• Journal of Astronomy and Space Sciences
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• v.35 no.2
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• pp.75-81
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• 2018

Solar activity has an important impact not only on the intensity of cosmic rays but also on the environment of Earth. In the present paper, a coupled oscillator model is proposed to explain solar activity. This model can be used to naturally reduce the 89-year Gleissberg cycle. Furthermore, as an application of the coupled oscillator model, we herein attempt to apply the proposed model to El $Ni{\tilde{n}}o$-southern oscillation (ENSO). As a result, the 22-year oscillation of the Pacific Ocean is naturally explained. Finally, we search for a possible explanation for coupled oscillators in actual solar activity.

• Journal of Astronomy and Space Sciences
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• v.35 no.4
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• pp.219-225
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• 2018

Cosmic rays are ions that move at relativistic speeds. They generate secondary cosmic rays by successive collisions with atmospheric particles, and then, the secondary particles reach the ground. The secondary particles are mainly neutrons and muons, and the neutrons are observed by the ground neutron monitor. This study compared the diurnal variation in cosmic ray intensity obtained via harmonic analysis and that obtained through the pile-up method, which was examined in a previous study. In addition, we analyzed the maximum phase of the diurnal variation using four neutron monitors with a cutoff rigidity below approximately 6 GV, located at similar longitudes to the Oulu and Rome neutron monitors. Expanding the data of solar cycles 20-24, we examined the time of the maximum cosmic ray intensity, that is, the maximum phase regarding the solar cyclic modulation. During solar cycles 20-24, the maximum phase derived by harmonic analysis showed no significant difference with that derived by the pile-up method. Thus, the pile-up method, a relatively straightforward process to analyze diurnal variation, could replace the complex harmonic analysis. In addition, the maximum phase at six neutron monitors shows the 22-year cyclic variation very clearly. The maximum phase tends to appear earlier and increase the width of the variation in solar cycles as the cutoff rigidity increases.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.3
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• pp.65-81
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• 2019

The twenty-four Solar Terms are Chinese traditional astronomical divisions that describe seasonal cycles of the year. Based on the analyses of meteorological data during 1979~2018, study results showed that the temperatures of the Solar Terms had increased in general in the Korean Peninsula. In North Korea, temperature increases were observed on 21 Solar Terms, and their seasonal mean temperatures were increased by $0.87^{\circ}C$, $1.19^{\circ}C$, $1.45^{\circ}C$, and $0.64^{\circ}C$ on average in spring, summer, fall, and winter, respectively. The duration of summer has lengthened due to the temperature rise in fall, and the magnitude of temperature change was greater in summer compared to winter. As for South Korea, increases in temperature were observed on 18 Solar Terms, and the temperature changes were more pronounced in fall and winter than spring and summer. The Great Snow temperature decreased more than any other Solar Terms during the study period, and this temperature change was observed both in North and South Koreas. The Great Cold, which represents the coldest day of the year, showed a significant temperature increase of $3.08^{\circ}C$, while the Slight Heat had a marginal temperature increase of $0.29^{\circ}C$. The hottest day and the first day of frost tended to come later than the Great Heat and the Frost's Decent. By contrast, the coldest day tended to occur later than the Great Cold in the study area. On average over the entire study period, the climatic fitness of the Great Heat and the Frost's Decent was higher in North Korea, and that of the Great Cold was higher in South Korea, respectively.

• Journal of The Korean Astronomical Society
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• v.40 no.4
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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.