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Deep learning-based prediction models for the vertical total electron content using GNSS satellite observations
(2026-08-01)
Mutasov, Gleb
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Myint, Lin Min Min
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Budtho, Jirapoom
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Perwitasari, Septi
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Nishioka, Michi
Ionospheric Total Electron Content (TEC) is a crucial parameter for characterizing the state of the ionosphere and assessing its impact on satellite-based navigation systems and on communication technologies. In equatorial and low-latitude regions, ionospheric irregularities, particularly equatorial plasma bubbles (EPBs), pose significant challenges for satellite navigation and communication due to their capacity to cause rapid TEC fluctuations and signal degradation. Since these effects are especially pronounced during ionospheric and geomagnetic disturbances, making accurate TEC prediction is an essential task for improving the reliability of GNSS-based positioning and space weather applications. This study presents a machine learning-based framework for one-day-ahead prediction of TEC with a 30-min resolution over the magnetic equator and low-latitude regions, with a focus on Southeast Asia. Unlike global models, our approach is tailored to local GNSS observations and directly predicts TEC values along specific satellite-receiver paths, defined by geographic location and satellite visibility. We integrate ionospheric pierce point (IPP) coordinates, geomagnetic indices, and solar activity indicators as features to enhance temporal and spatial forecasting accuracy. To address the nonlinear and nonstationary nature of TEC variations, we investigate and compare three deep learning architectures: a Transformer-based time-series model, a Temporal Kolmogorov–Arnold Network (TKAN), and a Long Short-Term Memory (LSTM). Additionally, the predictions are benchmarked against the empirical IRI-2020 model and a persistence baseline. The results demonstrate that both the Transformer and TKAN models outperform the LSTM and empirical approaches, particularly during different geomagnetic and ionospheric conditions, showing improved robustness and generalization. The proposed framework highlights the potential for accurate, resource-efficient TEC prediction in low-latitude regions and opens a pathway for further improvements by integrating multi-GNSS observations and additional space weather parameters.
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Study on the relationship between Global Positioning System Total Electron Content Anomalies and Earthquake Events in Thailand during Solar Cycle 24
(2025-10-29)
Pansong, Chollada
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Kenpankho, Prasert
This study investigates ionospheric Total Electron Content (TEC) anomalies in relation to earthquake events in Thailand from 2007 to 2020, encompassing Solar Cycle 24. TEC data were obtained from three sources: the Global Positioning System (GPS), the International GNSS Service (IGS), and the International Reference Ionosphere (IRI), and were compared to 473 earthquakes (Mw ≥ 3.0). While earthquake magnitudes below Mw 5.0 did not exhibit a clear correlation, earthquake events of Mw 5.0 or higher reflected in moderate negative correlation coefficients for GPS TEC, IGS TEC, and IRI TEC (-0.495,-0.501, and-0.303, respectively). Furthermore, a positive correlation coefficient (0.611) was found between Mw ≥ 5.0 earthquakes and geomagnetic storms with the Kp index. However, focusing specifically on geomagnetic storms and TEC variations on the day of an earthquake, no significant relationship was detected across GPS, IGS, and IRI data. Nevertheless, further research is needed to clarify the link between seismic activity and TEC fluctuations, potentially through alternative approaches or targeted case studies. This is especially important given the limited number of earthquakes above a magnitude of 5.0 in our study area, which restricts the available sample size.
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Exploring Ionospheric Disturbances Using GNSS: A STEM-Based Investigation of the 2024 Extreme Geomagnetic Storm
(2025-01-01)
Pansong, Chollada
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Buakao, Nitipat
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Keokhumcheng, Thanapon
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Phothila, Pharinya
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Intamas, Patcharin
This study investigates the effectiveness of a STEM-based instructional approach integrating GNSS satellite technology and ionospheric TEC (Total Electron Content) analysis during extreme geomagnetic storms. The objective was to enhance students' conceptual understanding, practical skills, and STEM-related attitudes through interdisciplinary learning activities. The SPACE model (Study, Plan, Analyze, Create, Evaluate) was applied as a pedagogical framework to guide students through real-world TEC anomaly detection using GPS RINEX data and computational tools. The integration of real satellite data and hands-on analysis enabled students to connect theoretical knowledge with real-world phenomena, deepening their engagement and inquiry-based thinking. Pre-and post-test results revealed statistically significant improvements, with scores increasing from a mean of 5 1. 8 (S D = 7. 5 4) to 7 5. 0(S D= 7.42) (p < 0. 000001). Moreover, the STEM Attitude Questionnaire reflected strong positive perceptions, especially in STEM career motivation (x¯=4.20) and perceived value of STEM (x¯= 4.13). These findings highlight the model's potential to foster STEM readiness. Integrating GNSS-based TEC analysis into the STEM framework significantly enhanced students' academic outcomes, practical skills, and STEM attitudes.
