• 천문학회보
• /
• 제37권2호
• /
• pp.123.2-123.2
• /
• 2012

We investigate the solar flare occurrence rate and daily flare probability in terms of the sunspot classification supplemented with sunspot area and its changes. For this we use the NOAA active region data and GOES solar flare data for 15 years (from January 1996 to December 2010). We consider the most flare-productive eleven sunspot classes in the McIntosh sunspot group classification. Sunspot area and its changes can be a proxy of magnetic flux and its emergence/cancellation, respectively. We classify each sunspot group into two sub-groups by its area: "Large" and "Small". In addition, for each group, we classify it into three sub-groups according to sunspot area changes: "Decrease", "Steady", and "Increase". As a result, in the case of compact groups, their flare occurrence rates and daily flare probabilities noticeably increase with sunspot group area. We also find that the flare occurrence rates and daily flare probabilities for the "Increase" sub-groups are noticeably higher than those for the other sub-groups. In case of the (M+X)-class flares in the 'Dkc' group, the flare occurrence rate of the "Increase" sub-group is three times higher than that of the "Steady" sub-group. Our results statistically demonstrate that magnetic flux and its emergence enhance the occurrence of major solar flares.

• 천문학회보
• /
• 제40권1호
• /
• pp.64.3-64.3
• /
• 2015

We have developed a set of daily solar flare peak flux forecast models for strong flares using multiple linear regression and artificial neural network methods. We consider input parameters as solar activity data from January 1996 to December 2013 such as sunspot area, X-ray flare peak flux and weighted total flux of previous day, and mean flare rates of McIntosh sunspot group (Zpc) and Mount Wilson magnetic classification. For a training data set, we use the same number of 61 events for each C-, M-, and X-class from Jan. 1996 to Dec. 2004, while other previous models use all flares. For a testing data set, we use all flares from Jan. 2005 to Nov. 2013. The best three parameters related to the observed flare peak flux are weighted total flare flux of previous day (r = 0.51), X-ray flare peak flux (r = 0.48), and Mount Wilson magnetic classification (r = 0.47). A comparison between our neural network models and the previous models based on Heidke Skill Score (HSS) shows that our model for X-class flare is much better than the models and that for M-class flares is similar to them. Since all input parameters for our models are easily available, the models can be operated steadily and automatically in near-real time for space weather service.

• 천문학회보
• /
• 제41권1호
• /
• 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.

• 천문학회보
• /
• 제46권1호
• /
• pp.42.1-42.1
• /
• 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.

• 천문학회보
• /
• 제39권2호
• /
• pp.97.1-97.1
• /
• 2014

We clarify the dynamics connecting a solar flare and a coronal mass ejection (CME) based on the results of a magnetohydrodynamic (MHD) simulation starting from a nonlinear force-free field (NLFFF) in Inoue et al. 2014. In previous studies, many authors proposed numerous candidates for triggering processes of a solar flare and the associated CME. Among them, the tether-cutting reconnection or the torus instability has been supported by recent simulations and observations. On the other hand, our MHD simulation in accordance with more realistic situations show that highly twisted field lines are first produced through a tether-cutting reconnection between the twisted field lines in the NLFFF, and then the newly formed, strongly twisted field erupts away from the solar surface because of a loss of equilibrium. This dynamics corresponds to the onset of a solar flare. Furthermore we have found that the strongly twisted erupting field reconnect with the weakly twisted ambient field during the eruption, creating a large flux tube, and then it rises over a critical height of the torus instability to trigger a CME. From these results, we conclude that the coupled process of tether-cutting reconnection and torus instability is important in the flare-CME relationship.

