太阳黑子月平均面积数活动周及其特征

我们对第12周至第22周的太阳黑子月平均面积数进行统计分析，并与相应的太阳黑子月平均数相比较，结果表明太阳黑子月平均面积数活动周与太阳黑子月平均数活动周有一定的关系。在多数情况下，太阳黑子出现最大值的时间与太阳黑子面积数出现最大值的时间上不一致；太阳黑子平滑月平均数活动周上升期与太阳黑子平滑月平均面积数上升期在大多数情况下不相同；太阳黑子平滑月平均数活动周平均效果的瓦德迈尔效应(Waldmeier effect)一般要比太阳黑子平滑平均面积数的活动周明显；文中还对太阳黑子平滑月平均面积数活动周的特征进行了分析．     

21,22,23太阳活动周上升期太阳黑子资料的分析

本文分析了第２１ 、２２ 、２３ 太阳活动周的上升期２３ 个月（ 月均值） 太阳黑子资料。结果表明：太阳黑子相对数和面积南北不对称。２３ 周的太阳黑子相对数和面积（２３ 个月的平均） 高于２２ 周,但低于２１ 周。我们估计第２３ 周峰年为２０００ 年３ 月或１９９９ 年１２ 月。     

第23太阳活动周不会是强活动周

利用最新的太阳黑子观测资料和线性相关统计模式,对第２３太阳活动周的极大月平滑黑子相对数和黑子数物极大年均值进行预测,预报因子分别是每个太阳周上升相第２６个月的月平滑黑子相对数和第三年的黑子数年均值,预测结果表明,第２３周太阳黑子数的极大值不会高,极大月平滑黑子相对为１１５．４±１４．９,极大年均值为１１８．９±１１．６,平滑黑子数极大不会出现在１９９９年,很可能出现在２０００年。     

第23太阳活动周不会是强活动周

利用最新的太阳黑子观测资料和线性相关统计模式,对第２３太阳活动周的极大月平滑黑子相对数和黑子数的极大年均值进行预测．预报因子分别是每个太阳周上升相第２６个月的月平滑黑子相对数和第三年的黑子数年均值．预测结果表明,第２３周太阳黑子数的极大值不会高,极大月平滑黑子相对数为１１５．４±１４．９,极大年均值为１１８．９±１１．６．平滑黑子数极大不会出现在１９９９年,很可能出现在２０００年．     

太阳与地磁活动的1.3–1.7 yr振荡关系分析

太阳和地磁活动中的1.3–1.7 yr周期研究对于理解日地空间耦合系统中可能发生的物理过程十分重要.黑子是太阳光球层上最突出的磁场结构, Ap指数则是表征全球地磁活动水平的重要指标.使用同步压缩小波变换得到太阳黑子数和地磁Ap指数的1.3–1.7yr周期,并用互相关方法分析研究它们之间的相位关系.结果如下:(1)太阳黑子数和地磁Ap指数的1.3–1.7 yr周期呈现间歇性的演化特征,且随着时间的变化而不断变化;(2)地磁Ap指数在奇数活动周比相邻的偶数活动周的周期分量更高,表现出上下波动的变化特性;(3)地磁Ap指数和太阳黑子数的相位关系不是一成不变的,在大多数情况下地磁Ap指数滞后太阳黑子数,仅在第18和第22活动周黑子数在相位上滞后.     

黑子面积随黑子数变化的统计研究

对1874年5月-2015年3月期间每月黑子数和黑子面积数进行数据分析,统计研究黑子数与黑子面积比例的变化。结果表明:黑子数与黑子面积非线性变化,在活动周极大时期更明显。二者的分布概率整体相似,都随数值增大而降低,但黑子面积下降得更明显,表明二者存在非线性关系。黑子数与黑子面积的比例在活动周极小时期约为10.2,在极大时期约为21.8,长期约为16.8。每个周的上升期和下降期确定的比例有微小差别,不同活动周确定的比例有小的差别,二者间的比例有减小的趋势。     

23周太阳活动的现状和展望

本文给出了太阳２３ 周开始时间的确定、从开始到现在近两年间太阳活动的状况以及２３周上升期间的一些特点。分析表明,１９９６ 年１０ 月是２３ 周的第一个月,它的月平滑值是８ ．８ ;２３ 周的太阳活动虽然可能是高活动周,例如,国际推荐值为２０００ 年３ 月的１６０ ,但它可能不会超过前两周。根据上升期太阳活动的一些特征,还给出了在２３ 周峰年联测和空间灾害性扰动事件预报和预报方法研究中应注意的几个问题     

