亚大地区F2电离层预测方法和CCIR方法的比较

本文将“国际参考电离层”所采用的CCIR频率预测方法和亚大地区F_2电离层预测方法预测的f_0F_2和M(3000)F_2,分别与第21太阳活动周的我国实测数据进行了比较。结果表明,从总体上看,在中国区域内亚大地区F_2电离层预测方法优于CCIR方法。文章建议,在没有更好方法的情况下,未来的“中国参考电离层”可以采用亚大地区F_2电离层预测方法。     

电离层暴时经验模型STORM在中国区域的适应性研究

利用中国区域内9个垂测站1976---1987年一个太阳活动周期的电离层暴时f0F2数据, 统计分析了电离层暴事件的等级, 以及不同等级的电离层暴随季节和地磁纬度的分布特征. 研究发现, 中小型电离层暴在春秋季发生的概率较大, 不同季节的发生次数与地磁纬度具有明显的关系. 利用STORM模型对电离层暴时f0F2和大型及特大型电离层暴时f0F2的预测值与月中值进行了比较. 结果表明, 除了冬季误差增大外, 发生电离层暴时STORM模型能够有效地改善月中值模型. 增加中国的暴时数据, 并提高对冬季的暴时参数f0F2的预测是改善STORM模型的重要因素. 建立合适的暴时指数来预测f0F2是未来研究的重点.      

NeQuick模式在亚太地区扇区电离层参数f0F2的预测能力

利用亚洲、澳大利亚地区8个电离层观测台站的F2层临界频率f0F2的历史观测数据,考察了NeQuick模式预报电离层基本参数f0F2在亚太扇区的适应性.对比分析表明,此模式能比较好地预测各地的F2层临界频率,其绝对误差在南半球各站相对北半球各站较大,太阳活动高年相对太阳活动低年较大,春秋季相对夏冬季较大.其误差均方根在太阳活动高年相对太阳活动低年较大.      

利用神经网络预报电离层f0F2

由中国武汉电离层台站和澳大利亚Hobart台站的电离层F2层临界频率(f0F2)的资料,利用三层前向反馈神经网络(BP网络),提出一种提前24h预测f0F2的方法,该方法以前5天观测的f0F2数据拟合的5个系数以及太阳活动参数作为输入,以当天24 h的f0F2作为输出对网络进行训练,训练好的网络可以实现对f0F2提前24 h的预报.预测结果显示,利用神经网络预测的f0F2与实际观测结果变化趋势较一致,并且比IRI的计算结果更加准确.误差分析表明,在南半球Hobart(-42.9°,147.3°)台站比中国武汉站(30.4°,114.3°)的结果要好,在低年比高年要好,在冬夏季节比春秋季节稍好.本文说明利用神经网络对电离层参量进行预报是一种切实可行的方法.     

台风影响电离层F2区的一种可能机制

在台风期间,特别是台风登陆前后,强烈的海气、陆气相互作用会增强低层大气中的湍流活动,并可能导致大气湍流层顶的抬升.这种抬升会改变高层大气结构,从而影响高层大气中的光化学过程,最终造成对电离层的影响.在台风活动抬升了湍流层顶的前提下,利用一个一维电离层物理模型,模拟了日本中纬地区(45°N,142°E)电离层F2区的响应.模拟结果很好地定性解释了如下观测事实,台风期间,电离层f0F2会下降,对给定频率电波的反射面会抬升;同时还表明以上过程会导致hmF2上升,这表明台风期间湍流层顶的抬升可能是台风影响电离层F2区的一种十分有效的机制.      

集合卡尔曼滤波在电离层短期预报中的应用

提出了一种利用集合卡尔曼滤波对电离层f0F2短期预报结果进行优化的方法. 利用训练好的神经网络对f0F2进行提前1~24 h的预报, 考虑前一天预报误差的反馈信息, 动态跟踪 f0F2的变化趋势, 引入集合卡尔曼滤波对神经网络的预报结果实行进一步修正和优化. 实验结果表明, 此方法的预报效果优于单纯的神经网络模型和IRI模型. 此方法还可以应用于其他电离层参量的短期预报.      

