Total Electron Content Values Research Articles

TEC disturbances caused by CME-triggered geomagnetic storm of September 6–9, 2017

This study investigates the ionospheric response to a geomagnetic storm triggered by a Coronal Mass Ejection (CME) during 6–9 September 2017, across GPS stations located in diverse geographical regions. We analyze the changes in the magnetic field component (ΔH), the Prompt Penetration Electric Fields (PPEF), and the Total Electron Content (TEC). We find that ΔH exhibits latitude-dependent responses during the storm, with high-latitude stations experiencing more significant reductions compared to low-latitude stations. The PPEF behavior is found to be directly correlated with solar wind disturbances. Particularly during the main phase of the storm, fluctuations in PPEF were clearly associated with negative values in the Dst index. The KIRU station, located at a high latitude, shows the most pronounced PPEF effects, indicating the increased susceptibility of high-latitude regions to solar wind interactions. The time series plot of TEC, covering a full month at different stations, shows a distinct diurnal pattern driven by solar ionization. Equatorial stations such as HYDE, BOU, HON (HNLC), and DODM exhibit the highest daily TEC values. During the geomagnetic storm, TEC disturbances are evident across all stations, with significant disturbances and varying trends in TEC depletion rate observed at different locations. The TEC values differ by 5–25 TECU during the storm period, suggesting intricate ionospheric responses to geomagnetic storms at different stations. This highlights the importance of considering different geographical regions to fully understand the ionospheric dynamics related to solar activities.

Analyzing the spatial variation characteristics of grid TEC using long-term GIM data

Global ionosphere maps (GIM), a grid model generated based on observations from global GNSS stations, is often used for ionospheric delay correction and total electron content (TEC) analysis, and is an important data for ionospheric modeling and code bias estimation, as well as related applications. Understanding the spatial variation characteristics of the grid TEC in GIM data can help the better use of GIM data. In this paper, the GIM data of CODE (Center for Orbit Determination in Europe) analysis center for a total of 15 years from 2008 to 2022 are used to analyze the variation of TEC of adjacent grid points and the difference of TEC of four grid points in each grid. The results show that the mean values of the grid TEC variations range from about 0.26–1.86 TECu in the longitude direction and 0.36–2.76 TECu in the latitude direction, and most of the differences in the TECs of neighboring grid points are within ± 2 TECu. In the low solar activity years, the STD of the TEC of the four grid points in most of the grids in the GIM data is less than 1.2 TECu, indicating that the TEC values of the four grid points in most of the grids in their GIMs are relatively close to each other and the differences are small, whereas the differences are larger in the high solar activity years. In other words, the TEC values of the areas covered by the GIM grids in the low solar activity years are close. The results of the study can provide some references and lessons for the interpolation and application of the TEC of the GIM grid, and the TEC modeling of the single-station area.

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.

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.

Seasonal Features of the Ionospheric Total Electron Content Response at Low Latitudes during Three Selected Geomagnetic Storms

In the present paper, the response of the ionospheric Total Electron Content (TEC) at low latitudes during several geomagnetic storms occurring in different seasons of the year is investigated. In the analysis of the ionospheric response, the following three geomagnetic events were selected: (i) 23–24 April 2023; (ii) 22–24 June 2015 and (iii) 16 December 2006. Global TEC data were used, with geographic coordinates recalculated with Rawer’s modified dip (modip) latitude, which improved the accuracy of the representation of the ionospheric response at low and mid-latitudes. By decomposition of the zonal distribution of the relative deviation of the TEC values from the hourly medians, the spatial distribution of the anomalies, the dependence of the response on the local time and their evolution during the selected events were analyzed. As a result of the study, it was found that the positive response (i.e., an increase in electron density relative to quiet conditions) in low latitudes occurs at the modip latitudes 30° N and 30° S. An innovative result related to the observed responses during the considered events is that they turn out to be relatively stationary. The longitude variation in the observed maxima changes insignificantly during the storms. Depending on the season, there is an asymmetry between the two hemispheres, which can be explained by the differences in the meridional neutral circulation in different seasons.

