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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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Analysis of ionospheric and geomagnetic fields changes in Thailand during the May 2024 geomagnetic storm
(2025-12-15)
Myint, Lin M.M.
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Perwitasari, Septi
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Nishioka, Michi
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Saito, Susumu
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Kaewthongrach, Rungnapa
The extreme geomagnetic storm of May 2024, the most severe in two decades of space weather history up to date, had widespread effects on the ionosphere, from the polar regions to the magnetic equator. This study examines the responses of the equatorial ionosphere and geomagnetic field over Thailand during this geomagnetic storm, utilizing data from GNSS receivers, magnetometers, and ionosondes near the magnetic equator and low-latitude regions of Thailand. We analyze the direct and indirect impacts of interplanetary magnetic field (IMF) and interplanetary electric field (IEF) variations, driven by solar storms, on local equatorial magnetic fields and ionospheric parameters. Our finding reveals that storm-driven electric fields, particularly prompt penetration electric fields (PPEF) and disturbance dynamo electric fields (DDEF), strongly influenced equatorial electric field (EEF), causing notable fluctuations in total electron content (TEC), critical frequency of F2 (foF2), and virtual height of F layer (h’F). The Pearson correlation analysis highlights the rapid coupling between interplanetary magnetic field (IMF) and local equatorial magnetic fields during geomagnetic storms. These observations enhance our understanding of geomagnetic storm impacts in equatorial regions, which is crucial for improving space weather forecasting and mitigation strategies, especially for GNSS-dependent systems and radio communications.
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Corrigendum to “Spatio-temporal characteristics of ionospheric irregularities in low latitude regions during the peak of solar cycle 25” [Adv. Space Res. 76(1) (2025) 254–268, (S0273117725004168), (10.1016/j.asr.2025.04.062)]
(2025-09-01)
Tongkasem, Napat
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Supnithi, Pornchai
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Thammavongsy, Phimmasone
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Nishioka, Michi
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Perwitasari, Septi
The authors regret that the following was omitted from the acknowledgment section: This research project is also financially supported by National Research Council of Thailand (NRCT) under grant N41A640235. The authors would like to apologise for any inconvenience caused.
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Spatio-temporal characteristics of ionospheric irregularities in low latitude regions during the peak of solar cycle 25
(2025-07-01)
Tongkasem, Napat
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Supnithi, Pornchai
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Thammavongsy, Phimmasone
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Nishioka, Michi
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Perwitasari, Septi
Equatorial plasma bubbles (EPBs) are a primary source of ionospheric irregularities (IIR) in low-latitude regions. The severity of EPBs depends on the intensity, penetration, and disturbance of electric fields generated in the ionosphere. In this work, we analyze the IIR associated with geomagnetic activity in the low-latitude region (0°N–25°N, 90°E–110°E) from 2022 to 2024. The total electron content (TEC) and the rate of TEC index (ROTI) are used to investigate the spatiotemporal characteristics of these IIRs, influenced by both local EPBs and global geomagnetic storms. During low-to-moderate geomagnetic activity, electric field penetration and disturbances have a low impact on EPB development. The high solar activity intensifies the electric field, leading to intense EPB occurrences that can affect the entire region for several hours. From January 2022 to October 2024, these intense EPB events accounted for 35% of all EPB occurrences. During strong geomagnetic storms, the prompt penetration of electric fields (PPEF), and disturbance dynamo electric field (DDEF) caused the depression and fluctuations of TECs.
