利用F_(10.7)短期预报电离层F_0F_2

通过对电离层历史数据和太阳射电流量F10.7的回归分析,提出了一种单站电离层f0F2的短期预报方法,以F10.7的流动平均值fc为输入,以未米3天的f0F2为输出,分别利用中国地区8个台站的数据进行检验,分析不同太阳活动水平、季节以及地方时预报误差的分布特征.结果表明,该方法能有效地预测未来1～3天的f0F2.该方法还可应用于其他电离层参量的短期预报.     

太阳10.7cm射电流量中期预报模型研究(Ⅰ)

采用时间序列模型中的自回归方法开展了F_(10.7)中期预报研究.预报试验和误差分析结果表明,在太阳活动水平较低、F_(10.7)的27天周期性明显时自回归预报方法的预报精度高,具有较为理想的预报效果,但在日面有大活动区产生和消亡时预报效果不理想.这说明时间序列模型中的自回归方法能够较好地反映太阳F_(10.7)的27天周期性特征,对F_(10.7)中期预报模型的建立有一定适用性.通过对2005年9月21日至2007年6月7日期间预报结果的比较可以看出,自回归分析方法预报的精度与美国空军预报的相当。     

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.      

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.      

用开环门限自回归分析方法探讨短波衰退事件的预报

本文基于电离层突然骚扰对强太阳活动的依赖关系,用开环门限自回归分析方法,以每日最大2800MHz射电爆发峰值流量为预报因子,探讨短波衰退事件(SWF)的预报.通过对1980.1—6月和1980.1—2月的短波衰退事件作中、短期预报的检验,表明,用此方法作中期预报的效果优于经验方法的预报;用于短期预报(3天)效果较好.      

Application of support vector machine combined with K-nearest neighbors in solar flare and solar proton events forecasting

The support vector machine (SVM) combined with K-nearest neighbors (KNN), called the SVM-KNN method, is new classing algorithm that take the advantages of the SVM and KNN. This method is applied to the forecasting models for solar flares and proton events. For the solar flare forecasting model, the sunspot area, the sunspot magnetic class, and the McIntosh class of sunspot group and 10 cm solar radio flux are chosen as inputs; for the solar proton event forecasting model, the inputs include the longitude of active regions, the flux of soft X-ray, and those for the solar flare forecasting model. Detailed tests are implemented for both of the proposed forecasting models, in which the SVM-KNN and the SVM methods are compared. The testing results demonstrate that the SVM-KNN method provide a higher forecasting accuracy in contrast to the SVM. It also gives an increased rate of ‘Low’ prediction at the same time. The ‘Low’ prediction means occurrence of solar flares or proton events with predictions of non-occurrence. This method show promise for forecasting models of solar flare and proton events.     

奇异谱分析在太阳10.7cm射电流量中期预测中的应用

首次尝试利用信号处理技术奇异谱分析方法预测太阳活动低年未来27天太阳10．7cm射电流量．选取的预报试验时间段是2004年4月30日至5月30日,此试验期内太阳活动水平相对较低．在样本时间序列的构建上,吸取了相似周数据分析思路,采取的是23周实时观测数据与其相似周第20周下降年部分数据相结合的方式,既增加了样本长度又避开了太阳活动的活跃期．这31天的预报试验结果表明,大部分情况下,预报值基本上体现出F10．7的变化趋势,平均相对误差为10．5％;比同时期美国空军预测值的平均相对误差小,前者为11．3％,后者为14．6％;除两天外,SSA每一次27天的预报结果的平均相对误差比美国空军(AAF)的要小;对不同的时间提前量而言,AAF提前1天到提前12天的预报准确性较奇异谱分析方法要高,即AAF较短期的预报效果更好．     

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.     

Predicting indices of the ionosphere response to solar activity for the ascending phase of the 25th solar cycle

The international reference ionosphere, IRI, and its extension to plasmasphere, IRI-Plas, models require reliable prediction of solar and ionospheric proxy indices of solar activity for nowcasting and forecasting of the ionosphere parameters. It is shown that IRI prediction errors could increase for the F2 layer critical frequency foF2 and the peak height hmF2 due to erroneous predictions of the ionospheric global IG index and the international sunspot number SSN1 index on which IRI and IRI-Plas models are built. Regression relation is introduced to produce daily SSN1 proxy index from new time series SSN2 index provided from June 2015, after recalibration of sunspots data. To avoid extra errors of the ionosphere model a new solar activity prediction (SAP) model for the ascending part of the solar cycle SC25 is proposed which expresses analytically the SSN1 proxy index and the 10.7-cm radio flux F10.7 index in terms of the phase of the solar cycle, Φ. SAP model is based on monthly indices observed during the descending part of SC24 complemented with forecast of time and amplitude for SC25 peak. The strength of SC25 is predicted to be less than that of SC24 as shown with their amplitudes for eight types of indices driving IRI-Plas model.     

