• The Bulletin of The Korean Astronomical Society
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• v.35 no.1
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• pp.33.2-33.2
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• 2010

We have investigated a relationship among the solar proton events (SPEs), coronal mass ejections (CMEs) and solar flares during the solar cycle 23 (1997-2006). Using 63 SPE dataset, we found that SPE rise time, duration time, and decrease times depend on CME speed and SPE peak intensity depends on the CME earthward direction parameter as well as CME speed and x-ray flare intensity. While inspecting the relation between SPE peak intensity and the CME earthward direction parameter, we found that there are two groups: first group consists of large 6 SPEs (> 10,000 pfu at >10 MeV proton channel of GOES satellite) and shows a very good correlation (cc=0.65) between SPE peak intensity and CME earthward direction parameter. The second group has a relatively weak SPE peak intensity and shows poor correlation between SPE peak intensity and the CME earthward direction parameter (cc=0.01). By investigating characteristics of 6 SPEs in the first group, we found that there are special common conditions of the extremely large proton events (group 1); (1) all the SPEs are associated with very fast halo CME (>1400km/s), (2) they are almost located at disk region, (3) they also accompany large flare (>M7), (4) all they are preceded by another wide CMEs, and (5) they all show helmet streamer nearby the main CME. In this presentation, we will give details of the energy spectra of the 6 SPE events from the ERNE/HED aboard the Solar and Heliospheric Observatory (SOHO), and onset time comparison among the SPE, flare, type II burst, and CME.

• Journal of Astronomy and Space Sciences
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• v.32 no.3
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• pp.181-187
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• 2015

Storm sudden commencements (SSCs) occur due to a rapid compression of the Earth's magnetic field. This is generally believed to be caused by interplanetary (IP) shocks, but with exceptions. In this paper we explore possible causes of SSCs other than IP shocks through a statistical study of geomagnetic storms using SYM-H data provided by the World Data Center for Geomagnetism - Kyoto and by applying a superposed epoch analysis to simultaneous solar wind parameters obtained with the Advanced Composition Explorer (ACE) satellite. We select a total of 274 geomagnetic storms with minimum SYM-H of less than -30nT during 1998-2008 and regard them as SSCs if SYM-H increases by more than 10 nT over 10 minutes. Under this criterion, we found 103 geomagnetic storms with both SSC and IP shocks and 28 storms with SSC not associated with IP shocks. Storms in the former group share the property that the strength of the interplanetary magnetic field (IMF), proton density and proton velocity increase together with SYM-H, implying the action of IP shocks. During the storms in the latter group, only the proton density rises with SYM-H. We find that the density increase is associated with either high speed streams (HSSs) or interplanetary coronal mass ejections (ICMEs), and suggest that HSSs and ICMEs may be alternative contributors to SSCs.

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

• Journal of Astronomy and Space Sciences
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• v.34 no.4
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• pp.315-330
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• 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
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• v.37 no.2
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• pp.132.2-132.2
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• 2012

We have investigated the characteristics of magnetic cloud (MC) and ejecta (EJ) associated coronal mass ejections (CMEs) based on the assumption that all CMEs have a flux rope structure. For this, we used 54 CMEs and their interplanetary counter parts (interplanetary CMEs: ICMEs) that constitute the list of events used by the NASA/LWS Coordinated Data Analysis Workshop (CDAW) on CME flux ropes. We considered the location, angular width, and speed as well as the direction parameter, D. The direction parameter quantifies the degree of asymmetry of the CME shape, and shows how closely the CME propagation is directed to Earth. For the 54 CDAW events, we found several properties of the CMEs as follows: (1) the average value of D for the 23 MCs (0.62) is larger than that for the 31 EJs (0.49), which indicates that the MC-associated CMEs propagate more directly to the Earth than the EJ-associated CMEs; (2) comparison between the direction parameter and the source location shows that the majority of the MC-associated CMEs are ejected along the radial direction, while many of the EJ-associated CMEs are ejected non-radially; (3) the mean speed of MC-associated CMEs (946 km/s) is faster than that of EJ-associated CMEs (771 km/s). For seven very fast CMEs (>1500 km/s), all CMEs with large D (>0.4) are associated with MCs and the CMEs with small D are associated with EJs. From the statistical analysis of CME parameters, we found the superiority of the direction parameter. Based on these results, we suggest that the CME trajectory essentially decides the observed ICME structure.

• Journal of Astronomy and Space Sciences
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• v.33 no.4
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• pp.279-285
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• 2016

The space radiation dose over air routes including polar routes should be carefully considered, especially when space weather shows sudden disturbances such as coronal mass ejections (CMEs), flares, and accompanying solar energetic particle events. We recently established a heliocentric potential (HCP) prediction model for real-time operation of the CARI-6 and CARI-6M programs. Specifically, the HCP value is used as a critical input value in the CARI-6/6M programs, which estimate the aviation route dose based on the effective dose rate. The CARI-6/6M approach is the most widely used technique, and the programs can be obtained from the U.S. Federal Aviation Administration (FAA). However, HCP values are given at a one month delay on the FAA official webpage, which makes it difficult to obtain real-time information on the aviation route dose. In order to overcome this critical limitation regarding the time delay for space weather customers, we developed a HCP prediction model based on sunspot number variations (Hwang et al. 2015). In this paper, we focus on improvements to our HCP prediction model and update it with neutron monitoring data. We found that the most accurate method to derive the HCP value involves (1) real-time daily sunspot assessments, (2) predictions of the daily HCP by our prediction algorithm, and (3) calculations of the resultant daily effective dose rate. Additionally, we also derived the HCP prediction algorithm in this paper by using ground neutron counts. With the compensation stemming from the use of ground neutron count data, the newly developed HCP prediction model was improved.

