The longitudinal dependence of the behavior of ionospheric parameters has been the subject of a number of works where significant variations are discovered.This also applies to the prediction of the ionospheric total ...The longitudinal dependence of the behavior of ionospheric parameters has been the subject of a number of works where significant variations are discovered.This also applies to the prediction of the ionospheric total electron content(TEC),which neural network methods have recently been widely used.However,the results are mainly presented for a limited set of meridians.This paper examines the longitudinal dependence of the TEC forecast accuracy in the equatorial zone.In this case,the methods are used that provided the best accuracy on three meridians:European(30°E),Southeastern(110°E)and American(75°W).Results for the stations considered are analyzed as a function of longitude using the Jet Propulsion Laboratory Global Ionosphere Map(JPL GIM)for 2015.These results are for 2 h ahead and 24 h ahead forecast.It was found that in this case,based on the metric values,three groups of architectures can be distinguished.The first group included long short-term memory(LSTM),gated recurrent unit(GRU),and temporal convolutional networks(TCN)models as a part of unidirectional deep learning models;the second group is based on the recurrent models from the first group,which were supplemented with a bidirectional algorithm,increasing the TEC forecasting accuracy by 2-3 times.The third group,which includes the bidirectional TCN architecture(BiTCN),provided the highest accuracy.For this architecture,according to data obtained for 9 equatorial stations,practical independence of the TEC prediction accuracy from longitude was observed under the following metrics(Mean Absolute Error MAE,Root Mean Square Error RMSE,Mean Absolute Percentage Error MAPE):MAE(2 h)is 0.2 TECU approximately;MAE(24 h)is 0.4 TECU approximately;RMSE(2 h)is less than 0.5 TECU except Niue station(RMSE(2 h)is 1 TECU approximately);RMSE(24 h)is in the range of 1.0-1.7 TECU;MAPE(2 h)<1%except Darwin station,MAPE(24 h)<2%.This result was confirmed by data from additional 5 stations that formed latitudinal chains in the equatorial part of the three meridians.The complete correspondence of the observational and predicted TEC values is illustrated using several stations for disturbed conditions on December 19-22,2015,which included the strongest magnetic storm in the second half of the year(min Dst=-155 nT).展开更多
In this paper, we studied the seasonal behavior of the total electron content (TEC) during a part of solar cycle 24 ascending, maximum and decreasing phases at Koudougou station (Latitude: 12°15'09"N Lon...In this paper, we studied the seasonal behavior of the total electron content (TEC) during a part of solar cycle 24 ascending, maximum and decreasing phases at Koudougou station (Latitude: 12°15'09"N Longitude: 2°21'45"W). Response of TEC to solar recurrent events is presented. The highest values of the TEC in 2014, 2015 and 2016 were recorded on March and October, while in 2013 they were recorded on April and November, corresponding to equinox months. This observation shows that TEC values at the equinoxes are higher than those of solstices. Moreover, the monthly TEC varies in phase with the sunspots number showing a linear dependence of the TEC on solar activity. The ionospheric electron contents are generally very low both before noon and during the night, but quite high at noon and after noon. This pattern of TEC variation is due to the fluctuation of incident solar radiation on the Earth’s equatorial ionosphere. During quiet periods, the number of free electrons generated is lower than that generated during recurrent periods, which shows a positive contribution of recurrent activity to the level of the TEC. Investigations have also highlighted a winter anomaly and equinoctial asymmetry in TEC behavior at Koudougou station.展开更多