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Compatibility of Low-Cost GNSS Receivers for Total Electron Content (TEC) Analysis
(2025-01-01)
Rana, Bhim Bahadur
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Supnithi, Pornchai
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Myint, Lin M.M.
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Tongkasem, Napat
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Budtho, Jirapoom
Although the geodetic GNSS receivers are highly precise, they are inaccessible to every user, especially in remote areas. Therefore, this work aimed to find the reasons that bolster the low-cost GNSS receivers to be used with high resolution over a wide area, instead of geodetic in space weather studies. A comparative analysis was conducted between a low-cost Ublox ZED-F9P GNSS receiver and a geodetic Novatel Propak6 GNSS receiver, focusing on ionospheric parameters such as slant total electron content (STEC), vertical total electron content (VTEC), and the number of satellites tracked using the Global Positioning System (GPS). Additionally, VTEC values were compared with the GIM model. Both receivers exhibited a similar pattern of TEC, with the R2 value of 0.9734 and the root mean square error of 3.4583. The number of satellites tracked by both receivers during the observed periods was also found to be similar. Moreover, the VTEC results obtained from the low-cost GNSS receiver showed compatibility with the GIM model, demonstrating the reliability of the low-cost receiver in comparison to the geodetic GNSS receiver.
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Total electron content prediction using singular spectrum analysis and autoregressive moving average approach
(2022-01-01)
Dabbakuti, J. R.K.Kumar
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Yarrakula, Mallika
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Panda, Sampad Kumar
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Jamjareegulgarn, Punyawi
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Haq, Mohd Anul
Continuous monitoring of ionospheric behavior and subsequent development or improvement of models for the prediction of its parameters with consistent accuracy remains an ongoing challenge. In this sense, an integrated approach by combining the signal extraction technique Singular Spectrum Analysis (SSA) with Autoregressive Moving Average (ARMA) is presented in this work to predict the ionospheric Total Electron Content (TEC) values that are responsible for causing ionospheric delays in the trans-ionospheric signal propagation associated with satellite-based communication, navigation, and timing applications. In general, SSA is a nonparametric spectral estimation procedure that decomposes the signals into interpretable and physically significant components. The observed TEC from two Global Positioning System (GPS) stations across the low latitude Saudi Arabian region are considered during the year 2017 that falls in the descending phase of solar cycle-24. The performance of the proposed hybrid model is evaluated by comparing with the sole estimation from the ARMA model and the observed GPS–TEC dataset for two different geomagnetic conditions: a) the regular geomagnetically quiet period of 15 to 29 December, 2017 (Ap < 24 and Dst > − 30 nT) and b) the geomagnetic storm period from 7 to 9 September, 2017 (Dst min = − 142 nT). The corresponding average Precision, Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) of the proposed SSA–ARMA model predictions are 1.79 TECU, 1.23 TECU, and 13.02%. In contrast, the respective values in the exclusive ARMA model are 2.01 TECU, 1.37 TECU, 14.42% at Oman station. The corresponding values for Magna station are 0.92 TECU, 0.61 TECU, and 10.76% (SSA–ARMA) and 1.01 TECU, 0.75 TECU, and 11.33% (ARMA). The results show an improved computational efficiency with minor improvement in the TEC predictions with the proposed SSA–ARMA method compared to the sole employment of the ARMA model by disregarding the extraneous components.
