dc.description.abstract | This dissertation comprises three interconnected projects, all tailored to advance our understanding of the variations and structure of ionospheric profiles. The first project forms the foundation by using mid-latitudinal 630.0 nm airglow emission to calculate atomic oxygen ions ([O+]) density within an altitude range of 150–450 km. Electron density (Ne) measured by digisonde DPS-4 validates the developed inversion model, unlocking the potential to use the airglow volume emission rate (VER) as ionospheric tracers on a global scale. Building on this foundational knowledge, the second project addresses the challenges of applying the inversion model to global satellite observations. As the accuracy of the VER profile at the tangent point is paramount, it requires geometry calibration. To this end, we deploy deep learning to optimize Abel inversion and reconstruct the missing upper segment (>300 km) of airglow intensity profiles. Through the Chapman function and three training algorithms: GDX (Gradient descent with momentum and adaptive learning rate), SCG (Scaled conjugate gradient), and LM (Levenberg-Marguardt), our system is trained to output an optimal set of slab thickness and type coefficient, essential for accurate global applications. The final project studies the ionospheric scintillation index (S4) and Ne profile observations obtained from the F7/C2 satellite constellation, to explore the variations of the ionospheric profile during geomagnetic events. These data are juxtaposed with a significant geomagnetic storm that affected SpaceX′s 2022 launches. Comparing the variation of the O/N2 ratio to the S4 distribution, a suppression of the S4 occurred when the O/N2 ratio decreased during the recovery phase. Additionally, the analysis illuminates the impacts of prompt penetration electric (PPE) fields and disturbance dynamo (DD) effects on the S4 index, as evidenced in the variation of the EIA crests. In summary, the integration of these three projects, each building on the achievements of the previous, provides a comprehensive and nuanced understanding of the variation and composition of the ionospheric profile, including disruptive factors and operative mechanisms. | en_US |