Geoeffectiveness Of Coronal Mass Ejections Research Articles

MHD Modeling of a Geoeffective Interplanetary Coronal Mass Ejection with the Magnetic Topology Informed by In Situ Observations

Variations of the magnetic field within a solar coronal mass ejection (CME) in the heliosphere depend on the CME’s magnetic structure as it leaves the solar corona and its interplanetary evolution. To account for this evolution, we developed a new numerical model of the inner heliosphere that simulates the propagation of a CME through a realistic solar wind background and allows various CME magnetic topologies. To this end, we incorporate the Gibson–Low CME model within our global MHD model of the inner heliosphere, GAMERA-Helio. We apply the model to study the propagation of the geoeffective CME that erupted on 2010 April 3, aiming to reproduce the temporal variations of the magnetic field vector during the CME’s passage by Earth. Parameters of the Gibson–Low CME are informed by STEREO white-light observations near the Sun. The magnetic topology for this CME—the tethered flux rope—is informed by in situ magnetic field observations near Earth. We performed two simulations testing different CME propagation directions. For an in-ecliptic direction, the simulation shows a rotation of all three magnetic field components within the CME, as seen at Earth, similar to that observed. However, the magnitudes of the components, particularly at the back of the CME, are underestimated by the model. With a southward direction, suggested by coronal imaging observations, the B x component lacks the observed change from negative to positive. In both cases, the model favors the east–west orientation of the flux rope, consistent with the orientation previously inferred from the images from STEREO/Heliospheric Imager.

Prediction of Geoeffective CMEs Using SOHO Images and Deep Learning

The application of machine learning to the study of coronal mass ejections (CMEs) and their impacts on Earth has seen significant growth recently. Understanding and forecasting CME geoeffectiveness are crucial for protecting infrastructure in space and ensuring the resilience of technological systems on Earth. Here we present GeoCME, a deep-learning framework designed to predict, deterministically or probabilistically, whether a CME event that arrives at Earth will cause a geomagnetic storm. A geomagnetic storm is defined as a disturbance of the Earth’s magnetosphere during which the minimum Dst index value is less than −50 nT. GeoCME is trained on observations from the instruments including LASCO C2, EIT, and MDI on board the Solar and Heliospheric Observatory (SOHO), focusing on a dataset that includes 136 halo/partial halo CMEs in Solar Cycle 23. Using ensemble and transfer learning techniques, GeoCME is capable of extracting features hidden in the SOHO observations and making predictions based on the learned features. Our experimental results demonstrate the good performance of GeoCME, achieving a Matthew’s correlation coefficient of 0.807 and a true skill statistics score of 0.714 when the tool is used as a deterministic prediction model. When the tool is used as a probabilistic forecasting model, it achieves a Brier score of 0.094 and a Brier skill score of 0.493. These results are promising, showing that the proposed GeoCME can help enhance our understanding of CME-triggered solar-terrestrial interactions.

CME Arrival Time Prediction Based on Coronagraph Observations and Machine-learning Techniques

The timely and precise prediction of the arrival time of coronal mass ejections (CMEs) is crucial in mitigating their potential adverse effects. In this study, we present a novel prediction method utilizing a deep-learning framework coupled with physical characteristics of CMEs and background solar wind. Time series images from synchronized solar white-light and EUV observations of 156 geoeffective CME events during 2000–2020 are collected for this study, according to the Richardson and Cane interplanetary CME directory and the SOHO/LASCO CME catalog of NASA/CDAW. The CME parameters are obtained from the CDAW website and the solar wind parameters are from OMNI2 website. The observational images are first fed into a convolutional neural network (CNN) to train a regression model as Model A. The results generated by the original CNN are then integrated with 11 selected physical parameters in additional neural network layers of Model B to improve the predictions. Under optimal configurations, Model A achieves a minimum mean absolute error (MAE) of 7.87 hr, whereas Model B yields a minimum MAE of 5.12 hr. During model training, we employed tenfold cross validation to reduce the occasionality of biased data. The average MAE of Model B on 10 folds is 33% lower than that of model A. The results demonstrate that combining the imaging observations with the physical properties of CMEs and background solar wind to train a machine-learning model can benefit the forecasting of CME arrival times.

