arXiv subjects

Tanja Amerstorfer

Publications and source records attributed to Tanja Amerstorfer.

At least 19 recordsLinked to original sources

arXiv2026

Polarimetric Tomography Applied to Synthetic Multi-Spacecraft White-Light Images: Observing Coronal Mass Ejections in 3D

A discrete tomography method has been developed that is able to reconstruct three-dimensional 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 localise 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 $(72\pm9)\%$, $(70\pm8)\%$ and $(52\pm12)\%$ 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; $0.003\pm0.004$\,au, $0.004\pm0.005$\,au and $0.005\pm0.004$\,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.

astro-ph.SR↗

arXiv2026

Validating a Non-conventional Method for Expansion of Coronal Mass Ejections (CMEs) and Investigating the Evolution of a CME Substructures Using Solar Orbiter and Wind Observations

We present a validation of our recently proposed non-conventional method, Constant Acceleration Accounted Perspective (CAAP), for estimating the instantaneous expansion speed of coronal mass ejection (CMEs), even when only single-point in situ observations are available. This validation is enabled by the radial alignment of SolO and Wind spacecraft (0.13 AU radial and 2.3 deg angular separation), providing simultaneous observations of the center (at Wind) and trailing edge (at SolO) of a CME associated magnetic cloud (MC) during 3-5 November 2021, allowing a direct measurement of its instantaneous expansion speed. These measurements are compared with CAAP-derived instantaneous expansion speed estimates at both spacecraft. The favorable spacecraft configuration also enables tracking the temporal evolution of CME substructures, including the shock, sheath, and MC. A discrepancy is noted between the low-inclination MC axis estimated from minimum variance analysis (MVA) and the highly inclined ENW-type MC axis suggested by visual inspection of in situ measurements. We also observe an apparent increase in the magnetic flux within the MC from SolO to Wind, indicating a noticeable deviation from magnetic flux conservation. During the CME's propagation from SolO to Wind, the shock becomes unexpectedly stronger at Wind, while the sheath thickness remains nearly the same, likely due to MC acceleration from back compression by a high-speed stream and ambient solar wind variability. Our results demonstrate the applicability of the CAAP method and the importance of accounting for temporal evolution in CME substructures for space weather studies.

astro-ph.SR↗

arXiv2025

Predicting CME Arrivals with Heliospheric Imagers from L5: A Data Assimilation Approach

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.

astro-ph.SR↗

arXiv2025

Beacon2Science: Enhancing STEREO/HI beacon data with machine learning for efficient CME tracking

Observing and forecasting coronal mass ejections (CME) 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, 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-minute 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 $\sim 0.5 ^\circ$ of elongation compared to $1^\circ$ with original beacon data. The work presented in this paper paves the way for its application to forthcoming missions such as Vigil and PUNCH.

physics.space-ph↗

arXiv2025

Solar Transient Recognition Using Deep Learning (STRUDL) for heliospheric imager data

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 towards 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.

astro-ph.SR↗

arXiv2025

First observations of a geomagnetic superstorm with a sub-L1 monitor

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 hours earlier at STEREO-A than at L1, consistent with the measured shock speed at L1, 710 km/s, and the radial distance of 0.04 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=-478.5 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.

physics.space-ph↗

arXiv2024

Flux rope modeling of the 2022 Sep 5 CME observed by Parker Solar Probe and Solar Orbiter from 0.07 to 0.69 au

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 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/WISPR images as input to the ELEvoHI model, 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/MAG and PSP/FIELDS data independently with the 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 Solar Orbiter, 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.

physics.space-ph↗

arXiv2022

Writhed Analytical Magnetic Flux Rope Model

Observations of magnetic clouds, within interplanetary coronal mass ejections (ICMEs), are often well described by flux rope models. Most of these assume either a cylindrical or toroidal geometry. In some cases, these models are also capable of accounting for non-axisymmetric crosssections but they generally all assume axial invariance. It can be expected that any ICME, and its flux rope, will be deformed along its axis due to influences such as the solar wind. In this work, we aim to develop a writhed analytical magnetic flux rope model which would allow us to analytically describe a flux rope structure with varying curvature and torsion so that we are no longer constrained to a cylindrical or toroidal geometry. In this first iteration of our model we will solely focus on a circular cross-section of constant size. We describe our flux rope geometry in terms of a parametrized flux rope axis and a parallel transport frame. We derive expressions for the axial and poloidal magnetic field components under the assumption that the total axial magnetic flux is conserved. We find an entire class of possible solutions, which differ by the choice of integration constants, and present the results for a specific example. In general, we find that the twist of the magnetic field locally changes when the geometry deviates from a cylinder or torus. This new approach also allows us to generate completely new types of in situ magnetic field profiles which strongly deviate from those generated by cylindrical or toroidal models.

astro-ph.SR↗

arXiv2022

Automatic Detection of Interplanetary Coronal Mass Ejections in Solar Wind In Situ Data

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.

