Modeling of ionospheric parameter variations in East Asia during the moderate geomagnetic disturbances

The results of modeling of ionospheric disturbances observed in the East Asian region during moderate storms are presented. The numerical model for ionosphere–plasmasphere coupling developed at the ISTP SB RAS is used to interpret the data of observations at ionospheric stations located in the longitudinal sector of 90–130°E at latitudes from auroral zone to equator. There is obtained a reasonable agreement between measurements and modeling results for winter and equinox. In the summer ionosphere, at the background of high ionization by the solar EUV radiation in the quiet geomagnetic period the meridional thermospheric wind strongly impacts the electron concentration in the middle and auroral ionosphere. The consistent calculations of the thermospheric wind permit to obtain the model results which are closer to summer observations. The actual information about the space-time variations of thermosphere and magnetosphere parameters should be taken into account during storms.     

大滞后温度智能控制系统的研究与实现

本文研究的是以MCS-51系列单片机为核心的计算机控制系统,用以实现化工催化剂活性检测过程床层温度程序控制。被控对象是具有3～8分钟的纯滞后,时变,非线性特性的催化剂电加热炉。控制难度大。 采用了广义最小方差自校正控制算法,具有时变滞后的极点自校正PID算法,极点对消直接PID三种算法进行了仿真与现场试验研究。前两种,由于工作过程允许的信号对系统激励不够充分,对象在线辩识有困难,试验结果表现,不能实现预期的控制要求。最终采用了极点对消直接PID算法,成功地实现了催化剂活性温度自动控制。该系统工作稳定、可靠,控制精度达到了检测标准要求。     

武昌地区急始型磁暴期间电离层电子总含量的变化

利用１９８０年４月至１９９０年１２月共１３６次急始型磁暴资料统计研究了武昌地区ＴＥＣ的变化。结果表明，ＴＥＣ的暴时变化出现正相，相对变化值ΔＴＥＣ的暴时变化形态与中高纬地区一些台站所观测到的结果差别较大；如果磁暴急始出现在白天，则急始后３６小时，会出现ΔＴＥＣ的极大值，如果急始出现在夜间，则不会出现极大值，这一现象与太阳黑子数，季节无关。     

Investigation of natural and artificial stimulation of the ionospheric Alfvén resonator at high latitude

A brief review is provided of recent progress in understanding the ionospheric Alfvén resonator (IAR) at high latitude. Firstly, naturally occurring resonances of the IAR as detected by pulsation magnetometers in the auroral zone at Sodankylä and in the polar cap at Barentsburg are considered. The characteristics of the IAR in the two regions are broadly similar, although the effects of solar illumination are less clear at the higher latitudes. Secondly we review recent attempts to stimulate the IAR through high-power radio frequency experiments both in the auroral zone at Tromsø with the European Incoherent SCATter (EISCAT) heater, and within the polar cap at Longyearbyen with the Space Plasma Exploration by Active Radar (SPEAR) facility. In the auroral zone at, Tromsø the stimulated IAR has been observed by ground-based magnetometers, and through electron acceleration observed on the FAST spacecraft. At SPEAR in the polar cap, the stimulated IAR has been investigated, with ground magnetometers, with the first results indicative of a positive detection.     

延误航班停机坪作业运行控制研究

随着航班航次的增加，机场变得越来越繁忙．地面作业操作不当所导致的飞机地面事故日益增加：在介绍航班停机坪作业的基础上，分析了地面作业的特点，在此基础上，根据航班延误时在作业资源充足和不足情况下，研究了航班停机坪作业的一般方法。在资源充足情况下，航班延误的地面作业控制主要采用AOE方法，缩短关键路线的作业时间。而资源不足情况下，除了控制关键路线作业时间外，还要根据排序算法合理分配关键作业的资源，以实现缩短延误时间的目的。     

45°(N)纬度带的Klobuchar like电离层延迟季节修正模型与评估

电离层延迟是全球卫星导航系统（GNSS）的主要误差源之一。对于装配GNSS单频接收机的航空器,选择简单有效的Klobuchar广播电离层模型来改正电离层延迟误差,其修正率为50%～60%。针对45°（N）纬度带,提出了更高电离层修正需求。考虑到季节因素对中高纬度地区电离层的显著影响,利用GIMs（Global Ionospheric Maps）分析了昼夜中TEC（Total Electron Content）的峰值和谷值随季节（年积日）的变化,建立了一种适用于45°（N）纬度带的Klobuchar like电离层模型。该模型不增加广播模型系数,新模型的夜间和VTEC高峰时电离层修正率分别达到了82%和80%,表明在穿刺点集中的45°(N)纬度地区使用该模型可以更精确地描述该地区的电离层,帮助航空器实现更高精度的定位。     

Comparison of observed ionospheric vertical TEC over the sea in Indian region with IRI-2016 model

