High‐latitude intermittent ionospheric scintillations: Exploring the Castaing distribution

Mezaoui, H.; Hamza, A. M.; Jayachandran, P. T.

doi:10.1002/2015ja021304

Cited by 6 publications

(3 citation statements)

References 26 publications

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“…More precisely, these functions include the normal distribution (Rino et al., 1976), the Nakagami distribution (Fremouv et al., 1980), the Rice distribution (Humphreys et al., 2009) and more recently the α – μ distribution (Moraes et al., 2014). To the best of our knowledge, a self‐consistent determination of the amplitude scintillation probability distribution including the characteristics of the ionospheric irregularity has yet to be developed (Mezaoui et al., 2015). Hence, the prediction of amplitude scintillation probability distributions remains a challenge in characterizing the radio signal propagation in the ionosphere (Wheelon, 2003).…”

Section: Introductionmentioning

confidence: 99%

On the Moments of Probability Distribution Function of Amplitude Scintillation in the Polar Region

2022

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The third and the fourth moments of probability distribution functions associated with ionospheric scintillation are empirically determined • The empirical determination of moments is inconsistent with moments the normal, log-normal, Nakagami, Rician, and α-μ distributions • The generalized Gaussian distribution, with a shape parameter 1 < β < 2, appears to be the best fit to account for the results

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Section: Introductionmentioning

confidence: 99%

On the Moments of Probability Distribution Function of Amplitude Scintillation in the Polar Region

2022

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“…For example, in a recent study [Mezaoui et al, 2014], higher-order moments of the probability density functions (PDFs), including skewness and kurtosis, were calculated for several events in order to identify patterns in both the amplitude and the phase of the scintillations. Furthermore, the multifractal nature of the scintillation has been exploited, and the probability density functions of the structure function of the GPS L1 signal components have been fitted to Castaing distributions for various differential scales [Mezaoui et al, 2015]. Moreover, the traditional indices (S4 and ) do not enable a classification of observations that are consistent with models, such as the weak scattering model [Yeh and Liu, 1982;Beach and Kintner, 1999].…”

Section: Introductionmentioning

confidence: 99%

Dynamic analysis of the polar ionosphere using the GPS signal: Toward an optimization of the cutoff scale

2017

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Using Global Navigation Satellite System observations, such as the amplitude and the phase components of the GPS L1 signal, ionospheric scintillation is characterized and quantified using indices derived from those observables. However, the background electron density of the ionosphere is not stationary, presenting a trend and a nonzero mean, and the GPS motion induces a Doppler shift that will contribute to the nonstationary aspect of the signal; hence, the multiscale nature of the diffracted signal makes it difficult to extract the components of the signal that correspond to scintillation. Constructing scintillation indices from a signal that has a nonscintillation component will lead to erroneous estimation and biased characterization of the scintillation. In this context, we present a technique aiming at retrieving the scintillation components from the raw, transionospheric radio signals. Using wavelet analysis, we define and maximize the entropy of the system, which is composed of two subsystems corresponding to scintillation and nonscintillation contributions. The Tsallis entropy has been considered for the power component, for which a non‐Gaussian behavior has been observed. This entropy is based on a nonextensive approach that introduces a parameter q, quantifying the nonextensivity. On the other hand, the phase presents a Gaussian behavior and is analyzed using the Shannon‐Gibbs entropy. In both cases, the optimum cutoff scale, delimiting the scintillation components, is estimated via the maximization of the entropy, which, as defined here, is a function of the temporal scale. This optimization of the cutoff scale will be key in the construction of an optimum, unbiased index quantifying the ionospheric scintillation using GPS signal.

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“…In the present work, tools developed in the context of neutral fluid and plasma turbulence theories are used in order to unveil pertinent and dominant length scales responsible for ionospheric scintillation. This path has been initiated in a previous report in which intermittency was explored in ionospheric scintillation (Mezaoui et al, 2015); a statistical mechanics' approach, which analyzes the properties of fluctuations of the local variable measured, was used. These fluctuations tend to deviate from the homogeneous and isotropic model of turbulence introduced by Taylor (1935Taylor ( , 1938, and have consistently been labeled as intermittent.…”

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confidence: 99%

Turbulence Signatures in High‐Latitude Ionospheric Scintillation

2022

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Ground‐based amplitude measurements of Global Navigation Satellite System signal during ionospheric scintillation are analyzed using prevalent data analysis tools developed in the fields of fluid and plasma turbulence. One such tool is the structure function of order q, with q = 1 to q = 6, which reduces to the computation of the second‐order difference in the GPS signal amplitude at various time lags, and allows for the exploration of dominant length scales in the propagation medium. We report the existence of a range where the structure function is linear with respect to time lag. This linear time segment could be considered as an analog to the inertial range in the context of neutral and plasma turbulence theory. Quantitatively, the slope of the structure function in the linear range is in good agreement with the scaling exponent determined from in situ measurements of the electrostatic potential at low altitude (E‐region) and the electron density at the topside ionosphere (F‐region). This in turn suggests the conjecture that scintillation could be considered a proxy for ionospheric turbulence. Furthermore, we have found that the probability distribution function of the second‐order difference in the signal amplitude has non‐Gaussian features at large time lags; a result that seems inconsistent with equilibrium statistical physics which suggests a Gaussian distribution for the conventional random‐walk processes. This could indicate that intermittency may occur at large time lags.

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High‐latitude intermittent ionospheric scintillations: Exploring the Castaing distribution

Cited by 6 publications

References 26 publications

On the Moments of Probability Distribution Function of Amplitude Scintillation in the Polar Region

On the Moments of Probability Distribution Function of Amplitude Scintillation in the Polar Region

Dynamic analysis of the polar ionosphere using the GPS signal: Toward an optimization of the cutoff scale

Turbulence Signatures in High‐Latitude Ionospheric Scintillation

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