Time Of Coronal Mass Ejections Research Articles

Prediction of the Transit Time of Coronal Mass Ejections with an Ensemble Machine-learning Method

Coronal mass ejections (CMEs), a kind of violent solar eruptive activity, can exert a significant impact on space weather. When arriving at the Earth, they interact with the geomagnetic field, which can boost the energy supply to the geomagnetic field and may further result in geomagnetic storms, thus having potentially catastrophic effects on human activities. Therefore, accurate forecasting of the transit time of CMEs from the Sun to the Earth is vital for mitigating the relevant losses brought by them. XGBoost, an ensemble model that has better performance in some other fields, is applied to the space weather forecast for the first time. During multiple tests with random data splits, the best mean absolute error (MAE) of ∼5.72 hr was obtained, and in this test, 62% of the test CMEs had absolute arrival time error of less than 5.72 hr. The average MAE over all random tests was ∼10 hr. It indicates that our method has a better predictive potential and baseline. Moreover, we introduce two effective feature importance ranking methods. One is the information gain method, a built-in method of ensemble models. The other is the permutation method. These two methods combine the learning process of the model and its performance to rank the CME features, respectively. Compared with the direct correlation analysis on the sample data set, they can help select the important features that closely match the model. These two methods can assist researchers to process large sample data sets, which often require feature selection in advance.

ICARUS, a new inner heliospheric model with a flexible grid

Context. Simulating the propagation and predicting the arrival time of coronal mass ejections (CMEs) in the inner heliosphere with a full three-dimensional (3D) magnetohydrodynamic (MHD) propagation model requires a significant amount of computational time. For CME forecasting purposes, multiple runs may be required for different reasons such as ensemble modeling (uncertainty on input parameters) and error propagation. Moreover, higher resolution runs may be necessary, which also requires more CPU time, for example for the prediction of solar energetic particle acceleration and transport or in the framework of more in-depth studies about CME erosion and/or deformation during its evolution. Aims. In this paper we present ICARUS, a new inner heliospheric model for the simulation of a steady background solar wind and the propagation and evolution of superposed CMEs. This novel model has been implemented within the MPI-AMRVAC framework which enables the use of stretched grids and solution adaptive mesh refinement (AMR). The usefulness and efficiency (speed-up) of these advanced features are explored. In particular, we model a typical solar wind with ICARUS and then launch a simple cone CME and follow its evolution. We focus on the effect of radial grid stretching and two specific methods or criteria to trigger solution AMR on this typical simulation run. Methods. For the solar background wind simulation run, we limited the mesh refinement to the area(s) of interest, in this case a co-rotating interaction region (CIR). For the CME evolution run, on the other hand, we apply AMR where the CME is located by the use of a tracing function. As such, the grid is coarsened again after the CME has passed. Results. The implemented AMR is flexible and only refines the mesh in a particular sector of the computational domain, for example around the Earth or a single CIR, and/or for a particular feature such as CIR or CME shocks. Radial grid stretching alone yields speed-ups of up to 4 and more, depending on the resolution. Combined with solution adaptive mesh refinement, the speed-ups can be much larger depending on the complexity of the simulation (e.g., number of CIRs in the background wind, number of CMEs) and on the chosen AMR criteria, thresholds and the number of refinement levels. Conclusions. The ICARUS model implemented in the MPI-AMRVAC framework is a new inner heliospheric 3D MHD model that uses grid stretching as well as AMR techniques. The flexibility in the grid and its resolution allows an optimization of the computational time required for CME propagation simulations for both scientific and forecasting purposes.

Drag-Based Model (DBM) Tools for Forecast of Coronal Mass Ejection Arrival Time and Speed

