• 천문학회지
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• 제47권6호
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• pp.255-258
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• 2014

Using sunspot number data from 270 historical stations for the period 1981-2013, we investigate their personal reduction coefficients (k) statistically. Chang & Oh (2012) perform a simulation showing that the k varies with the solar cycle. We try to verify their results using observational data. For this, a weighted mean and weighted standard deviation of monthly sunspot number are used to estimate the error from observed data. We find that the observed error (noise) is much smaller than that used in the simulation. Thus no distinct k-variation with the solar cycle is observed contrary to the simulation. In addition, the probability distribution of k is determined to be non-Gaussian with a fat-tail on the right side. This result implies that the relative sunspot number after 1981 might be overestimated since the mean value of k is less than that of the Gaussian distribution.

• 천문학회보
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• 제36권2호
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• pp.100.1-100.1
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• 2011

Everybody wants to see the future, but nobody does for sure. Reliably forecasting the solar activity in the near future looks like an easy task, but in fact still remains one of difficult problems in the solar-terrestrial research. We have sought for good univariate methods that can predict future smoothed sunspot numbers reasonably well based on past smoothed sunspot number data only. Here we consider a specific method we call principal component-and-autoregression (PCAR) method. The variation of sunspot number during a period of finite duration (past) before an epoch (present) is modeled by a linear combination of a small number of dominant principal components, and this model is extended to the period (future) beyond the epoch using the autoregressive model of finite order. From the application of this method, we find that the $24^{th}$ solar maximum is likely to occur near the end of the year 2013 (and there is a possibility that it occurs earlier near the start of 2013), and to have a peak sunspot number of about 86, indicating that the activity of the $24^{th}$ cycle will be weaker than the average. We will discuss how much this estimate is reliable.

• 천문학논총
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• 제14권2호
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• pp.91-102
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• 1999

We have made intensive calculations on the maximum relative sunspot number and the date of solar maximum of 23rd solar cycle, by using the statistical and precursor methods to predict solar activity cycle. According to our results of solar data processing by statistical method, solar maximum comes at between February and July of 2000 year and at that time, the smoothed sunspot number will reach to $114.3\~122.8$. while precursor method gives rather dispersed value of $118\~17$ maximum sunspot number. It is found that prediction by statistical method using smoothed relative sunspot number is more accurate than by any method to use any data of 10.7cm radio fluxes and geomagnetic aa, Ap indexes, from the full analysis of solar cycle pattern of these data. In fact, current ascending pattern of 23rd solar cycle supports positively our predicted values. Predicted results by precursor method for $Ap_{avg},\;aa_{31-36}$ indexes show similar values to those by statistical method. Therefore, these indexes can be used as new precursors for the prediction of 23rd or next solar cycle.

• Journal of Astronomy and Space Sciences
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• 제29권2호
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• pp.97-101
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• 2012

Monitoring sunspots consistently is the most basic step required to study various aspects of solar activity. To achieve this goal, the observers must regularly calculate their own correction factor $k$ and keep it stable. Relatively recently, two observing teams in South Korea have presented interesting papers which claim that revisions that take the yearly-basis $k$ into account lead to a better agreement with the international relative sunspot number $R_i$, and that yearly $k$ apparently varies with the solar cycle. In this paper, using artificial data sets we have modeled the sunspot numbers as a superposition of random noise and a slowly varying background function, and attempted to investigate whether the variation in the correction factor is coupled with the solar cycle. Regardless of the statistical distributions of the random noise, we have found the correction factor increases as sunspot numbers increase, as claimed in the reports mentioned above. The degree of dependence of correction factor $k$ on the sunspot number is subject to the signal-to-noise ratio. Therefore, we conclude that apparent dependence of the value of the correction factor $k$ on the phase of the solar cycle is not due to a physical property, but a statistical property of the data.

• Journal of Astronomy and Space Sciences
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• 제31권2호
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• pp.145-148
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• 2014

There was a research on the prolongation of solar cycle 23 by the solar cyclic variation of solar, interplanetary geomagnetic parameters by Oh & Kim (2013). They also suggested that the sunspot number cannot typically explain the variation of total solar irradiance any more. Instead of the sunspot number, a new index is introduced to explain the degree of solar activity. We have analyzed the frequency of sunspot appearance, the length of solar cycle, and the rise time to a solar maximum as the characteristics of solar cycle. Then, we have examined the predictability of solar activity by the characteristics of preceding solar cycle. We have also investigated the hemispheric variation of flare index for the periods that the leading sunspot has the same magnetic polarity. As a result, it was found that there was a good correlation between the length of preceding solar cycle and spotless days. When the length of preceding solar cycle gets longer, the spotless days increase. It is also shown that the shorter rise time to a solar maximum is highly correlated with the increase of sunspots at a solar maximum. Therefore, the appearance frequency of spotless days and the length of solar cycle are more significant than the general sunspot number as an index of declining solar activity. Additionally, the activity of flares leads in the northern hemisphere and is stronger in the hemisphere with leading sunspots in positive polarity than in the hemisphere with leading sunspots in negative polarity. This result suggests that it is necessary to analyze the magnetic polarity's effect on the flares and to interpret the period from the solar maximum to solar maximum as the definition of solar cycle.

