Obtaining more accurate electron density profiles from bending angle with GPS occultation data: FORMOSAT-3/COSMIC constellation

Since 1995, with the first GPS occultation mission on board Low Earth Orbiter (LEO) GPS/MET, inversion techniques were being applied to GPS occultation data to retrieve accurate worldwide distributed refractivity profiles, i.e. electron density profiles in the case of Ionosphere. Important points to guarantee the accuracy is to take into account horizontal gradients and topside electron content above the LEO orbit. This allows improving the accuracy from 20% to 50%, depending on the conditions, latitude and epoch regarding to Solar cycle as reported in previous works.     

Effects of space weather on the ionosphere and LEO satellites’ orbital trajectory in equatorial,low and middle latitude

We study the effects of space weather on the ionosphere and low Earth orbit (LEO) satellites’ orbital trajectory in equatorial, low- and mid-latitude (EQL, LLT and MLT) regions during (and around) the notable storms of October/November, 2003. We briefly review space weather effects on the thermosphere and ionosphere to demonstrate that such effects are also latitude-dependent and well established. Following the review we simulate the trend in variation of satellite’s orbital radius (r), mean height (h) and orbit decay rate (ODR) during 15 October–14 November 2003 in EQL, LLT and MLT. Nominal atmospheric drag on LEO satellite is usually enhanced by space weather or solar-induced variations in thermospheric temperature and density profile. To separate nominal orbit decay from solar-induced accelerated orbit decay, we compute $r\text{,}h$ and ODR in three regimes viz. (i) excluding solar indices (or effect), where $r=r_0\text{,}h=h_0$ and $\mathit{ODR}={\mathit{ODR}}_0$ (ii) with mean value of solar indices for the interval, where $r=r_m\text{,}h=h_m$ and $\mathit{ODR}={\mathit{ODR}}_m$ and (iii) with actual daily values of solar indices for the interval ($r\text{,}h$ and ODR). For a typical LEO satellite at h?=?450?km, we show that the total decay in r during the period is about 4.20?km, 3.90?km and 3.20?km in EQL, LLT and MLT respectively; the respective nominal decay ($r_0$) is 0.40?km, 0.34?km and 0.22?km, while solar-induced orbital decay ($r_m$) is about 3.80?km, 3.55?km and 2.95?km. h also varied in like manner. The respective nominal ${\mathit{ODR}}_0$ is about 13.5?m/day, 11.2?m/day and 7.2?m/day, while solar-induced ${\mathit{ODR}}_m$ is about 124.3?m/day, 116.9?m/day and 97.3?m/day. We also show that severe geomagnetic storms can increase ODR by up to 117% (from daily mean value). However, the extent of space weather effects on LEO Satellite’s trajectory significantly depends on the ballistic co-efficient and orbit of the satellite, and phase of solar cycles, intensity and duration of driving (or influencing) solar event.     

A neural network-based ionospheric model for Arecibo

The Arecibo Observatory (18°N, 66°W) has the world’s largest single dish antenna (300 m diameter). Beyond radio astronomy it can also operate as an incoherent scatter radar and in that mode its figure-of-merit makes it also one of the most powerful world-wide. For the present purpose all electron density data available on the web, from the beginning with the first erratic measurements in 1966 up to 2004 inclusive, were downloaded. The measurements range from about 100 km to beyond 700 km and are essentially evenly distributed, i.e. not dedicated to measure specific geophysical events. From manually edited/inspected data a neural network (NN) was established with season, hour of the day, solar activity and Kp as the input parameters. The performance of this model is checked against a – likewise NN based – global model of foF2, a measure of the maximum electron density of the ionosphere. Considering the diverse data sources and assumptions of the two models it can be concluded that they agree remarkably well.     

On the time of minimum ionization in the F2 region

Using measurements of the critical frequency of F2 region (f_oF2) the validity of the International Reference Ionosphere model to predict the time of minimum ionization is checked. Data obtained at different ionospheric stations have been considered. The CCIR and URSI options are used to model calculations. For CCIR option the results show that good predictions were obtained for about 40% of the considered cases. For the rest of the considered data, the model predicts the minimum at times earlier than that observed in the measurements. The percentages of good predictions obtained with URSI option are lower than those corresponding to CCIR one.     