Spectropolarimetric Radio Imaging of Faint Gyrosynchrotron Emission from a CME: A Possible Indication of the Insufficiency of Homogeneous Models

The geo-effectiveness of coronal mass ejections (CMEs) is determined primarily by their magnetic fields. Modeling of gyrosynchrotron (GS) emission is a promising remote sensing technique to measure the CME magnetic field at coronal heights. However, faint GS emission from CME flux ropes is hard to detect in the presence of bright solar emission from the solar corona. With high dynamic-range spectropolarimetric meter wavelength solar images provided by the Murchison Widefield Array, we have detected faint GS emission from a CME out to ∼8.3 R ⊙, the largest heliocentric distance reported to date. High-fidelity polarimetric calibration also allowed us to robustly detect circularly polarized emission from GS emission. For the first time in the literature, Stokes V detection has jointly been used with Stokes I spectra to constrain GS models. One expects that the inclusion of polarimetric measurement will provide tighter constraints on the GS model parameters. Instead, we found that homogeneous GS models, which have been used in all prior works, are unable to model both the total intensity and circular polarized emission simultaneously. This strongly suggests the need for using inhomogeneous GS models to robustly estimate the CME magnetic field and plasma parameters.

Employing the Coupled EUHFORIA‐OpenGGCM Model to Predict CME Geoeffectiveness

AbstractEUropean Heliospheric FORecasting Information Asset (EUHFORIA) is a physics‐based data‐driven solar wind and coronal mass ejections (CMEs) propagation model designed for space weather forecasting and event analysis investigations. Although EUHFORIA can predict the solar wind plasma and magnetic field properties at Earth, it is not equipped to quantify the geo‐effectiveness of the solar transients in terms of geomagnetic indices like the disturbance storm time (Dst) index and the auroral indices, that quantify the impact of the magnetized plasma encounters on Earth's magnetosphere. Therefore, we couple EUHFORIA with the Open Geospace General Circulation Model (OpenGGCM), a magnetohydrodynamic model of the response of Earth's magnetosphere, ionosphere, and thermosphere to transient solar wind characteristics. In this coupling, OpenGGCM is driven by the solar wind and interplanetary magnetic field obtained from EUHFORIA simulations to produce the magnetospheric and ionospheric response to the CMEs. This coupling is validated with two observed geo‐effective CME events driven with the spheromak flux‐rope CME model. We compare these simulation results with the indices obtained from OpenGGCM simulations driven by the measured solar wind data from spacecraft. We further employ the dynamic time warping (DTW) technique to assess the model performance in predicting Dst. The main highlight of this study is to use EUHFORIA simulated time series to predict the Dst and auroral indices 1–2 days in advance, as compared to using the observed solar wind data at L1, which only provides predictions 1–2 hr before the actual impact.

