基于极大验后推估理论的全球电离层地图预报

提出了一种基于极大验后估计理论的全球电离层预报方法，基于中国科学院电离层分析中心（CAS）提供的快速全球电离层地图（GIM），实现了1天、2天和5天GIM的预报。以国际GNSS服务组织（IGS）最终GIM、Jason测高卫星提供的电离层观测信息及全球GNSS基准站实测电离层总电子含量（TEC）为基准，评估了2008－2017年CAS电离层预报GIM在全球大陆及海洋区域的精度，并与欧洲定轨中心（CODE）、欧洲空间局（ESA）和西班牙加泰罗尼亚理工大学（UPC）的预报GIM进行对比。在评估时段内，与IGS-GIM相比，CAS预报GIM精度为2.4~3.1 TECU；与测高卫星TEC相比，CAS预报GIM的精度为5.1~6.6 TECU；与全球基准站实测TEC相比，CAS预报GIM的电离层延迟修正精度优于80%。总体来看，CAS预报GIM与CODE预报GIM精度相当，显著优于ESA和UPC预报GIM。      

地磁暴期间北半球高纬度地区电离层变化特征及对精密定位的影响

基于加拿大地区高纬度电离层观测网的电离层闪烁观测数据，分析了2018年8月26日地磁暴事件引发的北半球高纬度地区电离层总电子含量（TEC）异常变化、TEC变化率指数（ROTI）及电离层相位闪烁的变化特征.结果表明:加拿大地区最大异常值约6 TECU，磁暴引发全球电离层TEC异常峰值高达20 TECU；加拿大地区电离层相位闪烁发生率最大增至12.6%，而磁静日期间约为1%；强电离层闪烁期间，电离层相位闪烁指数与ROTI之间具有较强的一致性.对GPS双频精密单点定位（Precise Point Positioning，PPP）结果进行分析发现:无闪烁期间定位误差随测站纬度的增高呈现出增大趋势，但均方根误差小于0.4m；闪烁发生期间各测站的定位误差均显著增大，水平和垂直方向定位误差分别增至约0.9m及1.7m.      

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.      

利用GPS技术实时监测上海区域电离层变化

基于GPS技术实时监测电离层变化原理, 利用载波平滑伪距观测值建立区域电离层模型的方法, 计算了电离层延迟量和硬件延迟, 根据硬件延迟值相对稳定的特点, 采取一定时段求解出硬件延迟量, 对实时硬件延迟量进行预报, 进而实时分离GPS信号传播路径上的垂直总电子含量VTEC. 利用上海区域内的GPS网的观测数据, 建立实时上海区域电离层延迟模型, 监测上海区域的电离层变化. 数据分析结果表明, 这种方法的内符合精度优于3 TECU.      

IGS电离层产品在双向时间频率传递中的应用

利用IGS组织提供的全球电离层资料对卫星双向时间频率传递中的电离层误差进行修正。IGS提供特定时刻、固定经纬度网格点上的电离层总电子含量。对该电离层资料首先进行空间四点网格内插,然后利用双线性内插得到电离层穿刺点所需时刻的总电子含量,最后将得到电离层数据经过处理用于双向时间频率传递修正。结果表明：电离层对C波段的影响在（0～0.5）ns范围内,这对亚纳秒量级的时间比对是必须考虑的。IGS提供的电离层产品适合应用于双向时间频率传递,具有方法简单、准确度高和价格低廉等特点。     

南极GPS定位中的电离层延迟分析

基于南极地区国际GNSS服务组织(IGS,International GNSS Service)跟踪站的全球定位系统(GPS,Global Position System)双频实测数据,分析了南极地区电离层延迟的变化情况及其二阶项延迟对南极GPS定位结果的影响.结果表明:南极地区的总电子含量(TEC,Total Electron Content)日间波动频繁,其日间TEC最大值变化较中纬度地区剧烈;在南极地区夏季,电离层二阶项延迟对GPS定位结果的影响可达cm级.同时,由于欧洲定轨中心(CODE,The European Center for Orbit Determination)提供的全球电离层模型(GIM,Global Ionosphere Maps)在南极区域应用的局限性,通过选取南极地区6个IGS跟踪站作为基准站建立了区域电离层TEC模型(RIM,Regional Ionosphere Model).经实测数据计算证明,对于南极地区,RIM的定位精度在一定程度上优于全球电离层模型GIM.     

