Data Assimilation for Ionosphere-Thermosphere Storm-Time State Estimatio

This dissertation presents a data assimilation method for estimating the physical drivers of the Earth's ionosphere layer through the combination of Global Navigation Satellite System based (GNSS) ionospheric density measurements, Fabry-Perot interferometer (FPI) neutral wind measurements and several empirical models. The main contributions include: 1) Kalman filtering for multi-observation ingestion and multi-state estimation, 2) ingestion of FPI neutral wind measurements, 3) spherical harmonic basis functions for global electric potential estimation and 4) a study of storm-time ion drifts using globally ingested data.;The thermosphere is a region of Earth's atmosphere (80-1000 km) that contains a balance of particle density and solar ionizing radiation such that an ionosphere can form. During geomagnetic storm events, the ionosphere can be disturbed causing abrupt redistribution of the ionospheric plasma. These disruptions can cause blackouts for radio wave-based communications and navigation systems. Understanding what causes the ionosphere to change is therefore necessary as society becomes more dependent on navigation and communication technologies.;The first step in understanding the ionosphere is to quantify its physical drivers. Measurements of the ionosphere are limited both spatially and temporally because the region is so vast. Models, on the other hand, provide our best understanding and capability to simulate the ionosphere and its drivers but often fall short in capturing certain phenomena during severe geomagnetic storms. In this work, a data assimilation algorithm called Estimating Model Parameters from Ionospheric Reverse Engineering (EMPIRE) is further developed to combine both measurements and simulation data sets for estimating ionospheric drivers globally. EMPIRE ingests ionosphere plasma density rate measurements and subtracts model simulation results to produce an observation of the difference between measurements and simulation. EMPIRE then fits basis functions which represent physical drivers to the measurement-simulation discrepancy. The mapping from observation to physical driver happens using the ion continuity governing equation as a model.;The EMPIRE algorithm was originally developed in 2009 to perform regional data assimilation and used only plasma density measurements. In this work, EMPIRE is modified to use a Kalman filter so measurements and models can be ingested in an efficient and systematic manner. Direct physical driver measurements are provided by FPI neutral wind measurements using the newly developed Kalman filter. This thesis demonstrates the first ever use of FPIs and plasma density measurements in a data assimilative environment. Next, EMPIRE is modified to estimate coefficients to spherical harmonic basis functions rather than power series basis functions. Spherical harmonic functions allow EMPIRE to provide global estimates because they are continuous and orthogonal on a spherical domain (such as Earth). A study is then conducted to ingest global plasma density rate measurements and neutral winds to estimate ion drifts across the globe.