太阳活动对电离层TEC变化影响分析ormalsize

为研究太阳活动对电离层TEC变化的影响,从整体到局部分析了2000—2016年的太阳黑子数、太阳射电流量F_10.7指数日均值与电离层TEC的关系,并重点分析了2017年9月6日太阳爆发X9.3级特大耀斑前后15天太阳活动与电离层TEC变化的相关性.结果表明:由2000—2016年的数据整体看来,太阳黑子数、太阳F_10.7指数、TEC两两之间具有很强的整体相关性,但局部相关性强弱不均;此次耀斑爆发前后太阳黑子数、太阳F_10.7指数和TEC具有很强的正相关特性,太阳活动对TEC的影响时延约为2天;太阳活动对全球电离层TEC的影响不同步,从高纬至低纬约有1天的延迟,且对低纬度的影响远大于中高纬度.太阳活动是影响电离层TEC变化的主要原因,但局部也可能存在其他重要影响因素.      

基于小波与交叉小波分析的太阳黑子与宇宙线相关性研究

利用小波分析和交叉小波分析方法, 根据太阳黑子数以及Huancayo和Climax两个测站的月均宇宙线数据, 分析了两个测站的月均宇宙线周期变化, 同时利用太阳黑子数R₁₂对Climax站宇宙线流量进行预测研究. 小波分析结果表明, 太阳黑子与宇宙线除存在显著的11年周期外, 太阳活动高年期间还存在1～6个月尺度的周期特性, 在第22太阳周活动高年时还出现了6～8和1～22个月的变化周期; 交叉小波分析结果表明, 在130个月左右的周期上宇宙线与太阳黑子具有显著的负相关性, 并且宇宙线的变化滞后太阳黑子约8个月; 分别采用预测时刻和8个月前的太阳黑子数, 预测相对误差为3.8912%和3.2386%. 本文方法同样适用于估算其他空间天气参量之间的周期和相关性, 提高各种空间天气参量的预测或预报精度.      

Ionospheric heating due to solar flares as measured by SROSS-C2 satellite

The data on thermal fluctuations of the topside ionosphere have been measured by Retarding Potential Analyser (RPA) payload aboard the SROSS-C2 satellite over the Indian region for half of the solar cycle (1995–2000). The data on solar flare has been obtained from National Geophysical Data Center (NGDC) Boulder, Colorado (USA) and other solar indices (solar radio flux and sunspot number) were download from NGDC website. The ionospheric electron and ion temperatures show a consistent enhancement during the solar flares. The enhancement in the electron temperature is 28–92% and for ion temperature it is 18–39% compared to the normal day’s average temperature. The enhancement of ionospheric temperatures due to solar flares is correlated with the variation of sunspot and solar radio flux (F10.7cm). All the events studied in the present paper fall in the category of subflare with almost same intensity. The ionospheric electron and ion temperatures enhancement have been compared with the IRI model values.     

太阳黑子自动识别与特征参量自动提取

日面上黑子数目反映了太阳活动水平的高低.黑子形态的复杂性和磁场的非势性与太阳活动爆发密切相关.随着高时空精度的太阳观测数据量的急剧增长，快速准确地自动识别日面上的黑子以及对黑子群特征自动提取已成为太阳活动预报的现实需求.本文针对SDO/HMI的活动区白光数据，利用数学形态法开展黑子自动识别研究，并在黑子识别基础上对黑子群的面积和黑子数进行了计算.通过对利用2011-2017年HMI活动区数据计算得到的黑子群面积和黑子数与NOAA/SWPC发布的活动区相应参量进行比较，发现本文计算结果与SWPC发布数据的变化趋势基本一致，相关性较好.其中黑子群面积的相关系数为0.77，黑子数的相关系数为0.79.研究结果表明，利用本文方法对SDO/HMI数据进行处理，能够得到高时间分辨率的黑子群特征参量，可为太阳活动预报提供及时准确的输入.      

Essential features of long-term changes of areas and diameters of sunspot groups in solar activity cycles 12–24

We analyze the Greenwich catalog data on areas of sunspot groups of last thirteen solar cycles. Various parameters of sunspots are considered, namely: average monthly smoothed areas, maximum area for each year and equivalent diameters of groups of sunspots. The first parameter shows an exceptional power of the 19th cycle of solar activity, which appears here more contrastively than in the numbers of spots (that is, in Wolf’s numbers). It was found that in the maximum areas of sunspot groups for a year there is a unique phenomenon: a short and high jump in the 18th cycle (in 1946–1947) that has no analogues in other cycles. We also studied the integral distributions for equivalent diameters and found the following: (a) the average value of the index of power-law approximation is 5.4 for the last 13 cycles and (b) there is reliable evidence of Hale's double cycle (about 44?years). Since this indicator reflects the dispersion of sunspot group diameters, the results obtained show that the convective zone of the Sun generates embryos of active regions in different statistical regimes which change with a cycle of about 44?years.     

