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Author: search.filters.author.Panda, Sampad KumarSubject: search.filters.subject.Geomagnetic storm

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    Multi-instrument observations of unseasonal post-sunset equatorial plasma bubbles during two moderate geomagnetic storms in May and June 2024 over East/Southeast Asia
    (2025-12-01)
    Panda, Sampad Kumar
    ;
    Rajana, Siva Sai Kumar
    ;
    Vivek, Chiranjeevi G.
    ;
    Vankadara, Ramkumar
    ;
    Jamjareegulgarn, Punyawi
    This study investigated an unseasonal development of post-sunset EPBs in summer solstice period over the East/Southeast Asian longitude region by using multi-instrument observations during two consecutive moderate geomagnetic storm events (16–17 May 2024 and 28–29 June 2024). The results indicate, formation of EPBs during non-climatological plasma bubble season is primarily driven by sustained southward oriented IMF-Bz in the storm main phase, which facilitated the penetration of eastward electric fields into the equatorial ionosphere. These electric fields uplifted the F-region plasma to altitudes favorable for irregularities growth. Also, noteworthy hemispheric asymmetry is noticed in the formation of EPBs, manifesting more intense occurrence in Southern Hemisphere during the geomagnetic storm of 16–17 May 2024 and extended up to ∼20°S magnetic latitude. During the 28–29 June 2024 geomagnetic storm, EPBs are more prominent in the Northern Hemisphere and reached beyond EIA region up to ∼22°N magnetic latitude. In brief, the EPBs developed initially over the 100°E longitude sector, exhibited eastward drift and thereafter extended to 120°E longitude region during the 16–17 May 2024 geomagnetic storm. During the 28–29 June 2024 geomagnetic storm, EPBs formed over the 120°E longitude region and later drifted to 140°E longitude sector. These findings highlight the dominant role of storm-time electrodynamics in triggering EPBs and emphasize the need for continuous regional monitoring of EPBs to mitigate space weather impacts on satellite-based communication and GNSS systems.
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    Total electron content prediction using singular spectrum analysis and autoregressive moving average approach
    (2022-01-01)
    Dabbakuti, J. R.K.Kumar
    ;
    Yarrakula, Mallika
    ;
    Panda, Sampad Kumar
    ;
    Jamjareegulgarn, Punyawi
    ;
    Haq, Mohd Anul
    Continuous monitoring of ionospheric behavior and subsequent development or improvement of models for the prediction of its parameters with consistent accuracy remains an ongoing challenge. In this sense, an integrated approach by combining the signal extraction technique Singular Spectrum Analysis (SSA) with Autoregressive Moving Average (ARMA) is presented in this work to predict the ionospheric Total Electron Content (TEC) values that are responsible for causing ionospheric delays in the trans-ionospheric signal propagation associated with satellite-based communication, navigation, and timing applications. In general, SSA is a nonparametric spectral estimation procedure that decomposes the signals into interpretable and physically significant components. The observed TEC from two Global Positioning System (GPS) stations across the low latitude Saudi Arabian region are considered during the year 2017 that falls in the descending phase of solar cycle-24. The performance of the proposed hybrid model is evaluated by comparing with the sole estimation from the ARMA model and the observed GPS–TEC dataset for two different geomagnetic conditions: a) the regular geomagnetically quiet period of 15 to 29 December, 2017 (Ap < 24 and Dst > − 30 nT) and b) the geomagnetic storm period from 7 to 9 September, 2017 (Dst min = − 142 nT). The corresponding average Precision, Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) of the proposed SSA–ARMA model predictions are 1.79 TECU, 1.23 TECU, and 13.02%. In contrast, the respective values in the exclusive ARMA model are 2.01 TECU, 1.37 TECU, 14.42% at Oman station. The corresponding values for Magna station are 0.92 TECU, 0.61 TECU, and 10.76% (SSA–ARMA) and 1.01 TECU, 0.75 TECU, and 11.33% (ARMA). The results show an improved computational efficiency with minor improvement in the TEC predictions with the proposed SSA–ARMA method compared to the sole employment of the ARMA model by disregarding the extraneous components.