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Subject: search.filters.subject.ionospheric irregularity

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    Item type:Publication,
    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.
    ;
    Myint, L. M.M.
    ;
    Tongkasem, N.
    ;
    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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    A Novel Short-Term Prediction Model for Regional Equatorial Plasma Bubble Irregularities in East and Southeast Asia
    (2025-02-01)
    Zhao, Xiukuan
    ;
    Li, Guozhu
    ;
    Xie, Haiyong
    ;
    Hu, Lianhuan
    ;
    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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