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。      

IRI-2007预测TEC在广州地区的适用性分析

利用广州站(23.2°N, 113.3°E) GPS双频接收机监测的电离层TEC数据和IRI-2007模型不同电离层输入参数计算得到的TEC预测值, 对比分析了太阳活动低年(2008年)广州地区TEC的变化特征. 结果表明, TEC观测值周日变化在16:00LT左右达到最大值, 而IRI-TEC最大值出现时间较GPS-TEC提前1h左右. TEC季节变化在春秋分较高, 两至季节较低, 表现出明显的半年特性和季节依赖性, 并出现冬季异常现象. IRI-TEC与GPS-TEC在白天具有较好的一致性, 夜间偏差较大. 不同电离层输入参数得到的TEC预测值也相差较大, 选用顶部电子密度参数NeQuick、底部厚度参数B0 Table并用URSI系数计算F₂层峰值参数时, 能较好地反映TEC观测值的变化特征. 在对磁暴的响应上, 预测值无明显变化, 观测值则有比较明显的表现. 通过对比, 初步分析了利用IRI-2007模型预测TEC在广州地区的适用性, 并给出了合理的参数选择方案.      

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.      

相似预报法在电离层TEC短期预报中的应用

引入相似离度衡量样本间的相似程度, 并利用相似预报法对厦门一个GPS台站2004年电离层TEC观测数据进行了24,h预报试验. 结果表明, 预报相对误差与地磁活动水平密切相关, 地磁扰动条件下相对误差明显高于地磁平静时刻; 预报相对平均误差为18.022%, 地磁扰动时为44.896%, 地磁平静条件下为11.676%; 预报相对误差在10%, 20%, 30%, 40%以内的累积比例分别为38.209%, 65.075%, 84.984%,90.448%. 如果使用中纬地区或地磁平静期间的电离层TEC数据, 预报效果会更好.      

利用RDSS系统测量数据提取电离层TEC参数的研究

提出了一种基于无线测定卫星业务(RDSS)系统观测数据提取电离层TEC参数的方法,利用此方法计算分析了2006年5月地面中心地区电离层垂向TEC的日变化趋势.研究结果表明,利用RDSS系统观测数据提取的电离层垂向TEC,在北京时间每日凌晨0400时左右达到最小值,在午后1400时左右达到最大值,符合电离层TEC参数受太阳活动影响较大的物理规律.结果说明研究方法是可行且有效的,文章还对可能存在的误差进行了探讨.      

利用神经网络预报中国地区电离层f0F2

利用神经网络技术并考虑太阳和地磁活动对电离层的影响,提出了一种提前5 h预报中国地区电离层临界频率f0F2的方法.网络输入包括时间、季节、地理纬度、太阳天顶角、最近一天的12个观测值(F-23,F-22,F-21,F-20,F-19,F-18,F-5,F-4,F-3,F-2,F-1,F0)和前30天滑动平均值(A-24,A-23,A-22,A-4,A-3A-2,A-1,A0),网络输出分别为未来5 h的f0F2值F+1,F+2,F+3,F+4,F+5.选取乌鲁木齐、长春、重庆和广州站1958-1968年间的数据训练网络,利用中国9个电离层站的历史数据检验网络,根据均方根误差衡量网络性能的好坏.结果表明,神经网络的预报结果能较好地符合实测数据.这说明利用神经网络实现中国地区电离层f0F2的时空预报是可行的.     

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.     

用GPS 观测研究电离层TEC 水平梯度

双频GPS 用户能自动修正电离层总电子含量(TEC) 引起的延时误差, 但是对于电离层中的不规则体造成的信号闪烁而引起的误差则不能消除. 即使是差分GPS 系统, 电离层误差仍然是其主要的误差源, 其中电离层TEC 梯度将会影响到系统的定位精度和性能. 本文用GPS 方法研究了电离层TEC 的水平梯度问题, 用处于赤道异常区NTUS 台站的GPS 观测数据作了具体计算. 结果表明, 在日落以后到子夜前后电离层垂直TEC 出现了大的涨落, 电离层中的不规则体导致L 波段信号强的闪烁, 同时还伴随着大而快速变化的电离层~TEC 水平梯度. 对比发现, ROTI指数、电离层TEC 水平梯度和电离层垂直TEC 三者之间有很好的对应关系, 它们的变化特征均由电离层中的不规则体引起. 我们认为研究电离层闪烁, 特别是在缺乏S4指数时, 电离层TEC 梯度也可以作为一个重要的可选参数.      

一种用于电离层TEC监测的GNSS信号载波跟踪算法

全球卫星导航系统（GNSS）是电离层TEC监测中应用最普遍的手段. 目前方法通常是在传统导航用途的GNSS接收机输出的原始观测量基础上,经过数据后处理得到电离层TEC信息,其GNSS信号的跟踪处理算法依然采用GNSS导航接收机的算法. 针对GNSS系统用于电离层TEC监测的特殊性,提出一种称为GNSS双频信号和差联合跟踪的新算法,与传统方法相比,该算法直接跟踪电离层TEC的变化,可以提高电离层TEC跟踪的灵敏度和TEC的观测精度,改善电离层TEC监测性能.      

