Analysis of Polar Ionosphere Anomaly of Interplanetary Shock Events Based on GNSS TEC

Using large-scope and high-accuracy global navigation satellite system (GNSS) ionospheric monitoring, this study considered the polar ionosphere anomaly morphology caused by an interplanetary shock event that occurred on September 12, 2014. First, we used the continuous observation data of GNSS stations in the Antarctic region to estimate the carrier TEC epochs variation (dTEC) that reflects ionospheric electron density changes, and the abnormal signals were extracted. Through sequence data analyses of longitudinal chain stations, we found that the influence range of this anomaly lies in the geomagnetic region between 65<inline-formula><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> and 80 <inline-formula><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula>S. Furthermore, latitude chain analyses revealed that with the continuous fluctuation of the solar wind flow pressure, the polar ionospheric geomagnetic 75 <inline-formula><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula>S annular observation sequence gives a corresponding continuous fluctuation response with a range of 0.05–0.4 TECU and a westward anomaly trigger position. The average speed was close to the satellite speed during the study period, and it was determined that the anomaly occurred within an altitude region of 150–300 km. The physical mechanism of this anomaly is discussed in this article. Our results show that the GNSS dTEC and multisource data can accurately reflect the regional intensity, influence region, trigger source location, and velocity of the ionosphere anomaly.

T HE ionosphere is a high-altitude region of earth's atmosphere that is ionized by the excitation of high-energy solar radiation and cosmic rays. It is also an important part of the solar-terrestrial space environment and is closely related to human life. Recently, the use of satellite data to calculate the ionospheric total electronic content (TEC) has become the primary method for monitoring the abnormal activity and structure of the ionosphere [1], [2], leading to technologies such as ionospheric tomography being derived. After the emergence of global navigation satellite system (GNSS), new detection methods have been used to study and analyze ionospheric disturbances, focusing on ionospheric anomalies under extreme geological events, such as earthquakes, magnetic storms, and solar activity. The impact of interplanetary shock generated by solar activity on the ionosphere has recently become a hot spot of ionosphere anomaly detection [3], [4].
Currently, studies have shown that when the interplanetary shock reaches earth, the magnetosphere and ionosphere are affected in numerous ways [5], [6]. That is, when the interplanetary shock interacts with the magnetosphere, it generally leads to enhanced auroral activity on the day side and generates a shock aurora along the dawn and dusk sides of the aurora egg to the night side [7], [8], [9]. Furthermore, interplanetary shock compresses the magnetopause in the direction of earth and enhances the field-aligned current, leading to the deposition of electrons into the ionosphere and a change in the TEC [10]. The TEC measured by ground-based GNSS includes the ionosphere electron content contributions and the plasma layer electron content contributions. Its value changes significantly, particularly when electron deposition occurs in the polar ionosphere. Therefore, monitoring TEC variation can be an effective observation measurement to study the ionosphere and the plasma layer in response to interplanetary disturbances, such as magnetospheric compression [11], [12], [13], [14].
Meanwhile, many studies have shown that TEC content changes are related to the appearance and evolution of aurora and polar ionosphere irregularities [15], [16], [17]. For example, Coker et al. [18] used global positioning system (GPS) TEC data to detect auroral activity and found that the geographic location of the auroral-E ionization event was consistent with the magnetometer observation results, which verified the effectiveness of monitoring auroral activity using TEC variation. Kintner et al. [19] monitored two events of enhanced TEC content and found that they were closely related to the spatiotemporal characteristics of auroral appearances; therefore, they believed that the TEC variation was caused by the deposition of discrete aurora electrons and proposed that GPS TEC observations could detect individual aurora arcs. Jin et al. [10] studied the TEC variation in the polar ionosphere with GPS TEC data for an interplanetary shock event and found that the change in TEC content reflected the morphological characteristics of the shock aurora, which provided a new idea for studying the solar-terrestrial space coupling.
Based on raw GNSS TEC data from the Antarctic, this study eliminated the influence of integer ambiguity through the difference between epochs to obtain high-precision carrier dTEC sequences. Taking the polar ionosphere anomaly caused by an interplanetary shock event on September 12, 2014, as an example, this study comprehensively analyzed the anomaly from multiple perspectives, such as the arrival time, propagation range, trigger source location, and movement trend, and compared the analysis results with multisource data. , and the overall fluctuation tended to be gentle and kept at 10-20 nPa. These synchronous sudden increases in the IMF and flow pressure indicated that this event was a typical interplanetary shock event. According to the time when the flow pressure first showed an anomaly, it can be preliminarily inferred that the arrival time of the shock was approximately at the 1910 epochs (15:55 UT) [20].

