Structural characterization of the equatorial F region plasma irregularities in the multifractal context

In the emerging ionosphere–space–weather paradigm, investigating the dynamical properties of ionospheric plasma irregularities using advanced computational nonlinear algorithms provide new insights into their turbulent-seeming nature, for instance, the evidence of energy distribution via a multiplic...

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Bibliographic Details
Main Authors: Joshi, Neelakshi, Rosa, Reinaldo R., Savio, Siomel, Kherani, Esfhan Alam, de Meneses, Francisco Carlos, Stephany, Stephan, Muralikrishna, Polinaya
Format: Article
Language:English
Published: 2020
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Online Access:http://eprints.uanl.mx/23310/1/23310.pdf
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Summary:In the emerging ionosphere–space–weather paradigm, investigating the dynamical properties of ionospheric plasma irregularities using advanced computational nonlinear algorithms provide new insights into their turbulent-seeming nature, for instance, the evidence of energy distribution via a multiplicative cascade. In this study, we present a multifractal analysis of the equatorial F region in situ data obtained from two different experiments performed at Alcântara (2.4∘ S, 44.4∘ W), Brazil, to explore their scaling structures. The first experiment observed several medium- to large-scale plasma bubbles whereas the second experiment observed vertical uplift of the base of the F region. The multifractal detrended fluctuation analysis and the p-model fit are used to analyze the plasma density fluctuation time series. The result shows the presence of multifractality with degree of multifractality 0.53–0.93 and 0.3≤p≤0.4 cascading probability for the first experiment. Other experimental data also exhibit multifractality with degree of multifractality 0.19–0.27 and 0.42≤p≤0.44 cascading probability in ionospheric plasma irregularities. Our results confirm the nonhomogeneous nature of plasma irregularities and characterize the underlying nonhomogeneous multiplicative cascade hypothesis in the ionospheric medium. Differences in terms of scaling and complexity in the data belonging to different types of phenomena are also addressed.