磁暴期间电离层扰动的GPS台网观测分析

给出了一种利用GPS台网观测获取TEC快速变化的计算方法，并将该方法用于东亚一澳大利亚扇区的GPS台网观测数据，分析了2000年7月14—18H大磁暴期间的电离层响应，揭示出电离层暴期间赤道异常峰的压缩和移动等特性．计算结果表明，在站点分布不均匀、原始观测数据不足且随时间跳变等多种不利因素的影响下，这种新的算法仍能保持很好的计算稳定性，并能快速地提取给定时空范围内的三维TEC短时变化的特征，适用于研究电离层暴等情况下引起的TEC扰动．     

2000年7月和2003年10月大磁暴期间东亚地区中低纬电离层的GPS TEC的响应研究

利用武汉电离层观象台研制的GPS TEC的现报方法及现报系统,对东亚地区GPS台网的观测数据进行处理分析,特别对2000年7月14-18日和2003年10月28日至11月1日两次特大磁暴期间的数据进行了对比考察,文中分析了两次磁暴间的电离层响应,得到对应不同磁暴时段电离层TEC的不同变化情况,着重揭示了TEC赤道异常峰的压缩和移动以及赤道异常随时间的压缩—反弹—恢复的过程,并结合高纬电离层的部分响应机制进行了说明,结果显示,两次磁暴期的电离层响应表现出了各自不同的特点,从而反映出因季节变化引起的高纬电离层暴时能量注入的不同而造成的全球性电离层扰动的不同形态,由此看出,磁暴期间电离层TEC的变化直接与太阳扰动发生的时间及其对高纬电离层的耦合有关,若短时期内连续发生多次磁暴,则电离层反应更加复杂,不能简单地当做单一磁暴叠加处理。     

SHAKING: Adjusted spherical harmonics adding KrigING method for near real-time ionospheric modeling with multi-GNSS observations

Precise positioning based on Global Navigation Satellite System (GNSS) technique requires high accuracy ionospheric total electron content (TEC) correction models to account for the ionospheric path delay errors. We present an adjusted Spherical Harmonics Adding KrigING method (SHAKING) approach for regional ionospheric vertical TEC (VTEC) modeling in real time. In the proposed SHAKING method, the VTEC information over the sparse observation data area is extrapolated by the Adjusted Spherical Harmonic (ASH) function, and the boundary distortion in regional VTEC modeling is corrected by the stochastic VTEC estimated using Kriging interpolation. Using real-time GPS, GLONASS and BDS-2/3 data streams of the Crust Movement Observation Network of China (CMONOC), the SHAKING-based regional ionospheric VTEC maps are re-constructed over China and its boundary regions. Compared to GNSS VTECs derived from the independent stations, the quality of SHAKING solution improves by 13–31% and 6–33% with respect to the ASH-only solution during high and low geomagnetic periods, respectively. Compared to the inverse distance weighting (IDW) generated result, significant quality improved of SHAKING-based VTEC maps is also observed, especially over the edge areas with an improvement of 60–80%. Overall, the proposed SHAKING method exhibits notable advantage over the existing regional VTEC modeling techniques, which can be used for regional TEC modeling and associated high-precision positioning applications.     

A prediction model of short-term ionospheric foF2 based on AdaBoost

In this paper, the AdaBoost-BP algorithm is used to construct a new model to predict the critical frequency of the ionospheric F2-layer (foF2) one hour ahead. Different indices were used to characterize ionospheric diurnal and seasonal variations and their dependence on solar and geomagnetic activity. These indices, together with the current observed foF2 value, were input into the prediction model and the foF2 value at one hour ahead was output. We analyzed twenty-two years’ foF2 data from nine ionosonde stations in the East-Asian sector in this work. The first eleven years’ data were used as a training dataset and the second eleven years’ data were used as a testing dataset. The results show that the performance of AdaBoost-BP is better than those of BP Neural Network (BPNN), Support Vector Regression (SVR) and the IRI model. For example, the AdaBoost-BP prediction absolute error of foF2 at Irkutsk station (a middle latitude station) is 0.32 MHz, which is better than 0.34 MHz from BPNN, 0.35 MHz from SVR and also significantly outperforms the IRI model whose absolute error is 0.64 MHz. Meanwhile, AdaBoost-BP prediction absolute error at Taipei station from the low latitude is 0.78 MHz, which is better than 0.81 MHz from BPNN, 0.81 MHz from SVR and 1.37 MHz from the IRI model. Finally, the variety characteristics of the AdaBoost-BP prediction error along with seasonal variation, solar activity and latitude variation were also discussed in the paper.     