astro-ph.SR↗

arXiv2021

Predicting CMEs using ELEvoHI with STEREO-HI beacon data

Being able to accurately predict the arrival of coronal mass ejections (CMEs) at Earth has been a long-standing problem in space weather research and operations. In this study, we use the ELlipse Evolution model based on Heliospheric Images (ELEvoHI) to predict the arrival time and speed of 10 CME events that were observed by HI on the STEREO-A spacecraft between 2010 and 2020. Additionally, we introduce a Python tool for downloading and preparing STEREO-HI data, as well as tracking CMEs. In contrast to most previous studies, we use not only science data, which has a relatively high spatial and temporal resolution, but also low-quality beacon data, which is - in contrast to science data - provided in real-time by the STEREO-A spacecraft. We do not use data from the STEREO-B spacecraft. We get a mean absolute error of 8.81 $\pm$ 3.18 h / 59 $\pm$ 31 kms$^{-1}$ for arrival time/speed predictions using science data and 11.36 $\pm$ 8.69 h / 106 $\pm$ 61 kms$^{-1}$ for beacon data. We find that using science data generally leads to more accurate predictions, but using beacon data with the ELEvoHI model is certainly a viable choice in the absence of higher resolution real-time data. We propose that these differences could be minimized if not eliminated altogether if higher quality real-time data was available, either by enhancing the quality of the already available data or coming from a new mission carrying a HI instrument on-board.

astro-ph.SR↗

arXiv2021

Quantifying the uncertainty in CME kinematics derived from geometric modelling of Heliospheric Imager data

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.

astro-ph.SR↗

arXiv2021

Machine learning for predicting the Bz magnetic field component from upstream in situ observations of solar coronal mass ejections

Predicting the Bz magnetic field embedded within ICMEs, also known as the Bz problem, is a key challenge in space weather forecasting. We study the hypothesis that upstream in situ measurements of the sheath region and the first few hours of the magnetic obstacle provide sufficient information for predicting the downstream Bz component. To do so, we develop a predictive tool based on machine learning that is trained and tested on 348 ICMEs from Wind, STEREO-A, and STEREO-B measurements. We train the machine learning models to predict the minimum value of the Bz component and the maximum value of the total magnetic field Bz in the magnetic obstacle. To validate the tool, we let the ICMEs sweep over the spacecraft and assess how continually feeding in situ measurements into the tool improves the Bz prediction. Because the application of the tool in operations needs an automated detection of ICMEs, we implement an existing automated ICME detection algorithm and test its robustness for the time intervals under scrutiny. We find that the predictive tool can predict the minimum value of the Bz component in the magnetic obstacle with a mean absolute error of 3.12 nT and a Pearson correlation coefficient of 0.71 when the sheath region and the first 4 hours of the magnetic obstacle are observed. While the underlying hypothesis is unlikely to solve the Bz problem, the tool shows promise for ICMEs that have a recognizable magnetic flux rope signature. Transitioning the tool to operations could lead to improved space weather forecasting.

physics.space-ph↗

arXiv2021

Solar origins of a strong stealth CME detected by Solar Orbiter

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

astro-ph.SR↗

arXiv2021

Radial Evolution of the April 2020 Stealth Coronal Mass Ejection between 0.8 and 1 AU -- A Comparison of Forbush Decreases at Solar Orbiter and Earth

Aims. We present observations of the first coronal mass ejection (CME) observed at the Solar Orbiter spacecraft on April 19, 2020, and the associated Forbush decrease (FD) measured by its High Energy Telescope (HET). This CME is a multispacecraft event also seen near Earth the next day. Methods. We highlight the capabilities of HET for observing small short-term variations of the galactic cosmic ray count rate using its single detector counters. The analytical ForbMod model is applied to the FD measurements to reproduce the Forbush decrease at both locations. Input parameters for the model are derived from both in situ and remote-sensing observations of the CME. Results. The very slow (~350 km/s) stealth CME caused a FD with an amplitude of 3 % in the low-energy cosmic ray measurements at HET and 2 % in a comparable channel of the Cosmic Ray Telescope for the Effects of Radiation (CRaTER) on the Lunar Reconnaissance Orbiter, as well as a 1 % decrease in neutron monitor measurements. Significant differences are observed in the expansion behavior of the CME at different locations, which may be related to influence of the following high speed solar wind stream. Under certain assumptions, ForbMod is able to reproduce the observed FDs in low-energy cosmic ray measurements from HET as well as CRaTER, but with the same input parameters, the results do not agree with the FD amplitudes at higher energies measured by neutron monitors on Earth. We study these discrepancies and provide possible explanations. Conclusions. This study highlights that the novel measurements of the Solar Orbiter can be coordinated with other spacecraft to improve our understanding of space weather in the inner heliosphere. Multi-spacecraft observations combined with data-based modeling are also essential to understand the propagation and evolution of CMEs as well as their space weather impacts.