Forecasting the arrival time of coronal mass ejections (CMEs) and their associated shocks is one of the key aspects of space weather research. One of the commonly used models is the analytical drag-based model (DBM) for heliospheric propagation of CMEs due to its simplicity and calculation speed. The DBM relies on the observational fact that slow CMEs accelerate whereas fast CMEs decelerate and is based on the concept of magnetohydrodynamic (MHD) drag, which acts to adjust the CME speed to the ambient solar wind. Although physically DBM is applicable only to the CME magnetic structure, it is often used as a proxy for shock arrival. In recent years, the DBM equation has been used in many studies to describe the propagation of CMEs and shocks with different geometries and assumptions. In this study, we provide an overview of the five DBM versions currently available and their respective tools, developed at Hvar Observatory and frequently used by researchers and forecasters (1) basic 1D DBM, a 1D model describing the propagation of a single point (i.e., the apex of the CME) or a concentric arc (where all points propagate identically); (2) advanced 2D self-similar cone DBM, a 2D model which combines basic DBM and cone geometry describing the propagation of the CME leading edge which evolves in a self-similar manner; (3) 2D flattening cone DBM, a 2D model which combines basic DBM and cone geometry describing the propagation of the CME leading edge which does not evolve in a self-similar manner; (4) DBEM, an ensemble version of the 2D flattening cone DBM which uses CME ensembles as an input; and (5) DBEMv3, an ensemble version of the 2D flattening cone DBM which creates CME ensembles based on the input uncertainties. All five versions have been tested and published in recent years and are available online or upon request. We provide an overview of these five tools, as well as of their similarities and differences, and discuss and demonstrate their application.

Lessons learned from predictions of Solar Cycle 24

Solar Cycle 24 has almost faded and the activity of Solar Cycle 25 is appearing. We have learned much about predicting solar activity in Solar Cycle 24, especially with the data provided by SDO and STEREO. Many advances have come in the short-term predictions of solar flares and coronal mass ejections, which have benefited from applying machine learning techniques to the new data. The arrival times of coronal mass ejections is a mid-range prediction whose accuracy has been improving, mostly due to a steady flow of data from SoHO, STEREO, and SDO. Longer term (greater than a year) predictions of solar activity have benefited from helioseismic studies of the plasma flows in the Sun. While these studies have complicated the dynamo models by introducing more complex internal flow patterns, the models should become more robust with the added information. But predictions made long before a sunspot cycle begins still rely on precursors. The success of some categories of the predictions of Solar Cycle 24 will be examined. The predictions in successful categories should be emphasized in future work. The SODA polar field precursor method, which has accurately predicted the last three cycles, is shown for Solar Cycle 25. Shape functions for the sunspot number and F10.7 are presented. What type of data is needed to better understand the polar regions of the Sun, the source of the most accurate precursor of long-term solar activity, will be discussed.

Estimation of Arrival Time of Coronal Mass Ejections in the Vicinity of the Earth Using SOlar and Heliospheric Observatory and Solar TErrestrial RElations Observatory Observations

The arrival time of coronal mass ejections (CMEs) in the vicinity of the Earth is one of the most important parameters in determining space weather. We have used a new approach to predicting this parameter. First, in our study, we have introduced a new definition of the speed of ejection. It can be considered as the maximum speed that the CME achieves during the expansion into the interplanetary medium. Additionally, in our research we have used not only observations from the SOlar and Heliospheric Observatory (SOHO) spacecraft but also from Solar TErrestrial RElations Observatory (STEREO) spacecrafts. We focus on halo and partial-halo CMEs during the ascending phase of Solar Cycle 24. During this period the STEREO spacecraft were in quadrature position in relation to the Earth. We demonstrated that these conditions of the STEREO observations can be crucial for an accurate determination of the transit times (TTs) of CMEs to the Earth. In our research we defined a new initial velocity of the CME, the maximum velocity determined from the velocity profiles obtained from a moving linear fit to five consecutive height–time points. This new approach can be important from the point of view of space weather as the new parameter is highly correlated with the final velocity of ICMEs. It allows one to predict the TTs with the same accuracy as previous models. However, what is more important is the fact that the new approach has radically reduced the maximum TT estimation errors to 29 hours. Previous studies determined the TT with a maximum error equal to 50 hours.

