ST4.1

Coronal mass ejections (CMEs) and solar energetic particles (SEPs) are manifestations of the dynamic and explosive nature of solar activity and major drivers of space weather events. They often occur in concert, especially in the case of large and fast CMEs that are associated with strong flares and that are able to accelerate SEPs at the shocks ahead of them. Modelling efforts that aim to forecast and mitigate the effects of these solar phenomena range from empirical to analytical to numerical. Although the primary focus of space weather forecasts is naturally on Earth, the increased amount of heliospheric and planetary missions launched in the past ~15 years has provided new opportunities for CME and SEP measurements at other locations in the inner solar system. This has resulted in the possibility to test space weather forecasting models at multiple locations well separated in both heliocentric distance and longitude within the same event, which in turn represents a novel way to benchmark and validate the present capabilities.

In this presentation, we will first briefly review the current status of CME and SEP space weather forecasting, with particular attention given to the main challenges to overcome for advancing predictions. We will then present a few examples of CME and SEP events that were detected in situ at multiple locations in the inner heliosphere and show forecasting—or actually hindcasting—results for each of them. Finally, we will conclude by addressing possible future improvements that take advantage of model validation via multi-spacecraft measurements.

How to cite: Palmerio, E.: Space weather predictions of CMEs and SEPs through the inner heliosphere, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10831, https://doi.org/10.5194/egusphere-egu22-10831, 2022.

The configuration of the interplanetary magnetic field and features of the related ambient solar wind in the ecliptic and meridional plane are different. Therefore, one can expect that the orientation of the flux rope axis of a coronal mass ejection (CME) influences the propagation of the CME itself. However, the determination of the CME orientation remains a challenging task to perform. This study aims to provide a reference to different CME orientation determination methods in the near-Sun environment. Also, it aims to investigate the non-radial flow in the sheath region of the interplanetary CME (ICME) in order to provide the first proxy to relate the ICME orientation with its propagation. We investigated 22 isolated CME-ICME events in the period 2008-2015. We first determined the CME orientation in the near-Sun environment using a 3D reconstruction of the CME with the graduated cylindrical shell (GCS) model applied to coronagraphic images provided by the STEREO and SOHO missions. The CME orientation in the near-Sun environment was determined using an ellipse fitting technique to the CME outer front as determined from the SOHO/LASCO coronagraph. In the near-Earth environment, we obtained the orientation of the corresponding ICME using in-situ plasma and field data and also investigated the non-radial flow in its sheath region. The ability of GCS and ellipse fitting to determine the CME orientation is found to be limited to only distinguishing between the high or low inclination of the events. Most of the CME-ICME pairs under investigation were found to be characterized by a low inclination, and regardless of whether their inclination was high or low, the CME-ICME pairs maintained their inclination during interplanetary propagation. The observed non-radial flows in the sheath region show a greater y-direction to z-direction flow ratio for low-inclination events which suggests that there is a connection between the orientation and propagation of the observed CME-ICME pairs.

How to cite: Martinic, K., Dumbovic, M., Temmer, M., Veronig, A., and Vrsnak, B.: Determination of CME orientation and consequences for their propagation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3857, https://doi.org/10.5194/egusphere-egu22-3857, 2022.

Predicting the arrival time of Coronal Mass Ejections (CME) and the strength of their geomagnetic storms at Earth is crucial in space operations and essential to avoid losing our space instruments and not to risk our astronauts’ lives in space. Besides, protecting the power grids and pipelines on the ground from the side effects of geomagnetic storms. We contribute to this matter by implementing Neural Network (NN) models to predict the CME transit time and the minimum value of the Disturbed storm time (Dst) index of the associated geomagnetic storm.

For the first time, we employed the planets' ephemeris as input features. Taking the CME properties (angular width, linear speed, speed at 20 solar radii, measurement position angle, latitude, and longitude), the solar wind and plasma parameters (the differential speed between the CME and the solar wind, the average interplanetary magnetic field with its 3D components, proton density and plasma temperature, speed in 3D components, dynamic pressure, electric field, plasma beta parameter, Mach number, and magnetosonic Mach number), and the planets' ephemeris in the solar system (distance from the Sun, latitude, and longitude) as inputs, we performed a grid of NNs to predict not only the CME transit time but also the minimum disturbed storm time (Dst) index of the associated geomagnetic storm.

