• The Bulletin of The Korean Astronomical Society
• /
• v.46 no.1
• /
• pp.43.1-43.1
• /
• 2021

In this study, we suggest a new method to estimate the mass of a halo coronal mass ejection (CME) using synthetic CMEs. For this, we generate synthetic CMEs based on two assumptions: (1) the CME structure is a full ice-cream cone, (2) the CME electron density follows a power-law distribution (ρ_cme=ρ₀r^-n). The power-law exponent n is obtained by minimizing the root mean square error between the electron number density distributions of an observed CME and the corresponding synthetic CME at a position angle of the CME leading edge. By applying this methodology to 57 halo CMEs, we estimate two kinds of synthetic CME mass. One is a synthetic CME mass which considers only the observed CME region (M_cme1), the other is a synthetic CME mass which includes both the observed CME region and the occulted area larger than 4 solar radii (M_cme2). From these two cases, we derive conversion factors which are the ratio of a synthetic CME mass to an observed CME mass. The conversion factor for M_cme1 ranges from 1.4 to 3.0 and its average is 2.0. For M_cme2, the factor ranges from 1.8 to 5.0 with the average of 3.0. These results imply that the observed halo CME mass can be underestimated by about 2 times when we consider the observed CME region, and about 3 times when we consider the region including the occulted area. Interestingly these conversion factors have a very strong negative correlation with angular widths of halo CMEs.We also compare the results with the CME mass estimated from STEREO observations.

• The Bulletin of The Korean Astronomical Society
• /
• v.44 no.2
• /
• pp.43.3-43.3
• /
• 2019

A coronal mass ejection (CME) mass is generally estimated by the total brightness measured from white-light coronagraph observations. The total brightness are determined from the integration of the Thomson scattering by free electrons of solar corona along the line of sight. It is difficult to estimate the masses of halo CMEs due to the projection effect. To solve this issue, we construct a synthetic halo CME with a power-law density distribution (ρ = ρ₀r^-3) based on a full ice-cream cone model using SOHO/LASCO C3 observations. Then we compute a conversion factor from observed CME mass to CME mass for each CME. The final CME mass is determined as their average value of several CME masses above 10 solar radii. Our preliminary analysis for six CMEs show that their CME mass are well determined within the mean absolute relative error in the range of 4 to 15 %.

• The Bulletin of The Korean Astronomical Society
• /
• v.36 no.1
• /
• pp.39.2-39.2
• /
• 2011

In this study we have examined the occurrence probability of solar proton events (SEPs) and their peak fluxes depending two CME parameters, linear speed and angular width. For this we used the NOAA SPE events and their associated CME data from 1997 to 2006. As a result, the probability strongly depends on two parameters as follows. In the case of halo CME whose speed is equal to and faster than 1500km/s, 36.1% are associated with SPEs but in the case of partial halo CME ($120^{\circ}{\leq}AW$ < $359^{\circ}$) whose speed is $400{\leq}V$ < $1000km/s$, only 0.9% are associated with SPEs. When we consider only front-side CMEs, 45.3% are associated with SPEs in the first case and 1.8% are associated with them in the second case. Both of whole CME data group and front-side CME data group have similar tendencies. The probabilities are different as much as 4.9 to 23 times according to the CME speed and 1.6 to 6.5 times to the angular width. We have also examined the relationship between CME speed and proton peak flux as well as its dependence on angular width (partial halo CME and halo CME), longitude (east, center, and west) and direction parameter (< 0.4 and {\geq} 0.4). Our results show that the relationships strongly depend on longitude as well as direction parameter. In addition, the relationship using the radial CME speed based on a cone model has a higher correlation coefficient than that using the projected CME speed.

• The Bulletin of The Korean Astronomical Society
• /
• v.41 no.1
• /
• pp.82-82
• /
• 2016

To find the relationship between solar flares and halo CMEs using stereoscopic observations, we investigate 182 flare-associated halo CMEs among 306 front-side halo CMEs from 2009 to 2013. We have determined the 3D parameters (radial speed and angular width) of these CMEs by applying StereoCAT to multi-spacecraft data (SOHO and STEREO). For this work, we use flare parameters (peak flux and fluence) taken from GOES X-ray flare list and 2D CME parameters (projected speed, apparent angular width, and kinetic energy) taken from CDAW SOHO LASCO CME catalog. Major results from this study are as follows. First, the relationship between flare peak flux (or fluence) and CME speed is almost same for both 2D and 3D cases. Second, there is a possible correlation between flare fluence and CME width, which is more evident in 3D case than 2D one. Third, the flare fluence is well correlated with CME kinetic energy (CC=0.63). Fourth, there is an upper limit of CME kinetic energy for a given flare fluence (or peak flux). For example, a possible CME kinetic energy ranges from 1030.6 to 1033 erg for a given X1.0 class flare. Our results will be discussed in view of the physical mechanism of solar eruptions.

