A discrete tomography method has been developed that is able to reconstruct 3D coronal mass ejection (CME) density structure. We test the method by producing synthetic coronagraph imagery for three events using the CORona-HELiosphere (CORHEL) model. We combine images from different numbers of observing spacecraft and we perform the method separately using polarimetric and non-polarimetric reconstructions, as a means to test their relative effectiveness. We show that increasing the number of observing spacecraft consistently reduces the mean relative absolute error (MRAE) between the simulated and reconstructed density. Furthermore, the MRAE is generally lower when using polarimetric reconstructions compared to non-polarimetric reconstructions. Methods applied to localize the CME front work well for all spacecraft configurations, and are improved when using polarimetric, over non-polarimetric, reconstructions. The presence of a CME front can be identified with an accuracy of (72 +/- 9) per cent, (70 +/- 8) per cent, and (52 +/- 12) per cent for CME1, CME2, and CME3 via polarimetric reconstructions using only three spacecraft at L1, L4, and L5. The radial position of the CME front can be constrained to a high level of precision when using polarimetric reconstructions using the same three spacecraft; 0.003 +/- 0.004 , 0.004 +/- 0.005 , and 0.005 +/- 0.004 au for CME1, CME2, and CME3, respectively. We expect that at least four spacecraft are required in order to derive accurate information about 3D CME structure. We find no strong evidence of improvement when including out-of-ecliptic observers, but that their inclusion increases the volume of space within which the inversion can be performed.

The Solar TErrestrial RElations Observatory (STEREO) mission has laid a foundation for advancing real-time space weather forecasting by enabling the evaluation of heliospheric imager (HI) data for predicting coronal mass ejection (CME) arrivals at Earth. This study employs the ELEvoHI model to assess how incorporating STEREO/HI data from the Lagrange 5 (L5) perspective can enhance prediction accuracy for CME arrival times and speeds. Our investigation, preparing for the upcoming ESA Vigil mission, explores whether the progressive incorporation of HI data in real-time enhances forecasting accuracy. The role of human tracking variability is evaluated by comparing predictions based on observations by three different scientists, highlighting the influence of manual biases on forecasting outcomes. Furthermore, the study examines the efficacy of deriving CME propagation directions using HI-specific methods versus coronagraph-based techniques, emphasising the trade-offs in prediction accuracy. Our results demonstrate the potential of HI data to significantly improve operational space weather forecasting when integrated with other observational platforms, especially when HI data from beyond 35° elongation are used. These findings pave the way for optimising real-time prediction methodologies, providing valuable groundwork for the forthcoming Vigil mission and enhancing preparedness for CME-driven space weather events.

Timely and accurate prediction of coronal mass ejections (CMEs) is vital for mitigating the potential impact of severe space weather events on critical infrastructures. Currently, manual detection and tracking of CMEs as they traverse the heliosphere are the norm. This presentation introduces an innovative approach: the development and implementation of a machine learning algorithm for automatic detection and tracking of CMEs, leveraging data from various heliospheric imager (HI) instruments. The wealth of active spacecraft equipped with HI instruments provides a unique opportunity to train the algorithm using diverse datasets.This work gains significance in light of the upcoming launch of Vigil, a space weather monitor scheduled for deployment in the early 2030s at the L5 point. Vigil will continuously observe and provide real-time HI observations along the Sun-Earth line. Our presentation showcases preliminary outcomes from an automated CME detection and tracking algorithm, demonstrating its effectiveness with training on STEREO-HI data. We also discuss potential future steps and challenges in the development and testing of this algorithm, emphasizing its role in advancing operational space weather prediction capabilities, especially in anticipation of the Vigil mission.

Observing and forecasting Coronal Mass Ejections (CME) is crucial due to the potentially strong geomagnetic storms generated and their impact on satellites and electrical devices. With its near-real-time availability, STEREO-HI beacon data is the perfect candidate for efficient forecasting of CMEs. However, previous work concluded that prediction based on beacon data could not achieve the same accuracy as with high-resolution science data due to data gaps and lower quality. We have introduced a new method to improve the resolution and quality of near-real-time beacon data by using advanced machine-learning techniques while maintaining consistency between consecutive frames. This method also allows us to forecast intermediary and subsequent frames using a data-driven model for CME propagation within HI images. The output generated by our model produces smoother and more detailed time-elongation plots (J-plots) that are used as input for the Ellipse Evolution model based on Heliospheric Imager observations (ELEvoHl). We have compared the data produced by our model with the science data and analysed its impact on CME forecasting and propagation.

