太阳辐射指数F_(10.7)的中期预报方法

在低轨卫星的轨道计算中需要输入太阳辐射指数,它常用来描述太阳活动对高层大气密度的直接影响以及对轨道摄动的间接影响.因此太阳辐射指数的精度将影响轨道预报的精度.以太阳活动27 d短期震荡规律为基础,研究了一种利用135 d的辐射指数历史数据对F_(10.7)进行54 d中期预报的方法,能够预测太阳在未来2个自转周内辐射指数的变化.通过与其他预报方法的比较,表明:(1)该方法显著优于传统的三角函数长期预报法;(2)短期预报7 d时方法略优于美国空间天气预报中心(Space Weather Prediction Center,SWPC)的方法,RMS(Root Mean Square)下降约19%;(3)中期预报27 d时该方法与国内常用的54阶自回归模型精度基本相当,但方法的参数和需要的历史资料都明显减少,在轨道计算中使用更为简便,而且精度稳定,在54 d时预报值和实测值之间的相关系数仍然优于0.92.该方法的特点是只利用辐射指数较少的历史资料,不需要额外的太阳观测资料作支撑,能进行长达54 d的中期预报,为航天任务中的轨道中短期预报提供合理、可靠的辐射指数.     

10.7cm射电流量月平均变化的动力行为和可预报性研究

本文用非线性动力系统理论探讨了10.7cm射电流量月平均变化的动力行为和可预报性,计算了该过程的分维数(D=3.1±0.1)和最大Lyapunov指数(λ_1=0.045±0.003 bit/月).结果表明,这是一个具有有限自由度的复杂的浑沌系统,可用有限个参数描述,所需的变量最少是4个,最多为6个.从10.7cm射电流量月平均变化说明了太阳活动的浑沌行为.本文还讨论了10.7cm射电流量月均值的可预报时间尺度,平均可预报时间尺度为8个月,最大可预报时间尺度是22个月,     

Temporal variation of solar flare index during solar cycles 21–24

The present investigation attempts to quantify the temporal variation of Solar Flare Index(SFI)with other activity indices during solar cycles 21-24 by using different techniques such as linear regression,correlation,cross-correlation with phase lag-lead,etc.Different Solar Activity Indices(SAI)considered in this present study are Sunspot Number(SSN),10.7 cm Solar Radio Flux(F10.7),Coronal Index(CI)and MgⅡCore-to-Wing Ratio(MgⅡ).The maximum cycle amplitude of SFI and considered SAI has a decreasing trend from solar cycle 22,and cycle 24 is the weakest solar cycle among all other cycles.The SFI with SSN,F10.7,CI and MgⅡshows hysteresis during all cycles except for solar cycle 22 where both paths for ascending and descending phases are intercepting each other,thereby representing a phase reversal.A positive hysteresis circulation exists between SFI and considered SAI during solar cycles 22 and 23,whereas a negative circulation exists in cycles 21 and 24.SFI has a high positive correlation with coefficient values of 0.92,0.94,0.84 and 0.81 for SSN,F10.7,CI and MgⅡrespectively.According to crosscorrelation analysis,SFI has a phase lag with considered SAI during an odd-number solar cycle(solar cycles21 and 23)but no phase lag/lead during an even-numbered solar cycle(solar cycles 22 and 24).However,the entire smoothed monthly average SFI data indicate an in-phase relationship with SSN,F10.7 and MgⅡ,and a one-month phase lag with CI.The presence of those above characteristics strongly confirms the outcomes of different research work with various solar indices and the highest correlation exists between SFI and SSN as well as F10.7 which establishes that SFI may be considered as one of the prime activity indices to interpret the characteristics of the Sun’s active region as well as for more accurate short-range or long-range forecasting of solar events.     

