• 천문학논총
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• 제11권1호
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• pp.263-277
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• 1996

We have intensively carried out numerical calculations on flare predictions from the solar activity data for photospheric sunspots, chromospheric flare and plages, coronal X-ray intensities and 2800MHz radio fluxes, by using multilinear regression method. Intensities of solar flares for the next day have been predicted from the solar data between 1977-1982 and 1993-1996. Firstly, we have calculated flare predictions with the multilinear regression method, by using separate solar data in growth and decay phase of sunspot area and magnetic field strength from the whole data on solar activities. Secondly, the same operations as above have been made for the remaining data after removal of the data with large deviation from the mean calculated by the above prediction method. we have reached a conclusion that average hit ratio of correct predictions to total predictions of flares with class of M5 over has been as high as 70% for the first case and that of correct prediction number to total observation number has been shown as 61%.

• 천문학회지
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• 제36권spc1호
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• pp.37-44
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• 2003

In this paper, we review recent studies on the magnetic helicity changes of solar active regions by photospheric horizontal motions. Recently, Chae(200l) developed a methodology to determine the magnetic helicity change rate via photospheric horizontal motions. We have applied this methodology to four cases: (1) NOAA AR 8100 which has a series of homologous X-ray flares, (2) three active regions which have four eruptive major X-ray flares, (3) NOAA AR 9236 which has three eruptive X-class flares, and (4) NOAA AR 8668 in which a large filament was under formation. As a result, we have found several interesting results. First, the rate of magnetic helicity injection strongly depends on an active region and its evolution. Its mean rate ranges from 4 to $17 {\times} 10^{40}\;Mx^2\;h^{-1}$. Especially when the homologous flares occurred and when the filament was formed, significant rates of magnetic helicity were continuously deposited in the corona via photospheric shear flows. Second, there is a strong positive correlation between the magnetic helicity accumulated during the flaring time interval of the homologous flares in AR 8100 and the GOES X-ray flux integrated over the flaring time. This indicates that the occurrence of a series of homologous flares is physically related to the accumulation of magnetic helicity in the corona by photospheric shearing motions. Third, impulsive helicity variations took place near the flaring times of some strong flares. These impulsive variations whose time scales are less than one hour are attributed to localized velocity kernels around the polarity inversion line. Fourth, considering the filament eruption associated with an X1.8 flare started about 10 minutes before the impulsive variation of the helicity change rate, we suggest that the impulsive helicity variation is not a cause of the eruptive solar flare but its result. Finally, we discuss the physical implications on these results and our future plans.

• 천문학회지
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• 제36권2호
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• pp.61-66
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• 2003

We have examined the relationship between the speeds of coronal mass ejections (CMEs) and the GOES X-ray peak fluxes of associated flares. Noting that previous studies were possibly affected by projection effects and random association effects, we have considered two sets of carefully selected CME-flare events: four homologous events and four well-observed limb events. In the respective samples, good correlations are found between the CME speeds and the GOES X-ray peak fluxes of the associated flares. A similarly good correlation is found for all eight events of both samples when the CME speeds of the homologous events are corrected for projection effect. Our results suggest that a close relationship possibly exists between CME kinematics and flaring processes.

• 천문학회보
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• 제39권1호
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• pp.75.2-75.2
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• 2014

We have developed a set of daily solar flare peak flux forecast models using the multiple linear regression (MLR), the auto regression (AR), and artificial neural network (ANN) methods. We consider input parameters as solar activity data from January 1996 to December 2013 such as sunspot area, X-ray flare peak flux, weighted total flux $T_F=1{\times}F_C+10{\times}F_M+100{\times}F_X$ of previous day, mean flare rates of a given McIntosh sunspot group (Zpc), and a Mount Wilson magnetic classification. We compute the hitting rate that is defined as the fraction of the events whose absolute differences between the observed and predicted flare fluxes in a logarithm scale are ${\leq}$ 0.5. The best three parameters related to the observed flare peak flux are as follows: weighted total flare flux of previous day (r=0.5), Mount Wilson magnetic classification (r=0.33), and McIntosh sunspot group (r=0.3). The hitting rates of flares stronger than the M5 class, which is regarded to be significant for space weather forecast, are as follows: 30% for the auto regression method and 69% for the neural network method.