We proposed the new Best-of-the-Best (BOB) approach to optimize the NN hyperparameters using the GridSearch method in Python. We assembled our dataset from (Gopalswamy et al., 2010), (Micha lek et al., 2004), and (Richardson and Cane, 2010) with a total of 230 events between 1997 and 2020. This is the largest dataset of CME-ICME pairs along with solar wind indices and planets locations in the solar system so far.

Remarkably, for the given dataset, the best set of input features for predicting the CME transit time was the CME features and the planets' ephemeris, while for predicting the Dst index were the top correlated features, with a Mean Absolute Error (MAE) of 13.54 hr and 35.57 nT, respectively. More details are described in the manuscript. 

How to cite: Nedal, M., Shalaby, H., and Mahrous, A.: Predicting the Transit Time and Geo-effectiveness of Coronal Mass Ejections using Neural Networks, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2968, https://doi.org/10.5194/egusphere-egu22-2968, 2022.

Coronal mass ejections are one of the most significant drivers of space weather. The ToA predictions along with the Arrival speed of the CMEs are one of the crucial pieces of information for preparing for the possible geomagnetic storms. Geomagnetic storms can have adverse effects on several key components of modern society e.g. communications and electrical grids. The development of many machine learning methods provides us with the opportunity to use these tools in space weather applications. There have been several studies using machine learning methods for ToA predictions. In this study, we present an interactive dashboard to apply several machine learning methods (regression models) to test on the several CME databases used in the community. We also use this opportunity to benchmark various CME databases for TOA and CME arrival speed predictions. We also welcome the community to use this interactive dashboard as a tool to learn about machine learning.

How to cite: Tiwari, A., Camporeale, E., Del Moro, D., Foldes, R., Napoletano, G., de Gasperis, G., and Teunissen, J.: CME-learn: An interactive playground to benchmark CME databases for the time of arrival (ToA) prediction for using machine learning methods., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8887, https://doi.org/10.5194/egusphere-egu22-8887, 2022.

The estimation of the Coronal Mass Ejection (CME) arrival at Earth is an open issue in the field of Space Weather. We present a new near real-time algorithm based on heliospheric imaging (HI) observations of the CME front as a function of time. First, we transform the front elongation angle into radial distance using basic stereoscopic techniques (i.e. fixed-phi, harmonic mean and self-similar expansion). Then we adopt the assumptions that (1) CME accelerate (or decelerate) from the Sun up to some distance and (2) they move with a constant speed past that distance. This “two-phase kinematics” behavior forms the core of our algorithm. The resulting kinematic profiles provide estimates of the CME Time-of-Arrival (ToA) and Speed-on-Arrival (SoA) at 1 AU. This new tool is tested on a sample of CMEs where stereoscopic views were possible, from the STEREO-A and -B HIs were available. The algorithm is promising with predictions for the ToA of CMEs of the order of ±1 hour and for SoA of ±100 km/s. Our approach is in preparation for a possible future combination of HI data from missions at L5. We will test our method further for cases beyond the 1 AU studying ICMEs which has been spotted also on Mars (1.52 AUs).

How to cite: Paouris, E. and Vourlidas, A.: A potential near real-time algorithm for CME propagation utilizing heliospheric imaging observations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11702, https://doi.org/10.5194/egusphere-egu22-11702, 2022.

Accurate reconstruction of the magnetic field topology of coronal mass ejections (CMEs) is essential in space weather forecasting and thus in the spotlight of modelling efforts. The spheromak, a force-free, axisymmetric configuration within which plasma is confined by a twisted magnetic field that fills a spherical volume, is at the moment the most commonly employed flux rope model, which has entered numerous published event studies. Despite its widespread application, not much attention has been paid to the spheromak tilting, which not only affects the spheromak's orientation in the modelling domain, but also its direction of propagation. This can lead to implications when comparing simulation output to observations. The tilting of the spheromak occurs when its magnetic moment is at an angle with the ambient magnetic field. In this case a torque is exerted on the spheromak, forcing it to rotate, so that its magnetic moment aligns to the ambient magnetic field. In our study we used EUHFORIA to investigate the spheromak tilting under different conditions. We developed a method to monitor the spheromak's position and orientation in the EUHFORIA simulation output, and we quantified how the spheromak's total drift and rotation angle depend on various input parameters. We find that the spheromak experienced tilting in all studied scenarios resulting often in a significantly changed orientation to that which it had during insertion. We emphasize that in space weather modelling it is crucial to take into consideration the spheromak tilting, in particular when comparing the model output to observations.