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Double-thin-shell approach to deriving total electron content from GNSS signals and implications for ionospheric dynamics near the magnetic equator
(2021-12-01)
Maruyama, Takashi
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Hozumi, Kornyanat
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Ma, Guanyi
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Supnithi, Pornchai
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Tongkasem, Napat
A new technique was developed to estimate the ionospheric total electron content (TEC) from Global Navigation Satellite System (GNSS) satellite signals. The vertically distributed electron density was parameterized by two thin-shell layers (double-shell approach). The spatiotemporal variation of TEC (strictly speaking, partial electron content) associated with each shell was approximated by the functional fitting of spherical surface harmonics. The major improvements over the conventional single-shell approach were as follows: (1) the precise estimation of TEC was achieved; (2) the estimated TEC was less dependent on the choice of shell heights; and (3) the equatorial anomaly was captured more correctly. Furthermore, higher and lower shells exhibited a different pattern of local time vs latitude variation, providing information on the ionosphere–thermosphere dynamics. [Figure not available: see fulltext.]
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Empirical orthogonal function modelling of total electron content over Nepal and comparison with global ionospheric models
(2020-12-01)
Jamjareegulgarn, Punyawi
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Ansari, Kutubuddin
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Ameer, Afsheen
This analysis uses 3 years hourly observations of total electron content (TEC) from 5 global positioning system (GPS) station across Nepal to analyze the spatial trend. For this purpose, empirical orthogonal function modeling is used to investigate the annual and monthly variation inside the country. Empirical orthogonal function base functions and associated coefficients of TEC variability over Nepal have been studied to establish the relationship between observed and modeled TEC values. Both the observed GPS TEC values and the empirical orthogonal function modeled TEC values are compared with the global ionospheric models (global ionospheric map and international reference ionosphere) TEC values. The study shows an hourly pattern of TEC variation in which the TEC rises from dawn, reaches the highest TEC values about 40 TEC units during the peak hours of the day, then decreases at evening at the lowest diurnal values about 5 TEC units. Monthly TEC values over all sites are higher during the march and April (about 38 TEC unit), while they are lower values during December and January (about 10 TEC unit). Although the correlation coefficients between the GPS TEC values and the global modeled TEC values are higher, while it becomes highest with empirical orthogonal function modeled TEC values for both cases of hourly and monthly variations. We examined the root mean square errors between observed and modeled TEC values at each site by using tailor correlation plots and found that they are lower in case of empirical orthogonal function model. Monthly residuals between observed and empirical orthogonal function modeled TEC values are always lower as compared to other global modeled TEC values. These kinds of comparative analysis in the present work indicate that empirical orthogonal function model by using global geomagnetic activity works very well and is capable of depicting TEC variations accurately.
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Total electron content observations by dense regional and worldwide international networks of GNSS
(2018-06-01)
Tsugawa, Takuya
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Nishioka, Michi
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Ishii, Mamoru
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Hozumi, Kornyanat
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Saito, Susumu
Two-dimensional ionospheric total electron content (TEC) maps have been derived from ground-based Global Navigation Satellite System (GNSS) receiver networks and applied to studies of various ionospheric disturbances since the mid-1990s. For the purpose of monitoring and researching ionospheric conditions and ionospheric space weather phenomena, we have developed TEC maps of areas over Japan using the dense GNSS network, GNSS Earth Observation NETwork (GEONET), which consists of about 1300 stations and is operated by the Geospatial Information Authority of Japan (GSI). Currently, we are providing high-resolution, two-dimensional maps of absolute TEC, detrended TEC, rate of TEC change index (ROTI), and loss-of-lock on GPS signal over Japan on a real-time basis. Such high-resolution TEC maps using dense GNSS receiver networks are one of the most effective ways to observe, on a scale of several 100 km to 1000 km, ionospheric variations caused by traveling ionospheric disturbances and/or equatorial plasma bubbles, which can degrade single-frequency and differential GNSS positioning/navigation. We have collected all the available GNSS receiver data in the world to expand the TEC observation area. Currently, however, dense GNSS receiver networks are available in only limited areas, such as Japan, North America, and Europe. To expand the two-dimensional TEC observation with high resolution, we have conducted the Dense Regional and Worldwide International GNSS TEC observation (DRAWING-TEC) project, which is engaged in three activities: (1) standardizing GNSS-TEC data, (2) developing a new high-resolution TEC mapping technique, and (3) sharing the standardized TEC data or the information of GNSS receiver network. We have developed a new standardized TEC format, GNSS-TEC EXchange (GTEX), which is included in the Formatted Tables of ITU-R SG 3 Data-banks related to Recommendation ITU-R P.311. Sharing the GTEX TEC data would be easier than sharing the GPS/GNSS data among those in the international ionospheric researcher community. The DRAWING-TEC project would promote studies of medium-scale ionospheric variations and their effect on GNSS.
