太阳黑子相对数与人类疾病关系的初步分析

从日地关系的角度出发,探讨太阳活动对人类疾病有何影响。本文根据南京市传染病医院的资料,对每种疾病人数的相对比率与太阳黑子相对数对应作图比较,并求出其相关系数。我们的初步结果表明:伤寒和猩红热的发病率与太阳活动11年周期变化一致,它们在同年达到极大值,其它的不明确。     

太阳黑子数的子波分析

本文讨论了子波变换用于信号突变检测的原理，用它分析了１７００－１９９３年间的太阳黑子数的年均值．精确地检测到了太阳活动的突变点，用相邻两个突变点的时间长度求得了不同尺度下太阳黑子变化的周期．结果表明：利用子波变换检测太阳黑子周期与传统方法相比具有独到之处．     

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(Waldmeiereffect)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。     

紫金山天文台55 yr手绘黑子数据系统差分析

对紫金山天文台(简称紫台)自1954年至2011年共55 yr的手描黑子图进行了数字化.将紫台太阳黑子相对数(PRSN)和黑子群数(PGSN)与国际太阳影响数据分析中心(SIDC)中的对应数据(月平均太阳黑子相对数(IRSN)和月平均黑子群数(IGSN))进行对比研究,发现:(1)紫台黑子数据与SIDC黑子数据有很强的正相关性,说明紫台黑子数据的可靠性;(2) PRSN和IRSN、PGSN和IGSN的系统偏差分别处于7%左右、5%左右,紫台数据与SIDC数据在活动周的极小期的差异性显著大于极大期;(3)紫台的视宁度从1995年开始变差,直接导致了PRSN (PGSN)与IRSN (IGSN)的比值明显变大,表明视宁度的变化影响了紫台黑子的观测质量.     

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。     

太阳黑子数月平均变化的长期行为的可预报性

本文用非线性动力系统理论探讨了现代太阳周（１８５０年１月─１９９２年５月）黑子相对数月平均变化过程的可预报性。用时间延迟方法重构吸引子，计算它的最大Ｌｙａｐｕｎｏｖ指数（λ_１＝０．０２３±０．００４ｂｉｔｓ／月），估算了用这些黑子数进行确定性预报的理论时限（ｔ＝３．６±０．６年）．结果表明，动力系统的可预报性与它的最大Ｌｙａｐｕｎｏｖ指数有直接关系，黑子数月平均变化过程的演化不是周期的，也不是拟周期的，而是混沌的。即使今后找到了描述该过程的确定性方程，它的长期行为也不可能准确地预报，只能作短期预报，这是黑子数本身的混沌特性决定的。用于黑子数预报的纯粹数值统计方法仅对短期预报才有效。     

用自激励门限自回归分析方法模拟和预报太阳黑子数的长期变化

本文用一种新方法——自激励门限自回归分析方法对太阳黑子相对数年平均值进行拟合和预报检验,并对未来第22周逐年年均值作出预报。 目激励门限自回归分析模型的形式如下: 在对1956至1985年逐年太阳黑子相对数年均值的预报检验中,最大拟会误差为40.6,最小拟合误差为0.3,平均拟合误差为±12.5。 对1986至1997年逐年太阳黑子相对数年均值的预报见表(4)。定出第21周极小在1986年或1987年,极大在1990或1991年,极大值R_M=81.2±16.2。     

大行星轨道运动与太阳黑子数的中长周期变化

本文对不同序列的太阳黑子数资料作了分析研究,计算得到了可能的太阳黑子活动的中长周期变化,并分别与由大行星轨道运动引起的日心轨道角动量变化的周期进行比较,发现二者具有比较一致的谱结构。基于本文的讨论和文[17]的结论,我们进一步认为大行星轨道运动是太阳黑子数周期性变化的可能的外部因素。     

太阳黑子相对数的分维研究

本文用非线性动力系统理论控制了现代太阳周（１８５０年１月－１９９２年５月）黑子相对数月平均变化的动力行为和可预报性，计算了它的分维数。结果表明，太阳黑子数月均变化是一个复杂的低维浑沌系统，可用有限个参数描述，所需变量至少为３个，最多为７个，本文还讨论了黑子数月均值的可预报时间尺度，平均可预报时间尺度为１５０个月，本文建议利用最小二乘直线拟合的最小方差来判定最佳无标度区，所得分维数较客观，并简要讨论     

Short-Term Period Variation of Relative Sunspot Numbers

We use wavelet transform to analyze the daily relative sunspot number series over solar cycles 10-23. The characteristics of some of the periods shorter than ～ 600-day are discussed. The results exhibit not only the variation of some short periods in the 14 solar cy-cles but also the characteristics and differences around solar peaks and valley years. The short periodic components with larger amplitude such as ～27,～150 and ～360-day are obvious in some solar cycles,all of them are time-variable,also their lengths and amplitudes are vari-able and intermittent in time. The variable characteristics of the periods are rather different in different solar cycles.     

