Ionospheric Total Electron Content Research Articles

Different data-driven prediction of global ionospheric TEC using deep learning methods

Ionospheric Total Electron Content (TEC) is a crucial parameter for monitoring the ionosphere and space weather disasters. Its accurate prediction is vital for precise applications of Interferometric Synthetic Aperture Radar (InSAR) and Global Navigation Satellite System (GNSS). This study proposes a novel method for ionospheric TEC prediction that considers multiple TEC-related factors. We present the random forest method and the Autoformer deep learning with multilayer perceptron (Autoformer-MLP) to predict the global TEC by incorporating the geomagnetic and solar activity parameters. Two schemes with different ionospheric data, i.e., spherical harmonic coefficients (SHC) and vertical TEC (VTEC), are performed by Autoformer-MLP. Ionospheric products from 2009 to 2019 (Cycle 24), obtained from the Center for Orbit Determination in Europe (CODE), are collected to test and evaluate the proposed method. Experimental results demonstrate that the root mean square errors (RMSEs) of predicted global ionospheric maps (GIMs) using the SHC scheme are 3.79 and 1.39 TECU in 2015 and 2019, respectively, and those are 3.55 and 1.28 TECU for the VTEC scheme. Moreover, the RMSEs of the prediction results are 0.5 and 0.2 TECU lower than that of CODE's 1-day predicted global ionospheric map (GIM) product (C1PG) during high and low solar activity years, respectively. These analyses indicate that the proposed random forest and Autoformer-MLP deep learning methods exhibit high accuracy and stability for data-driven prediction of global ionospheric TEC in various scenarios.

Periodic Oscillation of VLF Transmitter Signals Measured in Low and Middle Latitude Regions

AbstractMeasurements of Very Low Frequency (VLF) signals from navy transmitters carry direct information about the D‐region ionosphere and have been widely utilized for detecting the electron density at D‐region altitudes, but not frequently for the atmospheric waves therein. Atmospheric waves have been extensively studied using the total ionospheric electron content, but if and how they are correlated with the D‐region ionosphere and VLF measurements still remains poorly investigated. In this study, we have conducted a comprehensive analysis using 7‐year measurements (2016–2022) of VLF signals from the JJI, NWC, and VTX transmitters as being recorded in Suizhou, China. These three transmitter‐receiver paths are representative and the corresponding observations constitute a valuable data set to investigate the periodicities of VLF data. Different from previous studies, we have utilized the ensemble empirical mode decomposition and Lomb‐Scargle methods to determine the periodicities of these data. By contrasting these paths, prominent periodicities ranging from 2 to 730 days have been found, with clear diurnal variation and suggestive latitudinal/longitudinal dependence. Moreover, we have found that the mesospheric temperature is closely related with the annual oscillation of VLF measurements, while this oscillation has a low correlation with solar Lyman‐α fluxes or geomagnetic activity. The oscillations with relatively shorter periods are likely atmospheric waves such as gravity waves, planetary waves, or harmonics of these waves. Our results suggest that, in addition to the electron density, the subionospheric VLF technique can be potentially utilized to remotely sense atmospheric waves that propagate up to or through the D‐region ionosphere.

Operational Forecasting of Global Ionospheric TEC Maps 1-, 2-, and 3-Day in Advance by ConvLSTM Model

In this paper, we propose a global ionospheric total electron content (TEC) maps (GIM) prediction model based on deep learning methods that is both straightforward and practical, meeting the requirements of various applications. The proposed model utilizes an encoder-decoder structure with a Convolution Long Short-Term Memory (ConvLSTM) network and has a spatial resolution of 5° longitude and 2.5° latitude, with a time resolution of 1 h. We utilized the Center for Orbit Determination in Europe (CODE) GIM dataset for 18 years from 2002 to 2019, without requiring any other external input parameters, to train the ConvLSTM models for forecasting GIM 1, 2, and 3 days in advance. Using the CODE GIM data from 1 January 2020 to 31 December 2023 as the test dataset, the performance evaluation results show that the average root mean square errors (RMSE) for 1, 2 and 3 days of forecasts are 2.81 TECU, 3.16 TECU, and 3.41 TECU, respectively. These results show improved performance compared to the IRI-Plas model and CODE’s 1-day forecast product c1pg, and comparable to CODE’s 2-day forecast c2pg. The model’s predictions get worse as the intensity of the storm increases, and the prediction error of the model increases with the lead time.