太阳黑子周极小年的统计性质——兼论第22周起始极小是否已经出现

探讨太阳周极小年的性质关系到确定极小值的位置及太阳周的长度,从而与太阳活动周的研究、太阳活动预报及水文、气象等地球物理现象的研究密切相关.当前对第22黑子周特征值的预报相当弥散,第22周起始极小是否已经出现的问题受到普遍关注.不同的太阳活动指标达到极值的时间不同,一般以太阳黑子数月均平滑最低值的位置来定义极小年.     

LSTAR模型用于太阳黑子相对数预测的初步研究

本采用了1891年至1988年期间的太阳黑子相对数的月均值资料序列，进行了用非线性时间序列分析的跳步门限自回归（LSTAR）模型作预测的研究。三年的预测结果表明，预测值与实际月均值之间的相对误差约为17％，预测值的平滑值与实际月均值的平滑值之间的相对误差接近8％左右，该方法预测的初步尝试结果表明，1992年每月的太阳黑子相对数仍将可能稳定在100以上，第22周太阳黑子相对数的谷值期将可能发生在1996年。     

预报第23周平滑月平均太阳黑子数的一种方法

利用已知的22个完整太阳活动周平滑月平均黑子数的记录，对正在进行的太阳周发展趋势给出了预测方法，并应用于第23周，同时与其他预报方法的结果进行了比较。     

Longterm Prediction of Solar Activity Using the Combined Method

The Combined Method is a non-parametric regression technique for long-term prediction of smoothed monthly sunspot numbers. Starting from a solar minimum, a prediction of the succeeding maximum is obtained by using a dynamo-based relation between the geomagnetic aa index and succeeding solar maxima. Then a series of predictions is calculated by computing the weighted average of past cycles of similar level. This technique leads to a good prediction performance, particularly in the ascending phase of the solar cycle where purely statistical methods tend to be inaccurate. For cycle 23 the combined method predicts a maximum of 160 (in terms of smoothed sunspot number) early in the year 2000.     

Prediction of maximum sunspot number in solar cycle 23

Instantaneous predictions of the maximum monthly smoothed sunspot number in solar cycle 23 have been made with a linear regressive model, which gives the predicted maximum value as a function of the smoothed sunspot numbers corresponding to a given month from the minimum in all preceding cycles. These predictions indicate that the intensity of solar activity in the current cycle will be at an average level.     

The Shape of Sunspot Cycles Described by Monthly Sunspot Areas

In this paper, we used the same four-parameter function as Hathaway, Wilson, and Reichmann (1994) proposed and studied the temporal behavior of sunspot cycles 12–22. We used the monthly averages of sunspot areas and their 13-point smoothed data. Our results show the following. (1) The four-parameter function may reduce to a function of only two parameters. (2) As a cycle progresses, the two-parameter function can be accurately determined after 4–4.5 years from the start of the cycle. A good prediction can be made for the timing and size of the sunspot maximum and for the behavior of the remaining 5–10 years of the cycle. (3) The solar activity in the remaining and forthcoming years of cycle 23 is predicted. (4) The smoothed monthly sunspot areas are more suitable to be employed for prediction at the maximum and the descending period of a cycle, whereas at the early period of a cycle the (un-smoothed) monthly data are more suitable.     

Synchronization of Sunspot Numbers and Sunspot Areas

Sunspot activity is usually described by either sunspot numbers or sunspot areas. The smoothed monthly mean sunspot numbers (SNs) and the smoothed monthly mean areas (SAs) in the time interval from November 1874 to September 2007 are used to analyze their phase synchronization. Both the linear method (fast Fourier transform) and some nonlinear approaches (continuous wavelet transform, cross-wavelet transform, wavelet coherence, cross-recurrence plot, and line of synchronization) are utilized to show the phase relation between the two series. There is a high level of phase synchronization between SNs and SAs, but the phase synchronization is detected only in their low-frequency components, corresponding to time scales of about 7 to 12 years. Their high-frequency components show a noisy behavior with strong phase mixing. Coherent phase variables should exist only for a frequency band with periodicities around the dominating 11-year cycle for SNs and SAs. There are some small phase differences between them. SNs lag SAs during most of the considered time interval, and they are in general more asynchronous around the minimum and maximum times of a cycle than at the ascending and descending phases.     