• 천문학회보
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• 제36권2호
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• pp.83.1-83.1
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• 2011

Solar flares are very spectacular, and are associated with various phenomena. Coronal shocks or disturbances are one of such flare-related phenomena. Although Moreton waves and X-ray waves are well explained with MHD first mode shocks propagating in the corona, there still remains a big problem on the nature of the waves, since they are very rare phenomena. On the other hand, EIT waves (or EUV waves) have been paid attention to as another phenomenon of coronal disturbances. However, the physical features (velocity, opening angle, and so on) are much different from those for Moreton waves and X-ray waves. We report detailed features of the coronal disturbances associated with the 2010 February 7 and the 2010 August 18 flares. For the former flare we analyzed the H-alpha images obtained by SMART at Hida Observatory, Kyoto University, Japan and by a flare telescope at National Astronomical Observatory of Japan, the X-rays images taken by Hinode/XRT, and the EUV images obtained by the both satellites of STEREO, and found the Moreton wave, X-ray wave, and EIT wave, simultaneously. In the latter flare, on the other hand, we observed a very fast EUV wave in EUV images taken by SDO/AIA. The propagating speed is comparable to the MHD first mode wave, while there is no obvious evidence of shocks for this flare. From these results, we discuss the nature of coronal disturbances.

• 천문학회지
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• 제29권spc1호
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• pp.291-294
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• 1996

The .Japanese sun observing satellite, Yohkoh, has been operational for five years and her scientific instruments are still in good condition. They have revealed ample of evidences that solar flares were triggered by magnetic reconnection, which was, for the first time, clearly indicated to take place in the solar corona. Cusp structures in soft X-rays and a new type of hard X-ray sources at the top of flaring loops have strongly supported the scenario originally proposed by C-S-H-KP. Nonthermal energy input in hard X-rays and thermal energy estimated from soft X-rays are fundamentally consistent with the interpretation of thick-target and chromospheric-evaporation models (Neupert effect). X-ray jets, another discovery of Yohkoh, were also associated with magnetic reconnection, as a result of the interaction of emerging fluxes with pre-existing coronal loops. Temperature structures of active regions, quiet sun, and coronal holes had very dynamic differential-emission-measure (DEM) distributions and high-temperature tails of DEM were considered to come from the contribution of flare-like activity.

• 한국우주과학회:학술대회논문집(한국우주과학회보)
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• 한국우주과학회 2010년도 한국우주과학회보 제19권1호
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• pp.38.2-38.2
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• 2010

We have examined the occurrence probability of solar proton events (SPEs) and their peak fluxes depending three flare parameters (X-ray peak flux, longitude, and impulsive time). For this we used NOAA SPEs from 1976 to 2006, and their associated X-ray flare data. As a result, we selected 166 proton events that were associated with major flares; 85 events associated with X-class flares and 81 events associated with M-class flares. Especially the occurrence probability strongly depends on these three parameters. In addition, the relationship between X-ray flare peak flux and proton peak flux as well as its correlation coefficient are strongly dependent on longitude and impulsive time. Among NOAA SPEs from 1997 to 2006, most of the events are related to both flares and CMEs but a few fraction of events (5/93) are only related with CMEs. We carefully identified the sources of these events using LASCO CME catalog and SOHO MDI data. Specifically, we examined the directions of CMEs related with the events and the history of active regions. As a result, we were able to determine active regions which are likely to produce SPEs without ambiguity as well as their longitudes at the time of SPEs by considering solar rotation rate. From this study, we found that the longitudes of five active regions are all between $90^{\circ}W$ and $120^{\circ}W$. When the flare peak time is assume to be the CME event time, we confirmed that the dependence of their rise times (proton peak time - flare peak time) on longitude are consistent with the previous empirical formula. These results imply that five events should be also associated with flares which were not observed because they occurred from back-side. Now we are examining the occurrence probability of SPEs depending on CME parameters. Finally, we will discuss the future prospects on the development of an empirical SPE forecast model based on the information of flares and CMEs.