A catalogue of observed geo-effective CME/ICME characteristics

One of the goals of Space Weather studies is to achieve a better understanding of impulsive phenomena, such as Coronal Mass Ejections (CMEs), to improve our ability to forecast their propagation characteristics and mitigate the risks to our technologically driven society. The essential part of achieving this goal is to assess the performance of forecasting models. To this end, the quality and availability of suitable data are of paramount importance. In this work, we merged publicly available data of CMEs from both in-situ and remote observations in order to build a dataset of CME properties. To evaluate the accuracy of the dataset and confirm the relationship between in-situ and remote observations, we have employed the Drag-Based Model (DBM) due to its simplicity and modest consumption of computational resources. In this study, we have also explored the parameter space for the drag parameter and solar wind speed using a Monte Carlo approach to evaluate how efficiently the DBM determines the propagation of CMEs for the events in the dataset. The geoeffective CMEs selected as a result of this work are compliant with the hypothesis of DBM (isolated CME, constant solar wind speed beyond 20 R⊙) and also yield further insight into CME features such as arrival time and arrival speed at L1 point, lift-off time, speed at 20 R⊙ and other similar quantities. Our analysis based on the acceptance rate in the DBM inversion procedure shows that almost 50% of the CME events in the dataset are well described by DBM as they propagate in the heliosphere. The dataset includes statistical metrics for the DBM model parameters. The probability distribution functions (PDFs) for the free parameters of DBM have been derived through a Monte Carlo-like inversion procedure. Probability distribution functions obtained from this work are comparable to PDFs employed in previous works. The analysis showed that there exist two different most probable values (median values) of solar wind speed for DBM input based on slow (wslow ≈ 386 km/s) and fast (wfast ≈ 547 km/s) solar wind type. The most probable value for the drag parameter (γ ≈ 0.687 × 10−7 km−1) in our study is somewhat higher than the values reported in previous studies. Using a data-driven approach, this procedure allows us to present a homogeneous, reliable, and robust dataset for the investigation of CME propagation. Additionally, possible CME events are identified where the DBM prediction is not valid due to model limitations and higher uncertainties in the input parameters. These events require further thorough investigation in the future.

Full Velocities and Propagation Directions of Coronal Mass Ejections Inferred from Simultaneous Full-disk Imaging and Sun-as-a-star Spectroscopic Observations

Coronal mass ejections (CMEs) are violent ejections of magnetized plasma from the Sun that can trigger geomagnetic storms, endanger satellite operations, and destroy electrical infrastructures on the Earth. After systematically searching Sun-as-a-star spectra observed by the Extreme Ultraviolet Variability Experiment on board the Solar Dynamics Observatory (SDO) from 2010 May to 2022 May, we identified eight CMEs associated with flares and filament eruptions by analyzing the blue-wing asymmetry of the O iii 52.58 nm line profiles. Combined with images simultaneously taken by the 30.4 nm channel of the Atmospheric Imaging Assembly on board SDO, the full velocity and propagation direction for each of the eight CMEs are derived. We find a strong correlation between geomagnetic indices (Kp and Dst) and the angle between the CME propagation direction and the Sun–Earth line, suggesting that Sun-as-a-star spectroscopic observations at extreme-ultraviolet wavelengths can potentially help to improve the prediction accuracy of the geoeffectiveness of CMEs. Moreover, an analysis of synthesized long-exposure Sun-as-a-star spectra implies that it is possible to detect CMEs from other stars through blue-wing asymmetries or blueshifts of spectral lines.

Self-consistent propagation of flux ropes in realistic coronal simulations

Context.The text has been edited to adhere to American English based on the spelling style used in the text. In order to anticipate the geoeffectiveness of coronal mass ejections (CMEs), heliospheric simulations are used to propagate transient structures injected at 0.1 AU. Without direct measurements near the Sun, the properties of these injected CMEs must be derived from models coming from observations or numerical simulations, and thus they contain a lot of uncertainty.Aims.The aim of this paper is to demonstrate the possible use of the new coronal model COCONUT to compute a detailed representation of a numerical CME at 0.1 AU after its injection at the solar surface and propagation in a realistic solar wind, as derived from observed magnetograms.Methods.We present the implementation and propagation of modified Titov-Démoulin flux ropes in the COCONUT 3D magnetohydrodynamics coronal model. Background solar wind was reconstructed in order to model two opposite configurations representing a solar activity maximum and minimum, respectively. Both configurations were derived from magnetograms that were obtained by the Helioseismic and Magnetic Imager on board the Solar Dynamic Observatory satellite. We tracked the propagation of 24 flux ropes that differ only by their initial magnetic flux. In particular, we investigated the geometry of the flux ropes during the early stages of their propagation as well as the influence of their initial parameters and solar wind configuration on 1D profiles derived at 0.1 AU.Results.At the beginning of the propagation, the shape of the flux ropes varied between simulations during low and high solar activity. We found dynamics that are consistent with the standard CME model, such as pinching of the CME legs and the appearance of post-flare loops. Despite the differences in geometry, the synthetic density and magnetic field time profiles at 0.1 AU are very similar in both solar wind configurations. These profiles are also similar to those observed further in the heliosphere and suggest the presence of a magnetic ejecta composed of the initially implemented flux rope and a sheath ahead of it. Finally, we uncovered relationships between the properties of the magnetic ejecta, such as relationships between density or speed and the initial magnetic flux of our flux ropes.Conclusions.The implementation of the modified Titov-Démoulin flux rope in COCONUT enables us to retrieve the major properties of CMEs at 0.1 AU for any phase of the solar cycle. When combined with heliospheric simulations, COCONUT could lead to more realistic and self-consistent CME evolution models and thus more reliable predictions.