The STEREO mission has paved the way for the forthcoming Vigil mission, set to launch around 2030. Based on the extensive data archives from STEREO's wide-angle cameras, the heliospheric imagers (HI), we aim to assess the suitability of these data for real-time space weather prediction.This study focuses on modeling the evolution of coronal mass ejections (CMEs) as they progress towards Earth, employing STEREO-A and STEREO-B observations from Vigil's future vantage point, the L5 point of the Sun-Earth system, with the drag-based ensemble model ELEvoHI.Our investigation aims to determine to what extent incorporating additional HI data (as it would be received in real-time) improves the forecasting accuracy and its impact on the prediction lead time.

Sub-L1 monitors are currently being researched in mission concepts for small satellites and may be deployed on distant retrograde orbits around the Earth in the future. Depending on the location of the sub-L1 monitor, the lead time for the arrival of coronal mass ejections (CMEs) and for determining their geo-effectiveness could be prolonged. If the sub-L1 monitor was to orbit the Earth at a distance of 0.05 AU, as is proposed for the MIIST mission, for example, Dst predictions could be made up to 5 hours in advance. The close encounter of STEREO-A and Wind from April 2023 to November 2023 represents such a constellation, and therefore allows us to investigate potential impacts of future sub-L1 missions. Following the method of Bailey et al. (2020), the data from STEREO-A are mapped to L1, taking into account an expansion of the CME. We then calculate the Dst of the temporally and spatially shifted data, and compare the result with the Dst calculated from L1 solar wind data and the observed Dst. In this way, we can analyse and quantify the implications of sub-L1 monitors on space weather forecasting.The events included in our study are part of the HELIO4CAST lineup catalog v2.0 (https://helioforecast.space/lineups). The catalogue includes CMEs that were observed by at least two spacecraft such as Solar Orbiter, Parker Solar Probe, BepiColombo, STEREO-A, and Wind. In contrast to single in situ measurements, which do not adequately capture the vast structure of CMEs, the events in the catalogue allow us to study the temporal and spatial evolution of CMEs and improve our current understanding of the large-scale structure of their magnetic flux ropes. In view of the upcoming maximum of solar cycle 25, further multipoint events are expected to be continuously added to the catalogue.

Coronal Mass Ejections (CMEs) are space weather phenomena capable of causing significant disruptions to both space- and ground-based infrastructure. The timely and accurate detection and prediction of CMEs is a crucial steps toward implementing strategies to minimize the impacts of such events. CMEs are commonly observed using coronagraphs and heliospheric imagers (HIs), with some forecasting methods relying on manually tracking CMEs across successive images in order to provide an estimate of their arrival time and speed. This process is time-consuming and results may exhibiting considerable interpersonal variation. We investigate the application of machine learning (ML) techniques to the problem of automated CME detection, focusing on data from the HI instruments aboard the STEREO spacecraft. HI data facilitates the tracking of CMEs through interplanetary space, providing valuable information on their evolution. Building on advances in image segmentation, we present the Solar Transient Recognition Using Deep Learning (STRUDL) model. STRUDL is designed to automatically detect and segment CME fronts in HI data. We address the challenges inherent to this task and evaluate the model's performance across a range of solar activity conditions. To complement segmentation, we implement a basic tracking algorithm that links CME detections across successive frames, thus allowing us to automatically generate time-distance profiles. Our results demonstrate the feasibility of applying ML-based segmentation techniques to HI data, while highlighting areas for future improvement, particularly regarding the accurate segmentation and tracking of faint and interacting CMEs.