In this work, the comparative study of total electron content (TEC) between recurrent and quiet geomagnetic periods of solar cycle 24 at Koudougou station with geographical coordinates 12°15'N;- 2°20'...In this work, the comparative study of total electron content (TEC) between recurrent and quiet geomagnetic periods of solar cycle 24 at Koudougou station with geographical coordinates 12°15'N;- 2°20'E was addressed. This study aims to analyze how geomagnetic variations influence the behavior of TEC in this specific region. The geomagnetic indices Kp and Dst were used to select quiet and recurrent days. Statistical analysis was used to interpret the graphs. The results show that the mean diurnal TEC has a minimum before dawn (around 0500 UT) and reaches a maximum value around 1400 UT, progressively decreasing after sunset. In comparison, the average diurnal TEC on recurrent days is slightly higher than on quiet days, with an average difference of 7 TECU. This difference increases with the level of geomagnetic disturbance, reaching 21 TECU during a moderate storm. The study also reveals significant monthly variations, with March and October showing the highest TEC values for quiet and recurrent days, respectively. Equinox months show the highest mean values, while solstice months show the lowest. Signatures of semi-annual, winter and equatorial ionization anomalies were observed. When analyzing annual variations, it was found that the TEC variation depends significantly on F10.7 solar flux, explaining up to 98% during recurrent geomagnetic activity and 92% during quiet geomagnetic activity.展开更多
Total Electron Content (TEC) is an important observable parameter of the ionosphere which forms the main source of error for space based navigation and positioning systems. Since the deployment of Global Navigation ...Total Electron Content (TEC) is an important observable parameter of the ionosphere which forms the main source of error for space based navigation and positioning systems. Since the deployment of Global Navigation Satellite Systems (GNSS), cost-effective esti- mation of TEC between the earth based receiver and Global Positioning System (GPS) sat- ellites became the major means of investigation of local and regional disturbance for earthquake precursor and augmentation system studies. International Reference Iono- sphere (IRI) extended to plasmasphere (IRI-Plas) is the most developed ionospheric and plasmaspheric climatic model that provides hourly, monthly median of electron density distribution globally. Recently, IONOLAB group {www.ionolab.org) has presented a new online space weather service that can compute slant TEC (STEC) on a desired ray path for a given date and time using IRI-Plas model (IRI-Plas-STEC). In this study, the performance of the model based STEC is compared with GPS-STEC computed according to the estimation method developed by the IONOLAB group and includes the receiver bias as IONOLAB-BIAS (IONOLAB-STEC). Using Symmetric Kullback-Leibler Distance (SKLD), Cross Correlation (CC) coefficient and the metric norm (L2N) to compare IRI-Plas-STEC and IONOLAB-STEC for the month of October 2011 over the Turkish National Permanent GPS Network (TNPGN- Active), it has been observed that SKLD provides a good indicator of disturbance for both earthquakes and geomagnetic storms.展开更多
电离层总电子含量(Total Electron Content,TEC)的监测与预报是空间环境研究的重要内容,对卫星通讯和导航定位等有重要意义.TEC值影响因素较多,很难确定精确物理模型来对其进行预测.本文设计了基于注意力机制的LSTM模型(Att-LSTM),采用...电离层总电子含量(Total Electron Content,TEC)的监测与预报是空间环境研究的重要内容,对卫星通讯和导航定位等有重要意义.TEC值影响因素较多,很难确定精确物理模型来对其进行预测.本文设计了基于注意力机制的LSTM模型(Att-LSTM),采用过去24小时TEC观测数据对未来TEC进行预测.选择北半球东经100°上,每2.5°纬度选择一个位置,共计36个位置来验证本文提出模型的性能,并与主流的深度学习模型如DNN、RNN、LSTM进行对比实验.取得了如下成果:(1)在选定的36个地区未来2小时单点预测上,基于本文的Att-LSTM模型的TEC预测性能明显优于其他对比模型;(2)讨论了纬度对Att-LSTM预测未来2小时TEC值时性能的影响,发现在北纬0°到60°之间,Att-LSTM预测性能随着纬度的升高而略有降低,在北纬62.5°~87.5°之间,模型预测性能出现扰动,预测效果略差;(3)讨论了磁暴期和磁静期模型的预测性能,发现无论是磁暴期还是磁静期,本文模型预测性能均较好;(4)还讨论了对未来多时点预测效果,实验结果表明,本文所提出的模型对未来2、4个小时的预测拟合度R-Square均超过0.95,预测结果比较可靠,对未来6、8、10个小时预测拟合度最高为0.7934,预测拟合度R-Square下降迅速,预测结果不可靠.展开更多
