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.      

Refinement of global ionospheric coefficients for GNSS applications: Methodology and results

We developed the methodology for the optimal estimation of global ionospheric coefficients of the current Global Navigation Satellite Systems (GNSSs), including the eight- and ten-parameter Klobuchar-like as well as NeQuick models. The ionospheric coefficients of those correction models are calculated from two sets of globally distributed tracking stations of the International GNSS Services (IGS). Performance of the re-estimated Klobuchar-like and NeQuick coefficients are validated during 2002–2014 over the continental and oceanic areas, respectively. Over the continental areas, GPS TECs derived from 40 ground GPS receivers are selected as reference. The eight-, ten-parameter Klobuchar-like and NeQuick models can mitigate the ionospheric delay by 65.8, 67.3 and 75.0%, respectively. Over the global oceans, the independent TECs derived from Jason-1&2 altimeters are used as reference. The re-estimated ionospheric correction models can mitigate 56.1–66.7% of the delay errors. Compared to the original GPS Ionospheric Correction Algorithm (ICA), performance of those eight-, ten-parameter Klobuchar-like and NeQuick models has improved 3.4, 5.9 and 13.4% during the whole test period, respectively. The methodology developed here takes the advantage of high-quality ionospheric TECs derived from the global network of GNSS receivers. The re-estimated ionospheric coefficients can be used as precise ionospheric products to monitor and assess GNSS broadcast ionospheric parameters and to improve the performance of various single-frequency GNSS applications.     

Precision analysis of troposphere sensing using GPS single-frequency signals

Various studies have been performed to investigate the accuracy of troposphere zenith wet delays (ZWDs) determined from GPS. Most of these studies use dual-frequency GPS data of large-scale networks with long baselines to determine the absolute ZWDs. For small-scale networks the estimability of the absolute ZWDs deteriorates due to high correlation between the solutions of the ZWDs and satellite-specific parameters as satellite clocks. However, as relative ZWDs (rZWDs) can always be estimated, irrespective of the size of the network, it is of interest to understand how the large-scale network rZWD-performance of dual-frequency GPS using an ionosphere-float model compares to the small-scale network rZWD-performance of single-frequency GPS using an ionosphere-weighted model. In this contribution such an analysis is performed using undifferenced and uncombined network parametrization modelling. In this context we demonstrate the ionosphere weighted constraints, which allows the determination of the rZWDs independent from signals on the second frequency. Based on an analysis of both simulated and real data, it is found that under quiet ionosphere conditions, the accuracy of the single-frequency determined rZWDs in the ionosphere-weighted network is comparable to that of the large-scale dual-frequency network without ionospheric constraints. Making use of the real data from two baselines of 15?days, it was found that the absolute differences of the rZWDs applying the two strategies are within 1?cm in over 90% and 95% of the time for ambiguity-float and -fixed cases, respectively.     

基于伴随理论的Spread-F最优初值扰动提取研究及数值模拟

通过讨论Spread-F扰动初值对于研究R-T不稳定性的必要性,分析初值扰动提取在人工诱导电离层方面的重要作用, 首次提出了应用伴随理论将电离层不稳定性基本方程与Spread-F观测资料相结合提取其扰动初值的方法. 推导了电离层不稳定性方程的伴随模式并进行数值模拟实验, 结果表明电离层不稳定性方程能够较好描述 Spread-F的发展过程; 伴随模式在利用不同迭代初值以及不同时刻观测资料进行反演时, 均能得到较为理想的结果, 表明利用伴随模式反演电离层 Spread-F初始扰动是可行的.      

Near real-time modeling of global ionospheric vertical total electron content using hourly IGS data

The International GNSS Service (IGS) has been providing reliable Global Ionospheric Maps (GIMs) since 1998. The Ionosphere Associate Analysis Centers (IAACs) model the global ionospheric Total Electron Content (TEC) and generate the daily GIM products within the context of the IGS. However, the rapid and final daily GIM products have a latency of at least one day and one week or so, respectively. This limits the value of GIM products in real-time GNSS applications. We propose and develop an approach for near real-time modeling of global ionospheric TEC by using the hourly IGS data. We perform an experiment in a real operating environment to generate near real-time GIM (named BUHG) products for more than two years. Final daily GIM products, Precise Point Positioning (PPP) based VTEC resources, and JASON-3 Vertical TEC (VTEC) measurements are collected for testing the performance of BUHG. The results show that the performance of BUHG is very close to that of the daily GIM products. Also, there is good agreement between BUHG and PPP-derived VTEC as well as with JASON-3 VTEC. It is possible that BUHG would be further improved with an increase in available hourly GNSS data.     