Characteristic analysis of the differences between total electron content (TEC) values in global ionosphere map (GIM) grids

Abstract. Using total electron content (TEC) from a global ionosphere map (GIM) for ionospheric delay correction is a common method of eliminating ionospheric errors in satellite navigation and positioning. On this basis, the TEC of a puncture point can be obtained by GIM grid TEC interpolation. However, in terms of grid, only few studies have analyzed the TEC value size characteristics of its four grid points, that is, the TEC difference characteristics among them. In view of this, by utilizing the GIM data from high solar-activity years (2014) and low solar-activity years (2021) provided by CODE (Center for Orbit Determination in Europe), this paper proposes the grid TEC difference as a way of analyzing TEC variation characteristics within the grid, which is conducive to exploring and analyzing the variation characteristics of the ionosphere TEC in the single-station area. The value is larger in high solar-activity years and generally small in low solar-activity years, and the value of high-latitude areas is always smaller than that of low-latitude areas. Specifically, in high solar-activity years, most of the GIM grid TEC internal differences are within 4 TECu (1 TECu = 1016 electrons m−2) in high-latitude and midlatitude regions, while only 78.17 % are in low-latitude regions. In low solar-activity years, the TEC difference values within a GIM grid are mostly less than 2 TECu, and most of them in the high and middle latitudes are within 1 TECu. The main finding of this analysis is that the grid TEC differences are small for most GIM grids, especially in the midlatitudes to high latitudes of low solar years. This means that relevant extraction methods and processes can be simplified when TEC within these GIM grids is needed.

Performance analysis of FFNN and Kriging model on prediction of ionospheric TEC during April 2022 – X 1.1 solar flare

Artificial Intelligence (AI) in data analysis has become an integral tool in recent times. In this research, Total Electron Content (TEC) prediction by the Feed Forward Neural Network (FFNN) model is compared with the Ordinary Kriging based Surrogate Model (OKSM) to verify the significance of the sample size required for FFNN and OKSM. To assess the credibility of the constructed models, the FFNN and OKSM prediction models were evaluated on 30 April 2022, during which a Solar Flare (SF) of intensity X 1.1 occurred. The TEC data is taken from Hyderabad (17.31°N and 78.55°E) from IONOLAB data servers. The solar parameters were collected from the NASA OMNIWEB data server. The surrogate model is built to predict the seventh-day TEC values by using the previous six days of TEC data, Whereas the FFNN model uses 146 days of TEC data as training data set to predict the subsequent four days of TEC. The performance of the models is evaluated using statistical parameters like Root Mean Square Error (RMSE), Correlation Coefficient (CC), Mean Absolute Error (MAE) and symmetric Mean Absolute Percentage Error (sMAPE). The results were represented as linear regression scatter plots, showing fewer residuals for the constructed prediction models.

Deep Learning‐Based Regional Ionospheric Total Electron Content Prediction—Long Short‐Term Memory (LSTM) and Convolutional LSTM Approach

AbstractThis study evaluates the performance of deep learning approach in the prediction of the ionospheric total electron content (TEC) during magnetically quiet periods. Two deep learning techniques, long short‐term memory (LSTM) and convolutional LSTM (ConvLSTM), are employed to predict TEC values 24 hr ahead in the vicinity of the Korean Peninsula (26.5°–40°N, 121°–134.5°E). The LSTM method predicts TEC at a single point based on time series of data at that point, whereas the ConvLSTM method simultaneously predicts TEC values at multiple points using spatiotemporal distribution of TEC. Both the LSTM and ConvLSTM models are trained using the complete regional TEC maps reconstructed by applying the Deep Convolutional Generative Adversarial Network–Poisson Blending (DCGAN‐PB) method to observed TEC data. The training period spans from 2002 to 2018, and the model performance is evaluated using 2019 data. Our results show that the ConvLSTM method outperforms the LSTM method, generating more reliable TEC maps with smaller root mean square errors when compared to the ground truth (DCGAN‐PB TEC maps). This outcome indicates that deep learning models can improve the prediction accuracy of TEC at a specific point by taking into account spatial information of TEC. We conclude that ConvLSTM is a reliable and efficient approach for the prompt ionospheric prediction.