Sporadic structures in equatorial ionosphere as revealed from GPS occultation data

A method for monitoring of sporadic formations in the lower ionosphere by use of the amplitude and phase variations of decimeter radio waves in the occultation trans-ionospheric link GPS satellite — LEO CHAMP satellite is described. Typical variations of the amplitude and phase of the occultation signal, caused by layered formations in the lower ionosphere, are considered. Parameters of sporadic structures measured during period of especially strong solar flashes from October 25 till November 9, 2003, are described. Results of statistical analysis of the occurrence frequency of sporadic layers, their altitude distribution, and thickness are presented. The electron density distribution in the lower ionosphere in the equatorial zone is estimated.     

多模多频卫星导航系统码偏差统一定义与处理方法

精确处理和修正观测量偏差（OSB）是有效发挥多模多频优势,提升GNSS服务和应用效能的前提和基础，已成为国内外卫星导航高精度定位领域研究的热点问题之一。国际GNSS服务组织（IGS）和国际大地测量协会（IAG）专门成立了工作组推动相关工作。然而，目前有关观测量偏差处理的方法仍存在着定义不统一、求解不严密、使用不便捷等突出问题，给实时高精度定位应用带来诸多不利因素。针对此，首先阐述了中国科学院（CAS）电离层分析中心码偏差产品的定义、估计策略和使用方法，然后给出了部分精度评估和分析。结果表明：CAS、CODE和DLR发布的码偏差参数一致性优于0.30ns，CAS码偏差月稳定性优于0.15ns。相关工作对于推动多模多频GNSS在电离层监测、高精度定位和授时定时等领域的应用具有重要参考价值。     

应用端部效应改善的LS+NN模型进行日长变化预报

现有ΔLOD(Delta Length-Of-Day,日长变化)预报模式在进行周期项与残差项拟合分离时,通常没有考虑LS(Least Squares,最小二乘)拟合序列的端部效应,预报精度难以取得较大提高。针对端部效应现象,首先采用时间序列分析模型在ΔLOD序列两端进行数据延拓,构成一个新序列,然后用新序列求得LS外推模型系数,再结合LS外推模型和NN(Neural Network,神经网络)对原始ΔLOD序列进行预测。算例表明,在ΔLOD序列两端增加延拓数据,能有效改善LS拟合序列的端部效应;端部效应改善的LS+NN模型的预报精度明显优于常规LS+NN模型,精度最大提高了17.86%。该方法不仅适用于LS+NN模型,也适用于LS外推模型与其他模型的组合。     

What is the optimum solar proxy for long-term ionospheric investigations?

The problem of optimum solar proxy is important for long-term and/or climatological studies of ionospheric parameters. Here we focus on possibly different optimum solar proxies for different ionospheric parameters, as they are affected by partly different spectral ranges of solar ionizing radiation. We use yearly average values of foF2 and foE of four European stations with long (1976–2014) and high-quality data (Juliusruh, Pruhonice, Rome, Slough/Chilton), and the global total electron content (G-TEC). Four solar proxies are used: F10.7, Mg II, solar Lymna-alpha flux Fα and sunspot numbers. The most important finding is that the optimum solar proxies are different for different ionospheric parameters. The most suitable solar proxy for foF2 is found to be Mg II, whereas for foE F10.7 evidently outperforms Mg II. Fα and sunspot numbers perform slightly worse but none of four solar proxies performs poorly. F10.7 is favored for G-TEC, to some extent surprisingly, as previous results favored rather Mg II.     

Prediction of ionospheric total electron content using adaptive neural network with in-situ learning algorithm

The Ionospheric Total Electron Content is responsible for the group delay of the signals from the Navigation satellites. This delay causes ranging error, which in turn degrades the accuracy of position estimated by the receivers. For critical applications, single frequency receivers resort to Satellite Based Augmentation Systems in order to have improved accuracy and integrity. The performance of these systems in terms of accuracy can be improved if predictions of the delays are available simultaneously with real measurements. This paper attempts to predict the Total Electron Content using adaptive recurrent Neural Network at three different locations of India. These locations are selected at the magnetic equator, at the equatorial anomaly crest and outside the anomaly range, respectively. In-situ Learning Algorithm has been used for tracking the non-stationary nature of the variation. Prediction is done for different prediction intervals. It is observed that, for each case, the mean and root mean square values of prediction errors remain small enough for all practical applications. Analysis of Variance is also done on the results.