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We investigate the effects of the evolutionary processes in the internal magnetic structure of two interplanetary coronal mass ejections (ICMEs) detected in situ between 2020 November 29 and December 1 by Parker Solar Probe (PSP). The sources of the ICMEs were observed remotely at the Sun in EUV and subsequently tracked to their coronal counterparts in white light. This period is of particular interest to the community since it has been identified as the first widespread solar energetic particle event of Solar Cycle 25. The distribution of various solar and heliospheric-dedicated spacecraft throughout the inner heliosphere during PSP observations of these large-scale magnetic structures enables a comprehensive analysis of the internal evolution and topology of such structures. By assembling different models and techniques, we identify the signatures of interaction between the two consecutive ICMEs and the implications for their internal structure. We use multispacecraft observations in combination with a remote-sensing forward modeling technique, numerical propagation models, and in-situ reconstruction techniques. The outcome, from the full reconciliations, demonstrates that the two CMEs are interacting in the vicinity of PSP. Thus, we identify the in-situ observations based on the physical processes that are associated with the interaction and collision of both CMEs. We also expand the flux rope modeling and in-situ reconstruction technique to incorporate the aging and expansion effects in a distorted internal magnetic structure and explore the implications of both effects in the magnetic configuration of the ICMEs.

Coronal Mass Ejections (CMEs) are large-scale eruptions of plasma and magnetic fields from the Sun. They are considered to be the main drivers of strong space weather events at Earth and their arrival time and associated shocks are one of the key aspects of space weather. Multiple models have been developed over the past decades to be able to predict the propagation of CMEs in the interplanetary space and their arrival time at Earth. Such models require input from observations, which can be used to fit the CME to an appropriate structure. The forecasting of CME arrival has proven to be exceedingly challenging. One of the major setbacks is the uncertainty of the CME observational input. When determining input parameters for CME propagation models, it is common procedure to derive kinematic parameters from remote-sensing data. The resulting parameters can be used as inputs for the CME propagation models to obtain an arrival prediction time of the CME f.e. at Earth. However, when fitting the CME structure to obtain the needed parameters for simulations, different geometric structures and also different parts of the CME structure can be fitted. These aspects, together with the fact that 3D reconstructions strongly depend on the subjectivity and judgement of the scientist performing them, may lead to uncertainties in the fitted parameters. Up to now, no large study has tried to map these uncertainties and to evaluate how they affect the modelling of CMEs. We will discuss these limits in the scope of the CME input analysis that is performed by the ISSI Bern team on "Understanding Our Capabilities In Observing and Modelling Coronal Mass Ejections".

Current efforts in space weather forecasting of CMEs have been focused on predicting their arrival time and magnetic structure. To make these predictions, methods have been developed to derive the true CME speed, size, position, and mass, among others. Difficulties in determining the input parameters for CME forecasting models arise from the lack of direct measurements of the coronal magnetic fields and uncertainties in estimating the CME 3D geometric and kinematic parameters after eruption. White-light coronagraph images are usually employed by a variety of CME reconstruction techniques that assume more or less complex geometries. This is the first study from our International Space Science Institute (ISSI) team “Understanding Our Capabilities in Observing and Modeling Coronal Mass Ejections”, in which we explore how subjectivity affects the 3D CME parameters that are obtained from the Graduated Cylindrical Shell (GCS) reconstruction technique, which is widely used in CME research. To be able to quantify such uncertainties, the “true” values that are being fitted should be known, which are impossible to derive from observational data. We have designed two different synthetic scenarios where the “true” geometric parameters are known in order to quantify such uncertainties for the first time. We explore this by using two sets of synthetic data: 1) Using the ray-tracing option from the GCS model software itself, and 2) Using 3D magnetohydrodynamic (MHD) simulation data from the Magnetohydrodynamic Algorithm outside a Sphere code. Our experiment includes different viewing configurations using single and multiple viewpoints. CME reconstructions using a single viewpoint had the largest errors and error ranges overall for both synthetic GCS and simulated MHD white-light data. As the number of viewpoints increased from one to two, the errors decreased by approximately 4 in latitude, 22 in longitude, 14 in tilt, and 10 in half-angle. Our results quantitatively show the critical need for at least two viewpoints to be able to reduce the uncertainty in deriving CME parameters. We did not find a significant decrease in errors when going from two to three viewpoints for our specific hypothetical three spacecraft scenario using synthetic GCS white-light data. As we expected, considering all configurations and numbers of viewpoints, the mean absolute errors in the measured CME parameters are generally significantly higher in the case of the simulated MHD white-light data compared to those from the synthetic white-light images generated by the GCS model.