Total Electron Content(TEC)and electron density enhancement were observed on the day before 17 March 2015 great storm in the China Region.Observations from ground-and space-based instruments are used to investigate th...Total Electron Content(TEC)and electron density enhancement were observed on the day before 17 March 2015 great storm in the China Region.Observations from ground-and space-based instruments are used to investigate the temporal and spatial evolution of the pre-storm enhancement.TEC enhancement was observed from 24°N to 30°N after 10:00 UT at 105°E,110°E and 115°E longitudes on March 16.The maximum magnitude of TEC enhancement was more than 10 TECU and the maximal relative TEC enhancement exceeded 30%.Compared with geomagnetic quiet days,the electron density of Equatorial Ionization Anomaly(EIA)northern peak from Swarm A/C satellites on March 16 was larger and at higher latitudes.NmF2 enhanced during 11:30—21:00 UT at Shaoyang Station and increased by 200%at~16:00 UT.However,TEC and electron density enhancement were not accompanied by a significant change of hmF2.Most research has excluded some potential mechanisms as the main driving factors for storm-time density enhancements by establishing observational constraints.In this paper,we observed pre-storm enhancement in electron density at different altitudes and Equatorial Electrojet(EEJ)strength results derived from ground magnetometers observations suggest an enhanced eastward electric field from the E region probably played a significant role in this event.展开更多
Possible ionospheric disturbances relating to the May 12, 2008, MsS.0 Wenchuan earthquake were identified by Global Positioning System (GPS)-derived total electron content (TEC), ion- osonde observations, the glob...Possible ionospheric disturbances relating to the May 12, 2008, MsS.0 Wenchuan earthquake were identified by Global Positioning System (GPS)-derived total electron content (TEC), ion- osonde observations, the global ionospheric map (GIM), and electron density profiles detected by the Constellation Observation System for Meteorology Ionosphere and Climate (COSMIC). We applied a statistical test to detect anomalous TEC signals and found that a unique enhancement in TEC, recorded at 16 GPS stations, appeared on May 9, 2008. The critical fre- quency at F2 peak (foF2), observed by the Chinese ionosondes, and maximal plasma frequency, derived from COSMIC data, revealed a characteristic similar to GPS TEC variations. The GIM showed that the anomalous variations of May 9 were located southeast of the epicenter. Using GPS data from 13 stations near the epicenter, we analyzed the TEC variations of satellite orbit traces during 04:00-11:00 UT. We found that TEC decreased to the east and increased to the southeast of the epicenter during this period. Results showed that the abnormal disturbance on May 9 was probably an ionosphenc precursor of the Wenchuan earthquake of May 12, 2008.展开更多
Recent ionospheric observations report anomalous total electron content (TEC) deviations prior strong earthquakes. We discuss common fetures of the pre-earthquake TEC disturbances on the basis of statistics covering 5...Recent ionospheric observations report anomalous total electron content (TEC) deviations prior strong earthquakes. We discuss common fetures of the pre-earthquake TEC disturbances on the basis of statistics covering 50 strong seismic events during 2005-2006. The F2-layer ionospheric plasma drift under action of the electric fields of seismic origin is proposed as the main reason of producing TEC anomalies. The origin of such electric fields is discussed in terms of the lithosphere-atmosphere-ionosphere coupling system. This theory is supported by numerical simulations using global Upper Atmosphere Model (UAM). UAM calculations show that the vertical electric current with the density of about 20 - 40 nA/m2 flowing between the Earth and ionosphere over an area of about 200 by 2000 km is required to produce the TEC disturbances with the amplitude of about 30% - 50% relatively to the non-disturbed conditions. Ionosphere responses on the variations of the latitudinal position, direction and configuration of the vertical electric currents have been investigated. We show that not only the vertical component of the ionospheric plasma drift but also horizontal components play an important role in producing pre-earthquake TEC disturbances.展开更多
利用2009—2019年间的全球电离层图(Global Ionospheric Map,GIM)产品,提出了一种基于神经网络技术的北京地区电离层总电子含量(Total Electron Content,TEC)经验模型建立方法。模型精度验证结果表明,本文提出的方法能够有效提高电离层...利用2009—2019年间的全球电离层图(Global Ionospheric Map,GIM)产品,提出了一种基于神经网络技术的北京地区电离层总电子含量(Total Electron Content,TEC)经验模型建立方法。模型精度验证结果表明,本文提出的方法能够有效提高电离层TEC经验模型精度,相对Klobuchar模型和BDGIM模型精度分别提高了62%和21%。BJFS站的单频定位验证结果表明,本文建立的电离层TEC经验模型能够帮助单频用户有效提高定位精度,相对Klobuchar模型和BDGIM模型的三维定位精度分别提高了32%和7.5%。展开更多
