• Journal of Astronomy and Space Sciences
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• 제35권2호
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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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• 제35권4호
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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.

• 한국지리정보학회지
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• 제22권3호
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• pp.65-81
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• 2019

24절기는 연중 계절 변화를 기술하기 위해 만들어진 중국의 전통적인 천문학적 구분 방식이다. 본 연구는 지난 40년간의 기상 자료 분석을 통해 한반도 절기 기온이 전반적으로 상승했음을 보여주었다. 북한의 경우 청명, 소설, 대설을 제외한 21개 절기 기온이 모두 상승했으며, 계절별로 봄, 여름, 가을, 겨울에 해당하는 절기의 평균기온은 각각 $0.87^{\circ}C$, $1.19^{\circ}C$, $1.45^{\circ}C$, $0.64^{\circ}C$ 상승했다. 가을철 기온 상승의 폭이 커진 결과 여름의 지속 기간이 길어졌고, 겨울에 비해 상대적으로 여름의 기온이 더 큰 폭으로 상승하였다. 남한의 경우, 18개 절기의 기온이 상승했는데, 계절적으로는 가을과 겨울철을 중심으로 상승하였다. 다른 절기에 비해 대설은 기온 하강 폭이 현저히 크게 나타났는데, 이러한 경향은 남북한 차이 없이 공통적인 특징으로 조사되었다. 연중 가장 추운 절기를 나타내는 대한의 기온은 연구 기간 동안 현저한 증가 추세($3.08^{\circ}C$)를 보인 반면, 여름 절기인 소서의 기온 증가 폭은 $0.29^{\circ}C$에 그쳐 상대적으로 미미하였다. 최근 연중 극서일과 첫서리일 발생은 남북한 지역 전체적으로 과거에 비해 대서와 상강 절기보다 각각 늦어지는 경향을 보인 반면, 극한일의 발생은 대한 절기보다 일찍 나타나는 추세를 보였다. 연구 기간 동안의 절기별 기온 변화를 분석한 결과, 대서와 상강의 절기 부합도는 북한 지역에서, 대한의 절기 부합도는 남한 지역에서 각각 더 높게 조사되었다.

• 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.