Parameter Study of Geomagnetic Storms and Associated Phenomena: CME Speed De-Projection vs. In Situ Data

In this study, we give correlations between the geomagnetic storm (GS) intensity and parameters of solar and interplanetary (IP) phenomena. We also perform 3D geometry reconstructions of geo-effective coronal mass ejections (CMEs) using the recently developed PyThea framework and compare on-sky and de-projected parameter values, focusing on the reliability of the de-projection capabilities. We utilize spheroid, ellipsoid and graduated cylindrical shell models. In addition, we collected a number of parameters of the GS-associated phenomena. A large variation in 3D de-projections is obtained for the CME speeds depending on the selected model for CME reconstruction and observer subjectivity. A combination of fast speed and frontal orientation of the magnetic structure upon its arrival at the terrestrial magnetosphere proves to be the best indicator for the GS strength. More reliable estimations of geometry and directivity, in addition to de-projected speeds, are important for GS forecasting in operational space weather schemes.

A Study on the Geoeffectiveness of Halo Coronal Mass Ejections During the Period of 1996- 2018

In this study, we investigate the solar origins and interplanetary properties of 83 geoeffective halo coronal mass ejections (CMEs) that produced intense geomagnetic storms (Dst ≤ -100 nT) on Earth, spanning the time interval from 1996 to 2018, which includes solar cycles 23 and 24. Observations indicate that full-halo CMEs are potential contributors to intense geomagnetic activity on Earth. However, it is worth noting that not all full-halo CMEs lead to significant geomagnetic storms, which adds complexity to the task of space weather forecasting. Our investigation delves into the solar origins and flare connections of these geoeffective CMEs and their interplanetary effects, specifically, solar wind speed, and the southward component of the interplanetary magnetic field Bz. The objective is to elucidate the relationship between solar and interplanetary parameters. Specifically, this study aims to identify solar parameters that govern key interplanetary parameters responsible for generating significant geomagnetic storms. Our findings indicate that fast full-halo CMEs, associated with powerful flares and originating from favourable locations (i.e., near the central meridian and at low to mid latitudes), are the most likely candidates for producing intense geomagnetic storms. Additionally, our results demonstrate that the intensity of geomagnetic storms is most strongly influenced by the southward component of the interplanetary magnetic field, but is less dependent on the initial speed of the CME.

Predicting CME arrival time through data integration and ensemble learning

The Sun constantly releases radiation and plasma into the heliosphere. Sporadically, the Sun launches solar eruptions such as flares and coronal mass ejections (CMEs). CMEs carry away a huge amount of mass and magnetic flux with them. An Earth-directed CME can cause serious consequences to the human system. It can destroy power grids/pipelines, satellites, and communications. Therefore, accurately monitoring and predicting CMEs is important to minimize damages to the human system. In this study we propose an ensemble learning approach, named CMETNet, for predicting the arrival time of CMEs from the Sun to the Earth. We collect and integrate eruptive events from two solar cycles, #23 and #24, from 1996 to 2021 with a total of 363 geoeffective CMEs. The data used for making predictions include CME features, solar wind parameters and CME images obtained from the SOHO/LASCO C2 coronagraph. Our ensemble learning framework comprises regression algorithms for numerical data analysis and a convolutional neural network for image processing. Experimental results show that CMETNet performs better than existing machine learning methods reported in the literature, with a Pearson product-moment correlation coefficient of 0.83 and a mean absolute error of 9.75 h.