Active region properties and irradiance variations

Total Solar Irradiance (TSI) has been measured for more than three decades. These observations demonstrate that total irradiance changes on time scales ranging from minutes to years and decades. Considerable efforts have been made to understand the physical origin of irradiance variations and to model the observed changes using measures of sunspots and faculae. In this paper, we study the short-term variations in TSI during the declining portion and minimum of solar cycle 22 and the rising portion of cycle 23 (1993–1998). This time interval of low solar activity allows us to study the effect of individual sunspot groups on TSI in detail. In this paper, we indicate that the effect of sunspot groups on total irradiance may depend on their type in the Zürich classification system and/or their evolution, and on their magnetic configuration. Some uncertainties in the data and other effects are also discussed.     

Impact of rogue active regions on hemispheric asymmetry

The solar dipole moment at activity minimum is a good predictor of the strength of the subsequent solar cycle. Through a systematic analysis using a state-of-the-art $2\times 2$D solar dynamo model, we found that bipolar magnetic regions (BMR) with atypical characteristics can modify the strength of the next cycle via their impact on the buildup of the dipole moment as a sunspot cycle unfolds. In addition to summarizing these results, we present further effects of such “rogue” BMRs. These have the ability to generate hemispheric asymmetry in the subsequent sunspot cycle, since they modify the polar cap flux asymmetry of the ongoing cycle. We found strong correlation between the polar cap flux asymmetry of cycle i and the total pseudo sunspot number asymmetry of cycle $i+1$. Good correlation also appears in the case of the time lag of the hemispheres of cycle $i+1$.     

Reconstruction of solar irradiance using the Group sunspot number

We present a reconstruction of total solar irradiance since 1610 to the present based on variations of the surface distribution of the solar magnetic field. The latter is calculated from the historical record of the Group sunspot number using a simple but consistent physical model. Our model successfully reproduces three independent data sets: total solar irradiance measurements available since 1978, total photospheric magnetic flux from 1974 and the open magnetic flux since 1868 (as empirically reconstructed from the geomagnetic aa-index). The model predicts an increase in the total solar irradiance since the Maunder Minimum of about 1.3 Wm⁻².     

The automated prediction of solar flares from SDO images using deep learning

In the last few years, there has been growing interest in near-real-time solar data processing, especially for space weather applications. This is due to space weather impacts on both space-borne and ground-based systems, and industries, which subsequently impacts our lives. In the current study, the deep learning approach is used to establish an automated hybrid computer system for a short-term forecast; it is achieved by using the complexity level of the sunspot group on SDO/HMI Intensitygram images. Furthermore, this suggested system can generate the forecast for solar flare occurrences within the following 24 h. The input data for the proposed system are SDO/HMI full-disk Intensitygram images and SDO/HMI full-disk magnetogram images. System outputs are the “Flare or Non-Flare” of daily flare occurrences (C, M, and X classes). This system integrates an image processing system to automatically detect sunspot groups on SDO/HMI Intensitygram images using active-region data extracted from SDO/HMI magnetogram images (presented by Colak and Qahwaji, 2008) and deep learning to generate these forecasts. Our deep learning-based system is designed to analyze sunspot groups on the solar disk to predict whether this sunspot group is capable of releasing a significant flare or not. Our system introduced in this work is called ASAP_Deep. The deep learning model used in our system is based on the integration of the Convolutional Neural Network (CNN) and Softmax classifier to extract special features from the sunspot group images detected from SDO/HMI (Intensitygram and magnetogram) images. Furthermore, a CNN training scheme based on the integration of a back-propagation algorithm and a mini-batch AdaGrad optimization method is suggested for weight updates and to modify learning rates, respectively. The images of the sunspot regions are cropped automatically by the imaging system and processed using deep learning rules to provide near real-time predictions. The major results of this study are as follows. Firstly, the ASAP_Deep system builds on the ASAP system introduced in Colak and Qahwaji (2009) but improves the system with an updated deep learning-based prediction capability. Secondly, we successfully apply CNN to the sunspot group image without any pre-processing or feature extraction. Thirdly, our system results are considerably better, especially for the false alarm ratio (FAR); this reduces the losses resulting from the protection measures applied by companies. Also, the proposed system achieves a relatively high scores for True Skill Statistics (TSS) and Heidke Skill Score (HSS).     

