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.      

太阳10.7 cm射电流量中期预报模型研究(Ⅱ)

太阳活动指数中期预报一直是空间环境业务预报的难点之一.本文在自回归方法模型的基础上,利用太阳活动区面积、位置等参数与10.7cm辐射流量之间的定量关系,根据活动区面积衰减规律,建立了一个基于活动区参数及演化规律的改进型太阳活动指数中期预报模型.通过对预报测试实例分析发现,在日面出现较大活动区导致F_(10.7)迅速增长并超过历史数据峰值的情况,在日面活动区消亡导致指数突然出现平静期的情况,新模型的预报准确性相比自回归模型有很大提高,预报的平均相对误差下降约5%～9%.由此可见,新模型在某些特定条件下提高了原有模型的精度.该研究为提高业务型太阳10.7cm射电流量中期预报模型的预报精度奠定了基础.     

神经网络方法在太阳质子事件短期预报中的应用

在大量统计结果的基础上,深入研究了太阳质子事件预报机理．总结了质子事件爆发与太阳活动区面积、位置、McIntosh结构、磁结构以及前两天活动区爆发耀斑事件数目之间的关系．然后,在神经网络的基础上建立了太阳质子事件短期预报模型,并对2000年以后12个未参加训练的样本进行测试,结果对事件预报的准确率为83％．此外,我们还利用该模型对2002年1-4月发生的几次质子事件进行了预报试验,结果发现,这期间发生的6次事件都被预报．其中3次质子事件系统预报提前了3天,两次事件预报提前了2天,一次事件提前1天预报．     

基于PCA方法对太阳活动区主要参量的分析

太阳耀斑与太阳质子事件的发生通常与太阳活动区存在非常密切的关系, 对这种关系的深入分析有助于太阳耀斑和太阳质子事件预报模型的建立. 本文利用主成分分析(Principal Component Analysis, PCA)方法对1997-2010年太阳质子事件所在活动区的主要参量进行分析, 选取的参量包括黑子磁分类、 McIntosh分类、太阳黑子群面积、10.7 cm射电流量、耀斑指数、质子耀斑位置和软X射线耀斑强度. 结果得到81个太阳活动主成分得分值排序(得分值代表每个事件的强弱), 与太阳质子事件峰值流量、太阳黑子年均值以及10.7 cm射电流量年均值的对比显示相似度非常高, 表明主成分得分值一定程度上可以反映太阳活动的强弱规律.      

太阳活动区光球磁场特征的量化

为有效解决太阳活动区磁场特征量化问题,对所有SOHO卫星MDI磁图预处理后,分割出日面角45°以内的活动区,分析活动区投影面积变形来源,研究建立Cosine面积校正因子,校正活动区面积,构建具有21个特征参数的活动区磁场特征量化指标体系,通过主成分分析法对量化结果计算累积方差,结合活动区10486爆发X17.2级耀斑时...     

基于深度学习的太阳黑子Wilson山磁类型识别方法

太阳黑子是太阳光球层中带有较强磁场的区域,通常是太阳爆发活动的源区。Wilson山磁分类是当前最为主流的太阳黑子分类方法之一,对研究太阳爆发有重要意义。利用2010－2017年间SDO/HMI成像仪观测到的720s_SHARP磁图和白光图数据,研究使用深度学习对太阳黑子群Wilson山磁分类的方法。实验结果表明,Xception网络在识别太阳黑子Wilson山磁类型上能取得最优的效果,其中对α类型黑子的F₁得分为96.50%,β类为93.20%,其他类型的黑子为84.65%。      

1986年2月太阳的高活动I活动区4711的演化和特征

本文使用太阳黑子、磁场、Hα色球、10.7cm射电及软X射线流量等观测资料,对太阳活动谷期的高活动区4711(SESC编号)从光球、色球和日冕三个方面做了综述.指出该活动区演化过程的特征是:(1)黑子群在主要发展阶段呈一个紧密的结构复杂的强磁区;(2)两次大的太阳爆发均发生在黑子群面积衰减阶段的初期;(3)黑子群的转动可能是活动区日冕加热和耀斑活动的主要供能机制;(4)色球暗条的频繁活动是爆发的先兆;(5) 10.7cm射电辐射和软X射线辐射的逐日流量有彼此不重合的双峰.      