CME Arrival Time Prediction Using Convolutional Neural Network

Abstract Fast and accurate prediction of the arrival time of coronal mass ejections (CMEs) at Earth is vital to minimize hazards caused by CMEs. In this paper, we use a deep-learning framework, i.e., a convolutional neural network (CNN) regression model, to analyze transit times from the Sun to Earth of 223 geoeffective CME events observed in the past 30 yr. 90% of them were used to build the prediction model, and the rest 10% have been used for test purpose. Unlike previous studies on this topic, our proposed CNN regression model does not require manually selected features for model training, it does not need time spent on feature collection, and it can deliver predictions without deeper expert knowledge. The only input to our CNN regression model is the instances of the white-light observations of CMEs. The mean absolute error of the constructed CNN regression model is about 12.4 hr, which is comparable to the average performance of the previous studies on this subject. As more CME data become available, we expect the CNN regression model will reveal better results.

Development of Ojih-Okeke modified emperical coronal mass ejection arrival (ECA) model for predicting arrival times of CMEs

A modified Empirical Coronal Mass Ejections Arrival time model (Ojih-Okeke Model) has been successfully developed. The model was applied in predicting the transit time of 33 fast Coronal Mass Ejections (CMEs) associated with intense geomagnetic storm (Dst ≤ − 100 nT). Interestingly, results obtained using this model indicate that the average error between the predicted CMEs and observed transit time is 5.7 h while fractional error is 0.14. These values are much less than those obtained by four other earlier existing models. Furthermore, this model appears to have greatly improved the accuracy of CMEs transit time prediction. Moreover, this newly modified model has thrown more light to the prediction accuracy of CMEs. However, it is recommended that Ojih-Okeke model be employed extensively in further studies to confirm its high prediction accuracy.

Forecasting the Arrival Time of Coronal Mass Ejections: Analysis of the CCMC CME Scoreboard

AbstractAccurate forecasting of the properties of coronal mass ejections (CMEs) as they approach Earth is now recognized as an important strategic objective for both NOAA and NASA. The time of arrival of such events is a key parameter, one that had been anticipated to be relatively straightforward to constrain. In this study, we analyze forecasts submitted to the Community Coordinated Modeling Center at NASA's Goddard Space Flight Center over the last 6 years to answer the following questions: (1) How well do these models forecast the arrival time of CME‐driven shocks? (2) What are the uncertainties associated with these forecasts? (3) Which model(s) perform best? (4) Have the models become more accurate during the past 6 years? We analyze all forecasts made by 32 models from 2013 through mid‐2018, and additionally focus on 28 events, all of which were forecasted by six models. We find that the models are generally able to predict CME‐shock arrival times—in an average sense—to within ±10 hr, but with standard deviations often exceeding 20 hr. The best performers, on the other hand, maintained a mean error (bias) of −1 hr, a mean absolute error of 13 hr, and a precision (standard deviation) of 15 hr. Finally, there is no evidence that the forecasts have become more accurate during this interval. We discuss the intrinsic simplifications of the various models analyzed, the limitations of this investigation, and suggest possible paths to improve these forecasts in the future.

Influence of Convective Effect of Solar Winds on the CME Transit Time

Based on an empirical model for predicting the transit time of coronal mass ejections (CMEs) proposed by Gopalswamy, 52 CME events which are related to the geomagnetic storms of Dst<−50 nT, and 10 CME events which caused extremely strong geomagnetic storms (Dst<−200 nT) in 1996– 2007 are selected, and combined with the observational data of the interplanetary solar winds that collected by the ACE satellite at 1AU, to analyze the influence of convective effect of ambient solar winds on the prediction of the CME transit time when it arrives at a place of 1 AU. After taking the convective effect of ambient solar winds into account, the standard deviation of predictions is reduced from 16.5 to 11.4hours for the 52 CME events, and the prediction error is less than 15hours for 68% of these events; while the standard deviation of predictions is reduced from 10.6 to 6.5hours for the 10 CME events that caused extremely strong geomagnetic storms, and the prediction error is less than 5hours for 6 of the 10 events. These results show that taking the convective effect of ambient solar winds into account can reduce the standard deviation of the predicted CME transit time, hence the convective effect of solar winds plays an important role for predicting the transit times of CME events.