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Sporadic E critical frequency detection using three EIA region ionosonde stations over Southeast Asia
(2025-03-01)
Wichaipanich, Noraset
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Nishioka, Michi
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Min Myint, Lin Min
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Supnithi, Pornchai
This paper presents the occurrence of the sporadic E layer critical frequency (foEs) measured from three ionosonde stations in the Southeast Asia equatorial ionization anomaly (EIA) regions. These three ionosonde stations include two in Thailand: Chiang Mai (18.76°N, 98.93°E, Dip 12.7°) and Chumphon (10.72°N, 99.37°E, Dip 3.0°), and one in Indonesia: Kototabang (0.2°S, 100.32°E, Dip −10.1°). The daily hourly foEs values observed during 2010 and 2015 were statistically analyzed for foEs occurrence during low and high solar activity periods. Additionally, the number of foEs occurrences was analyzed in terms of the percentage of occurrence (%foEs). The results show that the occurrences of foEs from all three stations were similar, with the monthly hourly occurrence of foEs peaking in the June solstice season (May, June, July, August). Meanwhile, foEs appeared relatively low during the September equinox (September, October) and the December solstice (November, December, January, February) seasons. Furthermore, the frequency of foEs occurrence peaks around 16–20 LT, except in 2015 at Chiang Mai and Chumphon, where peaks were observed at 10 LT and 15 LT, respectively. Additionally, comparing the three stations reveals that in 2010, the maximum number of foEs occurrences was at Chiang Mai (≈21 %), followed by Kototabang (≈19 %) and Chumphon (≈16 %). In 2015, the highest number was observed at Kototabang (≈17 %), followed by Chumphon (≈14 %) and Chiang Mai (≈8%). Furthermore, the maximum frequency of foEs was highest at Chiang Mai (20–25 MHz), followed by Chumphon (15–20 MHz) and Kototabang (10–15 MHz). Additionally, foEs occurrences during low solar activity (2010) were higher than those during high solar activity (2015). It was assumed that the occurrence of foEs in the Southeast Asian sector was anti-correlated with the solar cycle and asymmetric characteristics. We hope that this analytical information will be useful for future HF and VHF communications design in the Southeast Asia region.
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Was the Unseasonal Development of Post-Sunset Equatorial Plasma Bubbles in Southeast Asia Driven by Quasi-2-Day Planetary Waves?
(2025-03-01)
Dai, Guofeng
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Li, Guozhu
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Otsuka, Yuichi
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Hu, Lianhuan
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Sun, Wenjie
Previous studies suggest that the planetary waves in mesosphere and low thermosphere (MLT) could modulate the occurrence of equatorial plasma bubbles (EPBs) via altering post-sunset F layer height. Using simultaneous observations by Global Navigation Satellite System receiver networks, two ionosondes separated by about 10° in longitude, high frequency and very high frequency radars, we investigated the day-to-day variations of post-sunset F layer height and EPB occurrence in southeast Asia during the quasi-2-day planetary wave (QTDW) event in July 2023. The results showed that the post-sunset F layer height over Bac Lieu (9.3°N, 105.7°E) and EPB occurrence had a quasi-2-day (QTD) variation. However, such a 2 day variation of F layer height was confined in a very limited longitude, that is contradictory to the planetary scale characteristics of QTDW. We suggest that the QTD variations of post-sunset F layer height and EPB occurrence over the specific location were not necessarily due to the QTDW in MLT. The local seeding source, as characterized by satellite traces in ionosonde ionograms, could drive the small-scale longitudinal structure of F layer height and play an important role in shaping the QTD variation of EPB. The results implicate that the connection between planetary waves and the EPB occurrence over a specific location should be interpreted carefully, even if the day-to-day variation of post-sunset F layer height shows periodic behavior with planetary wave scale.
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A Novel Short-Term Prediction Model for Regional Equatorial Plasma Bubble Irregularities in East and Southeast Asia
(2025-02-01)
Zhao, Xiukuan
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Li, Guozhu
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Xie, Haiyong
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Hu, Lianhuan
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Sun, Wenjie
Equatorial plasma bubble (EPB) irregularities can significantly impact satellite-based communication and navigation systems. Accurate prediction of EPB occurrence is essential for mitigating these impacts. Using the GNSS receiver network and ionosonde data from East and Southeast Asia during 2010–2021, and the rate of TEC change index to characterize the occurrence of EPB irregularities, we developed a novel Spatio-Temporal deep learning model for regional EPB irregularities short-term Prediction (STEP). The model integrates the convolutional neural network and long short-term memory (LSTM) network, together with attention mechanisms, to capture both spatial and temporal features of regional ionospheric irregularities. The results show that for 5-min forecast, the STEP model achieves a root mean square error (RMSE) of 0.062 TECU/min and an R<sup>2</sup> of 0.818, reducing RMSE by 19.48% compared to LSTM and 27.06% compared to gated recurrent unit model. For 60-min prediction, the STEP model can still achieve reasonable accuracy with an RMSE of 0.110 TECU/min and an R<sup>2</sup> of 0.482, showing significant improvement over traditional models. The equatorial F layer height and regional TEC fluctuations were identified as the most critical factors for predicting the generation and duration of EPB irregularities, respectively. The spatial and temporal distributions of EPB irregularities, including their latitudinal variation and delayed onset after sunset, and the occurrence across different days in East and Southeast Asia, were well predicted by the STEP. It is expected that the STEP model would provide a valuable tool for improving the resilience of GNSS against ionospheric scintillations induced by EPB irregularities.
