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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.

Coronal mass ejections (CMEs) are the main drivers of geomagnetic storms at Earth and are capable of impacting power grids on the planet’s surface as well as satellites in orbit. Developing models to accurately predict the arrival time and speed of CMEs is a necessary step towards ensuring that we are capable of providing advance warning to mitigate potential severe space weather effects. We use the Ellipse Evolution model based on Heliospheric Imager observations (ELEvoHl) to predict the arrival of CMEs at Earth using data from STEREO-A’s heliospheric imagers. The HI instruments provide both real-time, lower-quality beacon data as well as higher- quality science data which is downlinked to Earth with some delay. Issuing alerts for approaching CMEs before they arrive at Earth necessitates the utilization of real-time data. HI data for each event and data type is compiled into a time-elongation plot (Jmap) in which every CME’s trajectory is tracked manually. We predict the arrival time and speed of 10 Earth-directed CMEs from 2010 to 2020 using both science and beacon data with the ELEvoHI model and compare the results in terms of accuracy across both data types. We find that predictions made using beacon data are generally worse than those made using science data and give an outlook on possible future perspectives for improving the real-time prediction of CMEs.

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.

Interplanetary coronal mass ejections (ICMEs) are one of the main drivers for space weather disturbances. In the past, different approaches have been used to automatically detect events in existing time series resulting from solar wind in situ observations. However, accurate and fast detection still remains a challenge when facing the large amount of data from different instruments. For the automatic detection of ICMEs we propose a pipeline using a method that has recently proven successful in medical image segmentation. Comparing it to an existing method, we find that while achieving similar results, our model outperforms the baseline regarding training time by a factor of approximately 20, thus making it more applicable for other datasets. The method has been tested on in situ data from the Wind spacecraft between 1997 and 2015 with a True Skill Statistic (TSS) of 0.64. Out of the 640 ICMEs, 466 were detected correctly by our algorithm, producing a total of 254 False Positives. Additionally, it produced reasonable results on datasets with fewer features and smaller training sets from Wind, STEREO-A and STEREO-B with True Skill Statistics of 0.56, 0.57 and 0.53, respectively. Our pipeline manages to find the start of an ICME with a mean absolute error (MAE) of around 2 hours and 56 minutes, and the end time with a MAE of 3 hours and 20 minutes. The relatively fast training allows straightforward tuning of hyperparameters and could therefore easily be used to detect other structures and phenomena in solar wind data, such as corotating interaction regions.

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 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.

Interplanetary coronal mass ejections (ICMEs) are one of the main drivers for space weather disturbances. In the past,different machine learning approaches have been used to automatically detect events in existing time series resulting fromsolar wind in situ data. However, classification, early detection and ultimately forecasting still remain challenges when facingthe large amount of data from different instruments. We propose a pipeline using a Network similar to the ResUNet++ (Jha et al. (2019)), for the automatic detection of ICMEs. Comparing it to an existing method, we find that while achieving similar results, our model outperforms the baseline regarding GPU usage, training time and robustness to missing features, thus making it more usable for other datasets.The method has been tested on in situ data from WIND. Additionally, it produced reasonable results on STEREO A and STEREO B datasetswith less input parameters. The relatively fast training allows straightforward tuning of hyperparameters and could therefore easily be used to detect other structures and phenomena in solar wind data, such as corotating interaction regions.

We present a catalogue of 35 interplanetary coronal mass ejections (ICMEs) observed by the Juno spacecraft during its cruise phase to Jupiter in conjunction with at least one other spacecraft at heliocentric distances near or less than 1 AU (by MESSENGER, Venus Express, Wind, or STEREO). Previous ICME catalogues are used to find conjunctions between spacecraft, and events with magnetic field features that can be matched unambiguously across different spacecraft are selected. We conduct a multi-spacecraft analysis of ICME properties between 0.3 and 2.2 AU: we first investigate the global expansion of ICMEs by tracking the variation in magnetic field strength with increasing heliocentric distance of individual events, finding significant variability in magnetic field relationships for individual events in comparison with statistical trends. With the availability of plasma data at 1 AU, local and global expansion rates are compared; despite following expected trends, the local and global expansion rates are weakly correlated. Finally, for those events with clearly identifiable magnetic flux ropes, we investigate their magnetic complexity as they propagate; we find that 60% of events undergo at least one change in complexity between observations at the innermost spacecraft and Juno. The multi-spacecraft catalogue produced in this study provides a valuable link between ICME observations in the inner heliosphere and beyond 1 AU, contributing to our understanding of ICME evolution in situ. We intend the catalogue to be a useful resource for future studies of ICMEs and space weather research at Earth and other planets.