English

Predicting the arrival time of Coronal Mass Ejections (CMEs) with a lower value of average error of the difference between the predicted and the observed transit time is very crucial in space weather forecast. A modified Empirical Coronal Mass Ejection Arrival (ECA) model was proposed, namely, Ojih-Okeke modified ECA model to predict the transit time of twenty eight fast CMEs from the sun to the earth. This is the first time the Ojih-Okeke modified ECA model is being applied in prediction of transit time of CMEs from the sun to the earth. The proposed modified model was tested using data obtained from coronagraph observations of large angle spectrometric aboard, the Solar and Heliospheric Observatory (SOHO/LASCO) CME catalogue from the period of 1997 to 2015. To ascertain the accuracy of the modified model, the three ECA model of Gopalswamy (G2000, G2001, and VG2002) were applied to our data points. Linear regression analyses were carried out on the data points and scatter plots were generated using excel software package. The average error of the difference between the CMEs transit time and models predicted transit time with their fractional errors were 4.27 h and 0.10 for the Ojih-Okeke modified model; 10.36 h and 0.23 for the VG2002 model; 12.93 h and 0.29 for G2001 model; and 14.42 h and 0.32 for the G2000 model. The proposed modified model has proved very effective in prediction of arrival time of CMEs. It is our recommendation that future work on prediction of the arrival time of CMEs be carried out employing our modified ECA model. &nbsp; Key words: Coronal mass ejections, arrival time, intense geomagnetic storm, observed transit time, earth and phase.

Predicting coronal mass ejections transit times to Earth with neural network

Predicting transit times of Coronal Mass Ejections (CMEs) from their initial parameters is a very important subject, not only from the scientific perspective, but also because CMEs represent a hazard for human technology. We used a neural network to analyse transit times for 153 events with only two input parameters: initial velocity of the CME, $v$, and Central Meridian Distance, CMD, of its associated flare. We found that transit time dependence on $v$ is showing a typical drag-like pattern in the solar wind. The results show that the speed at which acceleration by drag changes to deceleration is $v\approx$500 km s$^{-1}$. Transit times are also found to be shorter for CMEs associated with flares on the western hemisphere than those originating on the eastern side of the Sun. We attribute this difference to the eastward deflection of CMEs on their path to 1 AU. The average error of the NN prediction in comparison to observations is $\approx$12 hours which is comparable to other studies on the same subject.

INFLUENCE OF A CME’S INITIAL PARAMETERS ON THE ARRIVAL OF THE ASSOCIATED INTERPLANETARY SHOCK AT EARTH AND THE SHOCK PROPAGATIONAL MODEL VERSION 3

Predicting the arrival times of coronal mass ejections (CMEs) and their related waves at Earth is an important aspect of space weather forecasting. The Shock Propagation Model (SPM) and its updated version (SPM2), which use the initial parameters of solar flare-Type II burst events as input, have been developed to predict the shock arrival time. This paper continues to investigate the influence of solar disturbances and their associated CMEs on the corresponding interplanetary (IP) shock's arrival at Earth. It has been found that IP shocks associated with wider CMEs have a greater probability of reaching the Earth, and the CME speed obtained from coronagraph observations can be supplementary to the initial shock speed computed from Type II radio bursts when predicting the shock's arrival time. Therefore, the third version of the model, i.e., SPM3, has been developed based on these findings. The new version combines the characteristics of solar flare-Type II events with the initial parameters of the accompanying CMEs to provide the prediction of the associated IP shock's arrival at Earth. The prediction test for 498 events of Solar Cycle 23 reveals that the prediction success rate of SPM3 is 70%-71%, which is apparently higher than that of the previous SPM2 model (61%-63%). The transit time prediction error of SPM3 for the Earth-encountered shocks is within 9 hr (mean-absolute). Comparisons between SPM3 and other similar models also demonstrate that SPM3 has the highest success rate and best prediction performance.

On the arrival times of halo Coronal Mass Ejections in the vicinity of the Earth

It is well known that the arrival times of Coronal Mass Ejections (CMEs) in the vicinity of the Earth play an important role for solar terrestrial environment. It is necessary to predict the CMEs arrival time at 1AU, for the better forecasting of space weather. Here, using LASCO halo CMEs data of 248 events observed during time period 1996–2007, we have tried to predict the arrival times as accurately as possible of full halo CMEs only. We have also studied arrival time of halo CMEs associated with type II radio bursts and X-class soft X-ray bursts, separately. In this paper we discussed about location and speed of Earth directed CMEs. The results obtained in the present investigation are discussed in the light recent scenario of CMEs understanding.

