The Earth's polar ionosphere is a highly dynamic region of the upper atmosphere, and acts as the closure of the greater magnetospheric current system. This region plays host to many electrodynamic effects that impact terrestrial systems, such as power grids, railroads, and pipelines. These effects are fundamentally related to the currents, electric fields, and conductivity present in the polar ionosphere. Understanding and predicting the electrodynamics of this region is vital to being able to determine the physical impacts on terrestrial systems and provide predictions to government and commercial entities. Empirical models play a key role in the research and forecasting of the solar wind and interplanetary magnetic field's impact on the polar ionosphere, and is an active area of development and research. Recent interest in polar ionospheric conductivity has led to a community-wide campaign to develop our understanding of this portion of the electrodynamic system. Characterizing the interactions between the solar wind and the polar ionosphere is a difficult task, as the region of interest is highly data starved in many respects. In particular, satellite based data products are scarce due to being costly and logistically difficult. Recent advancements in data sources (such as the Swarm and CHAMP satellite missions) as well as continued research into the physical relationships between solar wind and interplanetary magnetic field drivers have provided the opportunity to develop new, novel tools to study this region of interest. In this dissertation, two polar ionosphere models are described in Chapters 3 and 4, along with the original research that their construction has produced in Chapter 1. These two models are combined to provide a foundation for future research in this area, which is described in Chapter 5. / Doctor of Philosophy / The Earth is subjected to a constant bombardment of solar particles and magnetic fields, known as the solar wind. Our planet’s geomagnetic field protects the atmosphere from this bombardment, and directs the plasma from the solar wind into the magnetic poles of the earth. This plasma flows through a region of the atmosphere called the ionosphere, where its energy is then dissipated. This energy has many impacts on the surface of the planet, including driving currents in power grids and generating auroral displays. The polar ionosphere is the fundamental connection between the solar wind and the planet, and being able to predict how and where this connection occurs is vital to studying its nature. This work describes two models of the plasma properties in the polar ionosphere, and provides some description of the original research that these models have garnered.

The Earth's equatorial ionosphere most often shows the occurrence of large plasma density and velocity fluctuations with a broad range of scale sizes and amplitudes. These night time ionospheric irregularities in the F-region are commonly referred to as equatorial spread F (ESF) or plasma bubbles (EPBs). This dissertation focuses on analysis of ground-based optical and radar measurements to investigate the development and dynamics of ESF, which can significantly disrupt radio communication and GPS navigation systems. OI (630.0 nm) airglow image data were obtained by the Utah State University all-sky CCD camera, primarily during the equinox period, from three different longitudinal sectors under similar solar flux conditions: Christmas Island in the Central Pacific Ocean, Ascension Island in South Atlantic, and Brasilia and Cariri in Brazil. Well-defined magnetic field-aligned depletions were observed from each of these sites enabling detailed measurements of their morphology and dynamics. These data have also been used to investigate day-to-day and longitudinal variations in the evolution and distribution of the plasma bubbles, and their nocturnal zonal drift velocities. In particular, comparative optical measurements at different longitudinal sectors illustrated interesting findings. During the post midnight period, the data from Christmas Island consistently showed nearly constant eastward bubble velocity at a much higher value (~80 m/s) than expected, while data from Ascension Island exhibited a most unusual shear motion of the bubble structure, up to 55 m/s, on one occasion with westward drift at low latitude and eastward at higher latitudes, evident within the field of view of the camera. In addition, long-term radar observations during 1996-2006 from Jicamarca, Peru have been used to study the climatology of post-sunset ESF irregularities. Results showed that the spread F onset times did not change much with solar flux and that their onset heights increased linearly from solar minimum to solar maximum. On average, radar plume onset occurred earlier with increasing solar flux, and plume onset and peak altitudes increased with solar activity. The F-region upward drift velocities that precede spread F onset increased from solar minimum to solar maximum, and were approximately proportional to the maximum prereversal drift peak velocities.