Ionosphere delay is one of the main sources of noise affecting global navigation satellite systems, operation of radio detection and ranging systems and very-long-baseline-interferometry. One of the most important and...Ionosphere delay is one of the main sources of noise affecting global navigation satellite systems, operation of radio detection and ranging systems and very-long-baseline-interferometry. One of the most important and common methods to reduce this phase delay is to establish accurate nowcasting and forecasting ionospheric total electron content models. For forecasting models, compared to mid-to-high latitudes, at low latitudes, an active ionosphere leads to extreme differences between long-term prediction models and the actual state of the ionosphere. To solve the problem of low accuracy for long-term prediction models at low latitudes, this article provides a low-latitude, long-term ionospheric prediction model based on a multi-input-multi-output, long-short-term memory neural network. To verify the feasibility of the model, we first made predictions of the vertical total electron content data 24 and 48 hours in advance for each day of July 2020 and then compared both the predictions corresponding to a given day, for all days. Furthermore, in the model modification part, we selected historical data from June 2020 for the validation set, determined a large offset from the results that were predicted to be active, and used the ratio of the mean absolute error of the detected results to that of the predicted results as a correction coefficient to modify our multi-input-multi-output long short-term memory model. The average root mean square error of the 24-hour-advance predictions of our modified model was 4.4 TECU, which was lower and better than5.1 TECU of the multi-input-multi-output, long short-term memory model and 5.9 TECU of the IRI-2016 model.展开更多
The analysis of existing method for calculation of total content of electrons (TEC) in ionosphere using GPS occultation method does show that due to different values of signal/noise ration in GPS signals ?and , the ne...The analysis of existing method for calculation of total content of electrons (TEC) in ionosphere using GPS occultation method does show that due to different values of signal/noise ration in GPS signals ?and , the new method of optimum measurements of relevant frequency components of TEC measured by phase and code methods should be developed. The optimum quantity of measurements of the above-mentioned frequency components is determined taking into account the limitation imposed on general number of necessary measurements.展开更多
电离层闪烁导致卫星导航信号在传播过程中幅度与相位发生随机波动,严重影响接收机的性能。为模拟真实的受电离层闪烁影响的卫星导航信号,以供接收机进行性能测试,提出了基于电离层总电子含量(Total Electron Content,TEC)数据的电离层...电离层闪烁导致卫星导航信号在传播过程中幅度与相位发生随机波动,严重影响接收机的性能。为模拟真实的受电离层闪烁影响的卫星导航信号,以供接收机进行性能测试,提出了基于电离层总电子含量(Total Electron Content,TEC)数据的电离层闪烁仿真方法。该方法通过输入卫星观测文件与导航电文获得电离层的TEC和仰角,利用TEC数据和各卫星仰角,估计出受电离层闪烁影响的各卫星导航信号幅度闪烁指数和相位闪烁指数,结合Cornell模型实现卫星导航信号模拟。该方法充分考虑了卫星导航信号闪烁指数获取困难,以及电离层闪烁对不同卫星导航信号的影响,能够高保真反演卫星导航信号。试验结果表明,该方法反演的电离层闪烁与实际发生的闪烁具有良好的一致性。展开更多
This research investigates the capability of artificial neural networks to predict vertical total electron content(VTEC)over central Anatolia in Turkey.The VTEC dataset was derived from the 19 permanent Global Positio...This research investigates the capability of artificial neural networks to predict vertical total electron content(VTEC)over central Anatolia in Turkey.The VTEC dataset was derived from the 19 permanent Global Positioning System(GPS)stations belonging to the Turkish National Permanent GPS NetworkActive(TUSAGA-Aktif)and International Global Navigation Satellite System Service(IGS)networks.The study area is located at 32.6°E-37.5°E and 36.0°N-42.0°N.Considering the factors inducing VTEC variations in the ionosphere,an artificial neural network(NN)with seven input neurons in a multi-layer perceptron model is proposed.The KURU and ANMU GPS stations from the TUSAGA-Aktif network are selected to implement the proposed neural network model.Based on the root mean square error(RMSE)results from 50 simulation tests,the hidden layer in the NN model is designed with 41 neurons since the lowest RMSE is achieved in this attempt.According to the correlation coefficients,absolute and relative errors,the NN VTEC provides better predictions for hourly and quarterly GPS VTEC.In addition,this paper demonstrates that the NN VTEC model shows better performance than the global IRI2016 model.Regarding the spatial contribution of the GPS network to TEC prediction,the KURU station performs better than ANMU station in fitting with the proposed NN model in the station-based comparison.展开更多
