnetosphere and its couplings to the ionosphere and solar wind, as well as novel machine learning approaches and solutions.
The Radiation Belt Forecasting Model and Framework (RBFMF) provides real-time forecasts and hindcasts of the radiation environment, which are used as inputs for the Satellite Charging Assessment Tool (Sat-CAT). Sat-Cat is used by satellite operators to model both long term and real-time effects of internal charging on satellite components. We will present the validation results of the RBFMF, and will show how combining real-time electron flux observations with physics based modelling improves hindcasting capabilities of either method used alone. We will also highlight how new missions must consider data provision for real-time operational applications, and discuss the observational requirements and model development necessary for improved specification of the radiation environment through hindcasts and forecasts.
Given the critical impact of accurate radiation belt modeling on space radiation environment restitution and forecasting, data assimilation has been employed to enhance physical models estimations since their earliest days. The method, allows the correction of the theoretical description of a given model, thanks to the ingestion of in-situ observations. Precisely, the widely adopted approach applies the correction solely at a model’s output level, with no impact on the model’s parameters or more generally on its physical description. Yet, the model's output biases are typically rooted in an inaccurate estimation of those same parameters within the underlying physical description. Moreover, one can explore within the same data assimilation framework, the correction of the model’s biases directly on the model’s faulty parameters, thanks to the state vector extension operation.
In this talk, we will present the principle of the state vector extension and how it can be leveraged in the radiation belt modeling context. For that, we will rely on twin experiments deployed on a realistic Fokker-Planck based model of the electron radiation belts. These numerical tests will help us evaluate the potential of the method as a new way to extract theoretical inferences from satellite observations or to detect the model’s trajectory deviation.
In previous years, space weather predictions have been developed as part of the Horizon 2020 funded projects PROGRESS (PRediction of Geospace Radiation Environment and Solar wind parameterS) and PAGER (Prediction of Adverse effects of Geomagnetic Storms and Energetic Radiation). Space weather predictions were initiated from observations of the Sun and provide a forecast of the radiation in space and its effects on satellite infrastructure. Real-time predictions allow for the evaluation of surface charging, and deep dielectric charging. The project provides 1-2 day probabilistic forecasts of ring current and radiation belt environments. As a backbone of the project, the most advanced codes from US and Europe were used to perform ensemble simulations and uncertainty quantification. This project includes a number of innovative tools, including data assimilation and uncertainty quantification, machine learning.
More recently, a new Horizon 2020 project, FLAG (Forecasts and Long-term probabilistic data Assimilative prediction of the effects of Geomagnetic storms) has been funded. FLAG will build upon PAGER and PROGRESS by reorganizing the coupling of the codes, providing realistic ensembles, adding new modules, carefully validating the new framework as a whole and transitioning the codes to operations at ESA. The final output of the FLAG early warning system will be simple to understand “traffic-light” indicators that will tell the stakeholders if their particular spacecraft, depending on the orbit and materials used, is in danger or not. FLAG will bring together researchers from the European Space Agency (ESA), the GFZ (Helmholtz-Zentrum für Geoforschung), the University of Warwick, TRAD Tests and Radiation, Université catholique de Louvain and BIRA (Royal Belgian Institute for Space Aeronomy).
The Van Allen Radiation belts are highly dynamic in both strength and location, meaning that the belts are difficult to predict for spacecraft operators. Forecasting models exist, in part, to minimise any additional damage caused by this natural hazard. Both physics-based and machine learning models already exist; physics-based models allow for a deeper understanding of the system, and machine learning models offer a computationally cheap way to make a forecast but do not necessarily provide physical insight.
We present a collection of machine learning models capable of predicting if the Outer Radiation Belt crosses set percentile thresholds with considerable skill up to 3-days in advance, and some skill up to 6-days in advance. We use a Random Forest classification model to predict if the daily ~2MeV electron flux level across the Outer Radiation Belt exceeds thresholds from the 60th to the 95th percentiles. Each model shows a high level of accuracy at nowcasting and skill at forecasting up to 6 days in advance, a longer forecast than current operational models. Using feature importance, we determine the key inputs into each model in order to gain an insight into which drivers are important in driving increasing flux levels and over what timescales they have an impact. Crucially, we find that only a small number of geomagnetic indices are required to be able to forecast radiation belt fluxes with good skill, meaning that models such as these could be operationally viable for space weather stakeholders.
There is an established tradition at ESWW for different forecast centres to present a Live Space Weather Forecast, either before or after the morning plenary session. Attending these forecasts enables participants to gain insights into the forecasting process and understand the real-world impact of space weather on end-users. It provides an opportunity to reflect on how we, as a community, can enhance our communication of these complex concepts to end-users and the public. With different forecast centres presenting, attendees benefit from a variety of forecasting perspectives and methodologies tailored to different end-users. Additionally, it offers an excellent opportunity for forecast centres to showcase their expertise and highlight the communication channels they use, fostering a deeper understanding and collaboration within the space weather community.
With an ever increasing interest in robotic and human exploration of Mars, the observation and understanding of space weather at Mars is becoming an important topic. The ultimate goal, as at Earth, will be to have a reliable capabilty of forecasting potential hazardous impacts of space weather events. The Martian plasma system, however, is very different from that at Earth, mainly as a result of the lack of an internal dipole field, but other aspects, such as a less dense atmosphere and a more eccentric orbit of the sun. At present a number of spacecraft at Mars provide a wealth of data during space weather events such as solar flares, solar energetic particles, interplanetary coronal mass ejections and stream interface regions. The number of extreme events available at present, however, is somewhat limited which means that untangling the impact of each of these types of events individually is somewhat difficult. In this presentation I shall provide an overview of our understanding of space weather events, illustrating this with examples from some of the more extreme events.
As we prepare for human missions beyond Low Earth Orbit (LEO), solar observations off the Sun-Earth line (SEL) at different vantage points become critical for accurate space weather predictions. These observations will help to improve our forecasting and understanding of the environment that these missions will encounter outside of the Earth’s protective magnetic field. The National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS) Office of Space Weather Observations (SWO) is implementing the Space Weather Next (SW Next) program that will provide the continuity of critical observations from the Lagrange point 1 (L1) and other relevant orbits, such as the Geostationary and Low Earth orbits, as well as Lagrange 5 (L5) in collaboration with the European Space Agency (ESA). The main objective for the project is to provide continuity of observations beyond the Space Weather Follow-On (SWFO)-L1 mission. In this presentation, we will describe the observational requirements, how the program is working to continue critical observations from the different vantage points and how these observations will support the space weather operational environment to safeguard the nation and to support government and commercial human exploration endeavors.
Panellists:
• Mark Lester, University of Leicester
• Yaireska Collado-Vega, NOAA NESDIS Space Weather Observations Office
• Teresa Nieves-Chinchilla, NASA Goddard Space Flight Center
• Iannis Dandouras, IRAP/CNRS