Supnithi, Pornchai

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Supnithi, Pornchai
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Supnithi, P.
Supnithi, Pomchai
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pornchai.su@kmitl.ac.th

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    Effects of Equatorial Plasma Bubbles over Real-Time Kinematic Positioning in Low-Latitude Region
    (2023-01-01)
    Thu, Phyo C.
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    Supnithi, Pornchai
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    Myint, Lin Min Min
    ;
    Budtho, Jirapoom
    ;
    Saito, Susumu
    Equatorial plasma bubbles (EPBs) refer to ionospheric irregularities in low-latitude regions, commonly observed after sunset. They originate at the magnetic equator and then potentially spread to mid-latitude region. As cm-level positioning techniques are increasingly important to various segments of society, the performance degradation of these systems due to EPB at low latitudes needs to be investigated. In this work, we analyze the EPB effects on the performances of real-time kinematic (RTK) positioning at the short, medium, and long baselines at low-latitude stations in Thailand. The low-latitudes local ionospheric disturbances such EPBs are shown to degrade the positioning accuracy of RTK in different seasons in 2022. It is found that the positioning errors are higher during the disturbance periods and more severe at the long baselines than the shorter ones, especially during the equinoctial months.
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    Comparative Analysis of Deep Learning Models for Daily Solar Indices Forecasting in Solar Cycle 25
    (2025-01-01)
    Min Myint, Lin Min
    ;
    Mutasov, Gleb
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    Supnithi, Pornchai
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    Budtho, Jirapoom
    Accurate forecasting of solar activity indices, particularly the Sunspot Number (SSN) and the F10.7 solar radio flux index (F10.7), is essential for effective space weather monitoring, as severe solar and ionospheric disturbances can significantly impact satellite operations, radio communications, and navigation systems. This paper presents a comparative analysis of deep learning models - Long Short-Term Memory (LSTM), Temporal Convolutional Networks (TCN), and encoder-only Transformer architectures - for daily forecasting of SSN and F10.7 up to 14 days ahead based on past 27 days. Considering relatively simple model structures, both single-step and multi-step prediction strategies are explored to evaluate the models' capability in handling short-and long-term dependencies in time series data. Daily solar activity data spanning seven solar cycles (Cycles 19-25), obtained from the GFZ Helmholtz Centre for Geosciences, are used for model training and evaluation. Experimental results show that LSTM consistently achieves the best performance across most forecast horizons, particularly in short-to medium-term predictions. The Transformer model delivers competitive and stable results, while TCN performs relatively less effectively, indicating the need for more complex architecture and optimization strategies. These findings highlight the strengths and limitations of each architecture for solar activity forecasting applications.
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    Equatorial spread-F forecasting model with local factors using the long short-term memory network
    (2023-12-01)
    Thammavongsy, Phimmasone
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    Supnithi, Pornchai
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    Myint, Lin Min Min
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    Hozumi, Kornyanat
    ;
    Lakanchanh, Donekeo
    The predictability of the nighttime equatorial spread-F (ESF) occurrences is essential to the ionospheric disturbance warning system. In this work, we propose ESF forecasting models using two deep learning techniques: artificial neural network (ANN) and long short-term memory (LSTM). The ANN and LSTM models are trained with the ionogram data from equinoctial months in 2008 to 2018 at Chumphon station (CPN), Thailand near the magnetic equator, where the ESF onset typically occurs, and they are tested with the ionogram data from 2019. These models are trained especially with new local input parameters such as vertical drift velocity of the F-layer height (Vd) and atmospheric gravity waves (AGW) collected at CPN station together with global parameters of solar and geomagnetic activity. We analyze the ESF forecasting models in terms of monthly probability, daily probability and occurrence, and diurnal predictions. The proposed LSTM model can achieve the 85.4% accuracy when the local parameters: Vd and AGW are utilized. The LSTM model outperforms the ANN, particularly in February, March, April, and October. The results show that the AGW parameter plays a significant role in improvements of the LSTM model during post-midnight. When compared to the IRI-2016 model, the proposed LSTM model can provide lower discrepancies from observational data. Graphical Abstract: [Figure not available: see fulltext.].