基金financially supported by the Ministry of Science and Higher Education of the Russian Federation(State contract GZ0110/23-10-IF)。
文摘The longitudinal dependence of the behavior of ionospheric parameters has been the subject of a number of works where significant variations are discovered.This also applies to the prediction of the ionospheric total electron content(TEC),which neural network methods have recently been widely used.However,the results are mainly presented for a limited set of meridians.This paper examines the longitudinal dependence of the TEC forecast accuracy in the equatorial zone.In this case,the methods are used that provided the best accuracy on three meridians:European(30°E),Southeastern(110°E)and American(75°W).Results for the stations considered are analyzed as a function of longitude using the Jet Propulsion Laboratory Global Ionosphere Map(JPL GIM)for 2015.These results are for 2 h ahead and 24 h ahead forecast.It was found that in this case,based on the metric values,three groups of architectures can be distinguished.The first group included long short-term memory(LSTM),gated recurrent unit(GRU),and temporal convolutional networks(TCN)models as a part of unidirectional deep learning models;the second group is based on the recurrent models from the first group,which were supplemented with a bidirectional algorithm,increasing the TEC forecasting accuracy by 2-3 times.The third group,which includes the bidirectional TCN architecture(BiTCN),provided the highest accuracy.For this architecture,according to data obtained for 9 equatorial stations,practical independence of the TEC prediction accuracy from longitude was observed under the following metrics(Mean Absolute Error MAE,Root Mean Square Error RMSE,Mean Absolute Percentage Error MAPE):MAE(2 h)is 0.2 TECU approximately;MAE(24 h)is 0.4 TECU approximately;RMSE(2 h)is less than 0.5 TECU except Niue station(RMSE(2 h)is 1 TECU approximately);RMSE(24 h)is in the range of 1.0-1.7 TECU;MAPE(2 h)<1%except Darwin station,MAPE(24 h)<2%.This result was confirmed by data from additional 5 stations that formed latitudinal chains in the equatorial part of the three meridians.The complete correspondence of the observational and predicted TEC values is illustrated using several stations for disturbed conditions on December 19-22,2015,which included the strongest magnetic storm in the second half of the year(min Dst=-155 nT).
文摘In this paper, we studied the seasonal behavior of the total electron content (TEC) during a part of solar cycle 24 ascending, maximum and decreasing phases at Koudougou station (Latitude: 12°15'09"N Longitude: 2°21'45"W). Response of TEC to solar recurrent events is presented. The highest values of the TEC in 2014, 2015 and 2016 were recorded on March and October, while in 2013 they were recorded on April and November, corresponding to equinox months. This observation shows that TEC values at the equinoxes are higher than those of solstices. Moreover, the monthly TEC varies in phase with the sunspots number showing a linear dependence of the TEC on solar activity. The ionospheric electron contents are generally very low both before noon and during the night, but quite high at noon and after noon. This pattern of TEC variation is due to the fluctuation of incident solar radiation on the Earth’s equatorial ionosphere. During quiet periods, the number of free electrons generated is lower than that generated during recurrent periods, which shows a positive contribution of recurrent activity to the level of the TEC. Investigations have also highlighted a winter anomaly and equinoctial asymmetry in TEC behavior at Koudougou station.
