Ionospheric Total Electron Content Data Research Articles

Forecasting of Ionospheric Total Electron Content Data Using Multivariate Deep LSTM Model for Different Latitudes and Solar Activity

The ionospheric state is becoming increasingly important to forecast for the reliable operation of terrestrial and space-based radio-communication systems which are influenced by ionospheric space weather. In this study, we have investigated and tested a multivariate long short-term memory (LSTM) deep learning model for its forecasting accuracy over different latitudinal regions during the solar quiet and solar active years. We also tested its prediction capability during the occurrence of a geomagnetic storm. Four stations qaq1 (60.7°N, 46.04°W), baie (49.18°N, 68.26°W), mas1 (27.76°N, 15.63°W), and bogt (4.64°N, 74.08°W) in the northern hemisphere were used in this study. To optimize the feature extraction process, we used heat map to find the correlation between TEC and the various exogenous parameters and finally nine correlated parameters were used as inputs to train the LSTM model. The performance of the LSTM model was validated by comparing it with the multilayer perceptron (MLP) machine learning algorithm using root mean square error (RMSE) and mean absolute error (MAE) as evaluation indices. The results showed an accuracy improvement of 70% and 64% over MLP during the solar quiet and active years, respectively. The prediction accuracy of our LSTM model was also 74% better than MLP during the geomagnetic storm event. These findings demonstrate the effectiveness of the developed LSTM model and the right selection of the exogenous parameters in estimating TEC, and suggest that this LSTM model can be used for short-term TEC forecasting.

An Empirical Orthogonal Function Study of the Ionospheric TEC Predicted Using the TIEGCM Model over the South Atlantic Anomaly in 2002 and 2008

In this study, the variability of the ionospheric total electron content (TEC) in the South Atlantic Anomaly (SAA) in the solar maximum of 2002 and the solar minimum of 2008 were compared by using an empirical orthogonal function (EOF) analysis. The ionospheric TEC data were simulated using the National Center for Atmospheric Research Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIEGCM). The first three EOFs accounted for 94.8% and 93.86% of the variability in the data in 2002 and 2008, respectively. The results showed that the TEC variations of the first three EOFs were generally consistent in 2002 and 2008. The first mode showed the equatorial anomaly caused by plasma drift and the east–west asymmetry possibly caused by the change in geomagnetic declination and zonal wind; EOF2 exhibited the zonal variation influenced by the solar EUV radiation and the semiannual variation possibly controlled by the [O/N2], solar zenith angle, and atmospheric circulation. EOF3 suggested an equatorial anomaly and winter anomaly influenced by the [O/N2] variation. However, the values and amplitude variations in the TEC were significantly greater in the solar maximum than that in the solar minimum, and the spring–autumn asymmetry of the TEC was more obvious in the solar minimum. In addition, we used the EOF method to extract the annual variation characteristics of the time coefficients and carried out a correlation analysis. The results showed that the annual variation in the TEC in 2002 was mainly affected by the solar EUV radiation, which was strongly correlated with F10.7 (r = 0.7348). In contrast, the TEC was mainly influenced by the geomagnetic activity in 2008 and had a strong correlation with Dst (r = −0.7898).

An Extreme Value Analysis of Long-Term GNSS Ionospheric Total Electron Content Data Observed at Japan Grid Point Location (34.95° N and 134.05° E)

An Extreme Value Analysis of Long-Term GNSS Ionospheric Total Electron Content Data Observed at Japan Grid Point Location (34.95° N and 134.05° E)

Analysis of ionospheric TEC response to solar and geomagnetic activities at different solar activity stages

