Maimaiti, Maimaitirebike2020-01-162020-01-162020-01-15vt_gsexam:23373http://hdl.handle.net/10919/96453The ionosphere carries a substantial portion of the electrical current flowing in Earth's space environment. Currents and electric fields in the ionosphere are generated through (1) the interaction of the solar wind with the magnetosphere, i.e. magnetic reconnection and (2) the collision of neutral molecules with ions leading to charged particle motions across the geomagnetic field, i.e. neutral wind dynamo. In this study we applied statistical and deep learning techniques to various datasets to investigate the driving influences of ionospheric electrodynamics at mid- and high-latitudes. In Chapter 2, we analyzed an interval on 12 September 2014 which provided a rare opportunity to examine dynamic variations in the dayside convection throat measured by the RISR-N radar as the IMF transitioned from strong By+ to strong Bz+. We found that the high-latitude plasma convection can have dual flow responses with different lag times to strong dynamic IMF conditions that involve IMF By rotation. We proposed a dual reconnection scenario, one poleward of the cusp and the other at the magnetopause nose, to explain the observed flow behavior. In Chapters 3 and 4, we investigated the driving influences of nightside subauroral convection. We developed new statistical models of nightside subauroral (52 - 60 degree) convection under quiet (Kp <= 2+) to moderately disturbed (Kp = 3) conditions using data from six mid-latitude SuperDARN radars across the continential United States. Our analysis suggests that the quiet-time subauroral flows are due to the combined effects of solar wind-magnetosphere coupling leading to penetration electric field and neutral wind dynamo with the ionospheric conductivity modulating their relative dominance. In Chapter 5, we examined the external drivers of magnetic substorms using machine learning. We presented the first deep learning based approach to directly predict the onset of a magnetic substorm. The model has been trained and tested on a comprehensive list of onsets compiled between 1997 and 2017 and achieves 72 +/- 2% precision and 77 +/- 4% recall rates. Our analysis revealed that the external factors, such as the solar wind and IMF, alone are not sufficient to forecast all substorms, and preconditioning of the magnetotail may be an important factor.ETDIn CopyrightIonospheric ElectrodynamicsMagnetic ReconnectionPenetration Electric FieldSubstorm Onset PredictionMachine learningDissertation