Forecasting ionospheric TEC using least squares support vector machine and moth-flame optimization methods in China

The total electron content (TEC) of the ionosphere is an important parameter to describe the ionosphere, and it is a great significance to monitor and predict it accurately. In this paper, a hybrid ionospheric TEC prediction model based on the least squares support vector machine (LSSVM) and the Moth-Flame Optimization (MFO) algorithm is proposed. The parameters of the LSSVM model are optimized by the MFO algorithm. We use observation data of 15 GNSS stations from the Crustal Movement Observation Network of China (CMONOC) to extract ionospheric TEC from 2012 to 2019. The ionospheric TEC is forecasted using solar and geomagnetic activity indices in both the low solar activity year (2019) and the high solar activity year (2015). The results show that the prediction performance of the MFO_LSSVM model is significantly better than that of the IRI model, SVM model, and LSSVM model. Compared with the other three models, there are more stable prediction results in the low and high solar activity years. At the same time, the predicted value of the MFO_LSSVM model has a good correlation with the measured value, and it also has good prediction potential in areas with active geomagnetic activity. The comparison with the Long Short-Term Memory (LSTM) model shows that the MFO_LSSVM model has better performance than the single LSTM model. In conclusion, the MFO_LSSVM model can accurately predict ionospheric TEC in China, and has better accuracy than traditional long-term and short-term models.

Detection of Traveling Ionospheric Disturbances Triggered by 2022 Tonga Volcanic Eruptions Through CubeSats Coherent GNSS‐Reflectometry Measurement

AbstractWe present two case studies of traveling ionospheric disturbances (TIDs) triggered by the 2022 Tonga volcanic eruption observed by low‐cost CubeSat‐based global navigation satellite system reflectometry (GNSS‐R) measurements. The GNSS‐R data used in this work are from Spire Global CubeSats. Our analysis shows that coherent GNSS signals reflected over the ocean can be used to derive precise ionospheric total electron content (TEC) measurements. The first case shows clear TID structures with a TEC disturbance magnitude of ∼1 TEC unit (TECu) and horizontal wavelength of ∼330 km over Northwest Australia on the day of Tonga volcanic eruption. The second case shows the TIDs with ∼0.05 TECu disturbance magnitude and horizontal wavelength of ∼240 km over East Russia the day after the eruption. The second case is likely a TIDs propagated outward from Tonga for the second time after it traversed around the Earth. The GNSS‐R observed TID may be associated with the incident or reflection signal ray path. In this paper, the appropriate ray path was identified using simultaneous observations from ground receiver networks in the areas of both ray paths.

Revisiting the Ionospheric Disturbances Over Low Latitude Region of China During Super Typhoon Hato

AbstractThe ionosphere exhibits complex variations due to the influences from above and below. To distinguish the source of ionospheric disturbances is important for understanding the variation process and the coupling mechanism among different regions. Using the ionospheric total electron content (TEC) derived from Global Navigation Satellite System observations, the ionospheric disturbances during the super typhoon Hato in 2017 that was accompanied by a weak geomagnetic storm are revisited, including the ionospheric deviation and traveling ionospheric disturbances (TIDs). It is found that the ionospheric TEC in the low‐latitude region of China experienced a significant enhancement (200% compared to the quiet geomagnetic day) on Hato landing day. This enhancement covers the northern and southern equatorial ionization anomaly (EIA) region from 80°E to 180°E. Considering the geomagnetic condition, the hmF2 and the O/N2 ratio in thermosphere, it is concluded that this enhancement is not related to the typhoon, but to the coinciding weak geomagnetic storm. Additionally, several medium‐scale TIDs are verified from differential TEC data in China low latitude region during Hato period. Most of them occur after sunset and their propagating direction is southwest that often occur in East‐Asian sector in summer months, which are not related to the typhoon. While a few TIDs with concentric wavefront (Concentric TIDs) are also observed on the day before Hato landfall that should be excited in the deep convective region of the typhoon. Because the ionosphere is affected by disturbances both from above and below, it should be careful to determine the source of the ionospheric disturbances.