利用小波分析研究近地空间高能电子的周期变化特征

利用小波变换对GOES (Geostationary Operational Environmental Satellites)系列卫星(GOES 10/11) 1999年3月至2010年12月和风云2号系列卫星(FY 2C/2D) 2004年10月至2012年5月记录的2 MeV高能电子通量变化情况进行了相关研究,发现GOES卫星观测到的高能电子通量存在明显的13.9 d、 27.7 d、 187.0 d和342.9 d周期, FY卫星观测到的高能电子通量存在明显的13.9 d、27.7 d、222.3 d和374.0 d周期,在某些年份GOES和FY卫星均存在9 d的周期,与地磁Dst (赤道环电流指数)、 AE (极光电射流指数)指数周期高度相似.将高能电子通量和Dst、AE指数进行交叉小波分析,并利用该算法的多分辨率特点以及时域、频域局部化分析方法,将数据按不同频率进行分解,从低频系数重构图像和交叉小波谱图可以清楚看出高能电子通量和地磁指数的关系.基于FY和GOES卫星高能电子通量良好的相关性,对多卫星高能电子通量变化短周期相同、中长周期不同进一步研究,对比发现不同地磁扰动引起的GOES和FY卫星高能电子通量变化存在各向异性,小磁暴也可以对高能电子通量造成和强磁暴一样的效果,并且某些时候存在地方时一致的24 h周期.这一结果表明对地磁宁静期高能电子研究至关重要,同时对理解太阳活动,预报高能电子能谱和预警深层充电事件以及验证预测磁暴、亚暴等事件具有重要意义.     

热层大气密度对太阳辐射27 d短期震荡的响应规律

在近地空间目标和碎片的定轨预报任务中,需要准确计算热层中性大气密度。太阳辐射约27 d周期的短期震荡是影响大气密度的一种重要的空间环境因素,它会引起大气密度在全球尺度上的涨落。选择CHAMP、GRACE-A和SWARM-C这3颗极轨卫星星载加速仪数据推导出的大气密度资料,提取其中的27 d短期变化信号,与太阳极紫外辐射(S₁₀指数)的27 d短期变化进行多元回归分析,研究热层大气密度对太阳辐射短期变化的响应规律,及其在不同高度、辐射水平、昼夜半球和纬度等方面的差异。结果表明：太阳辐射短期变化对大气密度的影响与高度负相关(主要因素)、与辐射水平正相关(次要因素);对白天半球的影响是夜间半球的约2倍;在白天半球,辐射影响随着纬度增大而减弱,夜间半球恰好相反。将以上观测结果与NRLMSISE00模型模拟结果对比,发现模型低估了太阳辐射短期变化对大气密度影响的(50%～60%),且低估了高度差异,但高估了辐射水平差异。利用SOHO卫星对太阳26～34 nm波长极紫外辐射1 h分辨率的测量值,研究了大气密度对辐射的响应延迟时间,约为18 h (0.75 d)。研究结果对于优...     

基于混沌时间序列的Volterra自适应滤波极移预报方法

针对极移复杂的时变特性, 根据混沌相空间坐标延迟重构理论, 提出一种基于Volterra自适应滤波的极移预报方法. 首先, 利用最小二乘拟合算法分离极移序列中的线性趋势项、钱德勒项和周年项, 获得线性极移、钱德勒极移和周年极移的外推值; 其次, 通过C-C关联积分法对最小二乘拟合残差序列进行相空间重构, 并利用小数据量法计算残差序列的最大Lyapunov指数验证其混沌特性, 在此基础上, 构建Volterra自适应滤波器对残差序列进行预测; 最后, 将线性极移、钱德勒极移和周年极移的外推值以及最小二乘拟合残差的预测值相加获得极移最终预报值. 利用国际地球自转服务局(International Earth Rotation and Reference Systems Service, IERS)提供的极移数据进行1--60d跨度预报, 并将预报结果分别与国际地球定向参数预报比较竞赛(Earth Orientation Parameters Prediction Comparison Campaign, EOP PCC)结果和IERS A公报发布的极移预报产品进行对比, 结果表明: 对于1--30d的短期预报, 该方法的预报精度与EOP PCC最优预报方法相当, 当预报跨度超过30d时, 该方法的预报精度低于EOP PCC最优预报方法, 优于参与EOP PCC的其他方法; 与IERS A公报相比, 该方法的短期预报效果较好, 当预报跨度增加时预报精度低于IERS A公报. 预报结果表明该方法更适合于极移短期预报.     