基于深度学习递归神经网络的电离层总电子含量经验预报模型

利用行星际太阳风参数与太阳活动指数、地磁活动指数、电离层总电子含量格点化地图数据,首次基于一种能处理时间序列的深度学习递归神经网络（Recurrent Neural Network,RNN）,建立提前24h的单站电离层TEC预报模型.对北京站（40°N,115°E）的预测结果显示,RNN对扰动电离层的预测误差低于反向传播神经网络（Back Propagation Neural Network,BPNN）0.49～1.46TECU,将太阳风参数加入预报因子模型后对电离层正暴预测准确率的提升可达16.8%.RNN对2001和2015年31个强电离层暴预报的均方根误差比BPNN低0.2TECU,将太阳风参数加入RNN模型可使31个事件的平均预报误差降低0.36～0.47TECU.研究结果表明深度递归神经网络比BPNN更适用于电离层TEC的短期预报,且在预报因子中加入太阳风数据对电离层正暴的预报效果有明显改善.      

Total electron content at low latitudes and its comparison with the IRI90

We used total electron content (TEC) data measured by Faraday rotation technique over Cachoeira Paulista (22.5°S, 45°W), in Brazil, to study the TEC variations with the solar flux at 10.7 cm (F10.7) and to compare the results with the IRI90 predictions. The data were divided into summer, equinox and winter. During the analysed period F10.7 varied from 66 up to 330. Our data shows that the observed TEC at 1600 LT (around the diurnal maximum) and at 0500 LT (around the diurnal minimum) increases with F10.7 until saturation is reached which occurs at F10.7≈210 to 220 for equinox and summer, and at F10.7≈180 for winter months. Comparison with the IRI90 predictions shows that IRI overestimates the TEC at 0500 LT for all solar flux values. At 1600 LT, IRI overestimates the observed TEC for low solar flux but underestimates it for high solar flux values.     

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.     

Solar activity prediction studies and services in NAOC

Solar activity prediction services started in 1960’s in National Astronomical Observatories, Chinese Academy of Sciences (NAOC). As one of the members of the International Space Environment Service (ISES), Regional Warning Center of China (RWC-China) was set up in 1990’s. Solar Activity Prediction Center (SAPC), as one of the four sub-centers of RWC-China, is located in NAOC. Solar activity prediction studies and services in NAOC cover short-term, medium-term, and long-term forecast of solar activities. Nowadays, certain prediction models, such as solar X-ray flare model, solar proton event model, solar 10 cm radio flux model, have been established for the practical prediction services. Recently, more and more physical analyses are introduced in the studies of solar activity prediction, such as the magnetic properties of solar active regions and magnetic structure of solar atmosphere. Besides traditional statistics algorithms, Machine Learning and Artificial Intelligence techniques, such as Support Vector Machine (SVM) method, are employed in the establishment of forecast models. A Web-based integrated platform for solar activity data sharing and forecast distribution is under construction.     

Variations of mid-term periodicities in solar activity physical phenomena

In this work we make an analysis of significant periodicities shown by phenomena linked to solar activity such as coronal hole area, radio emission in the 10.7 cm band and sunspots. We use the wavelet method that gives information in the frequency and time domains. Of particular interest are the mid-term periodicities (1–2 yrs). Over the whole period, coronal holes and radio variations show an important annual variation and a quasi-biannual periodicity. The increase in these variations is most important around the years of maximum solar activity. When the time series are separated in low and high frequencies, the latter are modulated by the general solar cycle. Although somewhat shifted in frequency, these periodicities might well correspond with those found in cosmic ray intensity, solar magnetic flux and other terrestrial and interplanetary phenomena as a wavelet coherence analysis of these series with the solar magnetic flux reveals.     

A nonlinear background removal method for seismo-ionospheric anomaly analysis under a complex solar activity scenario: A case study of the M9.0 Tohoku earthquake

A precise determination of ionospheric total electron content (TEC) anomaly variations that are likely associated with large earthquakes as observed by global positioning system (GPS) requires the elimination of the ionospheric effect from irregular solar electromagnetic radiation. In particular, revealing the seismo-ionospheric anomalies when earthquakes occurred during periods of high solar activity is of utmost importance. To overcome this constraint, a multiresolution time series processing technique based on wavelet transform applicable to global ionosphere map (GIM) TEC data was used to remove the nonlinear effect from solar radiation for the earthquake that struck Tohoku, Japan, on 11 March, 2011. As a result, it was found that the extracted TEC have a good correlation with the measured solar extreme ultraviolet flux in 26–34 nm (EUV_26–34) and the 10.7 cm solar radio flux (F_10.7). After removing the influence of solar radiation origin in GIM TEC, the analysis results show that the TEC around the forthcoming epicenter and its conjugate were significantly enhanced in the afternoon period of 8 March 2011, 3 days before the earthquake. The spatial distributions of the TEC anomalous and extreme enhancements indicate that the earthquake preparation process had brought with a TEC anomaly area of size approximately 1650 and 5700 km in the latitudinal and longitudinal directions, respectively.     