2011年8月4日螺旋状日冕物质抛射爆发事件

利用多卫星多波段的综合观测数据,通过追踪光球表面等离子体速度分析计算了耀斑爆发前后磁螺度的变化,发现耀斑爆发前活动区中光球表面存在强的水平剪切运动,活动区磁螺度的注入主要由这种剪切运动所产生;使用CESE-MHD-NLFFF重建了耀斑爆发前后活动区的磁场位形,推测出耀斑过程中存在磁绳结构的抛射.基于这些分析,给出了这一螺旋状抛射结构的形成机制:爆发前暗条西侧足点的持续剪切运动驱动磁通量绳增加扭转,高度扭缠的通量绳与东侧足点附近的开放磁力线重联并与东侧足点断开,进而向外抛出并伴随解螺旋运动.另外,利用1AU处WIND卫星的观测数据在对应的行星际日冕物质抛射中找到典型磁云的观测特征.这表明除了传统上双足点均在太阳表面的磁云模型,这种单足点固定于太阳表面的磁通量绳爆发图景同样可能在行星系际空间形成磁云结构.研究结果对进一步认识磁云结构具有重要意义.      

基于活动区面积统计的F10.7指数预报方法

太阳10.7cm射电流量（F_10.7）是反映太阳整体活动的重要指标,其主要源头是日面活动区.F_10.7指数与日面活动区具有显著的相关性,且不同面积的活动区与F_10.7并不遵循相同的线性关系.为进一步提高F_10.7预报的准确性,利用日面活动区面积与F_10.7的相关性,依据面积大小分类,提出F_10.7的预报公式并进行验证.采用2008-2018年SWPC （Space Weather Prediction Center）公布的活动区面积数据和CSWFC （Canadan Space Weather Forecast Center）公布的F_10.7实测数据计算预报公式系数,利用高年（2003年）和低年（1997年）的F_10.7预报验证其结果.研究结果表明,预报结果与实测值的相关系数分别为0.9318和0.9295,二者皆优于SWPC同时期的预报结果（相关系数分别为0.9186和0.8771）.本研究首次基于活动区的变化预测了F_10.7,提高了F_10.7预测的准确性.      

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.      

Detailed analysis of dynamic evolution of three Active Regions at the photospheric level before flare and CME occurrence

We present a combined analysis of the applications of the weighted horizontal magnetic gradient (denoted as ${\mathit{WG}}_M$ in Korsós et al. (2015)) method and the magnetic helicity tool (Berger and Field, 1984) employed for three active regions (ARs), namely NOAA AR 11261, AR 11283 and AR 11429. We analysed the time series of photospheric data from the Solar Dynamics Observatory taken between August 2011 and March 2012. During this period the three ARs produced a series of flares (eight M- and six X-class) and coronal mass ejections (CMEs). AR 11261 had four M-class flares and one of them was accompanied by a fast CME. AR 11283 had similar activities with two M- and two X-class flares, but only with a slow CME. Finally, AR 11429 was the most powerful of the three ARs as it hosted five compact and large solar flare and CME eruptions. For applying the ${\mathit{WG}}_M$ method we employed the Debrecen sunspot data catalogue, and, for estimating the magnetic helicity at photospheric level we used the Space-weather HMI Active Region Patches (SHARP’s) vector magnetograms from SDO/HMI (Solar Dynamics Observatory/Helioseismic and Magnetic Imager). We followed the evolution of the components of the ${\mathit{WG}}_M$ and the magnetic helicity before the flare and CME occurrences. We found a unique and mutually shared behaviour, called the U-shaped pattern, of the weighted distance component of ${\mathit{WG}}_M$ and of the shearing component of the helicity flux before the flare and CME eruptions. This common pattern is associated with the decreasing-receding phases yet reported only known to be a necessary feature prior to solar flare eruption(s) but found now at the same time in the evolution of the shearing helicity flux. This result leads to the conclusions that (i) the shearing motion of photospheric magnetic field may be a key driver for solar eruption in addition to the flux emerging process, and that (ii) the found decreasing-approaching pattern in the evolution of shearing helicity flux may be another precursor indicator for improving the forecasting of solar eruptions.     