How to cite: Asvestari, E., Rindlisbacher, T., Pomoell, J., and Kilpua, E.: The impact of the spheromak tilting in space weather modelling, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8908, https://doi.org/10.5194/egusphere-egu22-8908, 2022.

Coronal Mass Ejections (CMEs) are the main drivers of interplanetary shocks and space weather disturbances. One of the key parameters that determine the geo-effectiveness of the CME is its internal magnetic configuration. Strong CMEs directed towards Earth can have a severe impact on our planet and their prediction can mitigate possible damages. 

The novel heliospheric model Icarus, which is implemented within the framework of MPI-AMRVAC (Xia et al., 2018) introduces new capabilities to model the heliospheric wind and real CME events. Advanced techniques, such as adaptive mesh refinement and grid stretching are implemented. By imposing these techniques, we avoid cell deformation in the domain and only the necessary/desired areas are refined to higher spatial resolutions (and coarsened again when the high resolution is no longer necessary, e.g. behind a travelling shock wave). The refinement and coarsening conditions are controlled by the user. These techniques result in optimised computer memory usage and a significant speed-up, which is crucial for forecasting purposes. 

In order to model the magnetic field of the CME and its interaction with the solar wind, the Gibson and Low model is implemented in Icarus. In order to assess the ICARUS model capabilities to predict the solar wind conditions in the heliosphere, especially at L1, we consider a real CME event.  Further, we perform a comparison of the results of the existing Linear Force-Free Spheromak model and the new advanced model. To perform the full comparison we compare the time series data at L1 and other satellites, while we also monitor the time that simulations require to model the heliospheric wind and CME events. 

The solution mesh refinement is applied to the CMEs in order to model its arrival time and interior magnetic field better. To analyse the results, the radial, longitudinal and latitudinal components of the magnetic field are compared to the original EUHFORIA simulations and the observed data. As a result, the new magnetized model gives an opportunity to model the CME better and a bigger range of parameters to investigate to model the event as accurately as possible.  

This research has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 870405 (EUHFORIA 2.0).

How to cite: Baratashvili, T., Poedts, S., and Verbeke, C.: The effect of AMR on the advanced magnetized CME model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5020, https://doi.org/10.5194/egusphere-egu22-5020, 2022.

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.

How to cite: Amerstorfer, T., Bauer, M., Möstl, C., Barnard, L., Riley, P., Weiss, A. J., and Reiss, M. A.: Drag-based deformation of a multi-point CME event, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9162, https://doi.org/10.5194/egusphere-egu22-9162, 2022.

Drag-Based Ensemble Model (DBEM) is a probabilistic model for heliospheric propagation of Coronal Mass Ejections (CMEs) that predicts the CME hit chance, most probable arrival times and speeds, quantify the prediction uncertainties and calculate the confidence intervals. DBEM is based on the 2D analytical Drag-based Model (DBM) with very short computational time. By using CME cone geometry with flattening DBM calculates the CME arrival time and speed at Earth or any other given target in the solar system. DBEM considers the variability of model input parameters by making an ensemble of n different input parameters to obtain the distribution and significance of the DBM results. As an important tool for space weather forecasters, DBM/DBEM web application is integrated as one of the ESA Space Situational Awareness portal services (https://swe.ssa.esa.int/current-space-weather). Important requirement to perform DBM calculations is to assume that two input parameters namely background solar wind speed and the drag parameter γ are constant in order to have the analytical solution and fast computational times. However, this assumption is not always valid in more complex heliospheric conditions. Thus, to further increase the accuracy of CME propagation forecast we developed the new DBEMv4 version that calculates CME propagation in more steps with variable solar wind speeds. This allows also to employ as DBEMv4 input the dynamic solar wind data in real-time taken from simple persistence model under consideration of the CME propagation direction.

