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(1 - 3 of 3)
- Title
- MULTIYEAR AUTOMATED ANALYSIS OF AURORAL IMAGES TO CATEGORIZE IONOSPHERE IRREGULARITY LAYER
- Creator
- Stuart, David Jacques
- Date
- 2020
- Description
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This thesis presents a method of automated analysis of auroral all-sky images (ASIs) to determine the ionospheric layer of plasma...
Show moreThis thesis presents a method of automated analysis of auroral all-sky images (ASIs) to determine the ionospheric layer of plasma irregularities. These irregularities can perturb radio signals in an effect called scintillation, degrading and at worst preventing signal reception. One key question about scintillation-causing irregularities is whether they occur in the E or F layer of the ionosphere, whose dynamics differ.Previous studies have shown Global Positioning System (GPS) scintillation to be correlated with aurorae. The Scintillation Auroral GPS Array (SAGA) at Poker Flat Research Range, Alaska, was used to detect thousands of GPS L1 and L2C scintillation events over 2014-2015. Collocated auroral images of emissions are recorded nightly by both a keogram spectrograph (measuring intensity along a single longitude vs time) and an ASI filtered at 630.0 nm (red), 557.7 nm (green), and 427.8 nm (blue) wavelengths.In this work scattering layers are hypothesized based on optical measurements, through automated filtering of keograms followed by spectral analysis of aurorae, which tend to occur with these irregularities. A cloud detection method using the North-South keogram is implemented, where a time-averaged, intensity-corrected characteristic snapshot of cloudy times was built as a baseline response, and used as the gain in a flat field correction-like step to normalize the cloudy sky appearance. The coefficient of variation Cv is used as the test statistic to determine cloudy times. Cloud-free ASIs have the location of scintillating PRNs identified, and the ratio of red oxygen 630 nm to blue nitrogen 428 nm emissions in that direction. With an auroral model of characteristic energy, ratios above 0.5 are categorized E-Layer and ratios below F-Layer.Multiyear ASI irregularity layer determinations are used to categorize 364 of the initial 4174 SAGA scintillation events. A 77% majority of the events are hypothesized to be F-Layer based on ASI spectral classification. This disagrees with prior PFISR categorizations, which found scintillation events to be majority E-layer. This presents an outstanding question as to the possible reasons for the difference.
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- Title
- GLOBAL ESTIMATION AND ANALYSIS OF IONOSPHERIC DRIVERS WITH A DATA ASSIMILATION ALGORITHM
- Creator
- López Rubio, Aurora
- Date
- 2022
- Description
-
This dissertation studies a data assimilation algorithm that estimates the drivers of the ionosphere-thermosphere (IT) region of the Earth....
Show moreThis dissertation studies a data assimilation algorithm that estimates the drivers of the ionosphere-thermosphere (IT) region of the Earth. The algorithm, EMPIRE (Estimating Model Parameters from Ionospheric Reverse Engineering) can estimate 2 main drivers of the ionospheric behavior: neutral winds and electric potential by ingesting mainly ionospheric densities obtained through Global Satellite System (GNSS) measurements. Additionally, the algorithm can ingest FPI (Fabry-Perot interferometer) neutral wind measurements. The contributions include 1) Vector spherical harmonic basis function for neutral wind estimation, 2) Quantification of the representation error of the estimations of the algorithm EMPIRE, 3) Analysis of Nighttime Ionospheric Localized density Enhancement (NILE) events and 4) Ingestion of global ICON (Ionospheric Connection Explorer) neutral winds measurements. The IT region in the atmosphere is characterized by having a large concentration of free ions and electrons, electromagnetic radiation and Earth's magnetic field. The behavior of the region is dominated by the solar activity, that ionizes the free electrons of the region, forming ionospheric plasma and determining its density. Unusual solar activity or any atmospheric disturbance affects the distribution of the ionospheric plasma and the behavior of the IT region. The redistribution of the ionospheric density impacts technology widely used such as telecommunication or satellite navigation, so it is increasingly important to study the IT system response. The IT behavior can be characterized by what drives its changes. Two drivers that play a key role, the ones we focus on this dissertation, are electric potential, that directly affects the charged ions in the system, and neutral winds, that refers to the velocity of the neutral particles that form the thermosphere. To quantify these drivers, measurements and climate models are available. Measurements are limited as the IT region is vast and covers the entire globe. Climate models can provide information in all the region, but they are usually not as reliable during the unusual solar activity conditions or disturbances. In this dissertation we use a data assimilation algorithm, EMPIRE, that combines both sources of data, measurements and models, to estimate the IT drivers, neutral winds and electric potential. EMPIRE ingests measurements of the plasma density rate and models the physics of the region with the ion continuity equation. The drivers are represented with basis functions and their coefficients are estimated by fitting the expansions with a Kalman filter. In previous work and use of the algorithm, the neutral winds were expanded using power series basis function for each of the components of the vector. The first contribution of the dissertation is to use a vector spherical harmonic expansion to describe the winds, allowing a continuous expansion around the globe and self-consistent components of the vector. Before, EMPIRE estimated the correction of the drivers with respect climate model values. In this work, EMPIRE is also modified to directly estimate the drivers. Then, a study of the representation error, which is the discrepancy between the true physics and the discrete model that represents the physics of EMPIRE and its quantification is done. Next, EMPIRE is used to analyze two NILE events, using the global estimation of both winds, from the first contribution, and the electric potential, derived in previous work. Finally, global estimation of winds allows us to implement the ingestion of ICON global winds in EMPIRE, in addition to the plasma density rate measurements.
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- Title
- Improvement and Validation of Multiyear Auroral Analysis to Categorize Scintillation Event Layer
- Creator
- English, Breanna R.
- Date
- 2022
- Description
-
Ionospheric irregularities scintillate electromagnetic waves, such as Global Positioning System (GPS) signals, as they pass through the...
Show moreIonospheric irregularities scintillate electromagnetic waves, such as Global Positioning System (GPS) signals, as they pass through the ionosphere, especially in auroral zones. A previous method was developed to determine which layer of the ionosphere these scintillation events occurred in by analyzing optical all sky images (ASI). The results of determining the ionospheric scattering layer using the ratio of 630 nm (red) intensity to 428 nm (blue) intensity were compared to a radar-based method of determining the scintillation layer, and it was found that the results disagreed. In this work, the ASI method is critically analyzed to identify possible errors or sensitivities in the original method that might resolve the discrepancy. This is done by improving and validating the nighttime auroral cloud detection method by comparing to National Oceanic and Atmospheric Administration (NOAA) satellite cloud data. Then a sensitivity analysis is performed on the ASI method to determine which parameters of the method the results are sensitive to. The keogram cloud detection method is improved by automating the selection of the keogram time points that are used to calculate a flat-field gain correction, and by calculating the flat field gain for each year rather than calculatingit once and using it for all years of the study. Keogram cloud detection using the coefficient of variation is verified by comparing the keogram results to true sky conditions based on NOAA cloud mask data, and using detection theory to determine the optimal coefficient of variation threshold. We find that the ideal keogram threshold was 0.37 producing a disagreement rate of 22.4%. The ASI image analysis criteria tested are: the ASI azimuth and elevation mapping files, the magnetic zenith limit, the number of pixels of the ASI that are being analyzed, the duration of the scintillation event that is analyzed, and the red-to-blue ratio threshold. It is found that only changing the red-to-blue ratio threshold has a significant effect on the ASI method, with the red-to-blue ratio that minimizes the number of misattributed layers found to be 1.43.
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