Statistical Characteristics of Total Electron Content Intensifications on Global Ionospheric Maps

AbstractGlobal ionospheric total electron content (TEC) maps exhibit TEC intensifications and depletions of various sizes and shapes. Characterizing key features on TEC maps and understanding their dynamic coupling with external drivers can significantly benefit space weather forecasting. However, comprehensive analysis of ionospheric structuring over decades of TEC maps is currently lacking due to large data volume. We develop feature extraction software based on image processing techniques to extract TEC intensification regions, that is, contiguous regions with sufficiently elevated TEC values than surrounding areas, from global TEC maps. Applying the software to the Jet Propulsion Laboratory Global Ionospheric Map data, we generate a TEC intensification data set for years 2003–2022 and carry out a statistical study on the number and strength of TEC intensifications. We find that the majority of the TEC maps (about 86%) are characterized with one or two intensification(s), while the rest of the TEC maps have three or more intensifications. Both the number and strength of TEC intensifications exhibit semi‐annual variation that peaks near equinoxes and dips near solstices, as well as an annual asymmetry with larger values around December solstice compared to June solstice. The number and strength of intensifications increase with enhanced solar extreme‐violet irradiance. The strength of intensifications also increases with elevated geomagnetic activity, but the number of intensifications does not. In addition, the number of intensifications is not correlated with the strength of intensifications.

25-26 Ağustos 2018 Jeomanyetik Fırtına Sırasında Afrika Bölgesi Üzerindeki Manyetik Eşlenik Çiftleri Üzerindeki TEC Değişikliklerinin İncelenmesi

In this study, the electron transport process resulting from electromagnetic drift between two magnetic conjugate pairs over the African region during the August 25-26 2018 geomagnetic storm was investigated. The effects of geomagnetic conditions presented with Dst index and IMF Bz values on Total Electron Content (TEC) values at conjugate stations were compared separately for stormy and quiet periods. During the storm period, the effect of TEC values at stations in the northern hemisphere (Haifa and Djibouti) on the TEC values at stations in the southern hemisphere (Ambalavao and Malindi) is greater than the effect of TEC values in the southern hemisphere on TEC values in the northern hemisphere. According to this result, it can be said that the south-directed electromagnetic convection was more than the north-directed convection in the examined dates. When the coefficients are examined, it can be said that the interaction is more in the magnetic conjugate pair that is closer to the equator during the storm period, and the interaction is more in the magnetic conjugate pair that is far from the equator during the silent period. Considering the coefficients calculated for Dst and IMF Bz, it is seen that the TEC values are very small compared to their coefficients. From this it can be concluded that the effect of Dst and IMF Bz is much smaller than the effect of TEC values at a station on TEC values at its magnetic conjugates.

The capability of Artificial Neural Networks as a Model for Predicting Total Electron Content (TEC): A Review

The results of investigations from a complete analysis of ANN application on Total Electron Content (TEC) prediction are presented in this paper. TEC is important in defining the ionosphere and has many everyday applications, for example, satellite navigation, time delay and range error corrections for single frequency Global Positioning System (GPS) satellite signal receivers. The total electron content (TEC) in the ionosphere has been measured using GPS. GPS are not installed in every point on the earth to make global TEC measurements possible. As a result, it is crucial to have certain models that can aid to get data from places where there is not any in order to comprehend the global behavior of TEC. Neural Network (NN) models have been shown to accurately anticipate data patterns, including TEC. The capacity of neural networks to represent both linear and nonlinear relationships directly from the data being modeled is what makes them so powerful. The survey from literature reveals that, Levenberg-Marquardt algorithm is preferred and used mostly because of its speed and efficiency during learning process, and that ANN showed a good prediction of TEC compared to the IRI model. As a result, NNs are suitable for forecasting GPS TEC values at various locations if the model's input parameters are well specified.