太阳总辐照的演化特征分析

太阳总辐照在23和24太阳活动周的显著周期分别为35 d和26 d,进而推断太阳的准旋转周期在23和24太阳活动周也分别为35 d和26 d.太阳总辐照在24周极小期的值可能与蒙德极小期的值相近.在一个太阳旋转周到几个月的时间尺度上,太阳黑子是引起太阳总辐照变化的主要原因,但不是唯一的原因;在几天到一个太阳旋转周的时间尺度上,太阳总辐照的变化与MgⅡ特征指数是不相关的.     

遗传算法优化的BP神经网络卫星钟差预报

针对BP (Back Propagation)神经网络模型预测卫星钟差中权值和阈值的最优化问题, 提出了基于遗传算法优化的BP神经网络卫星钟差短期预报模型, 给出了遗传算法优化BP神经网络的基本思想、具体方法和实施步骤. 为验证该优化模型的有效性和可行性, 利用北斗卫星导航系统(BeiDou navigation satellite system, BDS)卫星钟差数据进行钟差预报精度分析, 并将其与灰色模型(GM(1,1))和BP神经网络模型预报的结果比较分析. 结果表明: 该模型在短期钟差预报中具有较好的精度, 优于GM(1,1)模型和BP神经网络模型.     

基于误差发散规律的低轨卫星大气阻力系数计算方法

低轨卫星轨道预报精度受到大气模型和大气阻力系数精度的制约,给一些高精度的空间和航天任务带来不利影响.提出了一种基于沿迹方向误差发散规律的大气阻力系数计算新方法.首先通过理论推导给出低轨卫星轨道预报中沿迹误差发散的分析表达式,定量描述初值误差和模型误差对沿迹误差的综合影响;提出利用定轨段的基本信息,优选预报段所采用的阻力系数,抑制沿迹误差的发散速率,从而降低沿迹方向预报误差的最大值,提高短期预报精度.以400 km附近的GRACE-A卫星的全弧段星载GPS高精度资料为基础,检验了方法的精度和成功率.结果表明:相对于传统的定轨预报方法,新方法能提高24 h短期预报精度约45%,成功率约71%,总体有效率约86%;方法对低、中、高等3种太阳辐射水平均有效,对于中低等级的地磁扰动也有效,具备较好的应用价值.     

太阳质子事件耀斑的短期预报

回顾产生太阳质子事件耀斑的短期预报,讨论短期预报在近期应做的研究。给出以下结论：（１）在６０年代和７０年代,质子事件耀斑的预报有相当大的进展;（２）新预报方法的探索和质子流在日冕与行星际的传播问题,是当前改进短期预报的关键;（３）对实际应用的短期预报工作的改进,可能需要从空间天气预报的角度,研究太阳活动区的分类。     

Solar cycle prediction using a long short-term memory deep learning model

In this paper,we propose a long short-term memory(LSTM)deep learning model to deal with the smoothed monthly sunspot number(SSN),aiming to address the problem whereby the prediction results of the existing sunspot prediction methods are not uniform and have large deviations.Our method optimizes the number of hidden nodes and batch sizes of the LSTM network structures to 19 and 20,respectively.The best length of time series and the value of the timesteps were then determined for the network training,and one-step and multi-step predictions for Cycle 22 to Cycle 24 were made using the well-established network.The results showed that the maximum root-mean-square error(RMSE)of the one-step prediction model was6.12 and the minimum was only 2.45.The maximum amplitude prediction error of the multi-step prediction was 17.2%and the minimum was only 3.0%.Finally,the next solar cycles(Cycle 25)peak amplitude was predicted to occur around 2023,with a peak value of about 114.3.The accuracy of this prediction method is better than that of the other commonly used methods,and the method has high applicability.     