基于NTCM-BC模型的全球卫星导航系统单频电离层延迟修正

选择NTCM-BC模型作为单频电离层延迟修正模型,通过非线性最小二乘拟合的方法,利用提前一天预测的电离层图（COPG文件）,计算得到NTCM-BC模型修正系数;利用Klobuchar模型和IGS发布的GIM数据对NTCM-BC模型进行比较和分析.对太阳活动高、中、低年实测数据的分析结果表明:全球平均水平上,NTCM-BC模型的电离层延迟修正性能明显优于Klobuchar模型,NTCM-BC模型的TEC平均误差和均方根误差比Klobuchar模型分别下降了41%和30%;模型的TEC计算误差与太阳活动剧烈程度成正相关,即太阳活动高年模型误差较大,太阳活动低年误差相对较低.相较于磁静日,磁扰日期间Klobuchar模型和NCTM模型的误差均有一定程度的增加.此外,模型的电离层修正误差同时存在明显的纬度、季节和地方时差异.      

电离层特征参量的自相关原理插值方法

通过选用合适的电离层平稳性参数, 建立相应的正定自相关系数模型, 利用自相关分析原理, 提出了一种针对电离层特征参量历史缺失数据插值处理的新方法. 该方法能够提高Muhtarov 和Kutiev 在1999 年提出的自相关系数法的插值精度, 通常情况下可以把误差降低1 到2 个百分点以上, 有时甚至能降低接近9 个百分点, 在很大程度上改善了对电离层历史缺失数据的插值处理效果. 此外, 本文还对插值误差随季节、太阳活动性和地理纬度等的变化规律进行了分析.      

基于因果卷积与LSTM网络的电离层总电子含量预报

电离层总电子含量（TEC）不仅是分析电离层形态的关键参数之一,同时为导航及定位等空间应用系统消除电离层附加时延提供重要支撑。由于电离层TEC的时空变化特征,本文融合因果卷积和长短时记忆网络,以太阳活动指数F_10.7、地磁活动指数Dst和电离层TEC历史数据作为特征输入,构建深度学习模型,实现提前24 h预报电离层TEC。进一步利用2005－2013年连续9年的CODE TEC数据,全面评估了模型在北京站（40°N,115°E）、武汉站（30.53°N,114.36°E）和海口站（20.02°N,110.38°E）的预报性能。结果显示不同太阳活动条件下三个站的TEC值与真实测量值的相关系数都大于0.87,均方根误差大都集中在0～1 TECU以内,且模型预报精度与纬度、太阳、地磁活动程度、季节变化相关。与仅由长短时记忆网络构成的预报模型相比,本实验模型均方根误差降低了15%,为电离层TEC预报模型的实际应用提供了参考。      

用Kriging方法构建中纬度区域电离层TEC地图

提出了中国中北部及周边(30°N～55°N,70°E～140°E范围内)区域电离层电子浓度总含量(TEC)地图(简称CNC TEC Map)的Kriging算法.比较了目前被广泛使用的电离层模型(Klobuchar,IRI和JPLGIM)与真实的TEC分布的符合情况;比较了常数漂移(普通Kriging方法)、线性漂移和二次漂移的Kriging方法;在三种不同的时间分辨率(5min,30min和120min)下,比较了Kriging算法、就近插值算法和多项式回归算法(5阶)绘制CNC TEC Map的效果.结果显示在本文研究区域内常用的电离层模型与真实TEC分布存在较大的区别;使用普通Kriging方法就可以较好的表示华北地区TEC分布;Kriging方法的结果优于其余两种插值算法,尤其是当时间分辨率提高到5 min时,Kriging算法的优势比较明显.      

Improving regional ionospheric TEC mapping based on RBF interpolation

Radial basis function (RBF) interpolation with multi-quadric is developed to perform ionospheric total electron content (TEC) mapping for the Chinese region between 15°N ~ 40°N and 100°E ~ 125°E. TEC measurements from the Centre for Orbit Determination in Europe (CODE) covering the solar maximum year 2011 are used to investigate the performance of the proposed RBF interpolation method. The differences between the RBF interpolated TEC and the CODE TEC are within 0.5 TECU and the root mean square error (RMSE) is very small when 49 data points are used. The maximum difference is ~5 TECU and the error is less than 1 TECU with 25 samples. Our study suggests that a random distribution of measurement points gives smaller RMSEs than a homogenous distribution when the number of sample points is low. The study indicates that RBF interpolation offers a powerful and reliable tool for ionospheric TEC mapping.     