• 대한전기학회논문지:시스템및제어부문D
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• 제54권6호
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• pp.390-395
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• 2005

Sunspots are dark areas that grow and decay on the lowest level of the sun that is visible from the Earth. Shot-term predictions of solar activity are essential to help plan missions and to design satellites that will survive for their useful lifetimes. This paper presents a parallel-structure fuzzy system(PSFS) for prediction of sunspot number time series. The PSFS consists of a multiple number of component fuzzy systems connected in parallel. Each component fuzzy system in the PSFS predicts future data independently based on its past time series data with different embedding dimension and time delay. An embedding dimension determines the number of inputs of each component fuzzy system and a time delay decides the interval of inputs of the time series. According to the embedding dimension and the time delay, the component fuzzy system takes various input-output pairs. The PSFS determines the final predicted value as an average of all the outputs of the component fuzzy systems in order to reduce error accumulation effect.

• Journal of Astronomy and Space Sciences
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• 제23권2호
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• pp.135-142
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• 2006

흑점수와 오로라의 관측 자료를 과거로 연장해 보면 dynamo 이론과 태양의 활동 그리고 기후간에 상당한 관계가 있음을 알 수 있다. 특히 흑점이 거의 나타나지 않았던 Maunder 극소기가 이상 혹한기였던 유럽의 소빙하기와 일치하고, 흑점의 출현과 오로라 발생 사이에 밀접한 관계가 있다는 사실이 알려졌다. 이에 따라 Maunder 극소기 시기의 흑점과 오로라의 관측 자료들을 간접적 solar proxy 자료들과 함께 조사하고, 이 시기에 나타난 태양 활동의 모습과 특징을 기후변화의 관계와 함께 알아보았다.

• 천문학회보
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• 제41권2호
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• pp.67.2-67.2
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• 2016

The Sun has diverse variations in solar atmosphere's layers due to solar activity. This solar variations can be recognized easily by sunspots which appear on the solar photosphere. Thus the sunspot on the photosphere is utilized by direct index of the solar activity. The other variation of the photosphere is center-to-limb variation (CLV). In this study, we analyze the relative intensity observed by SOHO, SDO. The data of photospheric intensity are from full disk images of SOHO/MDI intensity ($6768{\AA}$, from May 1994 to March 2011) and of SDO/HMI intensity ($6173-6174{\AA}$, from May 2010 to June 2016). As the result, we found the latitudinal variation of the intensity. The daily photospheric intensity showed the solar cyclic variation with sunspot number. It has a little difference of phase with sunspot number.

• 천문학논총
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• 제8권1호
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• pp.153-161
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• 1993

We analyze 211 data of the daily sunspot observations during the period of January 4 to November 30 in 1993 and present the daily relative sunspot numbers. During 334 days of the period, the preliminary annual average of the relative sunspot numbers is found to be 61.8 based on 27.1 distinct spots in a single group for 3.7 spot groups. According to the appearance of 203 spot groups, our analysis shows that the mean life time of spot group is about 1 day and 15.5 hours. Our records show that more number of sunspots have appeared in the southern hemisphere than in the northern hemisphere by some 2%, indicating that the solar activities of the northern and southern hemisphere are much the same during the period.

• 천문학회지
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• 제50권2호
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• pp.21-27
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• 2017

The autoregressive method provides a univariate procedure to predict the future sunspot number (SSN) based on past record. The strength of this method lies in the possibility that from past data it yields the SSN in the future as a function of time. On the other hand, its major limitation comes from the intrinsic complexity of solar magnetic activity that may deviate from the linear stationary process assumption that is the basis of the autoregressive model. By analyzing the residual errors produced by the method, we have obtained the following conclusions: (1) the optimal duration of the past time for the forecast is found to be 8.5 years; (2) the standard error increases with prediction horizon and the errors are mostly systematic ones resulting from the incompleteness of the autoregressive model; (3) there is a tendency that the predicted value is underestimated in the activity rising phase, while it is overestimated in the declining phase; (5) the model prediction of a new Solar Cycle is fairly good when it is similar to the previous one, but is bad when the new cycle is much different from the previous one; (6) a reasonably good prediction of a new cycle can be made using the AR model 1.5 years after the start of the cycle. In addition, we predict the next cycle (Solar Cycle 25) will reach the peak in 2024 at the activity level similar to the current cycle.