The vertical ionospheric TEC values obtained from GAGAN grid based ionospheric delay correction values over the sea in the Indian equatorial region have been compared with the corresponding values derived from the International Reference Ionosphere model, IRI-2016. The objective of this work is to study the deviation of the vertical TEC derived from the IRI model from ground truths over the sea for different conditions. This will serve the basic intention of assessing the candidature of the IRI model as an alternative ionospheric correction model in navigation receivers in terms of accuracy. We have chosen different solar activity periods, seasons, geomagnetic conditions, locations etc. for our comparison and analysis. The TEC values by the IRI-2016 were compared with the actual measured values for the given conditions and errors were obtained. The measured vertical TEC values at the ionospheric grid points were derived from the GAGAN broadcast ionospheric delay data and used as reference. The IRI model with standard internal functions was used in estimating the TEC at the same ionospheric grid points. The errors in the model derived values are statistically analysed. Broadly, the results show that, for the Indian sector over the sea, the IRI model performs better on quiet days in off equatorial regions, particularly in the northern region. The overall performance degrades for other conditions with the model generally underestimating the true TEC values and most severely in the equatorial region. The performance is worst in this region for the disturbed days of the equinoctial period. The comparison study is also done with the TEC data measured directly by dual frequency GPS receivers. The results were found to be in general agreement with those obtained by comparing the model with GAGAN broadcast data as reference. This study will be useful in considering the IRI-2016 model for real time estimates of TEC as an alternative to the current parametric model in a satellite navigation receiver in absence of other options.     

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.     

Ionospheric TEC disturbances over China during the strong geomagnetic storm in September 2017

From September 7 to 8, 2017, a G4-level strong geomagnetic storm occurred, which seriously impacted on the Earth’s ionosphere. In this work, the global ionospheric maps released by Chinese Academy of Sciences are used to investigate the ionospheric responses over China and its adjacent regions during the strong storm. The prominent TEC enhancements, which mainly associated with the neutral wind and eastward prompt penetration electric field, are observed at equatorial ionization anomaly crests during the main phase of the storm on 8 September 2017. Compared with those on 8 September, the TEC enhancements move to lower-latitude regions during the recovery phase on 9 September. A moderate storm occurred well before the start of the strong storm causes similar middle-latitude TEC enhancements on 7 September. However, the weak TEC depletion is observed at middle and low latitude on 9–10 September, which could be associated with the prevailing westward disturbance electric field or storm-time neural composition changes. In addition, the storm-time RMS and STD values of the ionospheric TEC grids over China increase significantly due to the major geomagnetic storm. The maximum of the RMS reaches 12.0 TECU, while the maximum of the STD reaches 8.3 TECU at ~04UT on 8 September.     

Establishment of atmospheric weighted mean temperature model in the polar regions

Due to the special geographical location and extreme climate environment, the polar regions (Antarctic and Arctic) have an important impact on global climate change. Atmospheric weighted mean temperature (Tm) is a crucial parameter in the retrieval of precipitable water vapor (PWV) from the zenith wet delay (ZWD) of ground-based Global Navigation Satellite System (GNSS) signal propagation. In this paper, the correlation between weighted mean temperature and surface temperature (Ts) is studied firstly. It is shown that the correlation coefficients between Tm and Ts are 0.93 in the Antarctic and 0.94 in the Arctic. The linear regression Tm model and quadratic function Tm model of the Antarctic and the Arctic are established respectively using the radiosonde profiles of 12 stations in the Antarctic and 58 stations in the Arctic from 2008 to 2015. The accuracies of the linear regression Tm model, the quadratic function Tm model and GPT2w Tm model which is a state-of-the-art global Tm model are verified using the radiosonde profiles from 2016 to 2018 in the Antarctic and Arctic. Root Mean Square (RMS) errors of the linear regression Tm model, the quadratic function Tm model and GPT2w Tm model in the Antarctic are 3.07 K, 2.87 K and 4.32 K respectively, and those in the Arctic are 3.53 K, 3.38 K and 4.82 K, which indicates that the quadratic function Tm model has a higher accuracy compared to linear regression Tm model, and the accuracies of the two regional Tm models are better than that of GPT2w Tm model in the polar regions. In order to better evaluate the accuracy of Tm in the PWV retrieval, the PWV values of radiosondes are used for comparisons as the reference value. The RMS errors of PWV derived from the two Tm models are similar for 1.28 mm in the Antarctic and 1 mm in the Arctic respectively. In addition, the spatial and temporal variation characteristics of Tm are analyzed in the polar regions by spectral analysis of Tm data using fast Fourier transform. The results show that the Tm has obvious seasonality and annual periodicity in the polar regions, and the maximum difference between warm season and cold season is about 63 K. After comparing and analyzing the influences of latitude, longitude and elevation on the Tm in the polar regions, it is found that latitude and elevation have a greater influence on the Tm than the longitude. As the latitude and elevation increase, the Tm decreases, and vice versa in the polar regions.