Observing and forecasting coronal mass ejections (CMEs) in real‐time is crucial due to the strong geomagnetic storms they can generate that can have a potentially damaging effect, for example, on satellites and electrical devices. With its near‐real‐time availability, Solar TErrestrial RElations Observatory‐heliospheric imagers (STEREO/HI) beacon data is the perfect candidate for early forecasting of CMEs. However, previous work concluded that CME arrival prediction based on beacon data could not achieve the same accuracy as with high‐resolution science data due to data gaps and lower quality. We present our novel machine‐learning pipeline entitled “Beacon2Science,” bridging the gap between beacon and science data to improve CME tracking. Through this pipeline, we first enhance the quality (signal‐to‐noise ratio and spatial resolution) of beacon data. We then increase the time resolution of enhanced beacon images through learned interpolation to match science data's 40‐min resolution. We maximize information coherence between consecutive frames with adapted model architecture and loss functions through the different steps. The improved beacon images are comparable to science data, showing better CME visibility than the original beacon data. Furthermore, we compare CMEs tracked in beacon, enhanced beacon, and science images. The tracks extracted from enhanced beacon data are closer to those from science images, with a mean average error of of elongation compared to with original beacon data. The work presented in this paper paves the way for its application to forthcoming missions such as Vigil and PUNCH.

Forecasting the geomagnetic effects of solar coronal mass ejections (CMEs) is currently an unsolved problem. CMEs, responsible for the largest values of the north‐south component of the interplanetary magnetic field, are the key driver of intense and extreme geomagnetic activity. Observations of southward interplanetary magnetic fields are currently only accessible directly through in situ measurements by spacecraft in the solar wind. On 10–12 May 2024, the strongest geomagnetic storm since 2003 took place, caused by five interacting CMEs. We clarify the relationship between the CMEs, their solar source regions, and the resulting signatures at the Sun–Earth L1 point observed by the ACE spacecraft at 1.00 AU. The STEREO‐A spacecraft was situated at 0.956 AU and 12.6 west of Earth during the event, serving as a fortuitous sub‐L1 monitor providing interplanetary magnetic field measurements of the solar wind. We demonstrate an extension of the prediction lead time, as the shock was observed 2.57 hr earlier at STEREO‐A than at L1, consistent with the measured shock speed at L1, 710 km, and the radial distance of 0.043 AU. By deriving the geomagnetic indices based on the STEREO‐A beacon data, we show that the strength of the geomagnetic storm would have been decently forecasted, with the modeled minimum SYM‐H nT, underestimating the observed minimum by only 8%. Our study sets an unprecedented benchmark for future mission design using upstream monitoring for space weather prediction.

As both Parker Solar Probe (PSP) and Solar Orbiter (SolO) reach heliocentric distances closer to the Sun, they present an exciting opportunity to study the structure of coronal mass ejections (CMEs) in the inner heliosphere. We present an analysis of the global flux rope structure of the 2022 September 5 CME event that impacted PSP at a heliocentric distance of only 0.07 au and SolO at 0.69 au. We compare in situ measurements at PSP and SolO to determine global and local expansion measures, finding a good agreement between magnetic field relationships with heliocentric distance, but significant differences with respect to flux rope size. We use PSP/Wide-Field Imager for Solar Probe images as input to the ELlipse Evolution model based on Heliospheric Imager data (or ELEvoHI), providing a direct link between remote and in situ observations; we find a large discrepancy between the resulting modeled arrival times, suggesting that the underlying model assumptions may not be suitable when using data obtained close to the Sun, where the drag regime is markedly different in comparison to larger heliocentric distances. Finally, we fit the SolO's magnetometer and PSP's FIELDS data independently with the 3D Coronal ROpe Ejection (or 3DCORE) model, and find that many parameters are consistent between spacecraft. However, challenges are apparent when reconstructing a global 3D structure that aligns with arrival times at PSP and SolO, likely due to the large radial and longitudinal separations between spacecraft. From our model results, it is clear the solar wind background speed and drag regime strongly affect the modeled expansion and propagation of CMEs and need to be taken into consideration.

AbstractThe field of heliophysics encompasses a diverse range of research areas and rich plasma environments, from the solar dynamo and solar wind turbulence, to the complex terrestrial thermosphere‐ionosphere‐magnetosphere (TIM) system, to investigations of both induced and intrinsic magnetospheres across the solar system. This perspective paper delves into the experiences and outcomes of a summer school held in person in Boulder, Colorado, USA, from 7–11 August, which offered a platform for interdisciplinary learning and collaboration. The event, supported by NASA's Living with a Star program and hosted by the University Corporation for Atmospheric Research and Cooperative Programs for the Advancement of Earth System Science, assembled a cohort of 26 graduate students and postdoctoral researchers from around the world, all engaged in heliophysics research. The summer school included a series of lectures, interactive activities, and a culminating capstone project. The participants' diverse backgrounds enriched discussions and encouraged novel approaches to traditional problems. The capstone projects spanned an array of topics, including investigating the origins of solar wind switchbacks, dissecting the sequence of events from solar eruptions and their corresponding terrestrial consequences, investigating the energy transfer from solar coronal mass ejections to Earth's magnetosphere, and advocating for the exploration of Coulomb collisions in understanding large‐scale global systems. Through this perspective, we shed light on the value of international and interdisciplinary collaboration in advancing heliophysics research. This perspective paper encapsulates the ethos of the summer school, serving as a testament to the continuing and collective pursuit of unraveling the mysteries of the heliosphere.