Studying the relationship of total electron content (TEC) to solar or geomagnetic activities at different solar activity stages can provide a reference for ionospheric modeling and prediction. On the basis of solar activity indices, geomagnetic activity parameters, and ionospheric TEC data at different solar activity stages, this study analyzes the overall variation relationships of solar and geomagnetic activities with ionospheric TEC, the characteristics of the quasi-27-day periodic oscillations of the three variables at different stages, and the delayed TEC response of solar activity by conducting correlation analysis, Butterworth band-pass filtering, Fourier transform, and time lag analysis. The following results are obtained. (1) TEC exhibits a significant linear relationship with solar activity at different solar activity stages. The correlation coefficients |R| are arranged as follows: |R|EUV > |R|F10.7 > |R|sunspot number. No significant linear relationship exists between TEC and geomagnetic activity parameters (|R| < 0.35). (2) TEC, solar activity indices, and geomagnetic activity parameters have a period of 10.5 years. The maximum amplitudes of the Fourier spectrum for TEC and solar activity indices are nearly 27 days and those of geomagnetic activity parameters are nearly 27 and 13.5 days. (3) The deviations of the quasi-27-day significant periodic oscillation of TEC and solar activity indices are consistent. (4) No evident relationship exists between the quasi-27-day periodic oscillation of TEC and geomagnetic activity parameters. (5) The delay time of TEC for the 10.7 cm solar radio flux and extreme ultraviolet is always consistent, whereas that for sunspot number varies at each stage.

The 15 January 2022 Hunga Tonga Eruption History as Inferred From Ionospheric Observations

AbstractOn 15 January 2022, the Hunga Tonga‐Hunga Ha’apai submarine volcano erupted violently and triggered a giant atmospheric shock wave and tsunami. The exact mechanism of this extraordinary eruptive event, its size and magnitude are not well understood yet. In this work, we analyze data from the nearest ground‐based receivers of Global Navigation Satellite System to explore the ionospheric total electron content (TEC) response to this event. We show that the ionospheric response consists of a giant TEC increase followed by a strong long‐lasting depletion. We observe that the explosive event of 15 January 2022 began at 04:05:54UT and consisted of at least five explosions. Based on the ionospheric TEC data, we estimate the energy released during the main major explosion to be between 9 and 37 Megatons in trinitrotoluene equivalent. This is the first detailed analysis of the eruption sequence scenario and the timeline from ionospheric TEC observations.

A Spatiotemporal Network Model for Global Ionospheric TEC Forecasting

In the Global Navigation Satellite System, ionospheric delay is a significant source of error. The magnitude of the ionosphere total electron content (TEC) directly impacts the magnitude of the ionospheric delay. Correcting the ionospheric delay and improving the accuracy of satellite navigation positioning can both benefit from the accurate modeling and forecasting of ionospheric TEC. The majority of current ionospheric TEC forecasting research only considers the temporal or spatial dimensions, ignoring the ionospheric TEC’s spatial and temporal autocorrelation. Therefore, we constructed a spatiotemporal network model with two modules: (i) global spatiotemporal characteristics extraction via forwarding spatiotemporal characteristics transfer and (ii) regional spatiotemporal characteristics correction via reverse spatiotemporal characteristics transfer. This model can realize the complementarity of TEC global spatiotemporal characteristics and regional spatiotemporal characteristics. It also ensures that the global spatiotemporal characteristics of the global ionospheric TEC are transferred to each other in both temporal and spatial domains at the same time. The spatiotemporal network model thus achieves a spatiotemporal prediction of global ionospheric TEC. The Huber loss function is also used to suppress the gross error and noise in the ionospheric TEC data to improve the forecasting accuracy of global ionospheric TEC. We compare the results of the spatiotemporal network model with the Center for Orbit Determination in Europe (CODE), the convolutional Long Short-Term Memory (convLSTM) model and the Predictive Recurrent Neural Network (PredRNN) model for one-day forecasts of global ionospheric TEC under different conditions of time and solar activity, respectively. With internal data validation, the average root mean square error (RMSE) of our proposed algorithm increased by 21.19, 15.75, and 9.67%, respectively, during the maximum solar activity period. During the minimum solar activity period, the RMSE improved by 38.69, 38.02, and 13.54%, respectively. This algorithm can effectively be applied to ionospheric delay error correction and can improve the accuracy of satellite navigation and positioning.