B. Data Source and Preprocessing
The data used in this study were divided into three main categories. The first category of data was the GNSS data, which were used to calculate the TEC epoch variation (dTEC) sequence, as GNSS dTEC estimations have high accuracies. Compared with the TEC accuracy estimated by phase-smoothed pseudorange observations, the dTEC accuracy doubled. Dense GNSS continuous observation stations are distributed in the Antarctic, especially in the southwest pole and the Antarctic Peninsula region. The approximately 40 stations could accurately and continuously monitor the ionospheric electron density anomaly. The data sampling rate was set to 30 s.
The second type of data was Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) occultation data from the COSMIC occultation constellation comprised of six low-earth-orbit satellites. This type of data has all-weather, global coverage, and high-resolution characteristics. They can cover the polar region well and study the polar ionosphere in depth and detail under the condition of an ionospheric anomaly [21]. As such, the electron density profile can be obtained from the COSMIC occultation data, which can be used to analyze the height distribution of the anomaly, but the amount of occultation data is small.
The third type of data was SWARM data from the SWARM satellite constellation, which consists of three small satellites, SWARM-A, SWARM-B, and SWARM-C, and operates at two orbital altitudes, with SWARM-A and SWARM-C being paired satellites at operating orbital altitudes of 450 km and inclinations of 87.4 • . Furthermore, these satellites are equipped with GNSS receivers, which can also analyze the abnormal height distribution status.
This study analyzed the ionosphere anomaly caused by this event based on the GNSS dTEC sequence, expressed the values of each dTEC sequence by combining the station name with the two-digit satellite pseudorandom noise code, and used the COSMIC occultation and SWARM data to speculate the anomaly height. Fig. 2(a) shows the distribution of data sources in the Antarctic region, with black symbols representing the GNSS ground observation stations, red symbols representing the location of the COSMIC occultation event, and blue lines representing the geomagnetic longitude (MLON) and geomagnetic latitude (MLAT). Since the GNSS could be continuously observed, and the flow pressure showed continuous fluctuations according to Fig. 1, the occurrence and development of the anomaly could be well reproduced by using the circular GNSS dTEC sequence.

C. GNSS dTEC Estimate
The GNSS continuous observation station can monitor the atmospheric environment (including tropospheric water vapor and ionospheric electron density) over a wide range with high accuracy in all weather conditions. The observation equation of GNSS carrier observation value is as follows: where ρ 0 is the geometric distance between the receiver and the satellite, and TEC is the total electron content on the propagation path; λ 1 and λ 2 represent the carrier wave lengths of different carriers; ϕ 1 and ϕ 2 are carrier phase measurements of different carriers; N 1 and N 2 are integer ambiguities; and Δ is a frequency-independent correction term including the receiver clock error, satellite clock error, antenna phase center error, relativistic effect correction, and tropospheric delay. GNSS carrier measurements have millimeter-level accuracy. The two equations in (1) are combined as follows: where f is a constant related to frequency. From (3), the absolute TEC magnitude cannot be determined using only carrier data because of the existence of integer ambiguity parameters. However, in the absence of cycle slip, the ambiguity parameters in a continuous arc segment do not change with time. Therefore, we continued to calculate the differences between epochs to obtain the estimation of TEC variation between epochs, as follows: The dTEC estimated value calculated by (5) eliminates the influence of ambiguity by taking the difference between epochs and has multiple improvements in accuracy compared with the estimated value obtained by the phase-smoothing pseudorange. Therefore, we use dTEC instead of TEC to accurately reflect the relative differences in ionospheric anomalies between different IPPs. The sliding window method can be used to use the average TEC of n epochs before and after as the background value to better extract the volatility of dTEC, as follows: , Aver is the average operator that can effectively extract small changes in the relative fluctuations of TEC through dTEC. Therefore, with the help of widely deployed GNSS stations, small-magnitude anomalies of the polar ionosphere can be continuously monitored with high accuracy.