astro-ph.SR↗

arXiv2021

Evaluation of CME Arrival Prediction Using Ensemble Modeling Based on Heliospheric Imaging Observations

In this study, we evaluate a coronal mass ejection (CME) arrival prediction tool that utilizes the wide-angle observations made by STEREO's heliospheric imagers (HI). The unsurpassable advantage of these imagers is the possibility to observe the evolution and propagation of a CME from close to the Sun out to 1 AU and beyond. We believe that by exploiting this capability, instead of relying on coronagraph observations only, it is possible to improve today's CME arrival time predictions. The ELlipse Evolution model based on HI observations (ELEvoHI) assumes that the CME frontal shape within the ecliptic plane is an ellipse, and allows the CME to adjust to the ambient solar wind speed, i.e. it is drag-based. ELEvoHI is used to perform ensemble simulations by varying the CME frontal shape within given boundary conditions that are consistent with the observations made by HI. In this work, we evaluate different set-ups of the model by performing hindcasts for 15 well-defined isolated CMEs that occurred when STEREO was near L4/5, between the end of 2008 and the beginning of 2011. In this way, we find a mean absolute error of between $6.2\pm7.9$ h and $9.9\pm13$ h depending on the model set-up used. ELEvoHI is specified for using data from future space weather missions carrying HIs located at L5 or L1. It can also be used with near real-time STEREO-A HI beacon data to provide CME arrival predictions during the next $\sim7$ years when STEREO-A is observing the Sun-Earth space.

physics.space-ph↗

arXiv2021

Why are ELEvoHI CME arrival predictions different if based on STEREO-A or STEREO-B heliospheric imager observations?

Accurate forecasting of the arrival time and arrival speed of coronal mass ejections (CMEs) is a unsolved problem in space weather research. In this study, a comparison of the predicted arrival times and speeds for each CME based, independently, on the inputs from the two STEREO vantage points is carried out. We perform hindcasts using ELlipse Evolution model based on Heliospheric Imager observations (ELEvoHI) ensemble modelling. An estimate of the ambient solar wind conditions is obtained by the Wang-Sheeley-Arge/Heliospheric Upwind eXtrapolation (WSA/HUX) model combination that serves as input to ELEvoHI. We carefully select 12 CMEs between February 2010 and July 2012 that show clear signatures in both STEREO-A and STEREO-B HI time-elongation maps, that propagate close to the ecliptic plane, and that have corresponding in situ signatures at Earth. We find a mean arrival time difference of 6.5 hrs between predictions from the two different viewpoints, which can reach up to 9.5 hrs for individual CMEs, while the mean arrival speed difference is 63 km s$^{-1}$. An ambient solar wind with a large speed variance leads to larger differences in the STEREO-A and STEREO-B CME arrival time predictions ($cc~=~0.92$). Additionally, we compare the predicted arrivals, from both spacecraft, to the actual in situ arrivals at Earth and find a mean absolute error of 7.5 $\pm$ 9.5 hrs for the arrival time and 87 $\pm$ 111 km s$^{-1}$ for the arrival speed. There is no tendency for one spacecraft to provide more accurate arrival predictions than the other.

astro-ph.SR↗

arXiv2020

Analysis of coronal mass ejection flux rope signatures using 3DCORE and approximate Bayesian Computation

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 which we then use to find global best-fit model parameters using an ABC sequential Monte Carlo (SMC) 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 Parker Solar Probe (PSP) shortly after its first fly-by 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 multi-point studies.

astro-ph.SR↗

arXiv2020

Prediction of the in situ coronal mass ejection rate for solar cycle 25: Implications for Parker Solar Probe in situ observations

The Parker Solar Probe (PSP) and Solar Orbiter missions are designed to make groundbreaking observations of the Sun and interplanetary space within this decade. We show that a particularly interesting in situ observation of an interplanetary coronal mass ejection (ICME) by PSP may arise during close solar flybys ($< 0.1$~AU). During these times, the same magnetic flux rope inside an ICME could be observed in situ by PSP twice, by impacting its frontal part as well as its leg. Investigating the odds of this situation, we forecast the ICME rate in solar cycle 25 based on 2 models for the sunspot number (SSN): (1) the forecast of an expert panel in 2019 (maximum SSN = 115), and (2) a prediction by McIntosh et al. (2020, maximum SSN = 232). We link the SSN to the observed ICME rates in solar cycles 23 and 24 with the Richardson and Cane list and our own ICME catalog, and calculate that between 1 and 7 ICMEs will be observed by PSP at heliocentric distances $< 0.1$ AU until 2025, including 1$σ$ uncertainties. We then model the potential flux rope signatures of such a double-crossing event with the semi-empirical 3DCORE flux rope model, showing a telltale elevation of the radial magnetic field component $B_R$, and a sign reversal in the component $B_N$ normal to the solar equator compared to field rotation in the first encounter. This holds considerable promise to determine the structure of CMEs close to their origin in the solar corona.

astro-ph.SR↗