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. Show less
