Field‐Aligned GPS Scintillation: Multisensor Data Fusion

Mrak, Sebastijan; Semeter, J. L.; Hirsch, Michael; Starr, Gregory; Hampton, D. L.; Varney, R. H.; Reimer, A. S.; Swoboda, John; Erickson, P. J.; Lind, Frank D.; Coster, A. J.; Pankratius, Victor

doi:10.1002/2017ja024557

Cited by 19 publications

(23 citation statements)

References 46 publications

(73 reference statements)

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“…The reduced electric field causes meter‐scale waves to propagate near the plasma acoustic speed, corresponding to the near‐threshold condition and matching observations of strong, narrow coherent radar spectra observed at the magnetic equator. The results presented here may also extend to auroral density structures produced by convection, auroral precipitation, and ionospheric cavitation (Mrak et al, ; Zettergren et al, ).…”

Section: Resultssupporting

confidence: 68%

Simulations of Secondary Farley‐Buneman Instability Driven by a Kilometer‐Scale Primary Wave: Anomalous Transport and Formation of Flat‐Topped Electric Fields

2019

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Since the 1950s, high frequency and very high frequency radars near the magnetic equator have frequently detected strong echoes caused ultimately by the Farley‐Buneman instability (FBI) and the gradient drift instability (GDI). In the 1980s, coordinated rocket and radar campaigns made the astonishing observation of flat‐topped electric fields coincident with both meter‐scale irregularities and the passage of kilometer‐scale waves. The GDI in the daytime E region produces kilometer‐scale primary waves with polarization electric fields large enough to drive meter‐scale secondary FBI waves. The meter‐scale waves propagate nearly vertically along the large‐scale troughs and crests and act as VHF tracers for the large‐scale dynamics. This work presents a set of hybrid numerical simulations of secondary FBIs, driven by a primary kilometer‐scale GDI‐like wave. Meter‐scale density irregularities develop in the crest and trough of the kilometer‐scale wave, where the total electric field exceeds the FBI threshold, and propagate at an angle near the direction of total Hall drift determined by the combined electric fields. The meter‐scale irregularities transport plasma across the magnetic field, producing flat‐topped electric fields similar to those observed in rocket data and reducing the large‐scale wave electric field to just above the FBI threshold value. The self‐consistent reduction in driving electric field helps explain why echoes from the FBI propagate near the plasma acoustic speed.

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Section: Resultssupporting

confidence: 68%

Simulations of Secondary Farley‐Buneman Instability Driven by a Kilometer‐Scale Primary Wave: Anomalous Transport and Formation of Flat‐Topped Electric Fields

2019

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“…If, rather than peak density altitude, we were to choose the altitude of the majority of peak ion or electron temperatures, we might mistakenly conclude that the 7 October 2015 event is an F-region scintillation event. As this contradicts the result determined by Mrak et al (2018), we select peak density as our test statistic.…”

Section: Radio Sciencementioning

confidence: 76%

“…The scintillation event described in this case study was determined to be due to the F-region based on all-sky images of emissions also. Another case study from the literature demonstrates E-region phase-only scintillation on 7 October 2015 (Mrak et al, 2018;Semeter et al, 2017). That study recorded array-wide loss of lock on multiple GPS signals associated with ionospheric scintillation.…”

Section: Radio Sciencementioning

confidence: 95%

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Automated Ionospheric Scattering Layer Hypothesis Generation for Detected and Classified Auroral Global Positioning System Scintillation Events

Sreenivash

Datta‐Barua

2020

Radio Science

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We describe a method to detect and classify global positioning system (GPS) scintillation, and then hypothesize a possible ionospheric layer scattering the signal. The objective is to routinely identify events of interest to investigate in detail in future work. Scintillation types include amplitude, phase, or both amplitude and phase. A scintillation event is one for which a scintillation index remains above a threshold across a majority of closely spaced receivers viewing a single satellite. Events are categorized by signal frequency and scintillation type. An event is then hypothesized to be due to the E or F layer using an independent data source. Data from the scintillation auroral GPS array located in Poker Flat Research Range, Alaska, are used to analyze L1 and L2C frequencies in 2014 and 2015. The irregularity layer associated with each scintillation event is hypothesized to be due to the activity in the E layer of the ionosphere (below 150 km) or in the F layer (above 195 km) using collocated Poker Flat Incoherent Scatter Radar electron density measurements. Events in a transition layer (150-195 km) and inconclusive results are also recorded. We find that nearly all of the over 4,000 events are phase scintillations. The majority of the events are hypothesized to occur when the peak density is at E-layer altitudes. This indicates that E-layer-related scintillation may be quite common at auroral latitudes and that GPS receivers are sensitive to the irregularities occurring both there and at F layer altitudes.Plain Language Summary As global positioning system (GPS) signals pass through Earth's atmosphere, they may "twinkle" when received at the ground due to variations in the number of charged particles present in the ionized layer of the Earth's atmosphere, the ionosphere. The twinkling or rapid fluctuation in the GPS signals is called ionospheric scintillation. We develop a way, using data from GPS receivers and a nearby radar, to detect and classify scintillations measured in Alaska in 2014 and 2015, and then make an educated guess about whether they are in the upper or lower layer of the ionosphere. This method will be useful for handling large data sets of scintillation, which are now becoming more common. We find that, even though most of the ionosphere's charged particles are usually above 195 km height, GPS is sensitive to scintillations at high latitudes due to variations in a lower layer below 150 km, and these happen a majority of the time.

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“…Due to the importance of particle precipitation to ionospheric scintillation (Mrak et al, ; Semeter et al, , ; Zou et al, ), we also incorporate data from the Oval Variation, Assessment, Tracking, Intensity, and Online Nowcasting (OVATION) Prime model (Newell et al, ) as potentially important input for prediction. OVATION Prime (freely available at http://sourceforge.net/projects/ovation-prime/) provides statistical distributions of particle precipitation in the middle‐ and high‐latitude ionosphere and was created from 11 years (roughly 50 satellite years) of observations from the Defense Meteorological Satellite Program satellites.…”

Section: Datamentioning

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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Field‐Aligned GPS Scintillation: Multisensor Data Fusion

Cited by 19 publications

References 46 publications

Simulations of Secondary Farley‐Buneman Instability Driven by a Kilometer‐Scale Primary Wave: Anomalous Transport and Formation of Flat‐Topped Electric Fields

Simulations of Secondary Farley‐Buneman Instability Driven by a Kilometer‐Scale Primary Wave: Anomalous Transport and Formation of Flat‐Topped Electric Fields

Automated Ionospheric Scattering Layer Hypothesis Generation for Detected and Classified Auroral Global Positioning System Scintillation Events

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

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