A Bayesian Analysis of the Correlations Among Sunspot Cycles

Sunspot numbers form a comprehensive, long-duration proxy of solar activity and have been used numerous times to empirically investigate the properties of the solar cycle. A number of correlations have been discovered over the 24 cycles for which observational records are available. Here we carry out a sophisticated statistical analysis of the sunspot record that reaffirms these correlations, and sets up an empirical predictive framework for future cycles. An advantage of our approach is that it allows for rigorous assessment of both the statistical significance of various cycle features and the uncertainty associated with predictions. We summarize the data into three sequential relations that estimate the amplitude, duration, and time of rise to maximum for any cycle, given the values from the previous cycle. We find that there is no indication of a persistence in predictive power beyond one cycle, and we conclude that the dynamo does not retain memory beyond one cycle. Based on sunspot records up to October 2011, we obtain, for Cycle 24, an estimated maximum smoothed monthly sunspot number of 97±15, to occur in January??C?February 2014 ± six months.     

The phase relation between sunspot numbers and soft X-ray flares

To better understand long-term flare activity, we present a statistical study on soft X-ray flares from May 1976 to May 2008. It is found that the smoothed monthly peak fluxes of C-class, M-class, and X-class flares have a very noticeable time lag of 13, 8, and 8 months in cycle 21 respectively with respect to the smoothed monthly sunspot numbers. There is no time lag between the sunspot numbers and M-class flares in cycle 22. However, there is a one-month time lag for C-class flares and a one-month time lead for X-class flares with regard to sunspot numbers in cycle 22. For cycle 23, the smoothed monthly peak fluxes of C-class, M-class, and X-class flares have a very noticeable time lag of one month, 5 months, and 21 months respectively with respect to sunspot numbers. If we take the three types of flares together, the smoothed monthly peak fluxes of soft X-ray flares have a time lag of 9 months in cycle 21, no time lag in cycle 22 and a characteristic time lag of 5 months in cycle 23 with respect to the smoothed monthly sunspot numbers. Furthermore, the correlation coefficients of the smoothed monthly peak fluxes of M-class and X-class flares and the smoothed monthly sunspot numbers are higher in cycle 22 than those in cycles 21 and 23. The correlation coefficients between the three kinds of soft X-ray flares in cycle 22 are higher than those in cycles 21 and 23. These findings may be instructive in predicting C-class, M-class, and X-class flares regarding sunspot numbers in the next cycle and the physical processes of energy storage and dissipation in the corona.     

High- And Low-Latitude Solar Activities: Who Leads Whom In Time Phase?

Using the data from observations of polar faculae by the National Astronomical Observatory of Japan from July 1951 to December 1998, we investigate whether there is a time lag between high-latitude solar activity and low-latitude solar activity. The cross-correlation analysis of the smoothed monthly numbers of the polar faculae with the smoothed monthly sunspot numbers shows that, high-latitude solar activity should lead low-latitude solar activity in time phase. The periodic characteristics of both of them also indicate that high-latitude activity evidently leads low-latitude activity.     

Predicting the start and maximum amplitude of solar cycle 24 using similar phases and a cycle grouping

We find that the solar cycles 9, 11, and 20 are similar to cycle 23 in their respective descending phases. Using this similarity and the observed data of smoothed monthly mean sunspot numbers (SMSNs) available for the descending phase of cycle 23, we make a date calibration for the average time sequence made of the three descending phases of the three cycles, and predict the start of March or April 2008 for cycle 24. For the three cycles, we also find a linear correlation of the length of the descending phase of a cycle with the difference between the maximum epoch of this cycle and that of its next cycle.Using this relationship along with the known relationship between the rise-time and the maximum amplitude of a slowly rising solar cycle, we predict the maximum SMSN of cycle 24 of 100.2±7.5 to appear during the period from May to October 2012.     

A new criterion for determining the time of a solar minimum

We propose a minimum level of the smoothed values for the solar constant during a period of low sunspot activity as a new additional criterion for determining the time of a minimum between solar cycles. An indicator for the time of a minimum between cycles is the time at which a minimum level in the average monthly values of the integral flux of solar radiation smoothed over thirteen months (when the last four values of the flux are greater than the previous minimum point) is achieved. We successfully tested the new criterion to determine the time of the previous minima between cycles 21 and 22, 22 and 23, and 23 and 24.