Testing the empirical shock arrival model using quadrature observations

The empirical shock arrival (ESA) model was developed based on quadrature data from Helios (in situ) and P‐78 (remote sensing) to predict the Sun‐Earth travel time of coronal mass ejections (CMEs). The ESA model requires earthward CME speed as input, which is not directly measurable from coronagraphs along the Sun‐Earth line. The Solar Terrestrial Relations Observatory (STEREO) and the Solar and Heliospheric Observatory (SOHO) were in quadrature during 2010–2012, so the speeds of Earth‐directed CMEs were observed with minimal projection effects. We identified a set of 20 full halo CMEs in the field of view of SOHO that were also observed in quadrature by STEREO. We used the earthward speed from STEREO measurements as input to the ESA model and compared the resulting travel times with the observed ones from L1 monitors. We find that the model predicts the CME travel time within about 7.3 h, which is similar to the predictions by the ENLIL model. We also find that CME‐CME and CME‐coronal hole interaction can lead to large deviations from model predictions.

Estimating Travel Times of Coronal Mass Ejections to 1 AU Using Multi-spacecraft Coronagraph Data

We study the relationship between the speeds of coronal mass ejections (CMEs) obtained close to the Sun and in the interplanetary medium during the low solar-activity period from 2008 to 2010. We use a multi-spacecraft forward-modeling technique to fit a flux-rope-like model to white-light coronagraph images from the STEREO and SOHO spacecraft to estimate the geometrical configuration, propagation in three-dimensions (3D), and the radial speeds of the observed CMEs. The 3D speeds obtained in this way are used in existing CME travel-time prediction models. The results are compared to the actual CME transit times from the Sun to STEREO, ACE, and Wind spacecraft as well as to the transit times calculated using projected CME speeds. CME 3D speeds give slightly better predictions than projected CME speeds, but a large scatter is observed between the predicted and observed travel times, even when 3D speeds are used. We estimate the possible sources of errors and find a weak tendency for large interplanetary CMEs (ICMEs) with high magnetic fields to arrive faster than predicted and small, low-magnetic-field ICMEs to arrive later than predicted. The observed CME transit times from the Sun to 1 AU show a particularly good correlation with the upstream solar-wind speed. Similar trends have not been observed in previous studies using data sets near solar maximum. We suggest that near solar minimum a relatively narrow range of CME initial speeds, sizes, and magnetic-field magnitudes led to a situation where aerodynamic drag between CMEs and ambient solar wind was the primary cause of variations in CME arrival times from the Sun to 1 AU.

An Analytical Model to Predict the Arrival Time of Interplanetary CMEs

Referring to the aerodynamic drag force, we present an analytical model to predict the arrival time of coronal mass ejections (CMEs). All related calculations are based on the expression for the deceleration of fast CMEs in the interplanetary medium (ICMEs), ˙ v =− 1 15 700 (v − VSW) 2 ,w hereV SW is the solar wind speed. The results can reproduce well the observations of three typical parameters: the initial speed of the CME, the speed of the ICME at 1 AU and the transit time. Our simple model reveals that the drag acceleration should be really the essential feature of the interplanetary motion of CMEs, as suggested by Vrsnak and Gopalswamy (J. Geophys. Res. 107, 1019, 2002).

Solar and interplanetary origins of the November 2004 superstorms

During the first half of November 2004, many solar flares and coronal mass ejections (CMEs) were associated with solar active region (AR) 10696. This paper attempts to identify the solar and interplanetary origins of two superstorms which occurred on 8 and 10 November with peak intensities of Dst = −373 nT and −289 nT, respectively. Southward interplanetary magnetic fields within a magnetic cloud (MC), and a sheath + MC were the causes of these two superstorms, respectively. Two different CME propagation models [Gopalswamy, N., Yashiro, S., Kaiser, M.L. et al. Predicting the 1-AU arrival times of coronal mass ejections. J. Geophys. Res. 106, 29207–29219, 2001; Gopalswamy, N.S., Lara, A., Manoharan, P.K. et al. An empirical model to predict the 1-AU arrival of interplanetary shocks. Adv. Space Res. 36, 2289–2294, 2005] were employed to attempt to identify the solar sources. It is found that the models identify several potential CMEs as possible sources for each of the superstorms. The two Gopalswamy et al. models give the possible sources for the first superstorm as CMEs on 2330 UT 4 November 2004 or on 1454 UT 5 November 2004. For the second superstorm, the possible solar source was a CME that on 0754 UT 5 November 2004 or one that occurred on 1206 UT 5 November 2004. We note that other propagation models sometimes agree and other times disagree with the above results. It is concluded that during high solar/interplanetary activity intervals such as this one, the exact solar source is difficult to identify. More refined propagation models are needed.