基于陆态网的区域电离层TEC空间变化特性分析

为研究中国陆态网区域电离层TEC在空间小尺度、高分辨率情况下的变化特性及适用精度范围,利用陆态网260个GNSS连续运行观测站数据,解算并生成2016-2017年731天陆态网区域电离层RIM格网,并进行精度验证.在同一RIM格网中,分别在经度和纬度方向上对间隔不同经纬度的TEC格网点作差分析.结果表明:陆态网区域内经度方向上TEC最大变化率和平均变化率分别为0.30TECU·（°）-1和0.11TECU·（°）-1;经度间隔1°时,TEC差值小于2TECU,且随着经度间隔的增大,其TEC差值也随之增大,并表现出一定的半年和周年变化规律;纬度方向上TEC最大变化率和平均变化率分别为1.7TECU·（°）-1和0.46TECU·（°）-1;陆态网区域内电离层TEC随纬度减小而增大,纬度间隔1°时,99.4%的TEC差值小于4TECU,且随着纬度间隔的增大,其TEC差值也随之增大,并表现出一定的半年和周年变化规律;间隔相同情况下,纬度方向上TEC的变化比经度方向上大.      

Ionospheric response to solar and magnetospheric protons during January 15–22, 2005: EAGLE whole atmosphere model results

We present an analysis of the ionosphere and thermosphere response to Solar Proton Events (SPE) and magnetospheric proton precipitation in January 2005, which was carried out using the model of the entire atmosphere EAGLE. The ionization rates for the considered period were acquired from the AIMOS (Atmospheric Ionization Module Osnabrück) dataset. For numerical experiments, we applied only the proton-induced ionization rates of that period, while all the other model input parameters, including the electron precipitations, corresponded to the quiet conditions. In January 2005, two major solar proton events with different energy spectra and proton fluxes occurred on January 17 and January 20. Since two geomagnetic storms and several sub-storms took place during the considered period, not only solar protons but also less energetic magnetospheric protons contributed to the calculated ionization rates. Despite the relative transparency of the thermosphere for high-energy protons, an ionospheric response to the SPE and proton precipitation from the magnetotail was obtained in numerical experiments. In the ionospheric E layer, the maximum increase in the electron concentration is localized at high latitudes, and at heights of the ionospheric F2 layer, the positive perturbations were formed in the near-equatorial region. An analysis of the model-derived results showed that changes in the ionospheric F2 layer were caused by a change in the neutral composition of the thermosphere. We found that in the recovery phase after both solar proton events and the enhancement of magnetospheric proton precipitations associated with geomagnetic disturbances, the TEC and electron density in the F region and in topside ionosphere/plasmasphere increase at low- and mid-latitudes due to an enhancement of atomic oxygen concentration. Our results demonstrate an important role of magnetospheric protons in the formation of negative F-region ionospheric storms. According to our results, the topside ionosphere/plasmasphere and bottom-side ionosphere can react to solar and magnetospheric protons both with the same sign of disturbances or in different way. The same statement is true for TEC and foF2 disturbances. Different disturbances of foF2 and TEC at high and low latitudes can be explained by topside electron temperature disturbances.     

The post sunset equatorial F- region zonal drift variability and its linkage with equatorial spread F onset and duration over Indian longitudes

The seasonal and solar activity variation of the post sunset F- region zonal plasma drift, at the magnetic equatorial region over Indian longitudes is analyzed using the Republic of China Satellite-1 data from January 2000 to April 2004. The post sunset F- region zonal drifts are observed to be higher in the years of high solar activity in comparison with low solar activity, while seasonally the drifts are minimum in summer with much higher values in other seasons. The seasonal and solar activity variations of zonal plasma drift are attributed to the corresponding variations in the neutral winds. The dependences of the F region peak vertical drift on the zonal plasma drift at 18.5 IST (Indian Standard Time) and the time difference of the conjugate points sunset times, are quantitatively analyzed. Further an integrated parameter (incorporating the above mentioned two independent factors), which is able to predict the peak vertical drift and growth rate of Rayleigh Taylor instability is proposed. The other major outcome of the study is the successful prediction of the Equatorial Spread F (ESF) onset time and duration using the new integrated parameter at 18.5 IST. ESF irregularities and associated scintillations adversely affect communication and navigation systems. Hence, the present methodology for the prediction of the characteristics of these nocturnal irregularities becomes relevant.     

A MARS-based method for estimating regional 2-D ionospheric VTEC and receiver differential code bias

The geometry-free linear combination of dual-frequency GNSS reference station ground observations are currently used to build the Vertical Total Electron Content (VTEC) model of the ionosphere. As it is known, besides ionospheric delays, there are differential code bias (DCB) of satellite (SDCB) and receiver (RDCB) in the geometry-free observation equation. The SDCB can be obtained using the International GNSS Service (IGS) analysis centers, but the RDCB for regional and local network receivers are not provided. Therefore, estimating the RDCB and VTEC model accurately and simultaneously is a critical factor investigated by researchers. This study uses Multivariate Adaptive Regression Splines (MARS) to estimate the VTEC approximate model and then substitutes this model in the observation equation to form the normal equation. The least squares method is used to solve the RDCB and VTEC model together. The research findings show that this method has good modeling effectiveness and the estimated RDCB has good reliability. The estimated VTEC model applied to GPS single-frequency precise point positioning has better positioning accuracy in comparison to the IGS global ionosphere map (GIM).     

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.