• 천문학회보
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• 제46권1호
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• pp.42.1-42.1
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• 2021

In this study, we present a visual explanation of a deep learning solar flare forecast model and its relationship to physical parameters of solar active regions (ARs). For this, we use full-disk magnetograms at 00:00 UT from the Solar and Heliospheric Observatory/Michelson Doppler Imager and the Solar Dynamics Observatory/Helioseismic and Magnetic Imager, physical parameters from the Space-weather HMI Active Region Patch (SHARP), and Geostationary Operational Environmental Satellite X-ray flare data. Our deep learning flare forecast model based on the Convolutional Neural Network (CNN) predicts "Yes" or "No" for the daily occurrence of C-, M-, and X-class flares. We interpret the model using two CNN attribution methods (guided backpropagation and Gradient-weighted Class Activation Mapping [Grad-CAM]) that provide quantitative information on explaining the model. We find that our deep learning flare forecasting model is intimately related to AR physical properties that have also been distinguished in previous studies as holding significant predictive ability. Major results of this study are as follows. First, we successfully apply our deep learning models to the forecast of daily solar flare occurrence with TSS = 0.65, without any preprocessing to extract features from data. Second, using the attribution methods, we find that the polarity inversion line is an important feature for the deep learning flare forecasting model. Third, the ARs with high Grad-CAM values produce more flares than those with low Grad-CAM values. Fourth, nine SHARP parameters such as total unsigned vertical current, total unsigned current helicity, total unsigned flux, and total photospheric magnetic free energy density are well correlated with Grad-CAM values.

• 천문학회지
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• 제40권4호
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• pp.99-106
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• 2007

Statistical analyses were performed to investigate the relative success and accuracy of daily maximum X-ray flux (MXF) predictions, using both multilinear regression and autoregressive time-series prediction methods. As input data for this work, we used 14 solar activity parameters recorded over the prior 2 year period (1989-1990) during the solar maximum of cycle 22. We applied the multilinear regression method to the following three groups: all 14 variables (G1), the 2 so-called 'cause' variables (sunspot complexity and sunspot group area) showing the highest correlations with MXF (G2), and the 2 'effect' variables (previous day MXF and the number of flares stronger than C4 class) showing the highest correlations with MXF (G3). For the advanced three days forecast, we applied the autoregressive timeseries method to the MXF data (GT). We compared the statistical results of these groups for 1991 data, using several statistical measures obtained from a $2{\times}2$ contingency table for forecasted versus observed events. As a result, we found that the statistical results of G1 and G3 are nearly the same each other and the 'effect' variables (G3) are more reliable predictors than the 'cause' variables. It is also found that while the statistical results of GT are a little worse than those of G1 for relatively weak flares, they are comparable to each other for strong flares. In general, all statistical measures show good predictions from all groups, provided that the flares are weaker than about M5 class; stronger flares rapidly become difficult to predict well, which is probably due to statistical inaccuracies arising from their rarity. Our statistical results of all flares except for the X-class flares were confirmed by Yates' $X^2$ statistical significance tests, at the 99% confidence level. Based on our model testing, we recommend a practical strategy for solar X-ray flare predictions.

• 천문학회지
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• 제37권4호
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• pp.171-177
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• 2004

X-ray plasma ejections often occurred around the impulsive phases of solar flares and have been well observed by the SXT aboard Yohkoh. Though the X-ray plasma ejections show various morphological shapes, there has been no attempt at classifying the morphological groups for a large sample of the X-ray plasma ejections. In this study, we have classified 137 X-ray plasma ejections according to their shape for the first time. Our classification criteria are as follows: (1) a loop type shows ejecting plasma with the shape of loops, (2) a spray type has a continuous stream of plasma without showing any typical shape, (3) a jet type shows collimated motions of plasma, (4) a confined ejection shows limited motions of plasma near a flaring site. As a result, we classified the flare-associated X-ray plasma ejections into five groups as follows: loop-type (60 events), spray-type (40 events), jet-type (11 events), confined ejection (18 events), and others (8 events). As an illustration, we presented time sequence images of several typical events to discuss their morphological characteristics, speed, CME association, and magnetic field configuration. We found that the jet-type events tend to have higher speeds and better association with CMEs than those of the loop-type events. It is also found that the CME association (11/11) of the jet-type events is much higher than that (5/18) of the confined ejections. These facts imply that the physical characteristics of the X-ray plasma ejections are closely associated with magnetic field configurations near the reconnection regions.