Forecasting the arrival time of coronal mass ejections (CMEs) and their associated shocks is one of the key aspects of space weather. In recent years many models have been developed by various research groups aiming to forecast CME arrival time. The models differ based on the input, approach, assumptions and complexity ranging from simple empirical and analytical to complex numerical and machine learning models. One of the commonly used models is, due to its simplicity and calculation speed, the analytical drag-based (ensemble) model [DB(E)M] for heliospheric propagation of CMEs. DB(E)M relies on the observational fact that slow CMEs accelerate whereas fast CMEs decelerate, and is based on the concept of MHD drag, which acts to adjust the CME speed to the ambient solar wind. However, regardless of the model, forecasting CME arrival time has proven to be exceedingly challenging. One of the major setbacks is the uncertainty of the CME observational input, which is still substantial despite state-of-the-art remote observational capacities such as high-resolution EUV imagers and stereoscopic observations. Another major setback is the uncertainty in the CME propagation itself, due to e.g. unrealistic background solar wind and/or complex interactions. These limits will be discussed in the scope of DB(E)M and the CME input analysis performed by the ISSI Bern team on the "Understanding Our Capabilities In Observing And Modeling Coronal Mass Ejections".

The Effective Acceleration Model (EAM) predicts the Time-of-Arrival (ToA) of the Coronal Mass Ejection (CME) driven shock and the average speed within the sheath at 1 AU. The model is based on the assumption that the ambient solar wind interacts with the interplanetary CME (ICME) resulting in constant acceleration or deceleration. The upgraded version of the model (EAMv3), presented here, incorporates two basic improvements: (i) a new technique for the calculation of the acceleration (or deceleration) of the ICME from the Sun to 1 AU and (ii) a correction for the CME plane-of-sky speed. A validation of the upgraded EAM model is performed via comparisons to predictions from the ensemble version of the Drag-Based model (DBEM) and the WSA-ENLIL+Cone ensemble model. A common sample of 16 CMEs/ICMEs, in 2013 - 2014, is used for the comparison. Basic performance metrics such as the mean absolute error (MAE), mean error (ME) and root mean squared error (RMSE) between observed and predicted values of ToA are presented. MAE for EAM model was 8.7 +/- 1.6 hours while for DBEM and ENLIL was 14.3 +/- 2.2 and 12.8 +/- 1.7 hours, respectively. ME for EAM was -1.4 +/- 2.7 hours in contrast with -9.7 +/- 3.4 and -6.1 +/- 3.3 hours from DBEM and ENLIL. We also study the hypothesis of stronger deceleration in the interplanetary (IP) space utilizing the EAMv3 and DBEM models. In particularly, the DBEM model perform better when a greater value of drag parameter, of order of a factor of 3, is used in contrast to previous studies. EAMv3 model shows a deceleration of ICMEs at greater distances, with a mean value of 0.72 AU.

The drag-based model (DBM) for heliospheric propagation of coronal mass ejections (CMEs) is a widely used analytical model which can predict CME arrival time and speed at a given heliospheric location. It is based on the assumption that the propagation of CMEs in interplanetary space is solely under the influence of magnetohydrodynamical drag, where CME propagation is determined based on CME initial properties as well as the properties of the ambient solar wind. We present an upgraded version, covering ensemble modelling to produce a distribution of possible ICME arrival times and speeds, the drag-based ensemble model (DBEM). Multiple runs using uncertainty ranges for the input values can be performed in almost real-time, within a few minutes. This allows us to define the most likely ICME arrival times and speeds, quantify prediction uncertainties and determine forecast confidence. The performance of the DBEM is evaluated and compared to that of ensemble WSA-ENLIL+Cone model (ENLIL) using the same sample of events. It is found that the mean error is $ME=-9.7$ hours, mean absolute error $MAE=14.3$ hours and root mean square error $RMSE=16.7$ hours, which is somewhat higher than, but comparable to ENLIL errors ($ME=-6.1$ hours, $MAE=12.8$ hours and $RMSE=14.4$ hours). Overall, DBEM and ENLIL show a similar performance. Furthermore, we find that in both models fast CMEs are predicted to arrive earlier than observed, most probably owing to the physical limitations of models, but possibly also related to an overestimation of the CME initial speed for fast CMEs., 15 pages, 8 figures

The drag-based model (DBM) for heliospheric propagation of ICMEs is a widely used simple analytical model which can predict ICME arrival time and speed at a given heliospheric distance (Vr_nak et al., 2013, SolPhys). It is based on the assumption that the heliospheric propagation of ICMEs, is solely under the influence of MHD drag, where ICME propagation is determined based on CME properties as well as the properties of the ambient solar wind. The current version of the DBM is operational as part of ESA's SSA programme (http://swe.ssa.esa.int/web/guest/graz-dbm-federated). The DBM takes into account the ICME geometry to track the whole leading edge of an ICME, it can estimate whether or not an ICME will reach the observer and calculate the transit time and impact speed. To estimate the uncertainty for a single event, Drag-Based Ensemble Model (DBEM) was developed (Dumbovic et al., 2018, ApJ) which utilizes an ensemble of the observation-based CME input and synthetic values of the ambient solar wind speed and drag parameter. Using multiple runs with different input parameters, distributions of predicted arrival times and speeds are obtained allowing to forecast the confidence in the likelihood of the ICME arrival. The DBEM was further developed to an on-line application to provide the real-time CME forecast, which is currently in a test phase, and will soon be a part of ESA-SSA Heliospheric Weather Expert Service Group (http://swe.ssa.esa.int/heliospheric-weather). We test the model and the on-line application using observations and compare the performance with other CME propagation models.