A Two-Step Regional Ionospheric Modeling Approach for PPP-RTK.

In the precise point positioning/real-time kinematic (PPP-RTK) technique, high-precision ionospheric delay correction information is an important prerequisite for rapid PPP convergence. The commonly used ionospheric modeling approaches in the PPP-RTKs only take the trend term of the ionospheric total electron content (TEC) variations into account. As a result, the residual ionospheric delay still affects the positioning solutions. In this study, we propose a two-step regional ionospheric modeling approach that involves combining a polynomial fitting model (PFM) and a Kriging interpolation (KI) model. In the first step, a polynomial fitting method is used to model the trend term of the ionospheric TEC variations. In the second step, a KI method is used to compensate for the residual term of the ionospheric TEC variations. Datasets collected from continuously operating reference stations (CORSs) in Hunan Province, China, are used to validate the PFM/KI method by comparing with a single PFM method and a combined PFM and inverse distance weighting interpolation (IDWI) method. The experimental results show that the two-step PFM/KI modeled ionospheric delay achieves an average root mean square (RMS) error of 1.8 cm, which is improved by about 48% and 23% when compared with the PFM and PFM/IDWI methods, respectively. Regarding the positioning performance, the PPP-RTK with the PFM/KI method takes an average of 1.8 min or 4.0 min to converge to a positioning accuracy of 1.3 cm or 2.5 cm in the horizontal and vertical directions, respectively. The convergence times are decreased by about 18% and 14% in the horizontal direction and 9% and 5% in the vertical direction over the PFM and the PFM/IDWI methods, respectively.

Variation in Total Electron Content Over Ethiopia During the Solar Eclipse Events

AbstractThis work studies variations of ionospheric total electron content (TEC) during four distinct solar eclipse events over the Ethiopia region. Dual‐frequency global positioning system (GPS) data obtained from UNAVCO over Addis Ababa (9.036°N, 38.76°E) and Bahir Dar (11.6°N, 37.34°E) stations are used to examine the ionospheric variability during two annular solar eclipses on 15 January 2010 and 1 September 2016, a partial solar eclipse on 4 January 2011, and a hybrid solar eclipse (the eclipse path starts out as annular but later changes to total) on 3 November 2013. The results show a significant decrease in TEC values during the occurrence of the solar eclipses. Specifically, the TEC values are reduced to −20% and −10% during the annular eclipse on 15 January 2010, −33% and −38% during the partial solar eclipse on 4 January 2011, −26% and −24% during the annular solar eclipse on 1 September 2016, over the Addis Ababa and Bahir Dar stations, respectively. There is only minimal change in TEC of −8% and −9% at Addis Ababa and Bahir stations, respectively, during the 3 November 2013 solar eclipse even if the obstruction rate is high over the study area. Furthermore, the study shows that the spatial gradient of TEC reduction varies at different locations, which is attributed to the distinct amount of reduction in solar radiation reaching the Earth's surface, resulting in reduced photo‐ionization. Overall, this study provides insightful information about the behavior of the ionospheric TEC during solar eclipses over Ethiopia and emphasizes the use of dual‐frequency GPS data in tracking the variations of the TEC.

Investigation of Radon, Total Electron Content and Linear and Nonlinear Variations of Meteorological Variables Due to Earthquakes: ARIMA and Monte Carlo Modelling

An Integrated Autoregressive Moving Average (ARIMA) - Monte Carlo Simulation (MCS) is proposed to analyze and model the anomalies of atmospheric and ground gases by an earthquake along the North Anatolian Fault Zone (Türkiye). Earthquakes, Soil radon gas and Total Electron Content (TEC) showed simultaneous anomalies. There are positive relationships between these three parameters. Also, positive relations between Rn, meteorology, and atmosphere are detected. The proposed ARIMA model and MCS for the Rn-TEC-Earthquake relationships of the measured data gave statistically significant results. This model and simulation showed statistically significant changes in the effects of microearthquakes, which are more difficult to detect than large earthquakes, especially on the ionospheric TEC.