• 천문학회보
• /
• 제41권1호
• /
• pp.80.2-81
• /
• 2016

We investigate the dependence of solar proton events (SPEs) on solar and interplanetary type II bursts associated with solar flares and/or CME-driven shocks. For this we consider NOAA solar proton events from 1997 to 2012 and their associated flare, CME, and type II radio burst data with the following subgroups: metric, decameter-hectometric (DH), and meter-to-kilometric (m-to-km) type II bursts. The primary findings of this study are as follows. First, about half (52%) of the m-to-km type II bursts are associated with SPEs and its occurrence rate is higher than those of DH type II bursts (45%) and metric type II bursts (19%). Second, the SPE occurrence rate strongly depends on flare strength and source longitude, especially for X-class flare associated ones; it is the highest in the central region for metric (46%), DH (54%), and m-to-km (75%) subgroups. Third, the SPE occurrence rate is also dependent on CME linear speed and angular width. The highest rates are found in the m-to-km subgroup associated with CME speed 1500 kms-1: partial halo CME (67%) and halo CME (55%). Fourth, in the relationships between SPE peak fluxes and solar eruption parameters (CME linear speed, flare flux, and longitude), SPE peak flux is mostly dependent on SPE peak flux for all three type II bursts (metric, DH, m-to-km). It is noted that the dependence of SPE peak flux on flare peak flux decreases from metric to m-to-km type II burst.

• 천문학회지
• /
• 제29권spc1호
• /
• pp.321-322
• /
• 1996

Using the data on the occurrences of the Ho: and soft X-ray flares for the time interval of January 1, 1986-May :31, 1994, we have studied the middle term(30-300days) pericities of the solar flare production during the activity cycle 22. Power analysis of the time seies of daily H$\alpha$ flare index in the northern hemisphere shows prominent periodicities at 220, 120, 109, and 92 days(see Figures l(a) and l(b)), while in the southern hemisphere, those at 267, 213, 183, 167, and 107 days are apparent, though their peaks are not so distint as those in the northern hemisphere. Periodogram of daily soft X-ray flare index also reveal the periodicities at 279, 205, 164, 117, and 91 days in the northern hemisphere, and at 266, 220, 199, 162, 120, and 100 days in the southern hemisphere. Howeer, the 155-day periodicity reported for the earlier cycles, 19, 20, and 21, could not be confirmed in our analysis. to be submitted to Solar Physics; an extended abstract.

• 천문학회보
• /
• 제36권1호
• /
• pp.40.2-40.2
• /
• 2011

We investigated solar flare occurrence probability depending on sunspot group classification and its area change. For this study, we used the McIntosh sunspot group classification and then selected most flare-productive six sunspot groups : DKI, DKC, EKI, EKC, FKI and FKC. For each group, we classified it into three sub-groups according to the sunspot group area change : increase, steady and decrease. For sunspot data, we used the NOAA's active region information for 19 years (from 1992.01 to 2010.12). As a result, we found that the probabilities of the all "increase" sub-groups is noticeably higher than those of other sub-groups. In case of FKC McIntosh sunspot group, for example, the M-class flare occurrence probability of the "increase" sub-group is 65% while the "decrease" and "steady" sub-groups are 50% and 44%, respectively. In summary, when sunspot group area increases, the probability of solar flares noticeably increases. This is statistical evidence that magnetic flux emergence is an very important mechanism for triggering solar flares.

• 천문학회보
• /
• 제36권2호
• /
• pp.90.1-90.1
• /
• 2011

We are developing empirical space weather (geomagnetic storms, solar proton events, and solar flares) forecast models based on solar information. These models have been set up with the concept of probabilistic forecast using historical events. Major findings can be summarized as follows. First, we present a concept of storm probability map depending on CME parameters (speed and location). Second, we suggested a new geoeffective CME parameter, earthward direction parameter, directly observable from coronagraph observations, and demonstrated its importance in terms of the forecast of geomagnetic storms. Third, the importance of solar magnetic field orientation for storm occurrence was examined. Fourth, the relationship among coronal hole-CIR-storm relationship has been investigated, Fifth, the CIR forecast based on coronal hole information is possible but the storm forecast is challenging. Sixth, a new solar proton event (flux, strength, and rise time) forecast method depending on flare parameters (flare strength, duration, and longitude) as well as CME parameter (speed, angular width, and longitude) has been suggested. Seventh, we are examining the rates and probability of solar flares depending on sunspot McIntosh classification and its area change (as a proxy of flux change). Our results show that flux emergence greatly enhances the flare probability, about two times for flare productive sunspot regions.

• 천문학회보
• /
• 제39권1호
• /
• pp.75.2-75.2
• /
• 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.