2009年7月22日日全食期间电离层参量的变化

利用多个电离层垂测站的数据和IGS-TEC数据资料, 结合日地空间环境指数, 分析了2009年7月22日日全食期间中国地区电离层参量(反射回波最低频率fmin及f0F2和TEC)的变化特征. 结果表明, 日食发生后fmin迅速降低, 日食结束后fmin迅速恢复到正常水平; 在食甚时刻附近, f0F2和TEC出现明显的降低, 显示了明显的光食效应. 日食结束后5~6 h, f0F2和TEC出现不同程度的正扰动, 在驼峰区更明显; 日食结束后9~10 h, f0F2和TEC出现较显著的负扰动. 由于此次日食发生时伴随着中等强度的磁暴和低纬电场穿透等空间天气事件, 给此次日食电离层效应的深入分析带来很大困难.      

地磁场与电离层异常现象及其与地震的关系

利用中国地磁台网与电离层台站资料,总结了大地震前出现的地磁低点位移、地磁日变异常及电离层f0F2(F2层临界频率)异常现象.对比研究了1997年11月8日玛尼7.5级与2001年11月14日昆仑山口西8.1级地震前磁场与电离层异常分布及特征.结果显示,两次巨大地震前磁场与电离层短临异常时空分布特征有较好的一致性,震中周围出现日变异常、拉萨台出现电离层f0F2明显异常;震前约1个月出现地磁低点位移,其突变分界线通过震中地区.      

武汉地区电离层TEC和NmF2及板厚的季节变化

通过利用武汉电离层观测站(114.4°E,30.6°N)1980-1990年对E8T-Ⅱ卫星信标的法拉第旋转测量的TEC(电子浓度总含量)数据,以及由测高仪测量的1980-1990年间的f0F2(F2层临界频率)数据,研究了武汉地区TEC,NmF2(最大电子浓度)和板厚的季节变化,同时比较了IRI和武汉单站模式在预测NmF2季节性方面的有效性.武汉单站模式在预测NmF2季节性变化方面优于IRI模式.      

基于地磁坐标和Kriging技术的高纬地区f0F2重构方法

为能够在高纬区域获取高精度电离层参数特性结果,提出了基于地磁坐标的高纬度区域电离层F₂层临界频率（f₀F₂）的重构方法.该方法确定了基于地磁坐标的变异函数,通过求解改进Kriging方程得出估计值.方法的确定取决于对2种坐标系、2类电离层距离计算方法以及尺度因子的选取.通过对俄罗斯6个垂直探测站在太阳活动高年（2013年）和低年（2017年）的f₀F₂历史观测数据使用月中值进行交叉验证,证明了引入地磁坐标和利用球面距离计算方法对高纬度地区进行重构能够达到最优效果.相比现有方法,其整体标准误差和绝对误差均有所降低.上述研究证实了该方法的有效性,对电子信息系统的可用频率预测以及通信效能保障具有重要意义.      

风云三号C星GNOS北斗掩星电离层探测初步结果

利用风云三号卫星C星GNOS掩星探测仪电离层数据,分析了2013年10月FY-3C GNOS探测的北斗掩星电离层廓线分布,将2013年10月1日至2015年10月10日期间FY-3C GNOS观测的F₂层峰值电子密度（N_mF₂）与地面电离层测高仪观测结果进行对比,验证了FY-3C GNOS北斗电离层掩星的探测精度.结果表明,FY3-C GNOS北斗电离层掩星与电离层测高仪探测的N_mF₂数据相关系数为0.96,平均偏差为10.21%,标准差为19.61%.在不同情况下其数据精度有如下特征:白天精度高于夜晚;夏季精度高于分季,分季精度高于冬季;中纬地区精度高于低纬地区,低纬地区精度高于高纬地区; BDS倾斜同步轨道（IGSO）卫星精度高于同步轨道（GEO）卫星和中轨道（MEO）卫星.FY-3C GNOS北斗电离层掩星与国际上其他掩星电离层数据精度的一致性对GNSS掩星探测资料的综合利用具有重大意义.      

Ionospheric response to the 17 March and 22 June 2015 geomagnetic storms over Wuhan region using GNSS-based tomographic technique

By using the data of GNSS (Global Navigation Satellite System) observation from Crustal Movement Observation Network of China (CMONOC), ionospheric electron density (IED) distributions reconstructed by using computerized ionospheric tomography (CIT) technique are used to investigate the ionospheric storm effects over Wuhan region during 17 March and 22 June 2015 geomagnetic storm periods. F-region critical frequency (foF2) at Wuhan ionosonde station shows an obvious decrease during recovery phase of the St. Patrick’s Day geomagnetic storm. Moreover, tomographic results present that the decrease in electron density begins at 12:00 UT on 17 March during the storm main phase. Also, foF2 shows a long-lasting negative storm effect during the recovery phase of the 22 June 2015 geomagnetic storm. Electron density chromatography presents the evident decrease during the storm day in accordance with the ionosonde observation. These ionospheric negative storm effects are probably associated with changes of chemical composition, PPEF and DDEF from high latitudes.     

