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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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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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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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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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