Abstract Coronal mass ejections (CMEs) can create significant disruption to human activities and systems on Earth, much of which can be mitigated with prior warning of the upstream solar wind conditions. However, it is currently extremely challenging to accurately predict the arrival time and internal structure of a CME from coronagraph images alone. In this study, we take advantage of a rare opportunity to use Solar Orbiter, at 0.5 au upstream of Earth, as an upstream solar wind monitor. In combination with low‐latency images from STEREO‐A, we successfully predicted the arrival time of two CME events before they reached Earth. Measurements at Solar Orbiter were used to constrain an ensemble of simulation runs from the ELEvoHI model, reducing the uncertainty in arrival time from 10.4 to 2.5 hr in the first case study. There was also an excellent agreement in the Bz profile between Solar Orbiter and Wind spacecraft for the second case study, despite being separated by 0.5 au and 10° longitude. The opportunity to use Solar Orbiter as an upstream solar wind monitor will repeat once a year, which should further help assess the efficacy upstream in‐situ measurements in real time space weather forecasting.

The 3D coronal rope ejection (3DCORE) model has proven to work quite well for fitting in situ magnetic fields of CME flux ropes. The model assumes an empirically motivated torus-like flux rope structure that expands self-similarly within the heliosphere, is influenced by a simplified interaction with the solar wind environment, and carries along an embedded analytical magnetic field. For the fitting part an approximate Bayesian computation sequential Monte Carlo algorithm is utilized, which allows us to generate estimates on the uncertainty of model parameters using only a single in situ observation.In the present study, we test the ability of 3DCORE to perform short term forecasts of an ICME’s magnetic field. Therefore, we use only the first couple of hours of an in situ observation to which 3DCORE fits a magnetic field and predicts the rest of the flux rope structure.

The problem of forecasting southward pointing magnetic fields in coronal mass ejections (CMEs) is closely tied to our ability to measure their magnetic field configuration between the Sun and 1 AU. I will review some of the ideas that have been developed to tackle this problem, from using solar proxies, heliospheric images, Faraday rotation and measuring the in situ magnetic field near the Sun Earth-line. Another way to make progress is to use the L1 data as boundary conditions for fast ensemble simulations, focusing on the flux rope parts inside CMEs. However, for any type of modeling and forecasting we need to better understand the global magnetic structure and shape of CME flux ropes from multi-spacecraft observations, now delivered by spacecraft such as Parker Solar Probe, Solar Orbiter, BepiColombo, STEREO-Ahead and Wind, ACE or DSCOVR. In the future, the PUNCH mission will allow for the first time to extract 3D information from heliospheric images, forming another trailblazer towards developing models for ESA's Vigil mission, and setting the stage for possible interplanetary fleets of small spacecraft.

On March 7, 2022 at 22:49 UT, a coronal mass ejection impacted Solar Orbiter, located almost exactly on the Sun-Earth line at a heliocentric distance of 0.49 AU. This exceptionally advantageous spacecraft location yielded the opportunity of constraining the ensemble of our CME propagation model, ELEvoHI, in a way that only the most accurate ensemble members at Solar Orbiter (in terms of predicted arrival time) contributed to the prediction for L1. ELEvoHI is based on STEREO's heliospheric imager data that is available in real time only in a reduced quality, i.e. lower spacial and time resolution compared to science data. However, considering the arrival at Solar Orbiter it was possible to precisely predict the arrival of the CME sheath at L1 in real time. These results emphasize the benefit of having (a) spacecraft situated between the Sun and Earth as an early warning system for Earth-directed CMEs.