A. Longitudinal Analysis of Anomaly Based on GNSS dTEC
To determine the latitude range affected by the anomaly, we calculated the longitude and latitude data of 393 pierce points based on 61 GNSS observation stations in Antarctica and finally selected two sets of longitude chains, as shown in Fig. 2(b). According to (6), the dTEC sequences of different stations for different GPS satellites in the study period were calculated and analyzed to select the magnetic latitude circle with the largest abnormal impact. Among them, only data with an elevation angle of more than 30 • were selected, and the background value was the average value of ten epochs before and after.
As shown in Fig. 3(a), the dTEC sequences located at magnetic latitudes of 75 • -85 • S along the magnetic longitude chain of 70 • W [the southeast magnetic longitude chain in Fig. 2(b)]. Clear dTEC sequences fluctuations were observed at the pierce points below 80 • S MLAT, among which the dTEC sequence fluctuations observed at the pierce points near 76 • S MLAT were the most sudden, with the overall fluctuation magnitude maintained at about 0.4 TECU, and the maximum value reaching 0.47 TECU. The fluctuation magnitude of the dTEC sequences at some pierce points above 80 • S MLAT was significantly reduced compared with the others, and no obvious fluctuation anomaly was observed, such as lwn005 (80.1 • S MLAT) and lwn029 (83.3 • S MLAT). The ionospheric anomaly caused by the shock event did not propagate to the area above 80 • S MLAT. For another part of the pierce points in the area above 80 • S MLAT, where obvious abnormal fluctuations were observed, the phenomenon was related to the midnight aurora enhanced by the compression wave generated by the shock impact position [22]. Fig. 3(b) shows the dTEC sequences located at 60 • -80 • S MLAT along chain of 14 • W MLON [the magnetic longitude chain in the southwest corner of Fig. 2(b)]. Clear dTEC sequence fluctuations were observed at the pierce points located above 65 • S MLAT, among which the dTEC sequence fluctuation observed at the pierce point located near 76 • S MLAT was also the most sudden; however, compared with the fluctuation amplitude of the pierce point in the same latitude area in Fig. 3(a), the fluctuation amplitude is lower, basically maintained at approximately 0.2 TECU, with the maximum value reaching 0.21 TECU. In addition, it can be clearly seen in Fig. 3

B. Latitudinal Analysis of Anomaly Based on GNSS dTEC
To further study the propagation of anomalies in latitude, based on the analysis results in the previous section, this study selected dTEC sequences of a circle of pierce point chains near 75 • S MLAT, as shown in Fig. 2(b) to study the propagation of ionospheric anomalies. In addition, according to the data analysis results of space weather parameters in Section II-A, both the flow pressure and IMF in the first 60 epochs experienced severe fluctuations during the study period, and the Bz component of the IMF in the 1972 epochs completed a southward transition. This study addressed the ionospheric dTEC in this process using sequential time data. The research period of this section is between the 1900 and 1980 epochs (15:50-16:30 UT), corresponding to 40 min. This short time frame meant that the magnetic longitude and latitude change of the pierce points was negligible; therefore, the motion of the satellite relative to the magnetic latitude circle is ignored in this section. Fig. 4 shows a comparison diagram of the flow pressure data and the dTEC sequence arranged clockwise at each piercing point on the magnetic latitude circle during the study period. To clearly display the dTEC sequence anomaly at each pierce point, the legend size of each pierce-point curve in the figure  was 0.1, 0.3, 0.2, 0.4, 0.4, 0.4, 0.4, 0.3, and 0 To further study the propagation characteristics of the anomaly in the shock event, we extracted four anomaly characteristic curves according to the abnormal fluctuations of dTEC sequences at each pierce point during the study period and marked the anomaly propagation path with different colors, as