We used extensive measurements made by the Jicamarca Unattended Long-Term Investigations of the Ionosphere and Atmosphere (JULIA) and Incoherent Scatter Radar (ISR) systems at Jicamarca, Peru during geomagnetic quiet conditions to determine the climatologies of lunar tidal effects on equatorial vertical plasma drifts. We use, for the first time, the expectation maximization (EM) algorithm to derive the amplitudes and phases of the semimonthly and monthly lunar tidal perturbations. Our results indicate, as expected, lunar tidal effects can significantly modulate the equatorial plasma drifts. The local time and seasonal dependent phase progression has been studied in much more detail than previously and has shown to have significant variations from the average value. The semimonthly drift amplitudes are largest during December solstice and smallest during June solstice during the day, and almost season independent at night. The monthly lunar tidal amplitudes are season independent during the day, while nighttime monthly amplitudes are largest and smallest in December solstice and autumnal equinox, respectively. The monthly and semimonthly amplitudes decrease from early morning to afternoon and evening to morning with moderate to large increases near dusk and dawn. We also examined these perturbation drifts during periods of sudden stratospheric warmings (SSWs). Our results show, for the first time, the enhancements of the lunar semimonthly tidal effects associated with SSWs to occur at night, as well as during the day. Our results also indicate during SSWs, monthly tidal effects are not enhanced as strongly as the semimonthly effects.

The 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. / Doctor of Philosophy / The Earth's ionosphere, ranging from about 60 km to 1000 km in altitude, is an electrically conducting region of the upper atmosphere that exists primarily due to ionization by solar ultraviolet radiation. The Earth's magnetosphere is the region of space surrounding the Earth that is dominated by the Earth's magnetic field. The magnetosphere and ionosphere are tightly coupled to each other through the magnetic field lines which act as highly conductive wires. The sun constantly releases a stream of plasma (i.e., gases of ions and free electrons) known as the solar wind, which carries the solar magnetic field known as the interplanetary magnetic field (IMF). The solar wind interacts with the Earth's magnetosphere and ionosphere through a process called magnetic reconnection, which drives currents and electric fields in the coupled magnetosphere and ionosphere. The ionosphere carries a substantial portion of the electrical currents flowing in the Earth's space environment. The interaction of the ionospheric currents and electric fields with plasma and neutral particles is called ionospheric electrodynamics. In this study we utilized statistical and machine learning techniques to study ionospheric electrodynamics in three distinct regions. First, we studied the influence of duskward IMF on plasma convection in the polar region using measurements from the Resolute Bay Incoherent Scatter Radar – North (RISR-N). Specifically, we analyzed an interval on Sep. 12, 2014 when the RISR-N radar made measurements in the high latitude noon sector while the IMF turned from duskward to strongly northward. We found that the high latitude plasma convection can have flow responses with different lag times during strong IMF conditions that involve IMF By rotation. Such phenomena are rarely observed and are not predicted by the antiparallel or the component reconnection models applied to quasi‐static conditions. We propose a dual reconnection scenario, with reconnection occurring poleward of the cusp and also at the dayside subsolar point on the magnetopause, to explain the rarely observed flow behavior. Next, we used measurements from six mid-latitude Super Dual Auroral Radar Network (SuperDARN) radars distributed across the continental United States to investigate the driving influences of plasma convection in the subauroral region, which is equatorward of the region where aurora is normally observed. Previous studies have suggested that plasma motions in the subaruroral region were mainly due to the neutral winds blowing the ions, i.e. the neutral wind dynamo. However, our analysis suggests that subauroral plasma flows are due to the combined effects of solar wind-magnetosphere coupling and neutral wind dynamo with the ionospheric conductivity modulating their relative importance. Finally, we utilized the latest machine learning techniques to examine the external drivers (i.e., solar wind and IMF) of magnetic substorms, which is a physical phenomenon that occurs in the auroral region and causes explosive brightening of the aurora. We developed the first machine learning model that forecasts the onset of a magnetic substorm over the next one hour. The model has been trained and tested on a comprehensive list of onsets compiled between 1997 and 2017 and correctly identify substorm onset ~75% of the time. In contrast, an earlier prediction algorithm correctly identified only ~21% of the substorm onsets in the same dataset. Our analysis revealed that external factors alone are not sufficient to forecast all substorms, and preconditioning of the nightside magnetosphere may be an important factor.