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    Ionospheric Scintillation Prediction Using Decision Tree and Rainforest Techniques
    (2024-01-01)
    Trachuentong, Sirasake
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    Supnithi, Pornchai
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    Myint, Lin Min Min
    ;
    Saito, Susumu
    The ionosphere contains electron density variation. When radio signals transmitted from global navigation satellite systems (GNSS) pass through such medium, additional delays are added. With ionospheric irregularity, fluctuation in GNSS signals known as scintillation are often observed resulting in reduced number of tracked satellites then degrade positioning performances. At present, scintillation is considered random, hence, the ability to detect or predict such phenomenon is crucial to efficient system operation. In this research, we design machine learning algorithm for scintillation prediction. Both Decision Tree (DT) and Random Decision Forest (RF), are implemented to predict daily ionospheric scintillation at King Mongkut's Institute of Technology Ladkrabang (KMITL) station in Thailand (13.73 ° E, 100.77° N). The rate of total electron content change index (ROTI) is also used. Modeling is carried out for four months in March (equinox), June (solstice), September (equinox), and December (solstice) in 2022, representing different seasons in space weather study. The prediction results are evaluated using the S 4 index observations at KMITL station and then compared between DT and RF methods. The designed model has a high potential for scintillation prediction.
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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
    ;
    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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    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
    ;
    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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    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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    Equatorial Ionospheric Irregularity Detection and Analysis Using 2-D ROTI Maps and VHF Radar Images During the Upcoming Solar Maximum
    (2026-01-01)
    Supnithi, P.
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    Myint, L. M.M.
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    Tongkasem, N.
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    Thanakulketsarat, T.
    ;
    Nishioka, M.
    In this work, we analyze the ionospheric irregularities at Chumphon station, Thailand, using observational data from GNSS receivers as well as VHF radar and ionosonde at Chumphon station, Thailand. The ionospheric irregularity event on 20 March 2020 and the super solar storms during 8–12 May 2024 are studied. Both instruments show traces the irregularities and interesting daytime fluctuation in total electron content over Thailand area. The statistics of ionospheric irregularities from 2020 to 2024 show that as we enter the solar maximum of the 25<sup>th</sup> solar cycle, more occurrences of ionospheric irregularities are clearly seen.
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    Artificial Intelligence Applications in Ionospheric Irregularities: A Bibliometric Analysis
    (2024-01-01)
    Kongthon, Alisa
    ;
    Supnithi, Pornchai
    Ionospheric irregularities such as equatorial plasma bubbles (EPBs) in low-latitude regions often lead to disruptions in trans-ionospheric radio systems, navigation systems and satellite communications. Understanding and monitoring equatorial plasma bubbles is important for improving the reliability of communication and navigation systems. Recently, artificial intelligence (AI) has been applied to a wide variety of satellite communication aspects including ionospheric irregularities detection. This paper aims to apply bibliometric analysis on research publications related to AI applications in ionospheric irregularities. Such analysis can help researchers understand the evolving trends in AI and its diverse sub-fields, guiding future research directions. In addition, researchers can use the results of bibliometric analysis to benchmark their own work against the broader research landscape, identifying areas where their contributions can have the most impact.
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    Local mitigation of higher-order ionospheric effects in DFMC SBAS and system performance evaluation
    (2024-04-01)
    Sophan, Somkit
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    Supnithi, Pornchai
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    Myint, Lin M.M.
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    Saito, Susumu
    ;
    Hozumi, Kornyanat
    Dual-frequency multi-constellation (DFMC) satellite-based augmentation system (SBAS) is a new SBAS standard for aeronautical navigation systems. It supports aircraft navigation from the enroute to approach phases via the L1 and L5 frequencies (1575.42 and 1176.45 MHz). Although the ionosphere-free (IF) combination in the DFMC SBAS operation removes the first-order ionospheric delays in the pseudorange measurement, remaining terms including the satellite-clock offset errors and higher-order ionospheric (HOI) delays are still unaccounted for. The DFMC SBAS accuracy and integrity can be affected by the HOI effects, especially during severe ionospheric disturbances. In this work, we present the local DFMC SBAS corrections with and without the mitigation of HOI delays. We first estimate the HOI delay terms using the received pseudorange followed by separate satellite and receiver bias estimations based on the minimum sum-variance technique. The integrity terms can then be obtained. The performances of DFMC SBAS using the global navigation satellite system (GNSS) data including GPS, Galileo, and QZSS are evaluated using obtained GNSS data at stations in Thailand on the ionospheric quiet and disturbed days. The results show that with the HOI mitigation, the vertical positioning errors (VPE) on the quiet and disturbed days can be improved by 12% and 9%, whereas the vertical protection levels (VPL) are improved by 16% and 21%, respectively. In addition, we perform a preliminary assessment of DFMC SBAS based on the International Civil Aviation Organization (ICAO) requirements of two categories: Localizer Performance with Vertical guidance (LPV-200) and Category I precision approach (CAT-I) showing promising results.