We present our most recent results for an analytical flux rope model that includes the effects of curvature and torsion on the magnetic field. Our analytical model consists of a cylindrical flux rope that can be bent or twisted in any manner under the condition of axial and poloidal flux conservation. We can furthermore configure our model to exhibit any arbitrary radial twist distribution but we do not necessarily assume the structure to be force-free. The approach can serve as a first stepping point to better model the deformations of ICME flux ropes that are expected to occur due to longitudinal velocity differentials in the solar wind. It is briefly discussed how this model can be implemented in real-world simulations and how it can be extended to non-cylindrical shapes.

We present the results of a search for multipoint in situ and imaging observations of interplanetary coronal mass ejections (ICMEs) with the Heliophysics System Observatory, from 2020 April to present day. This builds up on our recent publication in ApJ Letters introducing the living ICME lineup catalog available at https://helioforecast.space/lineups. We highlight a few new lineup events captured by those spacecraft from September to November 2021, when all were located within 50 degrees east of the Sun-Earth line. Multi-messenger observations of ICME flux ropes and shocks are much needed to make progress on the understanding of the global magnetic configuration of ICMEs, space weather forecasting, the magnetic connectivity of the solar wind to the Sun and the propagation of solar energetic particles.

Predicting the Bz magnetic field embedded in interplanetary coronal mass ejections (ICMEs), also called the Bz problem, is a core challenge in space weather research and prediction. We tackle this problem with a new approach by taking upstream in situ measurements of the ICME sheath region and the first few hours of the magnetic obstacle to predict the downstream Bz component. To do so, we trained a machine learning algorithm on 348 ICMEs (extracted from the open source ICMECATv2.0 catalog) observed by the Wind, STEREO-A, and STEREO-B satellites to predict the minimum value of Bz. The predictive tool was built to mimic a real-time scenario, where the ICMEs sweep over the spacecraft, which allows us to continually provide updates and improved predictions of Bz as time passes and more of the CME structure is observed. The final model, which is based on random forests, can predict the minimum value of Bz with a reasonable level of agreement compared to observations. In this presentation, we will discuss the main challenges we face in using a data-driven machine learning application to solve the Bz problem, and outline the lessons learned and future strategies for predicting and potentially mitigating the effects of ICMEs arriving at Earth.

Measurements of geomagnetically induced currents (GICs) in the Austrian power transmission grid have been carried out since 2014 at multiple locations. Following an analysis of the scales of GICs across the grid, we now look into forecasting the GICs from incoming solar wind data. Using nearby geomagnetic field measurements stretching back 26 years, we can estimate the local geoelectric field and consequently the GICs over longer time periods. We apply a machine learning method based on recurrent neural networks to this dataset combined with solar wind data as input. In this talk, we present the final method to forecast both the local geoelectric field E and the GICs in substations in the Austrian power grid, with our model results being compared to GIC measurements from recent years. We will discuss the current status of the model, outline limitations, and consider future applications.

Mirror mode structures arise whenever a temperature anisotropy is present in the plasma, classically in the wake of the bow shock in a quasi-perpendicular configuration with respect to the interplanetary magnetic field, or from pickup ion distribution effects. Born from space plasma instabilities and in competition with other wave modes, these ultra-low frequency waves contribute to energy exchanges between the different plasma populations present in the magnetosheath. At Mars and Venus, such structures have very similar scales: they last typically a few tens of seconds and appear as peaks or dips in the magnetic field data in antiphase with the local plasma density variations. As magnetometers are present on many space missions, magnetic field-only criteria are an ideal tool to study these structures across different magnetosheath environments. We present here for the first time a comparison of the statistical occurrence of magnetosheath mirror mode-like structures at Mars with MAVEN and at Venus with Venus Express. Based on magnetic field-only measurements, we use identical detection criteria at both planets to select quasi-linear structures in B-field measurements. We then present two-dimensional maps of mirror mode-like occurrence rates with respect to solar cycle variations and EUV flux levels, atmospheric seasons (for Mars) and the nature of the shock crossing (quasi-parallel or quasi-perpendicular configurations), and compare them between planets. Finally, we discuss ambiguities in the nature of the detected structures and their global effects on the magnetosheath.