Analysis of GPS TEC anomalies associated with Mw > 7 earthquakes in Japan

The Global Positioning System (GPS) provides insights into the Earthquakes (EQs) ionospheric anomalies. Different space-and-ground parameters are used to observe EQ precursors. This paper uses the Total Electron Content (TEC) from nearby operating GPS stations to detect perturbation in the ionosphere before and after four EQs in Japan in 2011. In addition, the TEC variations in relation to the depth and magnitude of the EQs are noticed. The analysis shows significant variations in TEC to depth and magnitude in association with each main shock. TEC value abruption starts on 8 March and continues for 6 days in March. In April, TEC abnormalities start 5 days before and after the man shock. The geomagnetic Kp index is higher than four from March 10 to 12. The anomalies in this study are clearly associated with geomagnetic storms, as the Kp index is active. Moreover, the Dst index is below the negative 50 (nT) in the seismic period of March and April. The ionosphere parameters will define the severity of seismic activity in future.

Responses of the Nigerian low-latitude ionosphere to geomagnetic storms of the ascending and maximum phases of solar cycle 24

This study investigates the responses of the Nigerian ionosphere to thirteen geomagnetic storms that occurred during the ascending and maximum phases of the solar cycle 24. The Total Electron Content (TEC) data obtained from the Nigerian Global Navigation Satellite System (GNSS) network (NIGNET) were used.The ionosphere over Nigeria recorded marked diurnal TEC variations around 1100–1700 LT, with the most significant variations between 1400 and 1600 LT time period, and minimum variation at 0600 LT. Another cogent piece of information from this study is that the values of TEC and ionospheric irregularities at all the five GNSS stations under investigation consistently matched one another. The implication of this is that the values of TEC and irregularities, at both quiet time and storm time, at any location in Nigeria could be used as the representative values at any other locations in the country, particularly in areas where there are no GNSS systems. The equinoctial maxima and June solstice minima effects were clearly observed in our data. Furthermore, ionospheric irregularities occurrences also followed semiannual patterns with two peaks in April and October, and the least occurrences in June, and generally localized around 2000–0100 LT. At daytime during geomagnetic storms in the equatorial/low-latitude regions, Prompt Penetration Electric Field (PPEF) intensified forward plasma fountain effect, while at nighttime, the reversed plasma fountain effect returns plasma backward to cause the Equatorial Ionization Anomaly (EIA) crests to contract, or totally coalesce the EIA structure to form a single strip of plasma density around the equator.

Performance evaluation of ionospheric models over equatorial Indian region

An extensive year-long evaluation of Total Electron Content (TEC) obtained from NCAR Thermosphere-Ionosphere Electrodynamics General Circulation Model (TIE-GCM) simulations along with TEC obtained from International Reference Ionosphere (IRI-2020) is carried out with respect to observations made by a GPS TEC receiver over the dip equatorial location of Thiruvananthapuram (8.5° N, 77° E, 0.5° magnetic), in the Indian region during solar minimum period of 2005–2006. The TIE-GCM model and IRI model were run for the period of 2005 (November December) and till October 2006 for each day, and the hourly values of TEC were compared to GPS TEC observations. Overall the temporal and seasonal characteristics of TEC over Thiruvananthapuram were simulated reasonably well by the TIE-GCM model and IRI model. The skill scores of normalized Root Mean Square Error (nRMSE), Prediction Efficiency (P.E), and Ratio between maximum and minimum values were estimated for both models to evaluate their performance. The mean percentage difference between the measured and the simulated TEC was found to be around −25% for IRI, indicating underestimation of the observations, and between −40% and −50% for TIE-GCM again indicating underestimation. A comprehensive analysis revealed that there is good conformity between the modeled and measured values for the whole observation period, with a correlation coefficient R-value of 0.8 and 0.9 for the TIE-GCM and IRI models respectively. This study brings out the competence of the two models in simulating the temporal and seasonal pattern of variability of ionospheric TEC over the equatorial Indian region.