Solar magnetic fields and the dynamo theory

Unlike Earth’s dipolar magnetic fields, solar magnetic fields consist of wide ranges of length-scales and strengths, and interestingly, they evolve in a cyclic fashion with a 22-year periodicity. A magnetohydrodynamic dynamo operating in the Sun is most likely responsible for producing the solar magnetic activity cycle. While the first solar dynamo models were built half a century ago, recent views differ significantly from those models. According to widely accepted present concepts, the large-scale solar dynamo is of flux-transport type, which involves three basic processes: (i) generation of toroidal fields by shearing the pre-existing poloidal fields by differential rotation (the Ω-effect); (ii) re-generation of poloidal fields by lifting and twisting the toroidal fluxtubes (the α-effect); (iii) flux transport by meridional circulation. This class of dynamos has been successful in explaining many large-scale solar cycle features, including a particularly difficult one – the correct phase relationship between the equatorward-migrating sunspot belt and the poleward drifting large-scale, diffuse fields. The dynamo cycle period in such models is primarily governed by the meridional flow speed near the bottom of the convection zone. After briefly reviewing the historical background, we will present the successes of flux-transport dynamos, including their predictive capability. For example, we will demonstrate how the meridional circulation plays a key role in governing the Sun’s memory about its own magnetic field, and how a flux-transport dynamo-based predictive tool can explain the cause of the very slow polar reversal in the so-called “peculiar” cycle 23 compared to those in cycles 20, 21 and 22. We will close by presenting explanations for certain long-term variability using these models, such as, what may have maintained the observed cyclic variation in slow solar wind flow during Maunder minima, in the presence of near zero solar activity.     

中等磁暴太阳周分布的统计研究

依据实际观测的中等磁暴数据,统计分析了中等磁暴的太阳周分布.分析结果表明,在一个太阳活动周内,每年中等磁暴随时间的变化出现多个峰值,其中,最大峰值均出现在太阳活动周的下降段,即中等磁暴的峰值比太阳黑子数平滑年均值的峰值要滞后,滞后的时间为2～3年.超过70%的中等磁暴出现在太阳活动周的下降段,这表明绝大多数中等磁暴出现在太阳活动周的下降段.通过对中等磁暴平滑月均值与太阳黑子数平滑月均值相位差的计算分析发现,中等磁暴峰值出现的时间比太阳黑子数峰值出现的时间要滞后,不同太阳活动周中等磁暴峰值出现的时间与太阳黑子数峰值时间滞后的程度不同.      

第21～24太阳周4类空间天气事件爆发特征统计分析

对第21～24太阳周不同等级的太阳X射线耀斑事件、太阳质子事件、地磁暴事件及高能电子增强事件的爆发频次特征进行统计,结果表明:太阳周耀斑爆发的总数量与该太阳周的黑子数峰值呈正比,耀斑总数、X级耀斑事件数与峰值的相关系数分别为0.974,0.997;太阳质子事件主要发生在峰年前后1～2年,约占总发生次数的80%,峰值通量大于10pfu （1 pfu=1 cm-2·sr-1·s-1）的质子事件中,84%伴有耀斑爆发,并且主要伴随M或X级耀斑,少量伴随C级耀斑,峰值通量大于1000pfu的质子事件中,98%伴随M或X级耀斑,并且以X级耀斑为主;第21,22,23和24太阳周发生地磁暴最频繁的时间分别在1982,1991,2003年和2015年,分别滞后黑子数峰值时间3年、2年、2年和1年;72%的高能电子增强事件发生在太阳周下降期,24%的高能电子增强事件发生在太阳周上升期.      

A note on the relationship between sunspot numbers and active days

We have studied the relationship between three different versions of the sunspot number (Group, International and American Sunspot Number) and the number of active days (i.e., the number of days with spots on the solar disk). We have detected an approximately linear relationship for low solar activity conditions. However, this relationship for the International Sunspot Number is very different to the ones obtained with the other versions of the sunspot number. The discordant values correspond to older observations.     

Latitudinal influences on the quiet daytime D-region

Four versions of a steady-state quiet D-region model are presented. They differ from each other as a result of latitudinal differences in total neutral particle concentrations, nitric oxide concentrations and cosmic ray ionization rates. The total ion concentration profiles of all four versions have minima near 70 km which range from about 10⁸ m^?3 at high latitudes to 3.5 × 10⁷ m^?3 at equatorial latitudes for a solar zenith angle of 60°. Neutral density differences among the four cases result in important vertical shifts for the respective D-region profiles relative to one another. A “C-layer” is evident for the high and mild-latitude models at large solar zenith angles. The altitude where the negative ion/electron concentrations ratio is unity varies from about 63 to 67 km. The computed results are compared briefly with the extensive data base in the literature.     