NRLMSISE-00大气模型与GRACE和CHAMP卫星大气密度数据的对比分析

利用GRACE(Gravity Recovery And Climate Experiment)和CHAMP(Challenging Mini-Satellite Payload)卫星2002-2008年的大气密度数据与NRLMSISE-00大气模型密度结果进行比较,分析了模型密度误差及其特点.结果显示,NRLMSISE-00大气模型计算的密度值普遍偏大,其相对误差随经纬度变化,在高纬度相对较小;相对误差随地方时变化,在02:00LT和15:00LT左右较大,10:00LT和20:00LT左右较小.通过模型密度相对误差与太阳F_10.7指数的对比分析发现,在太阳活动低年模型相对误差最大,而在太阳活动高年相对误差较小;将模型结果分别与GRACEA/B双星和CHAMP卫星的密度数据进行比较,发现对于轨道高度更高的GRACE卫星轨道,模型相对误差更大;在地磁平静期,相对误差与地磁ap指数(当前3h)相关性不强,但是在大磁暴发生时,误差急剧增大.      

太阳10.7 cm射电辐射流量预报方法初探

引进“相似周”方法,通过两种方式对第23周太阳10.7cm辐射流量（F10.7）月均值进行预报和预报,由“相似周”方法得到的第23周太阳黑子数月均平滑预测值来预测F10.7月均值和直接采用“相似周”方法对F10.7月均平滑值进行预测,通过对两种预报试验结果分析,得到以下结论。（1）两种预测结果与实际都比较吻合,都得到双峰结构。（2）直接通过相似周预测的F10.7的月均值结果较间接通过太阳黑子数的预测结果所推断的F10.7预测结果更接近实际观测结果。（3）使用“相似周”,预报方法,可以给出F10.7比较精细的剖面结构,这是其他普通预报方法很难做到的。     

Climatology of global,hemispheric and regional electron content variations during the solar cycles 23 and 24

We present the results of study on the variations of ionospheric total electron content (TEC) by using global, hemispheric, and regional electron contents computed from the global ionospheric maps (GIMs) for the period from 1999 to 2020. For a low and moderate solar activity, the global and regional electron contents vary linearly with solar 10.7 cm radio flux and EUV flux. While a saturation effect in the electron content verses EUV and F10.7 is found during the high solar activity periods at all regions, the maximum effect is observed at low-latitudes followed by high and mid-latitudes region. The extent of saturation effect is more pronounced for F10.7 as compared to EUV. A wavelet transform is applied to global and hemispheric electron contents to examine the relative strength of different variations. The semi-annual variations dominate in the northern hemisphere, whereas annual variations dominate in the southern counterpart. The amplitude of annual variations in southern hemisphere is found to be higher than northern counterpart at all latitudes. This asymmetry in the amplitude of annual variation is maximum at low-latitudes, followed by mid and high-latitudes, respectively. The semi-annual variations are in-phase in both hemisphere and follow the solar cycle. The northern hemisphere depicts relatively large amplitude of semi-annual variations and exhibit the maximum effect at high-latitudes.     

双通道辐射量计算太阳极紫外辐射E10.7指数方法初探

从太阳极紫外辐射研究的重要性出发, 介绍了太阳极紫外辐射E_10.7指 数及其作用, 详细阐述了利用两个能道的太阳辐射观测值计算极紫外辐射E_10.7指数的计算方法. 利用该方法对实测太阳辐射数据进行处理, 计算获得了2000-2005年的每日E_10.7指数, 并将计算结果 与Solar2000模式的输出结果进行对比分析, 验证了该计算方法的可行性, 对比结果表明, 最大相对误差在20%以内, 平均相对误差均在10%以内.      

Variations of total electron content over high latitude region during the ascending phase of 24th solar cycle

The solar cycle variation and seasonal changes significantly affects the ionization process of earth’s ionosphere and required to be monitored in real time basis for regional level refinement of existing models. In view of this, the present study has been carried out by using the ionospheric Total Electron Content (TEC) data observed with the help of Global Ionospheric Scintillation and TEC monitoring (GISTM) system installed at Indian Antarctic Research Station, “Maitri” [70°46′00″S 11°43′56″E] during the ascending phase of 24th solar cycle. The daily values of solar extreme ultraviolet (EUV) flux (0.1–50?nm wavelength), 10.7?cm radio flux F_10.7 and Sunspot number (SSN) has been taken as a proxy to represent the solar cycle variation to correlate with TEC. The linear regression results revels better correlation of TEC with EUV flux rather than F_10.7 and SSN. Also, the EUV and TEC show better agreement during summer as compared to winter and equinox period. Correlation between TEC and EUV appears significantly noticeable during ten internationally defined quiet days of each month (stable background geophysical condition) as compared to the overall days (2010–2014). Further, saturation effect has been observed on TEC values during the solar maxima year 2014. The saturation effects are more prominent during the night hours of winter and equinox season due to transportation losses manifested by the equator-ward direction of meridional wind.