Long-term equatorial ionosphere variations over Ethiopia

This paper focuses on the investigation of the long-term behavior of the equatorial ionosphere in Ethiopia. To explore the extended ionospheric variations, we analyze Total Electron Content (TEC) fluctuations in conjunction with solar activity indices, including F10.7 radio flux, EUV radiation, and Sunspot Number (SSN). The dataset used encompasses GPS-derived TEC records spanning 11 consecutive years from 2007 to 2017, corresponding to the period of solar cycle 24. Our analysis shows that the highest TEC levels generally occur during the post-noon hours, while the lowest values are observed around post-midnight or early morning. Notably, exceptions to this pattern are found in 2011 and 2013 when the highest TEC levels are observed during the December solstice. Otherwise, the seasonal TEC variation exhibits a semi-annual pattern, with peak TEC values occurring during the equinoxes and the lowest values during the solstices. Further examination demonstrates that in both solar activity phases, TEC magnitude is higher during the March Equinox compared to the September Equinox, and slightly higher during the December Solstice compared to the June Solstice. This reveals an asymmetry in the seasonal variations, encompassing the equinoxes, solstices, and the ascending and descending phases of the solar cycle. Our study confirms that long-term fluctuations in ionospheric TEC closely correspond to variations in solar activity indices. Correlation analysis indicates a strong link between all solar activity indices and TEC. Notably, while a negative correlation is observed in 2007, the correlation in 2011 is remarkably stronger. Additionally, we find that the long-term TEC variation exhibits stronger correlations with EUV and F10.7 radio flux than with SSN, with correlation coefficients of 0.86, 0.85, and 0.78, respectively. Lastly, our study extends previous research by identifying the long-term trends of vertical TEC (vTEC) in relation to various driving factors such as F10.7, SSN, and EUV.

Constructing a Regional Ionospheric TEC Model in China with Empirical Orthogonal Function and Dense GNSS Observation

Using Global Navigation Satellite Systems (GNSS) observation data for developing a high-precision ionospheric Total Electron Content (TEC) model is one of the essential subjects in ionospheric physics research and the application of satellite navigation correction. In this study, we integrate the Empirical Orthogonal Function (EOF) method with the TEC data provided by the Center for Orbit Determination in Europe (CODE), and observed by the dense GNSS receivers operated by the Crustal Movement Observation Network of China (CMONOC) to construct a regional ionospheric TEC model over China. The EOF analysis of CODE TEC in China from 1998 to 2010 shows that the first-order EOF component accounts for 90.3813% of the total variation of the ionospheric TEC in China. Meanwhile, the average value of CODE TEC is consistent with the spatial and temporal distribution characteristics of the first-order EOF base function, which mainly reflects the latitude and diurnal variations of TEC in China. The first-order coefficient after EOF decomposition shows an obvious 11-year period and semi-annual variations. The maximum amplitude of semi-annual variation mainly appears in March and October, which is closely associated with the variation in geographical longitude, the semi-annual change of the low-latitude electric field, and the ionospheric fountain effect. The second-order coefficient has an evident annual variation, the minimum amplitude mainly occurs in March, August, and September, and the amplitude values in the high solar activity years are more significant than those in the low solar activity years. The third-order coefficient mainly shows the characteristics of annual variation, and the fourth-order coefficient shows the noticeable semi-annual and annual variations. The third and fourth-order coefficients are both modulated by the solar activity index F10.7. The ionospheric TEC model in China, driven by CMONOC real-time GNSS observation data, can better reflect the latitude, local time and seasonal variation characteristics of ionospheric TEC over China. In particular, it can clearly show the spring and autumn asymmetry of ionospheric TEC in the low latitudes. The root mean square error of the absolute error between the model and the actual observation is mainly distributed around 2.45 TECU (1 TECU = 1016 electrons/m2). The values of the TEC model constructed in this study are closer to the actual observed values than those of the CODE TEC in China.

Instrumental Receiver Bias Estimation for Ionospheric Total Electron Content by Neural Network Model

Total Electron Content (TEC) is one of the most important parameters in the study of the ionosphere, especially for determining ionospheric disturbances. The TEC levels are typically estimated from dual-frequency GPS observation data. Since the measured TEC contains discrepancies such as satellite and receiver biases, they need to be removed to obtain more accurate TEC values. In this work, we estimate the receiver bias using a neural network technique. Based on the exhaustive evaluation, we design a neural network (NN) model with two-hidden layers, and it is trained with datasets from three GNSS observation stations in Thailand. The prediction from the proposed neural network deviates from the baseline reference using the minimum standard deviation method with significantly faster computational time. The trained NN model is also tested for estimating the receiver bias values at other untrained stations in Thailand.