Low Dimensional Chaos from the Group Sunspot Numbers

We examine the nonlinear dynamical properties of the monthly smoothed group sunspot number Rg and find that the solar activity underlying the time series of Rg is globally governed by a low-dimensional chaotic attractor. This finding is consistent with the nonlinear study results of the monthly Wolf sunspot numbers. We estimate the maximal Lyaponuv exponent (MLE) for the Rg series to be positive and to equal approximately 0.0187 ± 0.0023 (month-1). Thus, the Lyaponuv time or predictability time of the chaotic motion is obtained to be about 4.46 ± 0.5 years, which is slightly different with the predictability time obtained from Rz. However, they both indicate that solar activity forecast should be done only for a short to medium term due to the intrinsic complexity of the time behavior concerned.     

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.     

The Schwabe and Gleissberg Periods in the Wolf Sunspot Numbers and the Group Sunspot Numbers

Three wavelet functions: the Morlet wavelet, the Paul wavelet, and the DOG wavelet have been respectively performed on both the monthly Wolf sunspot numbers (R_z) from January 1749 to May 2004 and the monthly group sunspot numbers (R_g) from June 1795 to December 1995 to study the evolution of the Gleissberg and Schwabe periods of solar activity. The main results obtained are (1) the two most obvious periods in both the R_z and R_g are the Schwabe and Gleissberg periods. The Schwabe period oscillated during the second half of the eighteenth century and was steady from the 1850s onward. No obvious drifting trend of the Schwabe period exists. (2) The Gleissberg period obviously drifts to longer periods the whole consideration time, and the drifting speed of the Gleissberg period is larger for R_z than for R_g. (3) Although the Schwabe-period values for R_z and R_g are about 10.7 years, the value for R_z seems slightly larger than that for R_g. The Schwabe period of R_z is highly significant after the 1820s, and the Schwabe period of R_g is highly significant over almost the whole consideration time except for about 20 years around the 1800s. The evolution of the Schwabe period for both R_z and R_g in time is similar to each other. (4) The Gleissberg period in R_z and R_g is highly significant during the whole consideration time, but this result is unreliable at the two ends of each of the time series of the data. The evolution of the Gleissberg period in R_z is similar to that in R_g.     

Wavelet Analysis of Several Important Periodic Properties in the Relative Sunspot Numbers

We investigate the wavelet transform of yearly mean relative sunspot number series from 1700 to 2002. The curve of the global wavelet power spectrum peaks at 11-yr, 53-yr and 101-yr periods. The evolution of the amplitudes of the three periods is studied. The results show that around 1750 and 1800, the amplitude of the 53-yr period was much higher than that of the the 11-yr period, that the ca. 53-yr period was apparent only for the interval from 1725 to 1850, and was very low after 1850, that around 1750, 1800 and 1900, the amplitude of the 101-yr period was higher than that of the 11-yr period and that, from 1940 to 2000, the 11-yr period greatly dominates over the other two periods.     

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。     

Short-Term Period Variation of Relative Sunspot Numbers

We use wavelet transform to analyze the daily relative sunspot number series over solar cycles 10-23. The characteristics of some of the periods shorter than - 600-day are discussed. The results exhibit not only the variation of some short periods in the 14 solar cycles but also the characteristics and differences around solar peaks and valley years. The short periodic components with larger amplitude such as ～27, ～ 150 and ～360-day are obvious in some solar cycles, all of them are time-variable, also their lengths and amplitudes are variable and intermittent in time. The variable characteristics of the periods are rather different in different solar cycles.     

The Relative Phase Asynchronization between Sunspot Numbers and Polar Faculae

The monthly sunspot numbers compiled by Temmer et al. and the monthly polar faculae from observations of the National Astronomical Observatory of Japan, for the interval of March 1954 to March 1996, are used to investigate the phase relationship between polar faculae and sunspot activity for total solar disk and for both hemispheres in solar cycles 19, 20, 21 and 22. We found that (1) the polar faculae begin earlier than sunspot activity, and the phase difference exhibits a consistent behaviour for different hemispheres in each of the solar cycles, implying that this phenomenon should not be regarded as a stochastic fluctuation; (2) the inverse correlation between polar faculae and sunspot numbers is not only a long-term behaviour, but also exists in short time range; (3) the polar faculae show leads of about 50–71 months relative to sunspot numbers, and the phase difference between them varies with solar cycle; (4) the phase difference value in the northern hemisphere differs from that in the southern hemisphere in a solar cycle, which means that phase difference also existed between the two hemispheres. Moreover, the phase difference between the two hemispheres exhibits a periodical behaviour. Our results seem to support the finding of Hiremath (2010).