文摘In this work, the comparative study of total electron content (TEC) between recurrent and quiet geomagnetic periods of solar cycle 24 at Koudougou station with geographical coordinates 12°15'N;- 2°20'E was addressed. This study aims to analyze how geomagnetic variations influence the behavior of TEC in this specific region. The geomagnetic indices Kp and Dst were used to select quiet and recurrent days. Statistical analysis was used to interpret the graphs. The results show that the mean diurnal TEC has a minimum before dawn (around 0500 UT) and reaches a maximum value around 1400 UT, progressively decreasing after sunset. In comparison, the average diurnal TEC on recurrent days is slightly higher than on quiet days, with an average difference of 7 TECU. This difference increases with the level of geomagnetic disturbance, reaching 21 TECU during a moderate storm. The study also reveals significant monthly variations, with March and October showing the highest TEC values for quiet and recurrent days, respectively. Equinox months show the highest mean values, while solstice months show the lowest. Signatures of semi-annual, winter and equatorial ionization anomalies were observed. When analyzing annual variations, it was found that the TEC variation depends significantly on F10.7 solar flux, explaining up to 98% during recurrent geomagnetic activity and 92% during quiet geomagnetic activity.
基金supported by the joint grants of TUBITAK 112E568 and RFBR 13-02-91370-CT_a and TUBITAK 114E092Atmospheric Sciences Institute Czech Republic(AS CR) 14/001 projects
文摘Total Electron Content (TEC) is an important observable parameter of the ionosphere which forms the main source of error for space based navigation and positioning systems. Since the deployment of Global Navigation Satellite Systems (GNSS), cost-effective esti- mation of TEC between the earth based receiver and Global Positioning System (GPS) sat- ellites became the major means of investigation of local and regional disturbance for earthquake precursor and augmentation system studies. International Reference Iono- sphere (IRI) extended to plasmasphere (IRI-Plas) is the most developed ionospheric and plasmaspheric climatic model that provides hourly, monthly median of electron density distribution globally. Recently, IONOLAB group {www.ionolab.org) has presented a new online space weather service that can compute slant TEC (STEC) on a desired ray path for a given date and time using IRI-Plas model (IRI-Plas-STEC). In this study, the performance of the model based STEC is compared with GPS-STEC computed according to the estimation method developed by the IONOLAB group and includes the receiver bias as IONOLAB-BIAS (IONOLAB-STEC). Using Symmetric Kullback-Leibler Distance (SKLD), Cross Correlation (CC) coefficient and the metric norm (L2N) to compare IRI-Plas-STEC and IONOLAB-STEC for the month of October 2011 over the Turkish National Permanent GPS Network (TNPGN- Active), it has been observed that SKLD provides a good indicator of disturbance for both earthquakes and geomagnetic storms.
文摘电离层总电子含量(Total Electron Content,TEC)的监测与预报是空间环境研究的重要内容,对卫星通讯和导航定位等有重要意义.TEC值影响因素较多,很难确定精确物理模型来对其进行预测.本文设计了基于注意力机制的LSTM模型(Att-LSTM),采用过去24小时TEC观测数据对未来TEC进行预测.选择北半球东经100°上,每2.5°纬度选择一个位置,共计36个位置来验证本文提出模型的性能,并与主流的深度学习模型如DNN、RNN、LSTM进行对比实验.取得了如下成果:(1)在选定的36个地区未来2小时单点预测上,基于本文的Att-LSTM模型的TEC预测性能明显优于其他对比模型;(2)讨论了纬度对Att-LSTM预测未来2小时TEC值时性能的影响,发现在北纬0°到60°之间,Att-LSTM预测性能随着纬度的升高而略有降低,在北纬62.5°~87.5°之间,模型预测性能出现扰动,预测效果略差;(3)讨论了磁暴期和磁静期模型的预测性能,发现无论是磁暴期还是磁静期,本文模型预测性能均较好;(4)还讨论了对未来多时点预测效果,实验结果表明,本文所提出的模型对未来2、4个小时的预测拟合度R-Square均超过0.95,预测结果比较可靠,对未来6、8、10个小时预测拟合度最高为0.7934,预测拟合度R-Square下降迅速,预测结果不可靠.