• Journal of Astronomy and Space Sciences
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• v.41 no.1
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• pp.1-15
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• 2024

The Korean heliospheric community, led by the Korea Astronomy and Space Science Institute (KASI), is currently assessing the viability of deploying a spacecraft at the Sun-Earth Lagrange Point L4 in collaboration with National Aeronautics and Space Administration (NASA). The aim of this mission is to utilize a combination of remote sensing and in situ instruments for comprehensive observations, complementing the capabilities of the L1 and L5 observatories. The paper outlines longterm scientific objectives, underscoring the significance of multi-point in-situ observations to better understand critical heliospheric phenomena. These include coronal mass ejections, magnetic flux ropes, heliospheric current sheets, kinetic waves and instabilities, suprathermal electrons and solar energetic particle events, as well as remote detection of solar radiation phenomena. Furthermore, the mission's significance in advancing space weather prediction and space radiation exposure assessment models through the integration of L4 observations is discussed. This article is concluded with an emphasis on the potential of L4 observations to propel advancements in heliospheric science.

• Journal of Astronomy and Space Sciences
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• v.24 no.2
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• pp.125-134
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• 2007

In this paper, we analyze the orbital variation of the Korea Multi-Purpose SATellite-1(KOMPSAT-1) in a strong space environment due to satellite drag by solar and geomagnetic activities. The satellite drag usually occurs slowly, but becomes serious satellite drag when the space environment suddenly changes via strong solar activity like a big flare eruption or coronal mass ejections(CMEs). Especially, KOMPSAT-1 as a low earth orbit satellite has a distinct increase of the drag acceleration by the variations of atmospheric friction. We consider factors of solar activity to have serious effects on the satellite drag from two points of view. One is an effect of high energy radiation when the flare occurs in the Sun. This radiation heats and expands the upper atmosphere of the Earth as the number of neutral particles is suddenly increased. The other is an effect of Joule and precipitating particle heating caused by current of plasma and precipitation of particles during geomagnetic storms by CMEs. It also affects the density of neutral particles by heating the upper atmo-sphere. We investigate the satellite drag acceleration associated with the two factors for five events selected based on solar and geomagnetic data from 2001 to 2002. The major results can be summarized as follows. First, the drag acceleration started to increase with solar EUV radiation with the best cross-correlation (r = 0.92) for 1 day delayed F10.7. Second, the drag acceleration and Dst index have similar patterns when the geomagnetic storm is dominant and the drag acceleration abruptly increases during the strong geomagnetic storm. Third, the background variation of the drag accelerations is governed by the solar radiation, while their short term (less than a day) variations is governed by geomagnetic storms.

• Journal of Astronomy and Space Sciences
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• v.26 no.4
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• pp.479-486
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• 2009

We studied the solar proton events (SPEs) associated with coronal mass ejections (CMEs) during the solar cycle 23 (1997-2006). Using 63 SPE dataset, we investigated the relationship among SPE, flare, and CME, and found that (1) SPE rise time and duration time depend on CME speed and the earthward direction parameter of the CME, and (2) the SPE peak intensity depends on CME speed and X-ray Flare intensity. While inspecting the relation between SPE peak intensity and the direction parameter, we found there are two groups: first group consists of large six SPEs (> 10,000 pfu at > 10 MeV proton channel of GOES satellite) and shows strong correlation (cc = 0.65) between SPE peak intensity and CME direction parameter. The second group has a weak intensity and shows poor correlation between SPE peak intensity and the direction parameter (cc = 0.01). By investigating characteristics of the first group, we found that all the SPEs are associated with very fast halo CME (> 1400km/s) and also they are mostly located at central region and within ${\pm}20^{\circ}$ latitude and ${\pm}30^{\circ}$ longitude strip.

• Journal of The Korean Astronomical Society
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• v.56 no.2
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• pp.213-224
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• 2023

Type II solar radio bursts show frequency drifts from high to low over time. They have been known as a signature of coronal shock associated with Coronal Mass Ejections (CMEs) and/or flares, which cause an abrupt change in the space environment near the Earth (space weather). Therefore, early detection of type II bursts is important for forecasting of space weather. In this study, we develop a deep-learning (DL) model for the automatic detection of type II bursts. For this purpose, we adopted a 1-D Convolution Neutral Network (CNN) as it is well-suited for processing spatiotemporal information within the applied data set. We utilized a total of 286 radio burst spectrum images obtained by Hiraiso Radio Spectrograph (HiRAS) from 1991 and 2012, along with 231 spectrum images without the bursts from 2009 to 2015, to recognizes type II bursts. The burst types were labeled manually according to their spectra features in an answer table. Subsequently, we applied the 1-D CNN technique to the spectrum images using two filter windows with different size along time axis. To develop the DL model, we randomly selected 412 spectrum images (80%) for training and validation. The train history shows that both train and validation losses drop rapidly, while train and validation accuracies increased within approximately 100 epoches. For evaluation of the model's performance, we used 105 test images (20%) and employed a contingence table. It is found that false alarm ratio (FAR) and critical success index (CSI) were 0.14 and 0.83, respectively. Furthermore, we confirmed above result by adopting five-fold cross-validation method, in which we re-sampled five groups randomly. The estimated mean FAR and CSI of the five groups were 0.05 and 0.87, respectively. For experimental purposes, we applied our proposed model to 85 HiRAS type II radio bursts listed in the NGDC catalogue from 2009 to 2016 and 184 quiet (no bursts) spectrum images before and after the type II bursts. As a result, our model successfully detected 79 events (93%) of type II events. This results demonstrates, for the first time, that the 1-D CNN algorithm is useful for detecting type II bursts.