Magnetospheric Reconnection and Geomagnetic Storms: A Personal Perspective

AbstractThis commentary paper presents summaries of my research work on magnetospheric reconnection and on geomagnetic storms, to which I have devoted most of my professional career in space physics. Thus, the paper summarizes: (a) my early studies on the magnetospheric reconnection‐electric field, involving observations and a model of the related polar cap‐electric field during events with observed interplanetary driving parameters and (b) studies on several fundamental aspects associated with the origin of geomagnetic storms, especially leading to intense and extreme levels of activity, mostly associated with the arrival of geoeffective Coronal Mass Ejections (CMEs). The latter also involves studies on recurrent geomagnetic activity caused by interplanetary high‐speed streams, among which “High‐Intensity, Long‐Duration and Continuous Auroral Activity (HILDCAAs for short)” events were shown to represent a special class. In the final Section, I have added some comments on recent issues dealing with fundamental concepts on reconnection at the Earth's magnetopause.

Magnetohydrodynamic simulation of coronal mass ejections using interplanetary scintillation data observed from radio sites ISEE and LOFAR

Interplanetary scintillation (IPS) is a useful tool for detecting coronal mass ejections (CMEs) throughout interplanetary space. Global magnetohydrodynamic (MHD) simulations of the heliosphere, which are usually used to predict the arrival and geo-effectiveness of CMEs, can be improved using IPS data. In this study, we demonstrate an MHD simulation that includes IPS data from multiple stations to improve CME modelling. The CMEs, which occurred on 09–10 September 2017, were observed over the period 10–12 September 2017 using the Low-Frequency Array (LOFAR) and IPS array of the Institute for Space-Earth Environmental Research (ISEE), Nagoya University, as they tracked through the inner heliosphere. We simulated CME propagation using a global MHD simulation, SUSANOO-CME, in which CMEs were modeled as spheromaks, and the IPS data were synthesised from the simulation results. The MHD simulation suggests that the CMEs merged in interplanetary space, forming complicated IPS g-level distributions in the sky map. We found that the MHD simulation that best fits both LOFAR and ISEE data provided a better reconstruction of the CMEs and a better forecast of their arrival at Earth than from measurements when these simulations were fit from the ISEE site alone. More IPS data observed from multiple stations at different local times in this study can help reconstruct the global structure of the CME, thus improving and evaluating the CME modelling.

Predicting the Geoeffectiveness of CMEs Using Machine Learning

Coronal mass ejections (CMEs) are the most geoeffective space weather phenomena, being associated with large geomagnetic storms, and having the potential to cause disturbances to telecommunications, satellite network disruptions, and power grid damage and failures. Thus, considering these storms’ potential effects on human activities, accurate forecasts of the geoeffectiveness of CMEs are paramount. This work focuses on experimenting with different machine-learning methods trained on white-light coronagraph data sets of close-to-Sun CMEs, to estimate whether such a newly erupting ejection has the potential to induce geomagnetic activity. We developed binary classification models using logistic regression, k-nearest neighbors, support vector machines, feed-forward artificial neural networks, and ensemble models. At this time, we limited our forecast to exclusively use solar onset parameters, to ensure extended warning times. We discuss the main challenges of this task, namely, the extreme imbalance between the number of geoeffective and ineffective events in our data set, along with their numerous similarities and the limited number of available variables. We show that even in such conditions adequate hit rates can be achieved with these models.

Impacts of CMEs on Earth Based on Logistic Regression and Recommendation Algorithm

Coronal mass ejections (CMEs) are one of the major disturbance sources of space weather. Therefore, it is of great significance to determine whether CMEs will reach the earth. Utilizing the method of logistic regression, we first calculate and analyze the correlation coefficients of the characteristic parameters of CMEs. These parameters include central position angle, angular width, and linear velocity, which are derived from the Large Angle and Spectrometric Coronagraph (LASCO) images. We have developed a logistic regression model to predict whether a CME will reach the earth, and the model yields an F1 score of 30% and a recall of 53%. Besides, for each CME, we use the recommendation algorithm to single out the most similar historical event, which can be a reference to forecast CMEs geoeffectiveness forecasting and for comparative analysis.