太阳活动周期的小波分析

运用小波技术对太阳射电流量2800 MHz,太阳黑子数和太阳黑子面积数周期进行分析．其结果表明: (1)这3个系列的数据显示最显著的周期是10．69年,其他周期并不明显．(2)小波功率谱给出了全部时间-周期范围的功率谱变化,它显示了在某个周期处于某个时段的局部功率的变化,小波功率谱分析表明,小于1年的周期仅仅在太阳活动最大期附近比较明显．(3)太阳射电2800 MHz,太阳黑子数和太阳黑子面积数的几个周期(10．69年,5．11年, 155．5天)的小波功率谱比较相似,出现峰值的时间相同;曲线的起伏相似,周期越小,曲线起伏的频率越大．     

Forecast daily indices of solar activity, F10.7, using support vector regression method

The 10.7cm solar radio flux (F10.7), the value of the solar radio emission flux density at a wavelength of 10.7cm, is a useful index of solar activity as a proxy for solar extreme ultraviolet radiation. It is meaningful and important to predict F10.7 values accurately for both long-term (months-years) and short-term (days) forecasting, which are often used as inputs in space weather models. This study applies a novel neural network technique, support vector regression (SVR), to forecasting daily values of F10.7. The aim of this study is to examine the feasibility of SVR in short-term F10.7 forecasting. The approach, based on SVR, reduces the dimension of feature space in the training process by using a kernel-based learning algorithm. Thus, the complexity of the calculation becomes lower and a small amount of training data will be sufficient. The time series of F10.7 from 2002 to 2006 are employed as the data sets. The performance of the approach is estimated by calculating the norm mean square error and mean absolute percentage error. It is shown that our approach can perform well by using fewer training data points than the traditional neural network.     

What do the solar activity indices represent?

Sunspot number, sunspot area, and radio flux at 10.7 cm are the indices which are most frequently used to describe the long‐term solar activity. The data of the daily solar full‐disk magnetograms measured at Mount Wilson Observatory from 19 January 1970 to 31 December 2012 are utilized together with the daily observations of the three indices to probe the relationship of the full‐disk magnetic activity respectively with the indices. Cross correlation analyses of the daily magnetic field measurements at Mount Wilson observatory are taken with the daily observations of the three indices, and the statistical significance of the difference of the obtained correlation coefficients is investigated. The following results are obtained: (1) The sunspot number should be preferred to represent/reflect the full‐disk magnetic activity of the Sun to which the weak magnetic fields (outside of sunspots) mainly contribute, the sunspot area should be recommended to represent the strong magnetic activity of the Sun (in sunspots), and the 10.7 cm radio flux should be preferred to represent the full‐disk magnetic activity of the Sun (both the weak and strong magnetic fields) to which the weak magnetic fields mainly contribute. (2) On the other hand, the most recommendable index that could be used to represent/reflect the weak magnetic activity is the 10.7 cm radio flux, the most recommendable index that could be used to represent the strong magnetic activity is the sunspot area, and the most recommendable index that could be used to represent the full‐disk magnetic activity of the Sun is the 10.7cm radio flux. Additionally, the cycle characteristics of the magnetic field strengths on the solar disk are given. (© 2014 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

