Seismo-ionospheric anomalies in total electron content of the GIM and electron density of DEMETER before the 27 February 2010 M8.8 Chile earthquake

In this paper we examine the pre-earthquake ionospheric anomalies by the total electron content (TEC) extracted from GIM (global ionospheric map) and the electron density (Ne) observed by the DEMETER (Detection of Electro-Magnetic Emissions Transmitted from Earthquake Regions) satellite during the 2010 M8.8 Chile earthquake. Temporal variations show the nighttime TEC and Ne simultaneously increase 9–19 days before the earthquake. A cross-comparison of data recorded during the period of 1 February to 3 March in 2006–2010 confirms the above temporal anomalies specifically appear in 2010. The spatial analyses show that the anomalies tend to appear over the epicenter.     

Variations of ionospheric total electron content before three strong earthquakes in the Qinghai-Tibet region

We investigate the ionospheric total electron content (TEC) anomalies occurred in the Qinghai-Tibet region before three large earthquakes (M > 7.0). The temporal and spatial TEC variations were used to detect the ionospheric possible precursors of these earthquakes. We identified two TEC enhancements in the afternoon local time 9 days and 2–3 days before each earthquake, between which a TEC decrement occurred 3–6 days before earthquakes. These anomalies happened in the area of about 30° in latitude and the maximum is localized equatorward from the epicenters. These TEC anomalies can be found in all three earthquakes regardless the geomagnetic conditions. The features of these anomalies have something in common and may have differences from those caused by geomagnetic storms. Our results suggest that these ionospheric TEC perturbations may be precursors of the large earthquakes.     

Investigation into impact of tropical cyclones on the ionosphere using GPS sounding and NCEP/NCAR Reanalysis data

Ionospheric response to tropical cyclones (TCs) was estimated experimentally on the example of three powerful cyclones – KATRINA (23–31 August 2005), RITA (18–26 September 2005), and WILMA (15–25 October 2005). These TCs were active near the USA Atlantic coast. Investigation was based on Total Electron Content (TEC) data from the international network of two-frequency ground-based GPS receivers and the NCEP/NCAR Reanalysis data. We studied the spatial–temporal dynamics of wave TEC disturbances over two periods of ranges (02–20 min and 20–60 min). To select the ionospheric disturbances which were most likely to be associated with the cyclones, maps of TEC disturbances were compared with those of meteorological parameters.     

2006年4月5日中等强度磁暴期间观测到的大经度范围等离子体泡

利用分布在70°E～210°E和20°S～40°N之间的GPS台站的数据,分析了2006年4月5日夜间(中等强度磁暴期间)观测到的电离层等离子体泡的特性.结果表明,本次事件中,等离子体泡大约发生在当地日落后1～1.5 h;空间范围为经度90°E～160°E,纬度12°S～33°N.这是第一次利用地基设备观测到如此大经度范围内的等离子体泡.等离子体泡在南半球出现较早,并且存活时间较长.在其产生的过程中,在约1100 km高度上,映射到磁赤道向上的运动速度约为300m/s,并且等离子体泡在高度上有倾斜.东向电场的存在,对激发等离子体泡起到了一定的作用.      

武汉地区电离层TEC和NmF2及板厚的季节变化

通过利用武汉电离层观测站(114.4°E,30.6°N)1980-1990年对E8T-Ⅱ卫星信标的法拉第旋转测量的TEC(电子浓度总含量)数据,以及由测高仪测量的1980-1990年间的f0F2(F2层临界频率)数据,研究了武汉地区TEC,NmF2(最大电子浓度)和板厚的季节变化,同时比较了IRI和武汉单站模式在预测NmF2季节性方面的有效性.武汉单站模式在预测NmF2季节性变化方面优于IRI模式.      

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.      

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。     

Ionospheric parameters in the European sector during the magnetic storm of August 25–26, 2018

Variations of ionospheric parameters Total Electron Content (TEC) by GNSS, critical frequency (foF2) by vertical sounding and electron density (Ne) by low-altitude satellite were studied at high, mid and low latitudes of the European sector during the magnetic storm of August 25–26, 2018. During the main phase of the storm the ionospheric F2-layer was under the positive disturbance at mid and low latitudes. Then the transition from the positive to negative ΔfoF2 values occurred at all latitudes. The recovery phase was characterized by negative ionospheric disturbance at all latitudes. This is due to the decrease of thermospheric O/N2 ratio during the recovery phase of the storm. The intense Es layers screened the reflections from the F2-layer on August 26th at high and at low latitudes but at different times. Some blackouts occurred due to the high absorption level at high latitudes. In general, foF2 and TEC data were highly correlated. The major Ne changes were at the low latitudes. In general, Ne data confirmed the ionospheric dynamics revealed with foF2 and TEC.     

Temporal extrapolation of TEC using WNN during 2007–2018 and comparison against GIM,IRI2016 and Kriging

In this paper, a new method of temporal extrapolation of the ionosphere total electron content (TEC) is proposed. Using 3-layer wavelet neural networks (WNNs) and particle swarm optimization (PSO) training algorithm, TEC time series are modeled. The TEC temporal variations for next times are extrapolated with the help of training model. To evaluate the proposed model, observations of Tehran GNSS station (35.69°N, 51.33°E) from 2007 to 2018 are used. The efficiency of the proposed model has been evaluated in both low and high solar activity periods. All observations of the 2015 and 2018 have been removed from the training step to test the proposed model. On the other hand, observations of these 2 years are not used in network training. According to the F10.7, the 2015 has high solar activity and the 2018 has quiet conditions. The results of the proposed model are compared with the global ionosphere maps (GIMs) as a traditional ionosphere model, international reference ionosphere 2016 (IRI2016), Kriging and artificial neural network (ANN) models. The root mean square error (RMSE), bias, dVTEC = |VTEC_GPS ? VTEC_Model| and correlation coefficient are used to assess the accuracy of the proposed method. Also, for more accurate evaluation, a single-frequency precise point positioning (PPP) approach is used. According to the results of 2015, the maximum values of the RMSE for the WNN, ANN, Kriging, GIM and IRI2016 models are 5.49, 6.02, 6.34, 6.19 and 13.60 TECU, respectively. Also, the maximum values of the RMSE at 2018 for the WNN, ANN, Kriging, GIM and IRI2016 models are 2.47, 2.49, 2.50, 4.36 and 6.01 TECU, respectively. Comparing the results of the bias and correlation coefficient shows the higher accuracy of the proposed model in quiet and severe solar activity periods. The PPP analysis with the WNN model also shows an improvement of 1 to 12 mm in coordinate components. The results of the analyzes of this paper show that the WNN is a reliable, accurate and fast model for predicting the behavior of the ionosphere in different solar conditions.     

A MLP neural network as an investigator of TEC time series to detect seismo-ionospheric anomalies

Anomaly detection is extremely important for earthquake parameters estimation. In this paper, an application of Artificial Neural Networks (ANNs) in the earthquake precursor’s domain has been developed. This study is concerned with investigating the Total Electron Content (TEC) time series by using a Multi-Layer Perceptron (MLP) neural network to detect seismo-ionospheric anomalous variations induced by the powerful Tohoku earthquake of March 11, 2011.