Uncovering the process that transports magnetic helicity to coronal mass ejection flux ropes

Magnetic helicity, an intrinsic property of eruptive helical flux ropes (FRs) forming coronal mass ejections (CMEs), plays an important role in determining CME geoeffectiveness. In the solar atmosphere and heliosphere, helicity remains conserved in a closed volume. Considering this fact as a basis of our study, we perform a quantitative comparison between total magnetic helicity and twisted flux in interplanetary CMEs and those transported to CMEs via magnetic reconnection at low corona. At the source, twisted/poloidal flux (ϕpcme) of CMEs is directly estimated from total reconnection flux, and CME helicity (Hcme) is obtained by combining reconnection flux information with CME physical parameters. At 1 AU, the twisted/poloidal flux (ϕpmc) and helicity (Hmc) of CMEs are obtained from in situ observations. Considering uncertainties steaming from FR length, reconnection flux and CME physical parameter estimations, poloidal flux and helicity of CMEs at 1 AU are found to be highly relevant (ϕpmcϕpcme = 0.4–1.5, HmcHcme = 0.3–1) to low-corona magnetic reconnection at the wake of CMEs. This result remains unchanged despite CME association with pre-existing FRs. We show that a significant reduction in CME helicity during its heliospheric propagation may result from a high rate of FR erosion in the interplanetary medium. Our event analysis confirms that CME’s intrinsic magnetic properties are transported to CME FRs during magnetic reconnection at sheared coronal arcades. A one-to-one correspondence between the chirality of 1-AU CMEs and their pre-eruptive structures complies with the fact that the sense of field line rotations in FRs may remain unchanged during coronal reconnection at the source. By connecting intrinsic magnetic properties of FRs through Sun-Earth medium, this study provides important implications for the origin of geoeffectiveness in CMEs.

Using CME Progenitors to Assess CME Geoeffectiveness

Abstract There have been a few previous studies claiming that the effects of geomagnetic storms strongly depend on the orientation of the magnetic cloud portion of coronal mass ejections (CMEs). Aparna & Martens, using halo-CME data from 2007 to 2017, showed that the magnetic field orientation of filaments at the location where CMEs originate on the Sun can be used to credibly predict the geoeffectiveness of the CMEs being studied. The purpose of this study is to extend their survey by analyzing the halo-CME data for 1996–2006. The correlation of filament axial direction on the solar surface and the corresponding Bz signatures at L1 are used to form a more extensive analysis for the results previously presented by Aparna & Martens. This study utilizes Solar and Heliospheric Observatory Extreme-ultraviolet Imaging Telescope 195 Å, Michelson Doppler Imager magnetogram images, and Kanzelhöhe Solar Observatory and Big Bear Solar Observatory Hα images for each particular time period, along with ACE data for interplanetary magnetic field signatures. Utilizing all these, we have found that the trend in Aparna & Martens’ study of a high likelihood of correlation between the axial field direction on the solar surface and Bz orientation persists for the data between 1996 and 2006, for which we find a match percentage of 65%.

