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Improving One-Day-Ahead Forecasting of Low-Latitude Amplitude Scintillation Using an Upsampling-Enhanced LSTM
(2026-01-01)
Muangkammuen, Patinya
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Suthisopapan, Puripong
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Tongkasem, Napat
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Supnithi, Pornchai
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Kruesubthaworn, Anan
The scintillation in radio wave propagation, particularly in regions near the magnetic equator, is found to be introduced by the ionospheric irregularities causing unsatisfactory performance in satellite-based applications. In order to mitigate this effect, we design a long short-term memory (LSTM) model to forecast amplitude scintillation at 1-min resolution. In addition, the upsampling-based feature preprocessing is introduced to improve forecasting performance, especially for short-term severe scintillation events. In terms of R$^{2}$, which is a popular forecast evaluation metric, our proposed model exhibits about 20% improvement over the same LSTM model without upsampling. Furthermore, although existing studies achieve good forecasting accuracy up to 4 h ahead, the proposed model sets a benchmark with one-day-ahead forecasting, but at the cost of longer training time due to upsampling.
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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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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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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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Clustering of Ionospheric Irregularities based on Spatiotemporal ROTI Keogram Images
(2024-01-01)
Mutasov, Gleb
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Min Myint, Lin Min
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Supnithi, Pornchai
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Budtho, Jirapoom
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Tongkasem, Napat
Ionospheric irregularities associated with Equatorial plasma bubbles (EPB) can significantly impact navigation and communication systems. Therefore, their occurrences need to be studied and predicted. To solve the prediction problem, it is necessary to identify types of spatiotemporal characteristics as reference points for the predictive model. This work employs unsupervised machine learning algorithms to identify types of ionospheric irregularities due to EPB using the rate of total electron content index (ROTI) keograms. Two machine learning methods: two models, the Gaussian mixture model (GMM), and k-means, are considered. Comparative analysis is performed, and the optimal number of clusters is estimated using one classical, k-means and one additional - repeatability score, introduced in this work metric. The optimal GMM model successfully classifies three types of irregularity patterns offering valuable insights for the development of an effective EPB prediction model and enhancing our understanding of ionospheric behavior.
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Comparative Study of the Equatorial Plasma Bubbles using VHF Radar Images and Spatial ROTI Maps at Low-Latitude Region
(2023-01-01)
Tongkasem, Napat
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Myint, Lin M.M.
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Supnithi, Pornchai
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Hozumi, Kornyanat
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Nishioka, Michi
Equatorial Plasma Bubbles (EPBs) depict electron density depletion region originating at the bottom side of the F layer in the ionosphere. The EPBs are often observed in the low latitude region after post-sunset period, particularly, in equinoctial months. Since EPBs have a negative impact on high-precision positioning techniques, degrading convergence time and accuracy, it is essential to study the spatial variations of EPBs during their lifetime. In this work, we develop 2-D temporal-spatial maps based on the rate of change TEC change (ROTI) index analyzed from pseudorange information in a GNSS receiver network over Thailand. The area covers the magnetic equatorial and low-latitude regions including equatorial ionosphere anomaly (EIA). Using 2-D ROTI maps (longitude vs latitude), two types of ROTI keograms (time vs latitude and time vs longitude), we analyze the spatial and temporal changes of recent EPB events. Complementing this analysis, we propose to jointly anlayze the VHF radar images at Prachomklao Chumphon VHF radar station (Lat:10.72 N, Lon: 99.37, Magn. Lat: 1.34). The radar system can scan the ionosphere from geographic latitude 0° N to 20° N and from 140 to 860 km altitude range. The results show that with the three types of data methods, characterizations, speed, velocity and occurrences of EPB are obtained.
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Estimation and Validation of Vertical Total Electron Content Using Standalone Single-Frequency Observations
(2022-01-01)
Tongkasem, Napat
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Myint, Lin Min Min
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Supnithi, Pornchai
Nonuniform ionospheric delay is a well-known cause of degradations in radio wave propagations such as in satellite communication and positioning. In general, the ionospheric delay can be estimated using the Global Navigation Satellite System (GNSS) data from dual or multiple-frequency receivers; however, satellite differential code biases (DCBs) must be downloaded via network connection. For positioning based on standalone single-frequency receivers, the Klobuchar model, a well-known model in the GPS positioning system, is used to estimate the ionospheric delay based on solar activity, season, or region by using the eight coefficients in the broadcast navigation message. Although this model can reduce positioning errors by about 50 percent, the low-latitude disturbances such as the equatorial plasma bubble (EPB) phenomenon, significantly diminishes the accuracy of modeled delay estimation. In this work, we propose an ionospheric delay estimation technique based on observed single-frequency GPS data without requiring network-based corrections for DCB. The ionospheric delays estimated by the proposed method are compared with those from the GPS dual-frequency observation, the broadcast/network models in 2014 (high solar activity) and 2020 (low solar activity). According to the results, the proposed ionospheric delay estimation can correct the ionosphere errors better than the well-known Klobuchar model, by about 9.98 percent and 6.77 percent in 2014 and 2020, respectively. The proposed model increases the ionospheric error correction efficiency in vertical positioning by up to 81 percent in 2014 and 79 percent in 2020.
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Precise total electron content map monitoring in low latitude region
(2022-01-01)
Tongkasem, Napat
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Myint, Lin M.M.
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Supnithi, Pornchai
The need for accurate Global Navigation Satellite System (GNSS) positioning is necessary for GNSS applications such as Real-Time Kinematic (RTK), Precise Point Positioning RTK (PPP-RTK), etc. The ionosphere delay, especially in low latitude region, is a main cause of positioning error. The usage of electron maps in GNSS applications can help with the first ionosphere correction. The Global Ionosphere Map (GIM) is a large-scale service for the Total Electron Content (TEC) with a resolution of 2.5 for latitude and 5 for longitude which may not be proper to high resolution GNSS applications in the small or regional regions. Over the low latitude region, we apply the local TEC from 4 stations which has similar longitude to observe the differential TEC. Then, we generate the precise grid TEC maps from 18 GNSS stations with different resolution of grid for observe the appropriate values. The results are shown that the resolution of 1.5 for latitude and 3 for longitude has RMS 1.58 of TECu compare with the local TEC value, while GIM has RMS of 2.11 TECu.
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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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