基金Fundamental Research Funds for the Central Universities(No.B230201012)National Natural Science Foundation of China(No.42104009)China Postdoctoral Science Foundation(No.2022M720988)。
文摘Total Electron Content(TEC)and electron density enhancement were observed on the day before 17 March 2015 great storm in the China Region.Observations from ground-and space-based instruments are used to investigate the temporal and spatial evolution of the pre-storm enhancement.TEC enhancement was observed from 24°N to 30°N after 10:00 UT at 105°E,110°E and 115°E longitudes on March 16.The maximum magnitude of TEC enhancement was more than 10 TECU and the maximal relative TEC enhancement exceeded 30%.Compared with geomagnetic quiet days,the electron density of Equatorial Ionization Anomaly(EIA)northern peak from Swarm A/C satellites on March 16 was larger and at higher latitudes.NmF2 enhanced during 11:30—21:00 UT at Shaoyang Station and increased by 200%at~16:00 UT.However,TEC and electron density enhancement were not accompanied by a significant change of hmF2.Most research has excluded some potential mechanisms as the main driving factors for storm-time density enhancements by establishing observational constraints.In this paper,we observed pre-storm enhancement in electron density at different altitudes and Equatorial Electrojet(EEJ)strength results derived from ground magnetometers observations suggest an enhanced eastward electric field from the E region probably played a significant role in this event.
基金supported financially by Science for Earthquake Resilience(XH14064Y)the open foundation of the State Key Laboratory of Geodesy and Earth's Dynamics(SKLGED2014-5-2-E)
文摘Possible ionospheric disturbances relating to the May 12, 2008, MsS.0 Wenchuan earthquake were identified by Global Positioning System (GPS)-derived total electron content (TEC), ion- osonde observations, the global ionospheric map (GIM), and electron density profiles detected by the Constellation Observation System for Meteorology Ionosphere and Climate (COSMIC). We applied a statistical test to detect anomalous TEC signals and found that a unique enhancement in TEC, recorded at 16 GPS stations, appeared on May 9, 2008. The critical fre- quency at F2 peak (foF2), observed by the Chinese ionosondes, and maximal plasma frequency, derived from COSMIC data, revealed a characteristic similar to GPS TEC variations. The GIM showed that the anomalous variations of May 9 were located southeast of the epicenter. Using GPS data from 13 stations near the epicenter, we analyzed the TEC variations of satellite orbit traces during 04:00-11:00 UT. We found that TEC decreased to the east and increased to the southeast of the epicenter during this period. Results showed that the abnormal disturbance on May 9 was probably an ionosphenc precursor of the Wenchuan earthquake of May 12, 2008.
文摘Recent ionospheric observations report anomalous total electron content (TEC) deviations prior strong earthquakes. We discuss common fetures of the pre-earthquake TEC disturbances on the basis of statistics covering 50 strong seismic events during 2005-2006. The F2-layer ionospheric plasma drift under action of the electric fields of seismic origin is proposed as the main reason of producing TEC anomalies. The origin of such electric fields is discussed in terms of the lithosphere-atmosphere-ionosphere coupling system. This theory is supported by numerical simulations using global Upper Atmosphere Model (UAM). UAM calculations show that the vertical electric current with the density of about 20 - 40 nA/m2 flowing between the Earth and ionosphere over an area of about 200 by 2000 km is required to produce the TEC disturbances with the amplitude of about 30% - 50% relatively to the non-disturbed conditions. Ionosphere responses on the variations of the latitudinal position, direction and configuration of the vertical electric currents have been investigated. We show that not only the vertical component of the ionospheric plasma drift but also horizontal components play an important role in producing pre-earthquake TEC disturbances.