The CME event from October 2021 was in situ detected by BepiColombo, Solar Orbiter, DSCOVR and STEREO-A, whose Heliospheric Imagers (HI) additionally observed the event remotely. The latter observations are used to model the evolution of the CME through the inner heliosphere using the CME propagation model ELlipse Evolution based on HI (ELEvoHI). ELEvoHI assumes a drag-based interaction of the CME-sheath with the solar wind and allows it to deform according to local drag regimes. The ambient solar wind is provided by the time-dependent HelioMAS/HUXt model. Using the detected arrivals at the four different spacecraft we assess the ability of ELEvoHI to model the evolution of the shape of this CME.

We report the result of the first search for multipoint in situ and imaging observations of interplanetary coronal mass ejections (ICMEs) starting with the first Solar Orbiter (SolO) data in 2020 April - 2021 April. A data exploration analysis is performed including visualizations of the magnetic field and plasma observations made by the five spacecraft SolO, BepiColombo, Parker Solar Probe (PSP), Wind and STEREO-A, in connection with coronagraph and heliospheric imaging observations from STEREO-A/SECCHI and SOHO/LASCO. We identify ICME events that could be unambiguously followed with the STEREO-A heliospheric imagers during their interplanetary propagation to their impact at the aforementioned spacecraft, and look for events where the same ICME is seen in situ by widely separated spacecraft. We highlight two events: (1) a small streamer blowout CME on 2020 June 23 observed with a triple lineup by PSP, BepiColombo and Wind, guided by imaging with STEREO-A, and (2) the first fast CME of solar cycle 25 ($ \approx 1600$ km s$^{-1}$) on 2020 November 29 observed in situ by PSP and STEREO-A. These results are useful for modeling the magnetic structure of ICMEs and the interplanetary evolution and global shape of their flux ropes and shocks, and for studying the propagation of solar energetic particles. The combined data from these missions are already turning out to be a treasure trove for space weather research and are expected to become even more valuable with an increasing number of ICME events expected during the rise and maximum of solar cycle 25.

We present first results of a case study on a CME from October 2021 that was in situ detected by BepiColombo, Solar Orbiter, DSCOVR and STEREO-A, whose Heliospheric Imagers (HI) additionally observed the event remotely. The latter observations are used to model the evolution of the CME through the inner heliosphere using the CME propagation model ELlipse Evolution based on HI (ELEvoHI). ELEvoHI assumes a drag-based interaction of the CME-sheath with the solar wind and allows it to deform according to local drag regimes. The ambient solar wind is provided by the time-dependent HelioMAS/HUXt model. Using the arrivals at the four different spacecraft we are able to assess the ability of ELEvoHI to model the evolution of the shape of this CME.

On April 19th 2020 a CME was detected by Solar Orbiter at a heliocentric distance of 0.8 AU and was also observed in-situ on April 20th by both Wind and BepiColombo. During this time, BepiColombo had just completed a flyby of the Earth and therefore the longitudinal separation between BepiColombo and Wind was just 1.4°. The total longitudinal separation of Solar Orbiter and both spacecraft near the Earth was less than 5°, providing an excellent opportunity for a radial alignment study of the CME. We use the in-situ observations of the magnetic field at Solar Orbiter with those at Wind and BepiColombo to analyse the large-scale properties of the CME and compare results to those predicted using remote observations at STEREO-A, providing a global picture of the CME as it propagated from the Sun to 1 AU.

We present a major update to the 3D coronal rope ejection (3DCORE) technique for modeling coronal mass ejection flux ropes in conjunction with an approximate Bayesian computation (ABC) algorithm that is used for fitting the model to in situ magnetic field measurements. The model assumes an empirically motivated torus-like flux rope structure that expands self-similarly within the heliosphere, is influenced by a simplified interaction with the solar wind environment, and carries along an embedded analytical magnetic field. The improved 3DCORE implementation allows us to generate extremely large ensemble simulations that we then use to find global best-fit model parameters using an ABC sequential Monte Carlo algorithm. The usage of this algorithm, under some basic assumptions on the uncertainty of the magnetic field measurements, allows us to furthermore generate estimates on the uncertainty of model parameters using only a single in situ observation. We apply our model to synthetically generated measurements to prove the validity of our implementation for the fitting procedure. We also present a brief analysis, within the scope of our model, of an event captured by the Parker Solar Probe shortly after its first flyby of the Sun on 2018 November 12 at 0.25 au. The presented toolset is also easily extendable to the analysis of events captured by multiple spacecraft and will therefore facilitate future multipoint studies.