Ionospheric TEC data assimilation based on Gauss–Markov Kalman filter

Ionospheric Total Electron Content (TEC) is an important parameter for space weather research. Based on GNSS data from China Crustal Movement Observation Network (CMONOC) and International Reference Ionosphere (IRI) model, we develop a TEC model in China and adjacent region (15°N − 55°N and 70°E − 140°E) with spatial and temporal resolution of 1°×1° and 30 min. This model is developed by using Kalman filter algorithm. In the covariance matrix, the monthly average is used as the background reference field of the error matrix, and the spatial variation of TEC deviating from the reference background field in each day of the month is statistically analyzed. The assimilative results show that the data assimilation method can effectively combine the observational data with the background model to make up for the temporal and spatial limitations of the observations. In addition, the data assimilation algorithm can produce a short-term TEC forecast. The predicted results are consistent to the observations in the early stages of the forecast. With the increase of predicting time, the impact of the observational data on the predicted results weakens. The results become more biased toward the background data, and the biases can be corrected by observation data.

Analysis of spatiotemporal characteristics of internal coincidence accuracy in global TEC grid data

Evaluating the coincidence accuracy and time-varying characteristics of global total electron content (TEC) grid data can contribute to the improvement of the performance of global ionosphere maps (GIMs) and provide effective data support and theoretical verification for GIMs model update. This paper applies the global TEC grid data and GPS single-station ionospheric TEC data published by IGS from 1999 to 2020, as well as the F10.7 and SSN data series provided by the World Sunspot Index Data Center in Belgium. Using correlation, time series, and contour chart analyses, the correlation between TEC RMS and F10.7, SSN, and the accuracy-compliant space–time variation characteristics in the global TEC grid data are studied and analyzed. The results show the accuracy of the global TEC grid data is highly correlated with the solar activity intensity. The mean values of the correlation coefficients R between TEC RMS and F10.7, SSN are 0.755 and 0.707, respectively. The internal coincidence accuracy of single station GPS-TEC and global grid TEC is better than 3 TECU and is closely related to the solar activity. Globally, TEC RMS in land and ocean areas have significant differences. In general, the accuracy of the northern hemisphere is significantly higher than that of the southern hemisphere, and the accuracy of land is higher than that of the ocean. Among them, the accuracy of Europe and North America is the highest, the precision of the Pacific region is obviously on the low side, and “double hump” phenomenon can be observed. The accuracy of the global TEC grid data is closely related to the number of stations and satellites. The accuracy of the TEC grid data is higher in areas where the number of stations is dense, or when there are more satellites.

A case study based on ground observations of the conjugate ionospheric response to interplanetary shock in polar regions

Data acquired by imaging relative ionospheric opacity meters (riometers), ionospheric total electron content (TEC) monitors, and three-wavelength auroral imagers at the conjugate Zhongshan station (ZHS) in Antarctica and Yellow River station (YRS) in the Arctic were analyzed to investigate the response of the polar ionosphere to an interplanetary shock event induced by solar flare activity on July 12, 2012. After the arrival of the interplanetary shock wave at the magnetosphere at approximately 18:10 UT, significantly enhanced auroral activity was observed by the auroral imagers at the ZHS. Additionally, the polar conjugate observation stations in both hemispheres recorded notable evolution in the two-dimensional movement of cosmic noise absorption. Comparison of the ionospheric TEC data acquired by the conjugate pair showed that the TEC at both sites increased considerably after the interplanetary shock wave arrived, although the two stations featured different sunlight conditions (polar night in July in the Antarctic region and polar day in the Arctic region). However, the high-frequency (HF) coherent radar data demonstrated that different sources might be responsible for the electron density enhancement in the ionosphere. During the Arctic polar day period in July, the increased electron density over YRS might have been caused by anti-sunward convection of the plasma irregularity, whereas in Antarctica during the polar night, the increased electron density over ZHS might have been caused by energetic particle precipitation from the magnetotail. These different physical processes might be responsible for the different responses of the ionosphere at the two conjugate stations in response to the same interplanetary shock event. Citation : He F, Hu Z J, Hu H Q, et al. A case study based on ground observations of the conjugate ionospheric response to interplanetary shock in polar regions. Adv Polar Sci, 2021, 32(2): 139-158, doi: 10.13679/j.advps.2021.0012