Mitigation of receiver biases in ionospheric observables from PPP with ambiguity resolution

PPP (Precise Point Positioning) is a GNSS (Global Navigation Satellite Systems) positioning method that requires SSR (State Space Representation) corrections in order to provide solutions with an accuracy of centimetric level. The so-called RT-PPP (Real-time PPP) is possible thanks to real-time precise SSR products, for orbits and clocks, provided by IGS (International GNSS Service) and its associate analysis centers such as CNES (Centre National d'Etudes Spatiales). CNES SSR products also enable RT-PPP with integer ambiguity resolution. In GNSS related literature, PPP with ambiguity resolution (PPP-AR) in real-time is often referred as PPP-RTK (PPP – Real Time Kinematic). PPP-WIZARD (PPP - With Integer and Zero-difference Ambiguity Resolution Demonstrator) is a software that is made available by CNES. This software is capable of performing PPP-RTK. It estimates slant ionospheric delays and other GNSS positioning parameters. Since ionospheric effects are spatially correlated by GNSS data from active networks, it is possible to model and provide ionospheric delays for any position in the network coverage area. The prior knowledge ionospheric delays can reduce positioning convergence for PPP-RTK users. Real-time ionospheric models could benefit from highly precise ionospheric delays estimated in PPP-AR. In this study, we demonstrate that ionospheric delays obtained throughout PPP-AR estimation are actu ally ionospheric observables. Ionospheric observables are biased by an order of few meters caused by the receiver hardware biases. These biases prohibit the use of PPP-WIZARD ionospheric delays to produce ionospheric models. Receiver biases correction is essential to provide ionospheric delays while using PPP-AR based ionospheric observables. In this contribution, a method was implemented to estimate and mitigate receiver hardware biases influence on slant ionospheric observables from PPP-AR. In order to assess the proposed approach, PPP-AR data from 12 GNSS stations were processed over a two-month period (March and April 2018). A comparison between IGS ionospheric products and PPP-AR based ionospheric observables corrected for receiver biases, resulted in a mean of differences of −39 cm and 51 cm standard deviation. The results are consistent with the accuracy of the IGS ionospheric products, 2–8 TECU, considering that 1 TECU is ~16 cm in L1. In another analysis, a comparison of ionospheric delays from 5 pairs of short baselines GNSS stations found an agreement of 0.001 m in mean differences with 22 cm standard deviation after receiver biases were corrected. Therefore, the proposed solution is promising and could produce high quality (1–2 TECU) slant ionospheric delays. This product can be used in a large variety of modeling approaches, since ionospheric delays after correction are unbiased. These results indicate that the proposed strategy is promising, and could benefit applications that require accuracy of 1–2 TECU (~16–32 cm in L1).     

Ionospheric parameters in the European sector during the magnetic storm of August 25–26, 2018

Variations of ionospheric parameters Total Electron Content (TEC) by GNSS, critical frequency (foF2) by vertical sounding and electron density (Ne) by low-altitude satellite were studied at high, mid and low latitudes of the European sector during the magnetic storm of August 25–26, 2018. During the main phase of the storm the ionospheric F2-layer was under the positive disturbance at mid and low latitudes. Then the transition from the positive to negative ΔfoF2 values occurred at all latitudes. The recovery phase was characterized by negative ionospheric disturbance at all latitudes. This is due to the decrease of thermospheric O/N2 ratio during the recovery phase of the storm. The intense Es layers screened the reflections from the F2-layer on August 26th at high and at low latitudes but at different times. Some blackouts occurred due to the high absorption level at high latitudes. In general, foF2 and TEC data were highly correlated. The major Ne changes were at the low latitudes. In general, Ne data confirmed the ionospheric dynamics revealed with foF2 and TEC.     