文摘利用2009—2019年间的全球电离层图(Global Ionospheric Map,GIM)产品,提出了一种基于神经网络技术的北京地区电离层总电子含量(Total Electron Content,TEC)经验模型建立方法。模型精度验证结果表明,本文提出的方法能够有效提高电离层TEC经验模型精度,相对Klobuchar模型和BDGIM模型精度分别提高了62%和21%。BJFS站的单频定位验证结果表明,本文建立的电离层TEC经验模型能够帮助单频用户有效提高定位精度,相对Klobuchar模型和BDGIM模型的三维定位精度分别提高了32%和7.5%。
基金Project supported by the National Key Research and Development Program of China(Grant No.2016YFA0302101)the Initiative Program of State Key Laboratory of Precision Measurement Technology and Instrument。
文摘Ionosphere delay is one of the main sources of noise affecting global navigation satellite systems, operation of radio detection and ranging systems and very-long-baseline-interferometry. One of the most important and common methods to reduce this phase delay is to establish accurate nowcasting and forecasting ionospheric total electron content models. For forecasting models, compared to mid-to-high latitudes, at low latitudes, an active ionosphere leads to extreme differences between long-term prediction models and the actual state of the ionosphere. To solve the problem of low accuracy for long-term prediction models at low latitudes, this article provides a low-latitude, long-term ionospheric prediction model based on a multi-input-multi-output, long-short-term memory neural network. To verify the feasibility of the model, we first made predictions of the vertical total electron content data 24 and 48 hours in advance for each day of July 2020 and then compared both the predictions corresponding to a given day, for all days. Furthermore, in the model modification part, we selected historical data from June 2020 for the validation set, determined a large offset from the results that were predicted to be active, and used the ratio of the mean absolute error of the detected results to that of the predicted results as a correction coefficient to modify our multi-input-multi-output long short-term memory model. The average root mean square error of the 24-hour-advance predictions of our modified model was 4.4 TECU, which was lower and better than5.1 TECU of the multi-input-multi-output, long short-term memory model and 5.9 TECU of the IRI-2016 model.
文摘The analysis of existing method for calculation of total content of electrons (TEC) in ionosphere using GPS occultation method does show that due to different values of signal/noise ration in GPS signals ?and , the new method of optimum measurements of relevant frequency components of TEC measured by phase and code methods should be developed. The optimum quantity of measurements of the above-mentioned frequency components is determined taking into account the limitation imposed on general number of necessary measurements.
文摘电离层闪烁导致卫星导航信号在传播过程中幅度与相位发生随机波动,严重影响接收机的性能。为模拟真实的受电离层闪烁影响的卫星导航信号,以供接收机进行性能测试,提出了基于电离层总电子含量(Total Electron Content,TEC)数据的电离层闪烁仿真方法。该方法通过输入卫星观测文件与导航电文获得电离层的TEC和仰角,利用TEC数据和各卫星仰角,估计出受电离层闪烁影响的各卫星导航信号幅度闪烁指数和相位闪烁指数,结合Cornell模型实现卫星导航信号模拟。该方法充分考虑了卫星导航信号闪烁指数获取困难,以及电离层闪烁对不同卫星导航信号的影响,能够高保真反演卫星导航信号。试验结果表明,该方法反演的电离层闪烁与实际发生的闪烁具有良好的一致性。
文摘This research investigates the capability of artificial neural networks to predict vertical total electron content(VTEC)over central Anatolia in Turkey.The VTEC dataset was derived from the 19 permanent Global Positioning System(GPS)stations belonging to the Turkish National Permanent GPS NetworkActive(TUSAGA-Aktif)and International Global Navigation Satellite System Service(IGS)networks.The study area is located at 32.6°E-37.5°E and 36.0°N-42.0°N.Considering the factors inducing VTEC variations in the ionosphere,an artificial neural network(NN)with seven input neurons in a multi-layer perceptron model is proposed.The KURU and ANMU GPS stations from the TUSAGA-Aktif network are selected to implement the proposed neural network model.Based on the root mean square error(RMSE)results from 50 simulation tests,the hidden layer in the NN model is designed with 41 neurons since the lowest RMSE is achieved in this attempt.According to the correlation coefficients,absolute and relative errors,the NN VTEC provides better predictions for hourly and quarterly GPS VTEC.In addition,this paper demonstrates that the NN VTEC model shows better performance than the global IRI2016 model.Regarding the spatial contribution of the GPS network to TEC prediction,the KURU station performs better than ANMU station in fitting with the proposed NN model in the station-based comparison.