• Journal of Astronomy and Space Sciences
• /
• v.34 no.4
• /
• pp.315-330
• /
• 2017

Halo coronal mass ejections (CMEs) originating from solar activities give rise to geomagnetic storms when they reach the Earth. Variations in the geomagnetic field during a geomagnetic storm can damage satellites, communication systems, electrical power grids, and power systems, and induce currents. Therefore, automated techniques for detecting and analyzing halo CMEs have been eliciting increasing attention for the monitoring and prediction of the space weather environment. In this study, we developed an algorithm to sense and detect halo CMEs using large angle and spectrometric coronagraph (LASCO) C3 coronagraph images from the solar and heliospheric observatory (SOHO) satellite. In addition, we developed an image processing technique to derive the morphological and dynamical characteristics of halo CMEs, namely, the source location, width, actual CME speed, and arrival time at a 21.5 solar radius. The proposed halo CME automatic analysis model was validated using a model of the past three halo CME events. As a result, a solar event that occurred at 03:38 UT on Mar. 23, 2014 was predicted to arrive at Earth at 23:00 UT on Mar. 25, whereas the actual arrival time was at 04:30 UT on Mar. 26, which is a difference of 5 hr and 30 min. In addition, a solar event that occurred at 12:55 UT on Apr. 18, 2014 was estimated to arrive at Earth at 16:00 UT on Apr. 20, which is 4 hr ahead of the actual arrival time of 20:00 UT on the same day. However, the estimation error was reduced significantly compared to the ENLIL model. As a further study, the model will be applied to many more events for validation and testing, and after such tests are completed, on-line service will be provided at the Korean Space Weather Center to detect halo CMEs and derive the model parameters.

• The Bulletin of The Korean Astronomical Society
• /
• v.38 no.1
• /
• pp.61.1-61.1
• /
• 2013

In this study we apply Support Vector Machine (SVM) to the prediction of geo-effective halo coronal mass ejections (CMEs). The SVM, which is one of machine learning algorithms, is used for the purpose of classification and regression analysis. We use halo and partial halo CMEs from January 1996 to April 2010 in the SOHO/LASCO CME Catalog for training and prediction. And we also use their associated X-ray flare classes to identify front-side halo CMEs (stronger than B1 class), and the Dst index to determine geo-effective halo CMEs (stronger than -50 nT). The combinations of the speed and the angular width of CMEs, and their associated X-ray classes are used for input features of the SVM. We make an attempt to find the best model by using cross-validation which is processed by changing kernel functions of the SVM and their parameters. As a result we obtain statistical parameters for the best model by using the speed of CME and its associated X-ray flare class as input features of the SVM: Accuracy=0.66, PODy=0.76, PODn=0.49, FAR=0.72, Bias=1.06, CSI=0.59, TSS=0.25. The performance of the statistical parameters by applying the SVM is much better than those from the simple classifications based on constant classifiers.

• The Bulletin of The Korean Astronomical Society
• /
• v.37 no.2
• /
• pp.124.1-124.1
• /
• 2012

We have made a comparison of the WSA-ENLIL CME propagation model with three cone types and an empirical model using 29 halo CMEs from 2001 to 2002. These halo CMEs have cone model parameters from Michalek et al. (2007) as well as their associated interplanetary (IP) shocks. For this study we consider three different cone models (an asymmetric cone model, an ice-cream cone model and an elliptical cone model) to determine CME cone parameters (radial velocity, angular width and source location), which are used for input parameters of the WSA-ENLIL CME propagation model. The mean absolute error (MAE) of the arrival times at the Earth for the elliptical cone model is 10 hours, which is about 2 hours smaller than those of the other models. However, this value is still larger than that (8.7 hours) of an empirical model by Kim et al. (2007). We are investigating several possibilities on relatively large errors of the WSA-ENLIL cone model, which may be caused by CME-CME interaction, background solar wind speed, and/or CME density enhancement.

• Journal of Astronomy and Space Sciences
• /
• v.25 no.4
• /
• pp.383-394
• /
• 2008

We examine geoeffective directional parameters of coronal mass ejections (CMEs). We select 30 front-side halo CMEs from SOHO LASCO CMEs whose speed is larger than 1000km/s and longitude is less than ${\pm}30^{\circ}$. These are thought to be the most plausible candidate of geoeffective CMEs. We examine the relation between CMEs directional parameters (Earthward direction, eccentricity, ${\Delta}$ distance and central angle parameter) and the minimum value of the Dst index. We have found that the Earthward direction parameter has a good correlation with the Dst index, the eccentricity parameter has a much better correlation with the Dst index. The bo distance and central angle parameter has a poor correlation with the Dst index. It's, however, well correlated with the Dst index in very strong geomagnetic storms. Most of CMEs causing very strong storms (Dst ${\leq}$-200nT) are found to have large Earthward direction parameter $({\geq}0.6)$, small eccentricity, bo distance and central angle parameters $(E{\leq}0.4,\;{\Delta}X\;and\;sin\;{\theta}{\leq}0.2)$. These directional parameters are very important parameters that control the geoeffectiveness of very fast front-side halo CMEs.

• Bulletin of the Korean Space Science Society
• /
• 2003.05b
• /
• pp.41-41
• /
• 2003

• Journal of The Korean Astronomical Society
• /
• v.45 no.2
• /
• pp.31-38
• /
• 2012

In this study we apply Support Vector Machine (SVM) to the prediction of geo-effective halo coronal mass ejections (CMEs). The SVM, which is one of machine learning algorithms, is used for the purpose of classification and regression analysis. We use halo and partial halo CMEs from January 1996 to April 2010 in the SOHO/LASCO CME Catalog for training and prediction. And we also use their associated X-ray flare classes to identify front-side halo CMEs (stronger than B1 class), and the Dst index to determine geo-effective halo CMEs (stronger than -50 nT). The combinations of the speed and the angular width of CMEs, and their associated X-ray classes are used for input features of the SVM. We make an attempt to find the best model by using cross-validation which is processed by changing kernel functions of the SVM and their parameters. As a result we obtain statistical parameters for the best model by using the speed of CME and its associated X-ray flare class as input features of the SVM: Accuracy=0.66, PODy=0.76, PODn=0.49, FAR=0.72, Bias=1.06, CSI=0.59, TSS=0.25. The performance of the statistical parameters by applying the SVM is much better than those from the simple classifications based on constant classifiers.