Ionospheric TEC modeling using COSMIC-2 GNSS radio occultation and artificial neural networks over Egypt

Abstract The ionospheric delay significantly impacts GNSS positioning accuracy. To address this, an Artificial Neural Network (ANN) was developed using the high-quality COSMIC-2 ionospheric profile dataset to predict the Total Electron Content (TEC). ANNs are adept at addressing both linear and nonlinear challenges. For this research, eight distinct ANNs were cultivated. These ANNs were designed with the following inputs Year, Month, Day, Hour, Latitude, and Longitude. Along with solar and geomagnetic parameters such as the F10.7 solar radio flux index, the Sunspot Number (SSN), the Kp index, and the ap index. The goal was to discern the most influential parameters on ionosphere prediction. After pinpointing these key parameters, an enhanced model utilizing a pioneering technique of a secondary ANN was employed with the main ANN to predict TEC values for events in 2023. The study’s findings indicate that solar parameters markedly enhance the model’s accuracy. Notably, the augmented model featuring a prelude secondary network achieved a stellar correlation coefficient of 0.99. Distributionally, 41 % of predictions aligned within the (−1≤ ΔTEC ≤1) TECU spectrum, 28 % nestled within the (1< ΔTEC ≤2) and (−2≤ ΔTEC <−1) TECU ambit, while a substantial 30 % spanned the broader (2< ΔTEC ≤5) and (−5≤ ΔTEC <−2) TECU range. In essence, this research underscores the potential of incorporating solar parameters and advanced neural network techniques to refine ionospheric delay predictions, thus boosting GNSS positioning precision.

Synthesis‐Style Auto‐Correlation‐Based Transformer: A Learner on Ionospheric TEC Series Forecasting

AbstractAccurate 1‐day global total electron content (TEC) forecasting is essential for ionospheric monitoring and satellite communications. However, it faces challenges due to limited data and difficulty in modeling long‐term dependencies. This study develops a highly accurate model for 1‐day global TEC forecasting. We utilized generative TEC data augmentation based on the International Global Navigation Satellite Service (IGS) data set from 1998 to 2017 to enhance the model's prediction ability. Our model takes the TEC sequence of the previous 2 days as input and predicts the global TEC value for each hourly step of the next day. We compared the performance of our model with 1‐day predicted ionospheric products provided by both the Center for Orbit Determination in Europe (C1PG) and Beihang University (B1PG). We proposed a two‐step framework: (a) a time series generative model to produce realistic synthetic TEC data for training, and (b) an auto‐correlation‐based transformer model designed to capture long‐range dependencies in the TEC sequence. Experiments demonstrate that our model significantly improves 1‐day forecast accuracy over prior approaches. On the 2018 benchmark data set, the global root mean squared error (RMSE) of our model is reduced to 1.17 TEC units (TECU), while the RMSE of the C1PG model is 2.07 TECU. Reliability is higher in middle and high latitudes but lower in low latitudes (RMSE < 2.5 TECU), indicating room for improvement. This study highlights the potential of using data augmentation and auto‐correlation‐based transformer models trained on synthetic data to achieve high‐quality 1‐day global TEC forecasting.

Airglow observed by a full-band imager together with multi-instruments in Taiwan during nighttime of 1 November 2021

This study demonstrates an innovative approach of using a full-band chromatic all-sky imager, routinely operational for monitoring sky conditions at Lulin observatory (23.5°N, 120.9°E, 12.5° N magnetic latitude), Taiwan, to investigate equatorial plasma bubbles (EPBs). Distinct north–south aligned EPB depletions are identified by decomposing the color-scale images to respective red (centered at 630 nm) and green (520 nm) channels, where blue channel (470 nm) helps for background suppression. The intense EPBs, drifting eastwards at 60–100 m/s velocity, are also associated with reduced total electron content (TEC) values; increased ROTI (rate of TEC index); remarkable range spread-F; and prominent fluctuations in Doppler frequency shifts as well as FORMOSAT-7/COSMIC-2 electron density/S4 scintillation profiles. The results show that a full-band chromatic imager offers a cost-effective alternative to investigate the EPBs usually detected in OI 630.0 and 557.7 nm airglow emissions.