Even–odd cycle parity in complexity of hemispheric sunspot activity

We have used the Lempel–Ziv measure to describe the complexity in sunspot activity during the solar cycles 18–23. In particular, we examined the time series of daily sunspot numbers in the northern and southern hemispheres in each of the six cycles and calculated the Lempel–Ziv complexity (LZC) value for each time series. Our results indicate that in the even cycles, the LZC values of the sunspot numbers in the two hemispheres are very close to each other, whereas in the odd cycles they differ significantly between the two hemispheres. We also find that within each hemisphere the LZC varies from cycle to cycle. This even–odd cycle parity reflects the variations in inter-hemispheric strengths of the solar magnetic field leading to different temporal distributions of sunspots in the two hemispheres. The degree of complexity may influence the predictability of sunspot activity in the two hemispheres during the various cycles. Although the physical implication of the results is not clear, these results may stimulate new ideas into modeling the complex dynamics of the solar dynamo.     

Observed relation between solar luminosity and radius

We compile measurements of the total solar irradiance S_o made in the period 1967 to 1983 from balloons, rockets, and spacecrafts. These data, when corrected for atmospheric and calibration differences, suggest a systematic increase in S_o of about 0.025% per year. In 1979 and after the ACRIM/SMM and the ERB/NIMBUS data reveal a systematic decrease of about the same magnitude. The period of the early apparent increase embraces a full solar activity cycle of 11 years: hence the effect cannot be that of simple sunspot blocking. This trend of a slow increase followed by a similar decrease is compared with solar diameter measurements obtained from daily meridian transit timings of the Sun made during the same period at the Royal Greenwich Observatory and at the U.S. Naval Observatory. We find in both of these data sets an apparent increase in solar diameter of about 0.03″arc per year during the period of increasing solar irradiance and a weaker suggestion of a similar, subsequent decline. If the apparent trends in diameter and luminosity are real they allow us to estimate the empirical relationship between the two quantities. For this period we find W=ΔlogR/ΔlogL=0.078±0.026.     

Solar wind electron density and temperature over solar cycle 23: Thermal noise measurements on Wind

We present the solar wind plasma parameters obtained from the Wind spacecraft during more than nine years, encompassing almost the whole solar cycle 23. Since its launch in November 1994 Wind has frequently observed the in-ecliptic solar wind upstream of the Earth’s bow shock. The WIND/WAVES thermal noise receiver was specially designed to measure the in situ plasma thermal noise spectra, from which the electron density and temperature can be accurately determined. We present and discuss histograms of such measurements performed from 1994 to 2003. Using these large data sets, we study the density and core temperature variations with solar activity cycle and with different regimes of the solar wind. We confirm the anticorrelation of the electron density with the sunspot number, and obtain a positive correlation of the core temperature, with the sunspot number.     

Solar variability for periods of days to months

The time series of total solar irradiance determinations from ACRIM on the Solar Maximum Mission satellite (SMM) of 270 days and from the ERB experiment on NIMBUS 7 of 1445 days are analysed for periods greater than a few days. Comparison of the spectra of both with the spectrum of projected sunspot area over the corresponding time periods show high coherence for periods of 7 to about 25 days and for periods longer than about 30 to 35 days. In the vicinity and at the 27-day rotational period of the Sun, however, the coherence between sunspot area and irradiance is small, although both spectra show significant power at and around this period. This means that there is a signal in the irradiance which cannot be due to the sunspot area and the assumption of a straight forward sunspot blocking seems to be over simplified. This irradiance signal at 27 days has an amplitude of about ±0.012 per cent and is an enhancement.     

Sunspot cycle prediction using Warped Gaussian process regression

Solar cycle prediction is a key activity in space weather research. Several techniques have been employed in recent decades in order to try to forecast the next sunspot-cycle maxima and time. In this work, the Gaussian process, a machine-learning technique, is used to make a prediction for the solar cycle 25 based on the annual sunspot number 2.0 data from 1700 to 2018. A variation known as Warped Gaussian process is employed in order to deal with the non-negativity constraint and asymmetrical data distribution. Tests using holdout data yielded a root mean square error of 10.0 within 5 years and 25.0–35.0 within 10 years. Simulations using the predictive distribution were performed to account for the uncertainty in the prediction. Cycle 25 is expected to last from 2019 to 2029, with a peak sunspot number about 117 (110 by the median) occurring most likely in 2024. Thus our method predicts that solar Cycle 25 will be weaker than previous ones, implying a continuing trend of declining solar activity as observed in the past two cycles.