A comparison of ionospheric TEC between the South Atlantic anomaly region and the Indian Ocean region based on TIEGCM simulations in 2002

Using the Thermosphere-Ionosphere-Electrodynamic General Circulation Model (TIEGCM), a comparison of the ionospheric total electron content (TEC) between the South Atlantic anomaly (SAA) and the Indian Ocean (IO) at solar maximum is performed in this study. The results show that the average total electron content in the SAA is greater than that in the Indian Ocean in general. In order to further analyze the difference between the two regions, the empirical orthogonal function (EOF) are used to investigate the temporal and spatial characteristics of TEC. The empirical orthogonal function method separate part of the global four-peak structure (an equatorial ionization anomaly structure, distributed in Southeast Asia, South America, Africa, and central Pacific) and spatial variations in both regions. Moreover, the first mode of EOF shows the different distribution of Equatorial ionization anomaly in South America and central Pacific caused by deviation of geomagnetic field and tides between two regions, and the enhancement of TEC in SAA region at dusk is emphasized, but the enhancement of TEC in IO region at dawn is emphasized. The second mode performs the distribution of EIA in Africa related to solar radiation and E×B drift. The third mode indicates the similar spatiotemporal variations from the geomagnetic field. Besides, the correlation between TEC and Dst in two regions indicate that there are some deficiencies in simulation to the specificity of SAA, and the deficiencies are likely caused by the model’s inaccurate simulation of the magnetic field and particle deposition in the SAA region.

Forecasting Ionospheric TEC Changes Associated with the December 2019 and June 2020 Solar Eclipses: A Comparative Analysis of OKSM, FFNN, and DeepAR Models

This paper presents forecast and investigation of the variation in ionospheric Total Electron Content (TEC) during the solar eclipses (SEs) of December 2019 and June 2020 using three different methods: Deep Autoregressive model (DeepAR), Feed-Forward Neural Network (FFNN), and Ordinary Kriging-based Surrogate Model (OKSM), and the TEC data predicted by DeepAR, FFNN, and OKSM were compared with the actual TEC during the observation days. The study was conducted based on GPS data taken from the IISC receiver located in Bangalore, India, during the SEs which happened on 26.12.2019 and 21.06.2020. The TEC data were examined to assess the effect of solar eclipses on TEC values. Eighty-day prior TEC data for the IISC station are gathered from IONOLAB servers along with the other parameter data like Dst, Ap, F10.7, and Kp taken from OMNIWEB servers which were used to predict TEC. The reliability of the forecasted results is evaluated using numerical factors like Normalized Root Mean Square Error (NRMSE), Correlation Coefficient (CC), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R-squared. The study demonstrates the usefulness of combining multiple methods for analyzing TEC variations during SEs and highlights the potential of OKSM, FFNN, and DeepAR models for studying TEC variation in the same context. The findings may be useful for satellite broadcasting and navigational services and for further research into the influence of solar eclipses on the TEC changes.

Improved Ionospheric Total Electron Content Maps over China Using Spatial Gridding Approach

Improved Ionospheric Total Electron Content Maps over China Using Spatial Gridding Approach

A study of some research work on soil radon concentration and ionospheric total electron content as earthquake precursors

A study of some research work on soil radon concentration and ionospheric total electron content as earthquake precursors

Different Response of the Ionospheric TEC and EEJ to Ultra‐Fast Kelvin Waves in the Mesosphere and Lower Thermosphere