Responses of the Indian Equatorial Ionization Anomaly to two CME-induced geomagnetic storms during the peak phase of solar cycle 24

This work analyzes the geo-effectiveness of Coronal Mass Ejection- (CME-) induced storms by investigating the responses of ionospheric Vertical Total Electron Content (VTEC) and the Equatorial Ionization Anomaly (EIA) over the Indian sector to two storms. One of the storms occurred on February 19, 2014 (SYM-H: −120 nT), while the other occurred on June 23, 2015 (SYM-H: −204 nT). Both storms were driven by full halo CMEs. Global TEC maps were used to characterize VTEC variations during the storms. June 23, 2015 storm was characterized with stronger solar progenitors, right from its origin, although the VTEC response to the storm was not influenced by their strong progenitors. The CMEs that caused the selected storms are large (Halo CMEs). We inferred that irrespective of the strength of solar origin of a storm, the response of ionization distribution over equatorial and low-latitude regions to it depends on the season of storm occurrence, local time of the storm onset, and PPEF orientation. From the VTEC variations for the three Indian stations namely, Trivandrum (geographic latitude: 8.52°N, geographic longitude: 76.94°E, magnetic latitude: 0.37°N), Hyderabad (17.39°N, 78.49°E, 10.15°N) and Delhi (28.70°N, 77.10°E, 22.70°N), we observed that EIA disturbances were more prominent over Hyderabad than over Delhi. The February 19, 2014 storm was characterized by a localized EIA crest at latitude a little above Hyderabad, while in June 23, 2015 storm localized EIA crest was observed directly on Hyderabad. IRI-2016 model generally underestimated VTEC at the three Indian equatorial and low-latitude locations. Solar cycle 24 was characterized with low heliospheric pressure due to its weak polar field strength. The lower pressure allowed CMEs to expand greatly as they transit through space. As they expand, the strengths of the magnetic field inside them decrease, and such lower-strength magnetic fields cause geomagnetic storms that are less geoeffective, even when their solar/interplanetary progenitors are strong and healthy. This associated weak polar field strength of solar cycle 24 caused weak fountain effect with the attendant inability to exhibit storm-time super-fountain effect in the dayside of the equatorial/low-latitude regions.

Joint Geoeffectiveness and Arrival Time Prediction of CMEs by a Unified Deep Learning Framework

Fast and accurate prediction of the geoeffectiveness of coronal mass ejections (CMEs) and the arrival time of the geoeffective CMEs is urgent, to reduce the harm caused by CMEs. In this paper, we present a new deep learning framework based on time series of satellites’ optical observations that can give both the geoeffectiveness and the arrival time prediction of the CME events. It is the first time combining these two demands in a unified deep learning framework with no requirement of manually feature selection and get results immediately. The only input of the deep learning framework is the time series images from synchronized solar white-light and EUV observations. Our framework first uses the deep residual network embedded with the attention mechanism to extract feature maps for each observation image, then fuses the feature map of each image by the feature map fusion module and determines the geoeffectiveness of CME events. For the geoeffective CME events, we further predict its arrival time by the deep residual regression network based on group convolution. In order to train and evaluate our proposed framework, we collect 2400 partial-/full-halo CME events and its corresponding images from 1996 to 2018. The F1 score and Accuracy of the geoeffectiveness prediction can reach 0.270% and 75.1%, respectively, and the mean absolute error of the arrival time prediction is only 5.8 h, which are both significantly better than well-known deep learning methods and can be comparable to, or even better than, the best performance of traditional methods.

A Geoeffective CME Caused by the Eruption of a Quiescent Prominence on 29 September 2013

The eruption of a large prominence that occurred away of active regions in the SOL2013-09-29 event produced a fast coronal mass ejection (CME) and a shock wave. The event caused considerable geospace disturbances, including a proton enhancement that have been addressed in previous studies. Continuing with the analysis of this event, we focus on the development of the CME and shock wave, assess an expected geospace impact using simplest considerations, and compare the expectations with in situ measurements near Earth. The high CME speed in this non-flare-associated event was determined by a considerable reconnected flux that corresponds to a pattern established by different authors. Estimations based on a few approaches showed the reconnection flux in this event to be comparable with a typical value in flare-associated eruptions. The shock wave was most likely impulsively excited by the erupting prominence in the same way as in flare-associated events and changed to the bow-shock regime later. The trajectory calculated for this scenario reproduces the Type II emission observed from 30 MHz to 70 kHz; its interruptions were probably caused by propagation effects. Properties of the near-Earth proton enhancement are discussed considering the results of recent studies.