Adaptation of the NeQuick2 model for GNSS wide-area ionospheric delay correction in China and the surrounding areas

As an empirical three-dimensional ionospheric electron density model, NeQuick2 can generally represent the temporal and spatial characterization of ionospheric Total Electron Content (TEC) at a global scale. However, mismodeling errors usually exist between NeQuick2 representation and actual state of short-term ionospheric variation in small scales. In this contribution, an adaptation method is proposed for the NeQuick2 model based on data ingestion with GNSS-derived ionospheric TEC data. The relationship between the background NeQuick2 Slant TEC (STEC) estimations and real STEC measurements can be properly modelled with a ‘scaling factor’ in a highly efficient manner. In order to verify the feasibility and usefulness of the algorithm, validation experiments are carried out with GNSS tracking stations in China and the surrounding areas under different levels of solar activity. The results show that the bias of NeQuick2 corrections after adaptation are mostly within 1.0 TECu, suggesting that the systematic bias of the original NeQuick2 model can be significantly reduced. In comparison with about 50%-70% STEC RMS errors corrected by the original NeQuick2 model, the NeQuick2 model after adaptation can generally correct about 80% − 90% STEC RMS errors, which is also better than the CODE final GIM product as it has smaller residual bias and uses fewer reference stations. Therefore, the proposed algorithm can be satisfactorily applied in wide area GNSS ionospheric corrections.

Detecting All Possible Ionospheric Precursors by Kernel-Based Two-Dimensional Principal Component Analysis

Kernel-based two-dimensional principal component analysis (K2DPCA), a nonlinear method, was performed to examine ionospheric 2-D total electron content (TEC) variations obtained from the NASA Global Differential GPS (GDGPS) network, which consisted of low spatial resolution ionospheric TEC data, to detect TEC precursors prior to the China Ludian earthquake at 08:30:13 UT on 03 August 2014 ( $M = 6.1$ ). Simultaneously, two-dimensional principal component analysis (2DPCA), a linear method, was performed to examine data related to the same earthquake for comparison purposes. The results have shown that two TEC precursors were observed three days prior to this earthquake. The characteristics of all principal eigenvalues were considered to determine the TEC precursors of the earthquake in this paper. This paper aimed to differentiate the previous studies of Lin about 2DPCA using only the characteristic of the first principal eigenvalue to determine the TEC precursors of the earthquake. However, some other TEC precursors could be lost in processes that do not analyze the characteristics of other principal eigenvalues. Therefore, this research can be considered as a full analysis that examined the detailed surrounding TEC precursors of the earthquake and could be treated as a new study scope with 2DPCA and K2DPCA. The TEC precursor during 06:15 to 06:20 (UT) on 01 August 2014 localized near the epicenter was more intense compared with the characteristic of the first largest principal eigenvalue. However, another localized TEC precursor, which occurred from 06:15 to 06:20 (UT) on 01 August 2014 at a site west of the epicenter, was found to be more intense. The more plausible reason for the two TEC precursors could be the density variances caused by radon-gas release due to the fine crack stress transfer from the rocks according to Coulomb stress transfer theory as applied to a far TEC precursor. The durations of both TEC precursors were at least 5 min. This research could be treated as a full and detailed examination of surrounding TEC precursors. K2DPCA was better at identifying the TEC precursors.