A new method for improving the performance of an ionospheric model developed by multi-instrument measurements based on artificial neural network

There are remarkable ionospheric discrepancies between space-borne (COSMIC) measurements and ground-based (ionosonde) observations, the discrepancies could decrease the accuracies of the ionospheric model developed by multi-source data seriously. To reduce the discrepancies between two observational systems, the peak frequency (foF2) and peak height (hmF2) derived from the COSMIC and ionosonde data are used to develop the ionospheric models by an artificial neural network (ANN) method, respectively. The averaged root-mean-square errors (RMSEs) of COSPF (COSMIC peak frequency model), COSPH (COSMIC peak height model), IONOPF (Ionosonde peak frequency model) and IONOPH (Ionosonde peak height model) are 0.58 MHz, 19.59 km, 0.92 MHz and 23.40 km, respectively. The results indicate that the discrepancies between these models are dependent on universal time, geographic latitude and seasons. The peak frequencies measured by COSMIC are generally larger than ionosonde’s observations in the nighttime or middle-latitudes with the amplitude of lower than 25%, while the averaged peak height derived from COSMIC is smaller than ionosonde’s data in the polar regions. The differences between ANN-based maps and references show that the discrepancies between two ionospheric detecting techniques are proportional to the intensity of solar radiation. Besides, a new method based on the ANN technique is proposed to reduce the discrepancies for improving ionospheric models developed by multiple measurements, the results indicate that the RMSEs of ANN models optimized by the method are 14–25% lower than the models without the application of the method. Furthermore, the ionospheric model built by the multiple measurements with the application of the method is more powerful in capturing the ionospheric dynamic physics features, such as equatorial ionization, Weddell Sea, mid-latitude summer nighttime and winter anomalies. In conclusion, the new method is significant in improving the accuracy and physical characteristics of an ionospheric model based on multi-source observations.     

Response of equatorial,low- and mid-latitude F-region in the American sector during the intense geomagnetic storm on 24–25 October 2011

In this paper, we present and discuss the response of the ionospheric F-region in the American sector during the intense geomagnetic storm which occurred on 24–25 October 2011. In this investigation ionospheric sounding data obtained of 23, 24, 25, and 26 October 2011 at Puerto Rico (United States), Jicamarca (Peru), Palmas, São José dos Campos (Brazil), and Port Stanley, are presented. Also, the GPS observations obtained at 12 stations in the equatorial, low-, mid- and high-mid-latitude regions in the American sector are presented. During the fast decrease of Dst (about ∼54 nT/h between 23:00 and 01:00 UT) on the night of 24–25 October (main phase), there is a prompt penetration of electric field of magnetospheric origin resulting an unusual uplifting of the F region at equatorial stations. On the night of 24–25 October 2011 (recovery phase) equatorial, low- and mid-latitude stations show h′F variations much larger than the average variations possibly associated with traveling ionospheric disturbances (TIDs) caused by Joule heating at high latitudes. The foF2 variations at mid-latitude stations and the GPS-VTEC observations at mid- and low-latitude stations show a positive ionospheric storm on the night of 24–25 October, possibly due to changes in the large-scale wind circulation. The foF2 observations at mid-latitude station and the GPS-VTEC observations at mid- and high-mid-latitude stations show a negative ionospheric storm on the night of 24–25 October, probably associated with an increase in the density of molecular nitrogen. During the daytime on 25 October, the variations in foF2 at mid-latitude stations show large negative ionospheric storm, possibly due to changes in the O/N2 ratio. On the night of 24–25, ionospheric plasma bubbles (equatorial irregularities that extended to the low- and mid-latitude regions) are observed at equatorial, low- and mid-latitude stations. Also, on the night of 25–26, ionospheric plasma bubbles are observed at equatorial and low-latitude regions.     

Pre-earthquake ionospheric anomalies before three major earthquakes by GPS-TEC and GIM-TEC data during 2015–2017