C. Height Analysis of Anomaly Based on Occultation Data
For the auroral electron deposition observed in the polar region, the corresponding maximum ionization rate usually occurs in the area below a height of 300 km, and the intensity and area vary with the situation [10]. To confirm the altitude region of the ionospheric anomaly in the interplanetary shock event, this study first used occultation and spaceborne GNSS data from the SWARM satellites to confirm the ionospheric response at different altitudes during the research period. The occultation data used in this study were COSMIC data, and the data source distribution is shown in Fig. 2(a). Fig. 5 shows the electronic density profile of the COMIC data processed by the Hilbert-Huang transform (HHT) with a height resolution of 2.5 km. As shown in Fig. 5, the HHT spectrum did not show obvious anomalies before 16:00 UT. However, during 16:05-16:24 UT, obvious anomalies can be observed in the HHT spectrum at altitudes of 150-300 km, which can be preliminarily inferred that the height of the anomaly is about 250 km.
To further verify these conclusions, this study used spaceborne GNSS data from the SWARM satellites to calculate dTEC variation sequences to determine the TEC anomaly above 450-km orbital height, as shown in Fig. 6. SWARM satellites provide the slant TEC calculated by the carrier phase-smoothed pseudorange; therefore, their accuracy is not sufficient for smallscale TEC anomaly detection. Fig. 6 shows the dTEC sequences of SWARM-A (orbital height: 461 km) and GPS satellites 5 and 10 calculated according to (6), and the average before and after two epochs was selected as the background value. Meanwhile, to ensure the accuracy of detection, this study only uses observation values with elevation angles greater than 30 • to participate in the calculation. The SWARM-A satellite ran through the study area   Fig. 7(a) and (b) indicate the inferred locations of the anomaly trigger sources. The initial location  of the anomaly trigger source was near MLT00:00 (180 • W MLON), which is close to the initial location (172.90 W) inferred above based on the GNSS dTEC sequence, and it shows an obvious westward movement trend. The distance between the locations of the anomaly trigger source in the two figures was approximately 45 • and the interval was 27 min. It can be concluded that the movement speed of the trigger source is approximately 0.80 km/s, which is lower than the average speed of the same period in Section III-B, which is consistent with the speculated results above. Fig. 8 shows the movement paths of pierce points during the study period, which reflect the dTEC sequence anomaly earlier in the magnetic latitude circle selected in Section III-B. The three satellites represented by the three pierce points all show speed along the magnetic field line, and the direction is from east to west, which is consistent with the result that the location of the abnormal trigger source shows a westward trend. Among them, the pierce points dav129 and maw113 both moved in the direction far away from the 75 • S MLAT circle, while the pierce point dav129 was far away from the 75 • S MLAT circle, and maw113 moved approximately in the direction perpendicular to the magnetic field line, and the movement trend of the satellite along the tangential direction of the magnetic field line was not obvious; however, the pierce point dav116 moved in the direction close to the circle of the 75 • S MLAT, relatively close to the circle, and had an obvious tangential movement trend of the magnetic field line. Because IPPs are the intersections of the ionosphere with the GPS satellite raypaths, which means that GPS satellites have the same motion tendency as IPPs. Therefore, the GPS16 satellite was selected in this section for further analysis. According to the precise ephemeris of the satellite, the movement speed of the GPS satellite in this period was approximately 2.9 km/s, and the angle between the pierce point dav116 and the tangential direction of the magnetic field line was approximately 40 • , so the tangential velocity component was 2.2 km/s, which is close to the anomaly westward moving speed calculated in Section III-B.

IV. CONCLUSION
In this study, GNSS TEC data with an accuracy of 30 s were processed through epoch differences, and the sliding window method was used to process the results to obtain dTEC data that can accurately reflect the ionospheric electron density fluctuation trend, and the interplanetary shock event on September 12, 2014 was analyzed using it. The results are as follows.
1) The interplanetary shock arrived on earth near 15:55 UT on the same day, the duration of the anomaly was more than 30 min, and the height of the anomaly was approximately 250 km. 2) The initial position of the trigger source of the ionosphere anomaly caused by interplanetary shock was near 180 • W MLON and 76 • S MLAT, and it showed a westward movement trend, whereas its westward speed gradually decreased with time. The average speed in the research period was approximately 2.3 km/s, which is close to the 2.2-km/s tangential speed of the satellite along the magnetic latitude circle in the same period.
3) The longitudinal propagation region of the ionosphere anomaly in this event is within the 65 • -80 • S MLAT area, and the magnitude of the anomaly is the largest within the 180 • W-120 • E MLON area, which is consistent with the results shown in polar remote sensing image of SSUSI. Based on these analyses, it can be proven that the dTEC sequences obtained based on GNSS TEC in this study can accurately reflect the fluctuation of electron density during the ionosphere anomaly. Compared with the traditional prediction of the ionospheric TEC background value and setting tolerance to detect the ionosphere anomaly, dTEC eliminates the influence of integer ambiguity by the epoch difference of TEC. Compared with the accuracy of the TEC estimation obtained from the phase-smoothing pseudorange, dTEC has multiple improvements and unique advantages in high-precision ionosphere anomaly detection.