Analysis of a long-duration AR throughout five solar rotations: Magnetic properties and ejective events

Coronal mass ejections (CMEs), which are among the most magnificent solar eruptions, are a major driver of space weather and can thus affect diverse human technologies. Different processes have been proposed to explain the initiation and release of CMEs from solar active regions (ARs), without reaching consensus on which is the predominant scenario, and thus rendering impossible to accurately predict when a CME is going to erupt from a given AR. To investigate AR magnetic properties that favor CMEs production, we employ multi-spacecraft data to analyze a long duration AR (NOAA 11089, 11100, 11106, 11112 and 11121) throughout its complete lifetime, spanning five Carrington rotations from July to November 2010. We use data from the Solar Dynamics Observatory to study the evolution of the AR magnetic properties during the five near-side passages, and a proxy to follow the magnetic flux changes when no magnetograms are available, i.e. during far-side transits. The ejectivity is studied by characterizing the angular widths, speeds and masses of 108 CMEs that we associated to the AR, when examining a 124-day period. Such an ejectivity tracking was possible thanks to the multi-viewpoint images provided by the Solar-Terrestrial Relations Observatory and Solar and Heliospheric Observatory in a quasi-quadrature configuration. We also inspected the X-ray flares registered by the GOES satellite and found 162 to be associated to the AR under study. Given the substantial number of ejections studied, we use a statistical approach instead of a single-event analysis. We found three well defined periods of very high CMEs activity and two periods with no mass ejections that are preceded or accompanied by characteristic changes in the AR magnetic flux, free magnetic energy and/or presence of electric currents. Our large sample of CMEs and long term study of a single AR, provide further evidence relating AR magnetic activity to CME and Flare production.     

Solar filament eruptions and their physical role in triggering coronal mass ejections

Solar filament eruptions play a crucial role in triggering coronal mass ejections (CMEs). More than 80% of eruptions lead to a CME. This correlation has been studied extensively during the past solar cycles and the last long solar minimum. The statistics made on events occurring during the rising phase of the new solar cycle 24 is in agreement with this finding. Both filaments and CMEs have been related to twisted magnetic fields. Therefore, nearly all the MHD CME models include a twisted flux tube, called a flux rope. Either the flux rope is present long before the eruption, or it is built up by reconnection of a sheared arcade from the beginning of the eruption.     

基于长短期记忆神经网络的太阳耀斑短期预报

提出了一个基于长短期记忆神经网络的耀斑预报模型,利用过去24 h太阳活动区的磁场变化时序构建样本,通过长短期记忆神经网络对磁场特征时序演化进行分析,预报未来48 h内是否发生≥M级别耀斑事件。使用的数据集为2010年5月到2017年5月所有活动区样本,选取了SDO/HMI SHARP的10个磁场特征参量。在建模过程中通过XGBoost方法选取权重、增益率和覆盖率均较高的6个特征参量作为输入参数。通过测试对比,模型的虚报率和准确率与传统机器学习模型相近,报准率和临界成功指数分别为0.7483和0.7402,优于传统机器学习模型。模型总体效果优于传统机器学习模型。      

Correlation between solar flare productivity and photospheric vector magnetic fields

Studying the statistical correlation between the solar flare productivity and photospheric magnetic fields is very important and necessary. It is helpful to set up a practical flare forecast model based on magnetic properties and improve the physical understanding of solar flare eruptions. In the previous study ([Cui, Y.M., Li, R., Zhang, L.Y., He, Y.L., Wang, H.N. Correlation between solar flare productivity and photospheric magnetic field properties 1. Maximum horizontal gradient, length of neutral line, number of singular points. Sol. Phys. 237, 45–59, 2006]; from now on we refer to this paper as ‘Paper I’), three measures of the maximum horizontal gradient, the length of the neutral line, and the number of singular points are computed from 23990 SOHO/MDI longitudinal magnetograms. The statistical relationship between the solar flare productivity and these three measures is well fitted with sigmoid functions. In the current work, the three measures of the length of strong-shear neutral line, total unsigned current, and total unsigned current helicity are computed from 1353 vector magnetograms observed at Huairou Solar Observing Station. The relationship between the solar flare productivity and the current three measures can also be well fitted with sigmoid functions. These results are expected to be beneficial to future operational flare forecasting models.     

A study of acoustic halos in active region NOAA 11330 using multi-height SDO observations

We analyze data from the Helioseismic Magnetic Imager (HMI) and the Atmospheric Imaging Assembly (AIA) instruments on board the Solar Dynamics Observatory (SDO) to characterize the spatio-temporal acoustic power distribution in active regions as a function of the height in the solar atmosphere. For this, we use Doppler velocity and continuum intensity observed using the magnetically sensitive line at 6173?Å as well as intensity at 1600?Å and 1700?Å. We focus on the power enhancements seen around AR 11330 as a function of wave frequency, magnetic field strength, field inclination and observation height. We find that acoustic halos occur above the acoustic cutoff frequency and extends up to 10?mHz in HMI Doppler and AIA 1700?Å observations. Halos are also found to be strong functions of magnetic field and their inclination angle. We further calculate and examine the spatially averaged relative phases and cross-coherence spectra and find different wave characteristics at different heights.