Coronal Holes and Solar Wind High-Speed Streams: I. Forecasting the Solar Wind Parameters

We analyze the relationship between the coronal hole (CH) area/position and physical characteristics of the associated corotating high-speed stream (HSS) in the solar wind at 1 AU. For the analysis we utilize the data in the period DOY 25 – 125 of 2005, characterized by a very low coronal mass ejection (CME) activity. Distinct correlations between the daily averaged CH parameters and the solar wind characteristics are found, which allows us to forecast the solar wind velocity v, proton temperature T, proton density n, and magnetic field strength B, several days in advance in periods of low CME activity. The forecast is based on monitoring fractional areas A, covered by CHs in the meridional slices embracing the central meridian distance ranges [−40°,−20°], [−10°,10°], and [20°,40°]. On average, the peaks in the daily values of n, B, T, and v appear delayed by 1, 2, 3, and 4 days, respectively, after the area A attains its maximum in the central-meridian slice. The peak values of the solar wind parameters are correlated to the peak values of A, which provides also forecasting of the peak values of n, B, T, and v. The most accurate prediction can be obtained for the solar wind velocity, for which the average relative difference between the calculated and the observed peak values amounts to \(\overline{\vert\delta\vert}\approx10\) %. The forecast reliability is somewhat lower in the case of T, B, and n ( \(\overline{\vert\delta\vert}\approx20\) , 30, and 40%, respectively). The space weather implications are discussed, including the perspectives for advancing the real-time calculation of the Sun – Earth transit times of coronal mass ejections and interplanetary shocks, by including more realistic real-time estimates of the solar wind characteristics.

An empirical model to predict the 1-AU arrival of interplanetary shocks

We extend the empirical coronal mass ejection (CME) arrival model of Gopalswamy et al. [Gopalswamy, N. et al. Predicting the 1-AU arrival times of coronal mass ejections, J. Geophys. Res. 106, 29207, 2001] to predict the 1-AU arrival of interplanetary (IP) shocks. A set of 29 IP shocks and the associated magnetic clouds observed by the Wind spacecraft are used for this study. The primary input to this empirical shock arrival model is the initial speed of white-light CMEs obtained using coronagraphs. We use the gas dynamic piston–shock relationship to derive the ESA model which provides a simple means of obtaining the 1-AU speed and arrival times of interplanetary shocks using CME speeds.

Predictions of the arrival time of Coronal Mass Ejections at 1AU: an analysis of the causes of errors

Abstract. Three existing models of Interplanetary Coronal Mass Ejection (ICME) transit between the Sun and the Earth are compared to coronagraph and in situ observations: all three models are found to perform with a similar level of accuracy (i.e. an average error between observed and predicted 1AU transit times of approximately 11h). To improve long-term space weather prediction, factors influencing CME transit are investigated. Both the removal of the plane of sky projection (as suffered by coronagraph derived speeds of Earth directed CMEs) and the use of observed values of solar wind speed, fail to significantly improve transit time prediction. However, a correlation is found to exist between the late/early arrival of an ICME and the width of the preceding sheath region, suggesting that the error is a geometrical effect that can only be removed by a more accurate determination of a CME trajectory and expansion. The correlation between magnetic field intensity and speed of ejecta at 1AU is also investigated. It is found to be weak in the body of the ICME, but strong in the sheath, if the upstream solar wind conditions are taken into account. Key words. Solar physics, astronomy and astrophysics (flares and mass ejections) – Interplanetary physics (interplanetary magnetic fields; sources of the solar wind)