Geometric modelling of Coronal Mass Ejections (CMEs) is a widely used tool for assessing their kinematic evolution. Furthermore, techniques based on geometric modelling, such as ELEvoHI, are being developed into forecast tools for space weather prediction. These models assume that solar wind structure does not affect the evolution of the CME, which is an unquantified source of uncertainty. We use a large number of Cone CME simulations with the HUXt solar wind model to quantify the scale of uncertainty introduced into geometric modelling and the ELEvoHI CME arrival times by solar wind structure. We produce a database of simulations, representing an average, a fast, and an extreme CME scenario, each independently propagating through 100 different ambient solar wind environments. Synthetic heliospheric imager observations of these simulations are then used with a range of geometric models to estimate the CME kinematics. The errors of geometric modelling depend on the location of the observer, but do not seem to depend on the CME scenario. In general, geometric models are biased towards predicting CME apex distances that are larger than the true value. For these CME scenarios, geometric modelling errors are minimised for an observer in the L5 region. Furthermore, geometric modelling errors increase with the level of solar wind structure in the path of the CME. The ELEvoHI arrival time errors are minimised for an observer in the L5 region, with mean absolute arrival time errors of $8.2\pm1.2$~h, $8.3\pm1.0$~h, and $5.8\pm0.9$~h for the average, fast, and extreme CME scenarios., 37 pages, 12 figures. Accepted for publication in Space Weather

Right-wing populism as a desideratum of social-ecological transition research ;GAIA 30/1 (2021): 62-64 Keywords: climate denialism, environmental attidudes, right-wing populism, socio-ecological transformation Seien es die Etablierung nachhaltiger Mobilitatsysteme, Veranderungen im Agrarsektor oder die Transformation des Energiesystems - der Erfolg in unterschiedlichen Feldern einer sozial-okologischen Transformation ist nicht allein von Wissensbestanden, der Qualitat von Konzepten oder technischen Schlusselinnovationen abhangig The role of populist attitudes in explaining climate change skepticism and support for environmental protection Right-wing populism and the climate change agenda: exploring the linkages

The evolution and propagation of coronal mass ejections (CMEs) in interplanetary space is still not well understood. As a consequence, accurate arrival time and arrival speed forecasts are an unsolved problem in space weather research. In this study, we present the ELlipse Evolution model based on HI observations (ELEvoHI) and introduce a deformable front to this model. ELEvoHI relies on heliospheric imagers (HI) observations to obtain the kinematics of a CME. With the newly developed deformable front, the model is able to react to the ambient solar wind conditions during the entire propagation and along the whole front of the CME. To get an estimate of the ambient solar wind conditions, we make use of three different models: Heliospheric Upwind eXtrapolation model (HUX), Heliospheric Upwind eXtrapolation with time dependence model (HUXt), and EUropean Heliospheric FORecasting Information Asset (EUHFORIA). We test the deformable front on a CME first observed in STEREO-A/HI on February 3, 2010 14:49 UT. For this case study, the deformable front provides better estimates of the arrival time and arrival speed than the original version of ELEvoHI using an elliptical front. The new implementation enables us to study the parameters influencing the propagation of the CME not only for the apex, but for the entire front. The evolution of the CME front, especially at the flanks, is highly dependent on the ambient solar wind model used. An additional advantage of the new implementation is given by the possibility to provide estimates of the CME mass., 25 pages, 7 figures, 1 table, revised for Space Weather

Results of a study of the influence of solar-type host stars superflares on the gas dynamics of the extended envelopes of giant exoplanets are presented. During flare events, the radiation intensity of the host star in the extreme ultraviolet and soft X-ray can increase by several orders of magnitude for a short time, leading to strong local heating of the exoplanet atmosphere on the side facing the star, with the formation of shocks in the atmosphere. Computations of the gas-dynamical response of the atmosphere of the hot Jupiter HD 209458b to characteristic superflares of solar-like stars were carried out earlier in [1] using a one-dimensional aeronomical model correctly taking into account heating and chemical processes in the atmosphere. To investigate the outflow of atmospheric gas, the results obtained with this onedimensional model were used as simple boundary conditions for computations of the three-dimensional flow structure after a flare. The results of these three-dimensional gas-dynamical computations show that the mass ejection of the flare increases the size of the envelope over several hours, which could be detected with existing observing facilities. It is shown that the mass-loss rates for the most powerful superflare considered could be enhanced by an order of magnitude over several tens of hours after the flare.

We present an in-depth analysis of a time interval when quasi-linear mirror mode structures were detected by magnetic field and plasma measurements as observed by the NASA/Mars Atmosphere and Volatile EvolutioN (MAVEN) spacecraft. We employ ion and electron spectrometers in tandem to support the magnetic field measurements and confirm that the signatures are indeed mirror modes. Wedged against the magnetic pile-up boundary, the low-frequency signatures last on average $\sim$10 s with corresponding sizes of the order of 15-30 upstream solar wind proton thermal gyroradii, or 10-20 proton gyroradii in the immediate wake of the quasi-perpendicular bow shock. Their peak-to-peak amplitudes are of the order of 30-35 nT with respect to the background field, and appear as a mixture of dips and peaks, suggesting that they may have been at different stages in their evolution. Situated in a marginally stable plasma with $\beta_{||}\sim$1, we hypothesise that these so-called magnetic bottles, containing a relatively higher energy and denser ion population with respect to the background plasma, are formed upstream of the spacecraft behind the quasi-perpendicular shock. These signatures are very reminiscent of magnetic bottles found at other unmagnetised objects such as Venus and comets, also interpreted as mirror modes. Our case study constitutes the first unmistakable identification and characterisation of mirror modes at Mars from the joint points of view of magnetic field, electron and ion measurements. Up until now, the lack of high-temporal resolution plasma measurements has prevented such an in-depth study., Comment: 37 pages, 11 figures, 1 table

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.

