We investigate the solar cycle variation of microwave and extreme ultraviolet (EUV) intensity in latitude to compare microwave polar brightening (MPB) with the EUV polar coronal hole (CH). For this study, we used the full-sun images observed in 17 GHz of the Nobeyama Radioheliograph from 1992 July to 2016 November and in two EUV channels of the Atmospheric Imaging Assembly (AIA) 193 A° and 171 A° on the Solar Dynamics Observatory (SDO) from 2011 January to 2016 November. As a result, we found that the polar intensity in EUV is anti-correlated with the polar intensity in microwave. Since the depression of EUV intensity in the pole is mostly owing to the CH appearance and continuation there, the anti-correlation in the intensity implies the intimate association between the polar CH and the MPB. Considering the report of Gopalswamy et al. (1999) that the enhanced microwave brightness in the CH is seen above the enhanced photospheric magnetic field, we suggest that the pole area during the solar minimum has a stronger magnetic field than the quiet sun level and such a strong field in the pole results in the formation of the polar CH. The emission mechanism of the MPB and the physical link with the polar CH are not still fully understood. It is necessary to investigate the MPB using high resolution microwave imaging data, which can be obtained by the high performance large-array radio observatories such as the ALMA project.

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 118~171 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 Apavg,aa31−36 Apavg,aa31−36 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.

We have analyzed the sunspot and aurora data recorded in Go-Ryer-Sa. We have collected 35 records of sunspot observations for 46 days, and 232 records of auroral observations. To objectively estimate the periods of the solar activity appearing in these records a method of calculating the one-dimensional power spectrum from inhomogeneous data is developed, and applied to the sunspot and auroral data. We have found statistically significant 10.5 and 10 year periodicities in the distributions of sunspot and aurora records, respectively. These periods are consistent with the well-known solar activity cycle. There are indications of the long-term variations, but the period is not certain. We have also calculated the cross-correlations between the sunspot and auroral data. In particular, we have divided the aurora data into several subgroups to study their nature. We conclude that the historical records of strong auroral activity correspond to non-recurrent magnetic storms related to the sunspots. On the other hand, the records of weak auroral activity are thought to be related with the recurrent magnetic storms which occur frequently due to the coronal hole near the sunspot minimum.

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 Hα 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.

Utilizing a new version of the sunspot number and group sunspot number dataset available since 2015, we have statistically studied the relationship between solar activity parameters describing solar cycles and the slope of the linear relationship between the monthly sunspot numbers and the monthly number of active days in percentage (AD). As an effort of evaluating possibilities in use of the number of active days to predict solar activity, it is worthwhile to revisit and extend the analysis performed earlier. In calculating the Pearson’s linear correlation coefficient r, the Spearman’s rank-order correlation coefficient rs, and the Kendall’s τ coefficient with the rejection probability, we have calculated the slope for a given solar cycle in three different ways, namely, by counting the spotless day that occurred during the ascending phase and the descending phase of the solar cycle separately, and during the period corresponding to solar minimum ± 2 years as well. We have found that the maximum solar sunspot number of a given solar cycle and the duration of the ascending phase are hardly correlated with the slope of a linear function of the monthly sunspot numbers and AD. On the other hand, the duration of a solar cycle is found to be marginally correlated with the slope with the rejection probabilities less than a couple of percent. We have also attempted to compare the relation of the monthly sunspot numbers with AD for the even and odd solar cycles. It is inconclusive, however, that the slopes of the linear relationship between the monthly group numbers and AD are subject to the even and odd solar cycles.

We explore the associations between the total sunspot area, solar north-south asymmetry, and Southern Oscillation Index and the physical characteristics of clouds by calculating normalized cross-correlations, motivated by the idea that the galactic cosmic ray influx modulated by solar activity may cause changes in cloud coverage, and in turn the Earth’s climate. Unlike previous studies based on the relative difference, we have employed cloud data as a whole time-series without detrending. We found that the coverage of high-level and low-level cloud is at a maximum when the solar north-south asymmetry is close to the minimum, and one or two years after the solar north-south asymmetry is at a maximum, respectively. The global surface air temperature is at a maximum five years after the solar north-south asymmetry is at a maximum, and the optical depth is at a minimum when the solar north-south asymmetry is at a maximum. We also found that during the descending period of solar activity, the coverage of low-level cloud is at a maximum, and global surface air temperature and cloud optical depth are at a minimum, and that the total column water vapor is at a maximum one or two years after the solar maximum.

In solstices during the solar minimum, the hemispheric difference of the equatorial ionization anomaly (EIA) intensity (hereafter hemispheric asymmetry) is understood as being opposite in the morning and afternoon. This phenomenon is explained by the temporal variation of the combined effects of the fountain process and interhemispheric wind. However, the mechanism applied to the observations during the solar minimum has not yet been validated with observations made during other periods of the solar cycle. We investigate the variability of the hemispheric asymmetry with local time (LT), altitude, season, and solar cycle using the electron density taken by the CHAllenging Minisatellite Payload satellite and the global total electron content (TEC) maps acquired during 2001–2008. The electron density profiles provided by the Constellation Observing System for Meteorology, Ionosphere, and Climate satellites during 2007–2008 are also used to investigate the variation of the hemispheric asymmetry with altitude during the solar minimum. During the solar minimum, the location of a stronger EIA moves from the winter hemisphere to the summer hemisphere around 1200–1400 LT. The reversal of the hemispheric asymmetry is more clearly visible in the F-peak density than in TEC or in topside plasma density. During the solar maximum, the EIA in the winter hemisphere is stronger than that in the summer hemisphere in both the morning and afternoon. When the location of a stronger EIA in the afternoon is viewed as a function of the year, the transition from the winter hemisphere to the summer hemisphere occurs near 2004 (yearly average F10.7 index = 106). We discuss the mechanisms that cause the variation of the hemispheric asymmetry with LT and solar cycle.

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.

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 Ri, 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.