The dual-frequency satellite-based measurements from Global Positioning System (GPS) may provide feasible ways of studying and potentially detecting of earthquake (EQ) related anomalies in the ionosphere. In this paper, GPS based Total Electron Content (TEC) data are studied for three major M?>?7.0 EQs in Nepal and Iran-Iraq border during 2015–2017 by implementing statistical procedures on temporal and spatial scale. Previous studies presented different time interval of pre-seismic ionospheric anomalies, however, this study showed that EQs ionospheric precursors may occur within 10?days. Furthermore, the ionospheric anomalies on the suspected day occurred during UT?=?08:00–12:00?h before the main shock. The Global Ionospheric Map TEC (GIM-TEC) data retrieved over the epicenter of M7.8 (Nepal EQ) showed a significant increase of 6 TECU on April 24, 2015 (one day before the main shock), which is recorded by the ground GPS station data of Islamabad (station lies within the EQ preparation zone). Furthermore, the spatial GIM-TEC result imply significant anomalies over the epicenter during the time interval between UT?=?08:00–12:00?h (LT?=?13:00–17:00). For M7.3 (Nepal EQ), the TEC anomalies were detected on May 10, 2015 (2?days before the event) in the temporal data. The spatial TEC data imply the huge clouds over the epicenter at about UT?=?08:00–12:00?h on May 10, 2015, that may be associated with this EQ in the quiet geomagnetic storm conditions. Similarly, temporal and spatial TEC showed anomaly on November 3, 2017, during UT?=?08:00–12:00 (9?days before the Iran-Iraq border EQ) after implementing the statistical method on it. Conversely, there exists a short-term but low magnitude TEC anomaly synchronized with a geomagnetic storm on November 7–8, 2017 (4 to 5?days prior to M7.3 Iran-Iraq border EQ). The diurnal and hourly GIM-TEC and VTEC data also imply the execution of ionospheric anomalies within 10?days prior to all events. All these positive anomalies in TEC may be due to the existence of a huge energy from the epicenter during the EQ preparation period.     

实时电离层格网数据精度评估

电离层延迟是制约单频接收机定位精度的重要误差源之一.为提高单频接收机的实时电离层改正精度,需要实时电离层数据.以中国科学院空天信息创新研究院提供的实时电离层数据为例,对比分析不同太阳活动期实时电离层数据及预报电离层数据与IGS最终电离层数据之间的差值以及不同太阳活动期、不同纬度测站的电离层数据对电离层延迟进行改正后得到的定位精度.结果表明:在低太阳活动期和高太阳活动期,实时电离层数据无法很好地反映大部分海洋上空的电离层变化特性;对不同太阳活动期,实时电离层数据在高纬度测站的定位精度优于预报数据和广播模型,在中纬度测站的定位精度略低于预报数据而与广播模型定位精度相当,在低纬度测站的定位精度略优于预报数据和广播模型.      

Demonstration of the use of the Doppler Orbitography and Radio positioning Integrated by Satellite (DORIS) measurements to validate GPS ionospheric imaging

There is a lack of independent ionospheric data that can be used to validate GPS imaging results at mid latitudes over severe storm times. Doppler Orbitography and Radio positioning Integrated by Satellite (DORIS), a global network of dual-frequency ground to satellite observations, provides this missing data and here is employed as verification to show the accuracy of the ionospheric GPS images in terms of the total electron content (TEC). In this paper, the large-scale ionospheric structures that appeared during the strong geomagnetic storm of 20 November 2003 are reconstructed with a GPS tomographic algorithm, known as MIDAS, and validated with DORIS TEC measurements. The main trough shown in an extreme equatorward position in the ionospheric imaging over mainland Europe is confirmed by DORIS satellite measurements. Throughout the disturbed day, the variations of relative slant TECs between DORIS data and MIDAS results agree quite well, with the average of the mean differences about 2 TECu. We conclude that as a valuable supplement to GPS data, DORIS ionospheric measurements can be used to analyse TEC variations with a relatively high resolution, ∼10 s in time and tens of kilometres in space. This will be very helpful for identification of some highly dynamic structures in the ionosphere found at mid-latitudes, such as the main trough, TID (Travelling Ionospheric Disturbances) and SED (Storm Enhanced Density), and could be used as a valuable auxiliary data source in ionospheric imaging.     

不同太阳活动条件下电离层形态对估算GPS系统硬件延迟的影响

利用两个中纬度台站GPS观测数据提取的GPS卫星硬件延迟,分析了不同太阳活动情况下估算的硬件延迟稳定性和统计特征,结合同期电离层观测数据,研究了电离层状态对硬件延迟估算结果的影响.研究结果表明,基于太阳活动高年(2001年)GPS观测数据估算的硬件延迟稳定性要低于太阳活动低年GPS观测数据的估算结果,利用2001年GPS数据估算的卫星硬件延迟年标准偏差(RMS)平均值约为1TECU,而2009年GPS数据估算的卫星硬件延迟年标准偏差平均值约为0.8TECU.通过对2001年和2009年北京地区电离层F₂层最大电子密度(N_mF₂)变化性分析,结合GPS硬件延迟估算方法对电离层时空变化条件的要求,认为硬件延迟稳定性与太阳活动强度的联系是由不同太阳活动条件下电离层变化的强度差异引起的.