AbstractWe studied the response of ionospheric total electron content (TEC) and equatorial electrojet (EEJ) to the ultra‐fast Kelvin wave (UFKW) at the equator in the mesosphere using zonal wind data obtained from TIMED Doppler Interferometer (TIDI), EEJ data over the monitoring station Jicamarca (12°S, 77°W) and global TEC maps. The least squares fitting method is utilized to perform a spectral analysis of zonal wind, EEJ and TEC. Our analysis results demonstrate that UFKW events can be divided into four categories: (a) UFKW events with both TEC and EEJ response; (b) UFKW events with TEC response but without EEJ response; (c) UFKW events with EEJ response but without TEC response; (d) UFKW events without neither TEC response nor EEJ response. The first type of UFKW events occur the most often and is generally thought to generate a response in EEJ at approximately 105–110 km through the dynamo effect. The polarization electric field associated with EEJ then produces a response in the ionospheric TEC through the fountain effect. The lack of EEJ response in the second type of UFKWs may be due to the influence of eastward background winds. We found that all UFKW events with EEJ response have a response in TEC. The fourth type of UFKWs have smaller amplitudes, shorter vertical wavelengths and longer periods, which make them more likely to dissipate and cannot propagate to higher altitudes. These UFKWs cannot propagate to the altitude of EEJ and produce a response in EEJ, much less in TEC.

Analysis of factors affecting the estimation of the multi-GNSS satellite differential code biases (SDCBs)

The satellite hardware differential code biases (SDCBs) are one of the important factors affecting the accuracy of GNSS-based ionospheric total electron content (TEC) estimation. The accuracy of SDCB estimates is directly influenced by the data processing methodology. To analyze the impact of various factors on multi-GNSS SDCB estimation, we conducted a comprehensive analysis of the number of contributed stations, the data sampling rate, the contributed satellite systems, and the cut-off elevation angle. Two sets of GNSS data obtained from the International GNSS Service (IGS) multi-GNSS experiment (MGEX) network, covering both high and low solar activities during the ascending phase of solar cycle 25, were processed to estimate multi-GNSS SDCBs. The results indicate that the number of contributed stations is the primary factor that influences multi-GNSS SDCB estimation. To achieve a balance between computational efficiency and accuracy, a data sampling rate of 540 s is recommended, which results in a multi-GNSS SDCB root-mean-square (RMS) error of less than 0.01 ns compared to a 60-second sampling rate. The stability of SDCBs is hardly affected by incorporating observations from multiple GNSS systems, and the differences of the standard deviations of SDCBs for quad-, triple- and dual-system solutions are below 0.002 ns. Additionally, the results indicate that the optimal cutoff elevation angle for multi-GNSS SDCB estimation is between 20° and 30°, which ensures the best stability in the estimated multi-GNSS SDCBs.

2‐D Total Electron Content and 3‐D Ionospheric Electron Density Variations During the 14 October 2023 Annular Solar Eclipse

AbstractThis study investigates the ionospheric total electron content (TEC) responses in the 2‐D spatial domain and electron density variations in the 3‐D spatial domain during the annular solar eclipse on 14 October 2023, using ground‐based Global Navigation Satellite System (GNSS) observations, a novel TEC‐based ionospheric data assimilation system (TIDAS), ionosonde measurements, and satellite in situ data. The main results are summarized as follows: (a) The 2‐D TEC responses exhibited distinct latitudinal differences. The mid‐latitude ionosphere exhibited a more substantial TEC decrease of 25%–40% along with an extended recovery time of 3–4 hr. In contrast, the equatorial and low‐latitude ionosphere experienced a smaller TEC reduction of 10%–25% and a faster recovery time of 20–50 min. The minimal eclipse effect was observed near the northern equatorial ionization anomaly crest region. (b) The ionospheric electron density variations during the eclipse were effectively reconstructed by TIDAS data assimilation in the 3‐D domain, providing important altitude information with validity. (c) The ionospheric electron density variations showed a notable altitude‐dependent feature. The eclipse led to a substantial electron density reduction of 30%–50%, with the maximum depletion occurring around the ionospheric F2‐layer peak height (hmF2) of 250–350 km. The post‐eclipse recovery of electron density exhibited a relatively slower pace near the F2‐layer peak height than that at lower and higher altitudes.