Multifractal detrended fluctuation analysis of ionospheric total electron content data during solar minimum and maximum

The spatio-temporal variations in ionospheric vertical total electron content (TEC) data, which often reflect their scale invariant properties, can well be studied with multifractal analysis. We discuss the multifractal behaviour of TEC recorded at a total of 27 stations confined to a narrow longitude band (35°W-80°W) spanning from equator to high-latitude regions (30°S to 80°N) (geographic coordinates) during solar minimum (2008) and solar maximum (2014), using multifractal detrended fluctuation analysis (MFDFA). MFDFA provides an understanding of the multifractal scaling behaviour of a signal using the multifractal singularity spectra and the generalised Hurst exponents as diagnostic tools. The objectives of this study are to (i) understand the latitudinal dependence of the multifractal behaviour of TEC, (ii) compare the multifractal behaviour of TEC corresponding to the well-known 27-day variation (solar rotation period) and its harmonics and the 1-day (solar diurnal) periodicities, during 2008 and 2014 and (iii) understand the lunar tidal influence on TEC. Results indicate that except for the 1-day period, the TEC at all other periods shows a higher degree of multifractality during solar maximum compared to solar minimum. Further, irrespective of the solar activity, the degree of mutifractality in general decreases with increase in period for all latitude zones for periods of 27-day and its harmonics. However, the 1-day period exhibits monofractal behaviour regardless of the solar activity. The influence of semi-lunar tidal effects (having a periodicity of about 14.5 days) as a function of latitude is clearly seen in the 13.5-day periodicity (i.e., the 2nd harmonic of 27-day variation) of TEC. It manifests in the form of decreasing differences in the widths of the multifractal singularity spectra corresponding to the years 2008 and 2014, with increase in latitude. Results are discussed in the light of these observations.

Modelling the probability of ionospheric irregularity occurrence over African low latitude region

This study presents models of geomagnetically quiet time probability of occurrence of ionospheric irregularities over the African low latitude region. GNSS-derived ionospheric total electron content data from Mbarara, Uganda (0.60°S, 30.74°E, geographic, 10.22°S, magnetic) and Libreville, Gabon (0.35°N, 9.68°E, geographic, 8.05°S, magnetic) during the period 2001–2012 were used. First, we established the rate of change of total electron content index (ROTI) value associated with background ionospheric irregularity over the region. This was done by analysing GNSS carrier-phases at L-band frequencies L1 and L2 with the aim of identifying cycle slip events associated with ionospheric irregularities. We identified at both stations a total of 699 events of cycle slips. The corresponding median ROTI value at the epochs of the cycle slip events was 0.54TECU/min. The probability of occurrence of ionospheric irregularities associated with ROTI≥0.5TECU/min was then modelled by fitting cubic B-splines to the data. The aspects the model captured included diurnal, seasonal, and solar flux dependence patterns of the probability of occurrence of ionospheric irregularities. The model developed over Mbarara was validated with data over Mt. Baker, Uganda (0.35°N, 29.90°E, geographic, 9.25°S, magnetic), Kigali, Rwanda (1.94°S, 30.09°E, geographic, 11.62°S, magnetic), and Kampala, Uganda (0.34°N, 32.60°E, geographic, 9.29°S, magnetic). For the period validated at Mt. Baker (approximately, 137.64km, north west), Kigali (approximately, 162.42km, south west), and Kampala (approximately, 237.61km, north east) the percentages of the number of errors (difference between the observed and the modelled probability of occurrence of ionospheric irregularity) less than 0.05 are 97.3, 89.4, and 81.3, respectively.