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Classification of Equatorial Ionospheric Irregularities Using Unsupervised Machine Learning Based on Spatiotemporal ROTI Keograms
(2025-01-01)
Mutasov, Gleb
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Supnithi, Pornchai
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Budtho, Jirapoom
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Tongkasem, Napat
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Nishioka, Michi
Equatorial ionospheric irregularities, particularly those associated with equatorial plasma bubbles (EPB), can significantly disrupt satellite navigation and communication systems. As the demand for reliable Global Navigation Satellite System (GNSS) and communication services grows, the prediction of ionospheric irregularities becomes critical. A key step in the prediction process is to identify distinct spatiotemporal patterns of irregularities, including day-to-day, longitudinal, and seasonal variations. However, with large datasets, manually classification or identification of these irregularities is a complex and challenging task. In this work, we propose unsupervised machine learning techniques to recognize and group irregularity patterns in large, unlabeled Rate of Total Electron Content (TEC) Index (ROTI) keograms. Specifically, two machine learning models: Gaussian Mixture Model and k-means clustering are employed. The ROTI keograms are constructed using GNSS data from two low-latitude receiver stations in Thailand. To reduce redundancy in the keogram images, three feature extraction techniques are applied before the clustering process. A comparative analysis is performed to determine the optimal number of clusters using these models. Based on the results, the optimal combination of feature extraction and clustering technique is determined for the proposed clustering model. The resulting k-means model with contour extractor classifies five distinct patterns of ionospheric irregularity patterns, providing valuable insights for enhancing EPB prediction models and deepening our understanding of ionospheric dynamics. Furthermore, these five irregularity patterns are analyzed in relation to space weather parameters such as the solar radio flux index (F10.7), and the geomagnetic index (Kp). The findings contribute to the development of robust prediction models, improving the reliability of satellite-based communication and navigation systems.
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Predicting Equatorial Ionospheric Total Electron Content Using the Transformer-based Model with Observations From Ground GNSS Receivers and COSMIC-2 Satellites
(2025-01-01)
Mutasov, Gleb
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Supnithi, Pornchai
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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 key parameter for monitoring and studying the ionosphere, which induces significant delays in radio signals. Equatorial ionospheric irregularities, such as Equatorial plasma bubbles (EPB), can severely disrupt satellite navigation and communication. Predicting TEC is, therefore, essential for space weather monitoring and high-precision positioning applications. This study employs a Transformer-based model to predict TEC 24 hours in advance for specific satellites based on observations from a ground station and COSMIC-2 satellites. Unlike other approaches, our model directly forecasts TEC values for visible satellites within predefined longitude-latitude ranges. To enhance predictive accuracy, we also integrate additional features: ionospheric pierce points (IPP), geomagnetic (HP60), and solar activity indices, utilizing time-series Transformer architecture, and we consider a long-short-term memory (LSTM) model as a baseline. The proposed approach provides promising results for local TEC forecasting in the specific coverage area, with potential for further enhancements using additional GNSS or TEC measurements.
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Automatic classification of spread‐F types in ionogram images using support vector machine and convolutional neural network
(2024-12-01)
Benchawattananon, Phongsachot
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Siritaratiwat, Apirat
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Supnithi, Pornchai
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Nishioka, Michi
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Perwitasari, Septi
An ionogram image serves as a valuable data for examining the ionospheric bottom side characteristics and variabilities. Spread-F is indicated or identified by plasma irregularity in the ionospheric region. Diffused echo in the ionogram images particularly pose challenges for efficient interpretation required in further applications. An automatic classification of spread-F is presented in this study. Ionogram images are automatically classified using preprocessing techniques to improve the classification performance. In this study, the classification is designed by two machine learning algorithms, including support vector machine (SVM) and convolutional neural network (CNN). The CNN model with preprocessing technique outperforms the SVM alternative based on 4,692 labelled ionogram images from the FMCW-type ionosonde at Chumphon station, Thailand. The model successfully classified clear, frequency spread-F (FSF), range spread-F (RSF), strong spread-F (SSF), and unidentified class with an accuracy of 98.0%, 85.1%, 90.7%, 66.7%, and 99.2%, respectively. The proposed automatic classification models achieved to classify classes of ionogram images. In addition, the image filtering and data preprocessing are useful with ionogram images for improving the model classification performance. Graphical Abstract: (Figure presented.)

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