Modeling Ionospheric TEC Using Gradient Boosting Based and Stacking Machine Learning Techniques

AbstractAccurately predicting and modeling the ionospheric total electron content (TEC) can greatly improve the accuracy of satellite navigation and positioning and help to correct ionospheric delay. This study assesses the effectiveness of four different machine learning (ML) models in predicting hourly vertical TEC (VTEC) data for a single‐station study over Ethiopia. The models employed include gradient boosting machine (GBM), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM) algorithms, and a stacked combination of these algorithms with a linear regression algorithm. The models relied on input variables that represent solar activity, geomagnetic activity, season, time of the day, interplanetary magnetic field, and solar wind. The models were trained using the VTEC data from January 2011 to December 2018, excluding the testing data. The testing data comprised the data for the year 2015 and the initial 6 months of 2017. The RandomizedSearchCV algorithm was used to determine the optimal hyperparameters of the models. The predicted VTEC values of the four ML models were strongly correlated with the GPS VTEC, with a correlation coefficient of ∼0.96, which is significantly higher than the corresponding value of the International Reference Ionosphere (IRI 2020) model, which is 0.87. Comparing the GPS VTEC values with the predicted VTEC values based on diurnal and seasonal characteristics showed that the predictions of the developed models were generally in good agreement and outperformed the IRI 2020 model. Overall, the ML models used in this study demonstrated promising potential for accurate single‐station VTEC prediction over Ethiopia.

Features of the November 7, 2022 Geomagnetic Storm Development in the Ionospheric Total Electron Content Observations

Features of the November 7, 2022 Geomagnetic Storm Development in the Ionospheric Total Electron Content Observations

Prediction of ionospheric TEC using a GRU mechanism method

In this paper, we propose a gate recurrent unit (GRU) method to forecast ionospheric the total electron content (TEC) at different latitudes and longitudes. This method aims to address the issues of low accuracy in traditional neural networks and the challenges faced in training long-term time series. We construct a GRU model using TEC data from the European Orbit Determination Center (CODE) for the years 2016 to 2018, geomagnetic indices, and solar activity indices (16 points along the latitude and longitude lines of 120°E and 30°N). Comparative experiments are carried out with the international reference ionosphere (IRI) model and the long short-term memory (LSTM) model. The results show that the GRU model outperforms other models in terms of accuracy. On geomagnetic quiet days, the 5-day predicted average residual can reach 0.51 TECU, while on storm days it can reach 0.72 TECU. The root mean square error (RMSE) for these days is 0.64 TECU and 1.01 TECU, respectively. The correlation coefficients are 0.98 and 0.93, respectively. The RMSE of the GRU model ranges from 0.60 TECU to 2.62 TECU for both geomagnetic quiet days and storm days. In terms of TEC forecast accuracy in 2018, the RMSE of the GRU model is 1.6 TECU, which is smaller than that of the IRI model and LSTM model. These results demonstrate that the GRU model performs better than the IRI model and LSTM model.

Ionospheric TEC Prediction Based on Ensemble Learning Models

AbstractIn this paper, we propose the usage of an ensemble learning approach for predicting total electron content (TEC). The training data set spans from 2007 to 2016, while the testing data set is set to the year 2017. The model inputs in our study included Solar radio flux (F107), Solar Wind plasma speed, By, Bz, Dst, Ap, AE, day of year, universal time, 30‐day and 90‐day TEC averages. Specifically, eXtreme Gradient Boosting (XGBoost), Gradient Boosting Decision Tree, and Decision Tree were utilized for 1‐hr TEC prediction at high‐ (80°W, 80°N), mid‐ (80°W, 40°N), and low‐ latitudes (80°W, 10°N). Results indicate that all three models performed well in predicting TEC, with a mean error of only approximately 0.6 TECU at high‐ and mid‐ latitudes and 1.13 TECU at low latitudes. At the same time, we compared the model with 1‐day Beijing University of Aeronautics and Astronautics model during the period of magnetic storm from 25 August 2018 to 27 August 2018 and a quiet period from 13 December 2018 to 15 December 2018. In the magnetic storm period, Our model showed an average reduction of 1.83 TECU compared to BUAA model. During the quiet period, XGBoost exhibit an average error that is 1.14 TECU lower than that of BUAA model. Moreover, TEC prediction over the region between the 20°N–45°N and 70°E−120°E during geomagnetic storm has an error of 2.74 TECU, showing the stability and superiority of XGBoost. Overall, the ensemble learning approach exhibits its advantage in predicting TEC.