Aims.We aim to locate the origin of a stealth coronal mass ejection (CME) detected in situ by the MAG instrument on board Solar Orbiter, and make connections between the CME observed at the Sun, and the interplanetary CME (ICME) measured in situ. Methods. Remote sensing data are analysed using advanced image processing techniques to identify the source region of the stealth CME, and the global magnetic field at the time of the eruption is examined using Potential Field Source Surface (PFSS) models. The observations of the stealth CME at the Sun are compared with the magnetic field measured by the Solar Orbiter spacecraft, and plasma properties measured by the Wind spacecraft. Results. The source of the CME is found to be a quiet Sun cavity in the northern hemisphere. We find that the stealth CME has a strong magnetic field in situ, despite originating from a quiet Sun region with an extremely weak magnetic field. Conclusions. The interaction of the ICME with its surrounding environment is the likely cause of a higher magnetic field strength measured in situ. Stealth CMEs require multi-wavelength and multi-viewpoint observations in order to confidently locate the source region, however their elusive signatures still pose many problems for space weather forecasting. The findings have implications for Solar Orbiter observing sequences with instruments such as EUI that are designed to capture stealth CMEs, 6 pages, 7 figures, accepted for publication in A&A

In the last years, many kinds of CME models, based on a drag-based evolution through interplanetary space, have been developed and are now widely used by the community. The unbeatable advantage of those methods is that they are computationally cheap and are therefore suitable to be used as ensemble models. Additionally, their prediction accuracy is absolutely comparable to more sophisticated models.The ELlipse Evolution model based on heliospheric imager (HI) observations (ELEvoHI) assumes an elliptic frontal shape within the ecliptic plane and allows the CME to adjust to the ambient solar wind speed, i.e. it is drag-based. ELEvoHI is used as an ensemble simulation by varying the CME frontal shape within given boundary values. The results include a frequency distribution of predicted arrival time and arrival speed and an estimation of the arrival probability.In this study, we investigate the possibility of not only varying the parameters related to the CME's ecliptic extent but also the ambient solar wind speed for each CME ensemle member. Although we have used a range of +/-100 km/s for possible values of the solar wind speed in the past, only the best candidate was in the end used to contribute to the prediction. We present the results of this approach by applying it to a CME propagating in a highly structured solar wind and compare the frequency distribution of the arrival time and speed predictions to those of the usual ELEvoHI approach.

In this study we present a method for forecasting the ambient solar wind at L1 from coronal magnetic models. Ambient solar wind flows in interplanetary space determine how solar storms evolve through the heliosphere before reaching Earth, and accurately modelling and forecasting the ambient solar wind flow is therefore imperative to space weather awareness. We describe a novel machine learning approach in which solutions from models of the solar corona based on 12 different ADAPT magnetic maps are used to output the solar wind conditions some days later at the Earth. A feature analysis is carried out to determine which input variables are most important. The results of the forecasting model are compared to observations and existing models for one whole solar cycle in a comprehensive validation analysis. We find that the new model outperforms existing models and 27-day persistence in almost all metrics. The final model discussed here represents an extremely fast, well-validated and open-source approach to the forecasting of ambient solar wind at Earth, and is specifically well-suited for ensemble modelling or for application with other coronal models.

We present initial results for a triple-point analysis for the in situ magnetic field measurements of a CME observed at three independent locations. On the 19th of April 2020, Solar Orbiter observed a CME in situ at a radial distance of around 0.8 au. This CME was subsequently also detected by the Wind and Bepi Colombo satellites closer to Earth. This triple in situ measurement of a CME provides us the unique opportunity to test the consistency of the measurements with our own 3D Coronal Rope Ejection (3DCORE) model. A triple measurement allows for up to seven different data combinations to be analyzed (three single-point, three dual-point, and one single triple-point combination) which gives us information on how our analysis pipeline responds to multi-point measurements and how the results change with measurements at differing radial and longitudinal distances. The goal of this study is to test whether all three in situ measurements can still be described by a slightly bent flux rope geometry and how adding additional measurements can improve the accuracy of inferred model parameters.