Graduated Cylindrical Shell (GCS) model is a widely used tool to determine direction, kinematic and orientation properties of Coronal Mass Ejections (CME) using multi-viewpoint observations from SOHO and STEREO A&B coronagraphs. In this study, we estimate the subjective uncertainties typically seen while deriving these CME properties by comparing the GCS model results reported in multiple studies and catalogs for 56 CMEs. We find that the GCS estimates of latitude, longitude, and tilt show an average uncertainty of 5.7, 11.2, and 24.7 degrees with standard deviation of 5.5, 12.7, and 19.7 degrees respectively. We found that the uncertainties in estimated latitudes are correlated with uncertainties in estimated longitude, tilt, and speed, showing that some CMEs are inherently difficult to fit than others. We then introduced these uncertainty values in our 3-D magnetohydrodynamic flux rope based modified spheromak CME model to figure out their consequences for space weather prediction. We find that much better CME observations are required to reliably predict magnetic field of CMEs at 1 AU using flux rope based models, since the uncertainties in estimated GCS values can result in large differences in 1 AU signatures, especially for CMEs launched away from the Sun-Earth line.
Ensemble Simulations of the 2012 July 12 Coronal Mass Ejection with the Constant-turn Flux Rope Model
Abstract Flux-rope-based magnetohydrodynamic modeling of coronal mass ejections (CMEs) is a promising tool for prediction of the CME arrival time and magnetic field at Earth. In this work, we introduce a constant-turn flux rope model and use it to simulate the 2012 July 12 16:48 CME in the inner heliosphere. We constrain the initial parameters of this CME using the graduated cylindrical shell (GCS) model and the reconnected flux in post-eruption arcades. We correctly reproduce all the magnetic field components of the CME at Earth, with an arrival time error of approximately 1 hr. We further estimate the average subjective uncertainties in the GCS fittings by comparing the GCS parameters of 56 CMEs reported in multiple studies and catalogs. We determined that the GCS estimates of the CME latitude, longitude, tilt, and speed have average uncertainties of 5.°74, 11.°23, 24.°71, and 11.4%, respectively. Using these, we have created 77 ensemble members for the 2012 July 12 CME. We found that 55% of our ensemble members correctly reproduce the sign of the magnetic field components at Earth. We also determined that the uncertainties in GCS fitting can widen the CME arrival time prediction window to about 12 hr for the 2012 more »
- Publication Date:
- NSF-PAR ID:
- 10342182
- Journal Name:
- The Astrophysical Journal
- Volume:
- 933
- Issue:
- 2
- Page Range or eLocation-ID:
- 123
- ISSN:
- 0004-637X
- Sponsoring Org:
- National Science Foundation
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Abstract The magnetic fields of interplanetary coronal mass ejections (ICMEs), which originate close to the Sun in the form of a flux rope, determine their geoeffectiveness. Therefore, robust flux rope‐based models of CMEs are required to perform magnetohydrodynamic (MHD) simulations aimed at space weather predictions. We propose a modified spheromak model and demonstrate its applicability to CME simulations. In this model, such properties of a simulated CME as the poloidal and toroidal magnetic fluxes, and the helicity sign can be controlled with a set of input parameters. We propose a robust technique for introducing CMEs with an appropriate speed into a background, MHD solution describing the solar wind in the inner heliosphere. Through a parametric study, we find that the speed of a CME is much more dependent on its poloidal flux than on the toroidal flux. We also show that the CME speed increases with its total energy, giving us control over its initial speed. We further demonstrate the applicability of this model to simulations of CME‐CME collisions. Finally, we use this model to simulate the 12 July 2012 CME and compare the plasma properties at 1 AU with observations. The predicted CME properties agree reasonably with observational data.
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Abstract The arrival time prediction of coronal mass ejections (CMEs) is an area of active research. Many methods with varying levels of complexity have been developed to predict CME arrival. However, the mean absolute error (MAE) of predictions remains above 12 hr, even with the increasing complexity of methods. In this work we develop a new method for CME arrival time prediction that uses magnetohydrodynamic simulations involving data-constrained flux-rope-based CMEs, which are introduced in a data-driven solar wind background. We found that for six CMEs studied in this work the MAE in arrival time was ∼8 hr. We further improved our arrival time predictions by using ensemble modeling and comparing the ensemble solutions with STEREO-A and STEREO-B heliospheric imager data. This was done by using our simulations to create synthetic J-maps. A machine-learning (ML) method called the lasso regression was used for this comparison. Using this approach, we could reduce the MAE to ∼4 hr. Another ML method based on the neural networks (NNs) made it possible to reduce the MAE to ∼5 hr for the cases when HI data from both STEREO-A and STEREO-B were available. NNs are capable of providing similar MAE when only the STEREO-A data aremore »
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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.
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Context. Coronal mass ejections (CMEs) on the Sun are the largest explosions in the Solar System that can drive powerful plasma shocks. The eruptions, shocks, and other processes associated to CMEs are efficient particle accelerators and the accelerated electrons in particular can produce radio bursts through the plasma emission mechanism. Aims. Coronal mass ejections and associated radio bursts have been well studied in cases where the CME originates close to the solar limb or within the frontside disc. Here, we study the radio emission associated with a CME eruption on the back side of the Sun on 22 July 2012. Methods. Using radio imaging from the Nançay Radioheliograph, spectroscopic data from the Nançay Decametric Array, and extreme-ultraviolet observations from the Solar Dynamics Observatory and Solar Terrestrial Relations Observatory spacecraft, we determine the nature of the observed radio emission as well as the location and propagation of the CME. Results. We show that the observed low-intensity radio emission corresponds to a type II radio burst or a short-duration type IV radio burst associated with a CME eruption due to breakout reconnection on the back side of the Sun, as suggested by the pre-eruptive magnetic field configuration. The radio emission consists ofmore »
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- https://doi.org/10.3847/1538-4357/ac73f3