An Early Prediction of the Amplitude of Solar Cycle 25

A “Solar Dynamo” (SODA) Index prediction of the amplitude of Solar Cycle 25 is described. The SODA Index combines values of the solar polar magnetic field and the solar spectral irradiance at 10.7 cm to create a precursor of future solar activity. The result is an envelope of solar activity that minimizes the 11-year period of the sunspot cycle. We show that the variation in time of the SODA Index is similar to several wavelet transforms of the solar spectral irradiance at 10.7 cm. Polar field predictions for Solar Cycles 21?–?24 are used to show the success of the polar field precursor in previous sunspot cycles. Using the present value of the SODA index, we estimate that the next cycle’s smoothed peak activity will be about \(140 \pm30\) solar flux units for the 10.7 cm radio flux and a Version 2 sunspot number of \(135 \pm25\). This suggests that Solar Cycle 25 will be comparable to Solar Cycle 24. The estimated peak is expected to occur near \(2025.2 \pm1.5\) year. Because the current approach uses data prior to solar minimum, these estimates may improve as the upcoming solar minimum draws closer.     

Historical Dataset Reconstruction and a Prediction Method of Solar 10.7 cm Radio Flux

We reconstruct the developing history of solar 10.7 cm radio flux (F10.7) since 1848, based on the yearly sunspot number and the variations. A relationship between the maximum and the linear regression slope of the first 3 years starting from minimum of the solar cycle is considered. We put forward a method of predicting the maximum of F10.7 by means of the slope-maximum relationship. Running tests for cycles 19 to 23 indicate that the method can properly predict the peak of F10.7.     

An Analysis of the Variation of Solar Activity

Using the method of Morlet wavelet transform, we analysed the sunspot relative numbers and obtained some meaningful results. The solar activity possesses the periods of 10.7a and 101a, and the period of 10.7a is very prominent. The variation of intensity of the solar activity exhibits certain stages. In 1950 there occurred abrupt changes of climate, and since then the solar activity has become more and more intense. It is predicted that it would be weaker for some time interval in the future.     

Features of the Solar Active Cycles

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10.7-cm Solar radio flux and the magnetic complexity of active regions

During sunspot cycles 20 and 21, the maximum in smoothed 10.7-cm solar radio flux occurred about 1.5 yr after the maximum smoothed sunspot number, whereas during cycles 18 and 19 no lag was observed. Thus, although 10.7-cm radio flux and Zürich suspot number are highly correlated, they are not interchangeable, especially near solar maximum. The 10.7-cm flux more closely follows the number of sunspots visible on the solar disk, while the Zürich sunspot number more closely follows the number of sunspot groups. The number of sunspots in an active region is one measure of the complexity of the magnetic structure of the region, and the coincidence in the maxima of radio flux and number of sunspots apparently reflects higher radio emission from active regions of greater magnetic complexity. The presence of a lag between sunspot-number maximum and radio-flux maximum in some cycles but not in others argues that some aspect of the average magnetic complexity near solar maximum must vary from cycle to cycle. A speculative possibility is that the radio-flux lag discriminates between long-period and short-period cycles, being another indicator that the solar cycle switches between long-period and short-period modes.Operated by the Association of Universities for Research in Astronomy, Inc. under contract with the National Science Foundation.     

Automatic Detection of Sunspots and Extractionof Their Feature Parameters

Sunspots are solar features located in active regions of the Sun, whose number is an indicator of the Sun's magnetic activity. With a substantial increase in the quantity of solar image data, the automated detection and verification of various solar features have become increasingly important for the accurate and timely forecasts of solar activity and space weather. In order to use the high time-cadence SDO/HMI data to extract the main sunspot features for forecasting solar activities, we have established an automatic detection method of sunspots based on mathematical morphology, and calculated the sunspot group area and sunspot number. By comparing our results with those obtained from the Solar Region Summary compiled by NOAA/SWPC, it is found that the sunspot group areas and sunspot numbers computed with our algorithm are in good agreement with the active region values released by SWPC, and the corresponding correlation coefficients for the sunspot group area and sunspot number are 0.77 and 0.79, respectively. By using the method of this paper, the high time-cadence feature parameters can be obtained from the HMI data to provide the timely and accurate inputs for the solar activity forecast.