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A new expression for computing the bottomside thickness parameter and comparisons with the NeQuick and IRI-2012 models during declining phase of solar cycle 23 at equatorial latitude station, Chumphon, Thailand
(2017-07-15)
Jamjareegulgarn, Punyawi
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Supnithi, Pornchai
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Watthanasangmechai, Kornyanat
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Yokoyama, Tatsuhiro
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Tsugawa, Takuya
This paper proposes a new expression for computing the bottomside thickness parameter at equatorial latitude station, Chumphon (10.72°N, 99.37°E), Thailand. Its diurnal variations from 2004 to 2006 at this location are then studied. The proposed expression is derived based on two experimental data sources: FMCW ionosonde and dual-frequency GPS system, and some expressions of the NeQuick 2 model. Hence, after both the bottomside thickness parameter computed by the proposed equation, B2bot_Pro, and the bottomside shape parameter (namely, B1_Pro in this work) are computed, the bottomside electron density and the height where the bottomside electron density drops down to be 24% of the NmF2 (namely, h0.24) can be computed and shown in this work using the analytical functions of the IRI model. Moreover, the diurnal variations of the B2bot_Pro are compared with those computed from the NeQuick model, B2bot_NeQ, and the predicted B0 of the IRI-2012 model with ABT-2009 and Bil-2000 options (namely, “B0_ABT” and “B0_Bil”, respectively). The averaged, minimum, and maximum values of percentage deviations among these bottomside thickness parameters are also computed and shown in this work. Our results show that the diurnal variations of B2bot_Pro at Chumphon station have the following patterns: they start to increase during nighttime to the first peaks during pre-sunrise hours, and then decrease abruptly to their minimum values during sunrise hours. Afterward, they increase again to reach the second peaks around local noontime and fall gradually to their starting times during 20–04 LT. The diurnal variations of B2bot_Pro follow generally the same trends as those of the B2bot_NeQ and the B0_ABT, except pre-sunrise hours. The pre-sunrise peaks and sunrise collapses in both the B2bot_NeQ and the B0_ABT can be found occasionally. On the other hand, the diurnal variations in B2bot_Pro differ from those in B0_Bil due to the flattened variation in B0_Bil and the pre-sunrise peaks as well as sunrise collapses in B0_Bil disappear. The pre-sunrise peaks of the B2bot_Pro at the Chumphon station are higher than those of the B2bot_NeQ, the B0_ABT, and the observed B0 at other regions. Furthermore, the percentage deviations between the B2bot_Pro and the B0_ABT (PD_B2B0ABT) are mostly lower than 30% for all seasons of the studied years, opposite to the other percentage deviations studied in this work. The proposed B2bot_Pro parameters in this work follow a similar trend to the B2bot_NeQ and the B0_ABT, but it is not conclusive that the proposed values are equivalent to them.
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TEC prediction with neural network for equatorial latitude station in Thailand
(2012-01-01)
Watthanasangmechai, Kornyanat
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Supnithi, Pornchai
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Lerkvaranyu, Somkiat
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Tsugawa, Takuya
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Nagatsuma, Tsutomu
This paper describes the neural network (NN) application for the prediction of the total electron content (TEC) over Chumphon, an equatorial latitude station in Thailand. The studied period is based on the available data during the low-solar-activity period from 2005 to 2009. The single hidden layer feed-forward network with a back propagation algorithm is applied in this work. The input space of the NN includes the day number, hour number and sunspot number. An analysis was made by comparing the TEC from the neural network prediction (NN TEC), the TEC from an observation (GPS TEC) and the TEC from the IRI-2007 model (IRI-2007 TEC). To obtain the optimum NN for the TEC prediction, the root-mean-square error (RMSE) is taken into account. In order to measure the effectiveness of the NN, the normalized RMSE of the NN TEC computed from the difference between the NN TEC and the GPS TEC is investigated. The RMSE, and normalized RMSE, comparisons for both the NN model and the IRI-2007 model are described. Even with the constraint of a limited amount of available data, the results show that the proposed NN can predict the GPS TEC quite well over the equatorial latitude station. Copyright © The Society of Geomagnetism and Earth, Planetary and Space Sciences (SGEPSS).

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