WAAS系统中电离层折射校正的新方法及计算结果

电离层介质的色散性是影响电磁波信号进行卫星导航定位精度的重要因素之一.配合北斗二代分系绩研制任务,提出了一种新的电离层折射校正算法,并利用2000年7月1日到3日的双频GPS观测数据对6个用户站进行试算,进一步将试算所得均方根误差和电离层网格算法得到的误差进行比较.结果表明,对于中纬区域的用户站,估算的TEC误差约为0.5 m左右;而低纬用户误差相对增大,为1 m左右.文中给出的算法与电离层网格模型所提供的精度相差不多,在未来中国自主的卫星增强系统中采用新方法进行电离层进行修正是可行的及有效的.      

Dayside ionospheric uplift during strong geomagnetic storms as detected by the CHAMP,SAC-C,TOPEX and Jason-1 satellites

Ionosphere response to severe geomagnetic storms that occurred in 2001–2003 was analyzed using data of global ionosphere maps (GIM), altimeter data from the Jason-1 and TOPEX satellites, and data of GPS receivers on-board CHAMP and SAC-C satellites. This allowed us to study in detail ionosphere redistribution due to geomagnetic storms, dayside ionospheric uplift and overall dayside TEC increase. It is shown that after the interplanetary magnetic field turns southward and intensifies, the crests of the equatorial ionization anomaly (EIA) travel poleward and the TEC value within the EIA area increases significantly (up to ∼50%). GPS data from the SAC-C satellite show that during the main phase of geomagnetic storms TEC values above the altitude of 715 km are 2–3 times higher than during undisturbed conditions. These effects of dayside ionospheric uplift occur owing to the “super-fountain effect” and last few hours while the enhanced interplanetary electric field impinged on the magnetopause.     

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.     

Temporal extrapolation of TEC using WNN during 2007–2018 and comparison against GIM,IRI2016 and Kriging

In this paper, a new method of temporal extrapolation of the ionosphere total electron content (TEC) is proposed. Using 3-layer wavelet neural networks (WNNs) and particle swarm optimization (PSO) training algorithm, TEC time series are modeled. The TEC temporal variations for next times are extrapolated with the help of training model. To evaluate the proposed model, observations of Tehran GNSS station (35.69°N, 51.33°E) from 2007 to 2018 are used. The efficiency of the proposed model has been evaluated in both low and high solar activity periods. All observations of the 2015 and 2018 have been removed from the training step to test the proposed model. On the other hand, observations of these 2 years are not used in network training. According to the F10.7, the 2015 has high solar activity and the 2018 has quiet conditions. The results of the proposed model are compared with the global ionosphere maps (GIMs) as a traditional ionosphere model, international reference ionosphere 2016 (IRI2016), Kriging and artificial neural network (ANN) models. The root mean square error (RMSE), bias, dVTEC = |VTEC_GPS ? VTEC_Model| and correlation coefficient are used to assess the accuracy of the proposed method. Also, for more accurate evaluation, a single-frequency precise point positioning (PPP) approach is used. According to the results of 2015, the maximum values of the RMSE for the WNN, ANN, Kriging, GIM and IRI2016 models are 5.49, 6.02, 6.34, 6.19 and 13.60 TECU, respectively. Also, the maximum values of the RMSE at 2018 for the WNN, ANN, Kriging, GIM and IRI2016 models are 2.47, 2.49, 2.50, 4.36 and 6.01 TECU, respectively. Comparing the results of the bias and correlation coefficient shows the higher accuracy of the proposed model in quiet and severe solar activity periods. The PPP analysis with the WNN model also shows an improvement of 1 to 12 mm in coordinate components. The results of the analyzes of this paper show that the WNN is a reliable, accurate and fast model for predicting the behavior of the ionosphere in different solar conditions.     

GNSS single frequency ionospheric range delay corrections: NeQuick data ingestion technique

A study of the performance of the NeQuick model and the Klobuchar model for GNSS single frequency range delay correction on a global scale was done using data for moderate solar activity. In this study NeQuick was used in the way intended for Galileo. This study is to assess the performance of the two models at each ionospheric geographic region during moderate solar activity as previously published studies were concentrated only on high solar activity. The results obtained showed that NeQuick outperformed Klobuchar for the whole year at the three geographical regions of the ionosphere. In terms of monthly root mean square of mismodeling, NeQuick outperformed Klobuchar by 15 TECU or more at low-latitudes, 5 TEC or more at mid-latitudes, and 1 TECU or more at high-latitudes.