Budtho, Jirapoom

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Budtho, Jirapoom
Alternative Name
Budtho, J.
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Email
jirapoom.bu@kmitl.ac.th

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    Item type:Publication,
    Identifying Geomagnetic Storms with Ionospheric Storm Scale for GNSS and Disaster Prevention
    (2020-03-01)
    Jamjareegulgarn, Punyawi
    ;
    Duangsuwan, Sarun
    ;
    Supnithi, Pornchai
    ;
    Budtho, Jirapoom
    ;
    Tangtrakunphaisan, Udomsit
    This paper proposes an ionospheric storm scale (I-scale) for identifying the impact of geomagnetic or ionospheric storms in the Ionosphere for GNSS (global navigation satellite system) service and disaster prevention. The I-scale in this work is computed based on the observed foF2 at Chumphon station (10.72°N, 99.37°E) over equatorial latitude from January 2004 to July 2018. The results report that the severe geomagnetic storms, i.e., IP3 and IN3, seldom occur at Chumphon with the probabilities of 0.02% and 0.07%, respectively. The probability of quiet ionospheric condition is the maximum value of 70.73%. Meanwhile, the other I-scales sometimes occur and range from 0.60% to 13.97%. The benefits of the foF2-based I-scale are to indicate the violence level of geomagnetic storms and to announce the ionospheric irregularities in practice.
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    Item type:Publication,
    Instrumental Receiver Bias Estimation for Ionospheric Total Electron Content by Neural Network Model
    (2023-10-01)
    Thu, Phyo C.
    ;
    Supnithi, Pornchai
    ;
    Budtho, Jirapoom
    ;
    Saekow, Apitep
    ;
    Sopon, Thanomsak
    Total Electron Content (TEC) is one of the most important parameters in the study of the ionosphere, especially for determining ionospheric disturbances. The TEC levels are typically estimated from dual-frequency GPS observation data. Since the measured TEC contains discrepancies such as satellite and receiver biases, they need to be removed to obtain more accurate TEC values. In this work, we estimate the receiver bias using a neural network technique. Based on the exhaustive evaluation, we design a neural network (NN) model with two-hidden layers, and it is trained with datasets from three GNSS observation stations in Thailand. The prediction from the proposed neural network deviates from the baseline reference using the minimum standard deviation method with significantly faster computational time. The trained NN model is also tested for estimating the receiver bias values at other untrained stations in Thailand.