Possibility of early detection of GPS ionospheric total electron content anomalies associated with large earthquakes using principal component analysis

The goal of this study is to determine whether principal component analysis (PCA) can be used to process latitude-time ionospheric TEC data on a monthly basis to identify earthquake associated TEC anomalies earlier than 5 days before a large (M ≥ 6) earthquake. PCA is applied to latitude-time (mean-of-a-month) ionospheric total electron content (TEC) records collected from the Japan GEONET system to detect TEC anomalies associated with 26 earthquakes in Japan (M ≥ 6.0) from 2004 to 2005. According to the results, PCA was able to discriminate clear TEC anomalies in the months when all 26 earthquakes occurred. After reviewing months when no M ≥ 6.0 earthquakes occurred but geomagnetic storm activity was present, it is possible that the maximal principal eigenvalues PCA returned for these 26 earthquakes indicate earthquake associated TEC anomalies. Previously, PCA has been used to discriminate earthquake-associated TEC anomalies recognized by other researchers who found that statistical association between large earthquakes and TEC anomalies could be established in the 5 days before earthquake nucleation; however, since PCA uses the characteristics of principal eigenvalues to determine earthquake related TEC anomalies, it is possible to show that such anomalies existed earlier than this 5-day statistical window. In this paper, this is shown through the application of PCA to one-dimensional TEC data relating to the Kyushu earthquake of 20 March 2005 (M = 6.6). The analysis is applied to daily TEC and reveals a large principal eigenvalue (representative of an earthquake associated anomaly) for March 9, 11 days before the March 20 earthquake.

Latitude-Time Total Electron Content Anomalies as Precursors to Japan's Large Earthquakes Associated with Principal Component Analysis

The goal of this study is to determine whether principal component analysis (PCA) can be used to process latitude-time ionospheric TEC data on a monthly basis to identify earthquake associated TEC anomalies. PCA is applied to latitude-time (mean-of-a-month) ionospheric total electron content (TEC) records collected from the Japan GEONET network to detect TEC anomalies associated with 18 earthquakes in Japan () from 2000 to 2005. According to the results, PCA was able to discriminate clear TEC anomalies in the months when all 18 earthquakes occurred. After reviewing months when no earthquakes occurred but geomagnetic storm activity was present, it is possible that the maximal principal eigenvalues PCA returned for these 18 earthquakes indicate earthquake associated TEC anomalies. Previously PCA has been used to discriminate earthquake-associated TEC anomalies recognized by other researchers, who found that statistical association between large earthquakes and TEC anomalies could be established in the 5 days before earthquake nucleation; however, since PCA uses the characteristics of principal eigenvalues to determine earthquake related TEC anomalies, it is possible to show that such anomalies existed earlier than this 5-day statistical window.

Winter Anomaly and Traveling Ionospheric Disturbances over Japan during DYANA Campaign.

The winter anomaly and medium-scale traveling Ionospheric disturbances (MSTIDs) detected during the DYANA campaign in 1990 are analyzed to investigate their characteristics and possible relationship to meteorological phenomena in the lower atmosphere. Anomalous f-min enhancements (FMEs), an indication of the winter anomaly, occurred around January 15 and between January 20 and 29, 1990. For the latter period, the FME region extended down to 31°N. D-region electron density enhancements associated with the FMEs were also recognized from phase measurements of the 10.2kHz Omega signals. The FMEs are not due to solar flares or geomagnetic disturbances, but are related to meteorological processes in the lower atmosphere. Comparison of the FMEs with neutral wind flows observed by the DYANA rockets and MU radar indicates that the FMEs are largest for a few days before the reversal of the stratospheric wind direction from westward to eastward caused by planetary wave activity. There is a 5-day delay between the onsets of the FME and the lower stratospheric (20-hPa) temperature increase. A minor stratospheric sudden warming that appeared around 60°N from February 1 to 14 did not affect the D-region (no FMEs) at least over Japan.A large number of MSTIDs were identified from ionospheric total electron content data. Although a few of these MSTIDs were caused by geomagnetic activities, the majority have sources in the lower atmosphere. We find that daily and diurnal variations of the MSTID occurrence are controlled by F-region electron density (foF2).