How to cite: Čalogović, J., Dumbović, M., Vršnak, B., Temmer, M., and Veronig, A.: Drag-Based Ensemble Model (DBEMv4) with variable solar wind speed input, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3816, https://doi.org/10.5194/egusphere-egu22-3816, 2022.

The empirical solar wind forecast (ESWF) model is an ESA service to forecast the solar wind speed at Earth with 4 days lead time. The model uses a simple empirical relation between the area of coronal holes (CHs) as measured in meridional slices in EUV at the Sun and the in-situ measured solar wind speed at 1 AU (Vršnak, Temmer, Veronig, 2007). The relation has the drawback that Gaussian type speed profiles are produced as the CH rotates in and out of the meridional slice. With adaptations to the ESWF algorithm we aim to improve the precision of the ESWF speed profile by implementing compression and rarefaction effects occurring between SW streams of different velocities in the interplanetary space. By considering the propagation times for plasma parcels between the Sun and Earth and their interactions, we achieve the asymmetrical shape of the speed profile that is characteristic of high-speed streams (HSS). We present a statistical analysis for the period 2012 - 2019 showing that our adaptions improve the ability to predict HSS speed profiles as well as smaller structures with higher precision.

How to cite: Milosic, D., Temmer, M., Heinemann, S., Podladchikova, T., Veronig, A., and Vršnak, B.: Improving the Empirical Solar Wind Forecast (ESWF) model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4063, https://doi.org/10.5194/egusphere-egu22-4063, 2022.

Coronal holes (CHs) are the source of high-speed solar wind streams (HSSs), which interact with the slow solar wind and form corotating interaction regions (CIRs) in the heliosphere. These high-speed streams and their associated structures influence the geomagnetic activity, causing recurrent geomagnetic storms. The propagation time of solar wind from Sun to Earth is about 1–5 days, creating a natural lead-time for early warning. However, the magnetic structure of an interplanetary perturbation, in particular the southward component Bz of the interplanetary magnetic field (IMF), driving the storm, cannot be determined from solar observations yet, which strongly limits the possibility of storm forecast several days in advance. Current approaches to quantitative storm predictions are often limited to a short-term forecast based on measurements of IMF and solar wind at the Lagrange point L1, which is possible due to the 1-hour difference in the propagation time from L1 to Earth between the radio signal and solar wind.

In this study, we focus on predictions of CIR-driven geomagnetic storms from solar observations with the aim of increasing the warning lead-time from hours to days. We develop a prediction technique of geomagnetic storms using coronal holes at the Sun as well as corresponding solar magnetic field data (cf. Vrsnak et al. 2007). The method is based on establishing empirical relations between the time-series of coronal hole areas on the Sun derived from SDO/AIA images and the solar wind speed at L1; between remote-sensing magnetic field maps of the solar photosphere and that measured in-situ at L1, and finally between coronal hole areas, corresponding magnetic field at Sun and geomagnetic Dst and Kp indices. We demonstrate that the inward/outward direction of the magnetic field originating from the base of a coronal hole is preserved in more than 80% of cases when compared to the related magnetic field measured at Earth. This opens the possibility to use the magnetic field derived from solar observations instead of that at L1. Additionally, to improve the predictions for which we need to derive the Bz component, we analyze the Russel-McPherron effect, which reflects the change of the Bz component and the associated geomagnetic activity through the seasons. The approach proposed in this study for forecasting the Dst/Kp indices makes use of the Gaussian Process Regression, a non-parametric Bayesian model, suitable in case of limited available data, flexible non-linear problems and known prior information about the output (e.g. periodicity). Testing the developed forecasting technique for the whole SDO period 2010-2020, we obtained that the correlation coefficient between the predicted and observed Dst (Kp) index reaches r = 0.68/0.73 (0.67/0.76), for coronal holes having the positive/negative polarity on the Sun. These results demonstrate that the proposed technique opens a possibility to predict CIR-driven geomagnetic storms from solar observations resulting in the extension of the lead time from hours up to several days, which is highly important for warnings of the space weather conditions in the near-Earth environment and other space weather applications.

How to cite: Nitti, S., Podladchikova, T., M. Veronig, A. M., Hofmeister, S., Verbanac, G., and Bandić, M.: Geomagnetic storms forecasting from solar coronal holes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8660, https://doi.org/10.5194/egusphere-egu22-8660, 2022.