Regional modeling of the ionosphere using adaptive neuro-fuzzy inference system in Iran

In recent years, new techniques and algorithms such as Artificial Neural Networks (ANNs), Fuzzy Inference Systems (FIS) and Genetic Algorithm (GA) have been used as alternative statistical tools in modeling and forecasting issues. These methods have been extensively used in the field of geosciences and atmospheric physics. The main purpose of this paper is to combine FIS and ANNs for local modeling of the ionosphere Total Electron Content (TEC) in Iran. An Adaptive Neuro-Fuzzy Inference System (ANFIS) is developed for TEC modeling. Also, Multi-Layer Perceptron ANN (MLP-ANN) and ANN based on Radial Base Functions (RBF) have been designed for analyzing ANFIS results. Observations of 29 Global Positioning System (GPS) stations from the Iranian Permanent GPS Network (IPGN) have been used in 3 different seasons in 2015 and 2016. These stations are located at geomagnetic low latitudes region. Out of these 29 stations, 24 stations for training and 5 stations for testing and validating were selected. The relative and absolute errors have been used to evaluate the accuracy of the proposed model. Also, the results of this paper are compared with the International Reference Ionosphere model (IRI2016). The maximum values of the average relative error for RBF, MLP-ANN, ANFIS and IRI2016 methods are 13.88%, 11.79%, 10.06%, and 18.34%, respectively. Also, the maximum values of the average absolute error for these methods are 2.38, 2.21, 1.5 and 3.36 TECU, respectively. Comparison of diurnal predicted TEC from the ANFIS, RBF, MLP-ANN and IRI2016 models with GPS-TEC revealed that the ANFIS provides more accurate predictions than the other methods in the test area.     

Comparative analysis of TEC disturbances over tropical cyclone zones in the North–West Pacific Ocean

In this paper, we study ionospheric total electron content (TEC) disturbances associated with tropical cyclones (TCs). The study relies on the statistical analysis of six cyclones of different intensity which occurred in the North–West Pacific Ocean in September–November 2005. We have used TEC data from the international network of two-frequency ground-based GPS receivers and NCEP/NCAR meteorological archive. TEC variations of different period ranges (02–20 and 20–60 min) are shown to be more intense during TC peaks under quiet geomagnetic conditions. The highest TEC variation amplitudes are registered when the wind speed in the cyclone and the TC area are maximum. The intensification of TEC disturbances is more pronounced when several cyclones occur simultaneously. We have revealed that the ionospheric response to TC can be observed only after the cyclone has reached typhoon intensity. The ionospheric response is more pronounced at low satellite elevation angles.     

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.     

Using TensorFlow-based Neural Network to estimate GNSS single frequency ionospheric delay (IONONet)

In the last 20?years, and in particular in the last decade, the availability of propagation data for GNSS has increased substantially. In this sense, the ionosphere has been sounded with a large number of receivers that provide an enormous amount of ionospheric data. Moreover, the maturity of the models has also been increased in the same period of time. As an example, IGS has ionospheric maps from GNSS data back to 1998, which would allow for the correlation of these data with other quantities relevant for the user and space weather (such as Solar Flux and Kp). These large datasets would account for almost half a billion points to be analyzed. With the advent and explosion of Big Data algorithms to analyze large databases and find correlations with different kinds of data, and the availability of open source code libraries (for example, the TensorFlow libraries from Google that are used in this paper), the possibility of merging these two worlds has been widely opened. In this paper, a proof of concept for a single frequency correction algorithm based in GNSS GIM vTEC and Fully Connected Neural Networks is provided. Different Neural Network architectures have been tested, including shallow (one hidden layer) and deep (up to five hidden layers) Neural Network models. The error in training data of such models ranges from 50% to 1% depending on the architecture used. Moreover, it is shown that by adjusting a Neural Network with data from 2005 to 2009 but tested with data from 2016 to 2017, Neural Network models could be suitable for the forecast of vTEC for single frequency users. The results indicate that this kind of model can be used in combination with the Galileo Signal-in-Space (SiS) NeQuick G parameters. This combination provides a broadcast model with equivalent performances to NeQuick G and better than GPS ICA for the years 2016 and 2017, showing a 3D position Root Mean Squared (RMS) error of approximately 2?m.     

The occurrence of the mid-latitude ionospheric trough in GPS-TEC measurements

Simultaneous GPS observations from about 150 stations of European Permanent Network (EPN) have been used for studying dynamics of latitudinal profiles and structure of mid-latitude ionospheric trough (MIT). For the analyses, the TEC maps over Europe were created with high spatial and temporal resolution. The latitudinal profiles were produced from TEC maps with one-hour interval for geographic latitude range from 35N to 75N. The structure of latitudinal profiles relates to the occurrence of the ionospheric trough. The location of the trough depends on season, local time, and both geophysical and geomagnetic conditions. The trough structure in GPS-TEC demonstrates a smooth shape. The trough occurrence as a distinguished structure is more distinct during winter. The relation of TEC in the trough minimum to the equator and polar walls amounted to a factor of 2–4.