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Author: search.filters.author.Myint, Lin Min MinSubject: search.filters.subject.IRI-2016 model

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    Equatorial spread-F forecasting model with local factors using the long short-term memory network
    (2023-12-01)
    Thammavongsy, Phimmasone
    ;
    Supnithi, Pornchai
    ;
    Myint, Lin Min Min
    ;
    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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    Investigating the F2-layer peak height of IRI-2016 model at the equatorial station during the deepest solar activity for 70 years ago
    (2023-01-01)
    Jamjareegulgarn, Punyawi
    ;
    Taugtragoonpaisan, Udomsit
    ;
    Myint, Lin Min Min
    ;
    Supnithi, Pornchai
    The paper investigates the F2-layer peak height (hmF2) of IRI-2016 model at Sao Luis on the magnetic Equator in 2019. Since the deepest solar activity occurred surprisingly in year 2019 for 70 years ago, hence, the IRI-2016 model prediction should be investigated to know the anomalous ionosphere and the deviations between the observation and IRI prediction. The hmF2 is selected to be studied in this work. Our studied results show that the observed hmF2 and the three hmF2 models of IRI-2016 prediction generally show the similar variations only about 60% during this deepest solar activity and the variations of the four kinds of studied hmF2 values show the similar trends for all seasons, except June solstice. The diurnal variation of hmF2_Giro show three peaks for December solstice months and the equinoctial months, while they show four peaks amazingly in June solstice. The hmF2_SHU is the best option that can agree reasonably well to the observed hmF2 at Sao Luis, excluding the pre-sunrise hmF2 peak for all seasons and a very deep trough in June solstice.