During the declining phase of the current solar cycle, heliospheric activity has suddenly and drastically increased starting from a simple sunspot in Active Region (AR) 2673, which transformed into a complex region with three X-class flares accompanied by several Earth-directed Coronal Mass Ejections (CME) from 4th to 6th of September. Only a few days later, on 10th September, the same AR 2673 produced solar energetic particles (SEPs) which were registered as a ground level enhancement (GLE) at Earth and the biggest GLE on the surface of Mars as observed by the Radiation Assessment Detector (RAD) since the landing of the Curiosity rover in August 2012. Both Earth and Mars saw an impulsive and intense enhancement of the accelerated protons with energies larger than hundreds of MeV whereas STEREO-A, despite being at the back side of the event, detected gradually increasing fluxes of particles transported there across the heliospheric magnetic field. These high energy particles were mainly accelerated by the flares and shocks which were associated with three consecutive CMEs launched on 9th and 10th of September. Based on STEREO-A and SOHO coronograph images, we identified the initial three-dimensional kinematics of the three CMEs using the Graduated Cylindrical Shell (GCS) model. The first two CMEs had moderate launch speeds while the last one was extremely fast (larger than 2500 km/s at 20 solar radii). These three CMEs interacted as they propagated outwards into the heliosphere and the resulting complex interplanetary CME (ICME) together with its associated shock was highly likely related to the effective acceleration of particles at such high energies causing GLE at both Earth and Mars. The arrival of the ICME at Mars caused a very significant Forbush decrease seen by the Radiation Assessment Detector (RAD) on the surface of Mars and the arrival time is only a few hours later than that at Earth which is about 0.5 AU closer to the Sun than Mars. We investigated the interaction of three CMEs and propagation of the consequent ICME using the Drag Based Model (DBM) as well as the WSA-ENLIL plus cone model and the simulated results are compared with in-situ measurements at both Earth and Mars. The comparison shows that in order to better predict the ICME arrival and its potential space weather impact at Earth and other heliospheric locations, it is essential to 1) analyze the evolution of the ICME kinematics, especially during interactions of different CMEs and 2) better understand the spatially and temporally varying interplanetary conditions of the heliosphere.

The drag-based model for heliospheric propagation of coronal mass ejections (CMEs) is a widely used analytical model that can predict CME arrival time and speed at a given heliospheric location. It is based on the assumption that the propagation of CMEs in interplanetary space is solely under the influence of magnetohydrodynamical drag, where CME propagation is determined based on CME initial properties as well as the properties of the ambient solar wind. We present an upgraded version, the drag-based ensemble model (DBEM), that covers ensemble modeling to produce a distribution of possible ICME arrival times and speeds. Multiple runs using uncertainty ranges for the input values can be performed in almost real- time, within a few minutes. This allows us to define the most likely ICME arrival times and speeds, quantify prediction uncertainties, and determine forecast confidence. The performance of the DBEM is evaluated and compared to that of ensemble WSA-ENLIL+Cone model (ENLIL) using the same sample of events. It is found that the mean error is ME = −9.7 hr, mean absolute error MAE = 14.3 hr, and root mean square error RMSE = 16.7 hr, which is somewhat higher than, but comparable to ENLIL errors (ME = −6.1 hr, MAE = 12.8 hr and RMSE = 14.4 hr). Overall, DBEM and ENLIL show a similar performance. Furthermore, we find that in both models fast CMEs are predicted to arrive earlier than observed, most likely owing to the physical limitations of models, but possibly also related to an overestimation of the CME initial speed for fast CMEs.

Supporting the growing field of space weather forecasting, we propose a framework to analyze ensemble simulations of coronal mass ejections. As the current simulation technique requires manual input, uncertainty is introduced into the simulation pipeline which leads to inaccurate predictions. Using our system, the analyst can compare ensemble members against ground truth data (arrival time and geo-effectivity) as well as information derived from satellite imagery. The simulations can be compared on a global basis, based on time-resolved quality measures, and as a 3D volumetric rendering with embedded satellite imagery in a multi-view setup. This flexible framework provides the expert with the tools to increase the knowledge about the, as of yet not fully understood, principles behind the formation of coronal mass ejections.