Investigating high‐latitude ionospheric turbulence using global positioning system data

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

doi:10.1002/2014gl061331

Cited by 15 publications

(12 citation statements)

References 25 publications

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“…The relationship between kurtosis and skewness and the associated coefficients for mirror modes in the Earth's magnetosheath is similar to those found in a wide range of turbulent environments with no apparent physical connections [ Labit et al , ; Sura and Sardeshmukh , ; Hamza and Meziane , ; Mezaoui et al , ]. Consequently, our results provide additional evidence that, despite inherently different physical processes, geophysical turbulent systems dominated by second‐order nonlinearities might hold universal statistical properties and be explained under a combined mathematical formalism.…”

Section: Resultssupporting

confidence: 82%

Universal properties of mirror mode turbulence in the Earth's magnetosheath

Osmane

Dimmock

Pulkkinen

2015

Geophysical Research Letters

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We use more than 7 years of Time History of Events and Macroscale Interactions during Substorms observations to quantify the non‐Gaussian properties associated with mirror mode turbulence in the Earth's magnetosheath. We find that non‐Gaussian statistics of mirror modes lead to the parabolic collapse of kurtosis as the square of the skewness (K = aS2+B with a ∼ 1.3 and b ∼− 1). The parabolic scaling is a global constraint for the magnetosheath and is dictated by kinetic processes. This parabolic scaling is qualitatively independent of the distance to the magnetopause or bow shock, which implies that, even though the bow shock is driving the mirror mode instability, the dynamical evolution of mirror structures is independent of the source region and due to local processes. The parabolic relationship and coefficients between kurtosis and skewness for mirror modes in the Earth's magnetosheath are also similar to those found in a wide range of geophysical and laboratory turbulent environments, providing further evidence that turbulent systems dominated by non‐Gaussian fluctuations hold universal statistical properties.

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Section: Resultssupporting

confidence: 82%

Universal properties of mirror mode turbulence in the Earth's magnetosheath

Osmane

Dimmock

Pulkkinen

2015

Geophysical Research Letters

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“…In other studies, it has been found that the profile is parabolic centered on zero, indicating that skewness and kurtosis have an empirical quadratic relationship [44]. In relevant ionospheric studies, this is considered an indication of the local dynamics of the plasma within the scattering ionospheric layer [45]. In this work, we also investigate the skewness and kurtosis profile using the data from 2010 to 2019 of all scintillation intensities, and the profile tends to collapse to the parabolic line, which is coincident with previous studies.…”

Section: Goodness Of Fit For Different Distributionsmentioning

confidence: 94%

Study of the Ionospheric Scintillation Radio Propagation Characteristics with Cosmic Observations

et al. 2022

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The ionosphere has important influences on trans-ionosphere radio propagation. When signals pass through ionospheric irregularities, their amplitude and phase are often attenuated and distorted. In this work, the statistical features of scintillation observed by the Global Navigation Satellite System (GNSS) and low earth orbit (LEO) satellites are investigated with Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) data in solar cycle 24. The amplitude scintillation propagation channel is fitted by the Nakagami-m, α-μ and κ-μ models. The performance is evaluated in terms of root mean square error (RMSE), kurtosis and information entropy. The results reveal that the α-μ model achieves the best performance in all considered scintillation intensities, while the Nakagami-m model achieves better performance under severe scintillation in the GNSS-LEO propagation channels.

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“…Both explanations suggest that the ionosphere at these spatial scales exhibits ‘memory’ at least on a time scale of one hour and may be predictable (Siscoe & Solomon, ). Intuitively, the spectral slope partially describes the size of scintillation‐causing irregularities (Chartier et al, ; Forte et al, ; Mezaoui et al, ; Wernik, ; Yeh & Liu, ) and we should expect a relationship between spatial size and/or irregularity lifetime and future scintillation (i.e., larger irregularities would be expected to affect a given area for periods exceeding that of smaller irregularities and/or have longer lifetimes).…”

Section: Prediction Methodsmentioning

confidence: 99%

New Capabilities for Prediction of High‐Latitude Ionospheric Scintillation: A Novel Approach With Machine Learning

et al. 2018

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As societal dependence on transionospheric radio signals grows, space weather impact on these signals becomes increasingly important yet our understanding of the effects remains inadequate. This challenge is particularly acute at high latitudes where the effects of space weather are most direct and no reliable predictive capability exists. We take advantage of a large volume of data from Global Navigation Satellite Systems (GNSS) signals, increasingly sophisticated tools for data-driven discovery, and a machine learning algorithm known as the support vector machine (SVM) to develop a novel predictive model for high-latitude ionospheric phase scintillation. This work, to our knowledge, represents the first time an SVM model has been created to predict high-latitude phase scintillation. We use the true skill score to evaluate the SVM model and to establish a benchmark for high-latitude ionospheric phase scintillation prediction. The SVM model significantly outperforms persistence (i.e., current and future scintillation are identical), doubling the predictive skill according to the true skill score for a 1-hr lead time. For a 3-hr lead time, persistence is comparable to a random chance prediction, suggesting that the memory of the ionosphere in terms of high-latitude plasma irregularities is on the order of, or shorter than, a few hours. The SVM model predictive skill only slightly decreases between the 1-and 3-hr predictive tasks, pointing to the potential of this method. Our findings can serve as a foundation on which to evaluate future predictive models, a critical development toward the resolution of space weather impact on transionospheric radio signals.Plain Language Summary Society is increasingly dependent on radio signals, particularly those from the Global Navigation Satellite Systems (GNSS), and the technologies (e.g., navigation and financial transactions) that they enable. The integrity and reliability of these signals is threatened by their travel from the GNSS satellites to the ground, which includes passage through a charged region between 100 and 1,000 km known as the ionosphere. Disturbances to the ionosphere from solar energy, or space weather, cause variations in GNSS signals that adversely affect the dependent systems and technologies. Currently, the effect of the ionosphere on these signals cannot be reliably predicted, and the challenge is particularly important at latitudes above 45 ∘ where space weather impacts are most direct. We have compiled a large volume of data from the regions important to space weather (i.e., from the Sun to the Earth) to develop a novel machine learning model capable of skillfully predicting disruptions to GNSS signals at high latitudes.To our knowledge, this model is the first of its kind. We find that the new model is capable of more accurate predictions than current methods and position this model as a benchmark on which future predictive models can be measured.

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Investigating high‐latitude ionospheric turbulence using global positioning system data

Cited by 15 publications

References 25 publications

Universal properties of mirror mode turbulence in the Earth's magnetosheath

Universal properties of mirror mode turbulence in the Earth's magnetosheath

Study of the Ionospheric Scintillation Radio Propagation Characteristics with Cosmic Observations

New Capabilities for Prediction of High‐Latitude Ionospheric Scintillation: A Novel Approach With Machine Learning

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