Physical understanding and forecasting of the thermospheric structure and dynamics

Kodikara, N 2019, Physical understanding and forecasting of the thermospheric structure and dynamics, Doctor of Philosophy (PhD), Science, RMIT University.


Document type: Thesis
Collection: Theses

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Title Physical understanding and forecasting of the thermospheric structure and dynamics
Author(s) Kodikara, N
Year 2019
Abstract Radiation from the Sun drives, for the most part, the variations in temperature and energy in the thermosphere, which is the region of space approximately between 80 and 700 km altitudes. Despite decades of progress in upper atmospheric research, developing a precise model of the thermosphere remains a significant challenge. One motivation to better understand the thermosphere is the growing number of satellites in the region. A prime requirement for the management of satellites (and therefore, space-based services and technologies) is to avoid satellite collisions, which requires the capability to precisely track and predict their orbits. The unpredictability attached to the safety of operational satellites and human space operations in the near-Earth orbit is higher than ever due to the increasing anthropogenic space debris that orbits alongside. In this enterprise of orbit tracking and prediction, the most significant uncertainty in the low Earth orbit region (160-2,000 km altitudes) originates from the poor estimation of atmospheric mass density vis-á-vis atmospheric drag.

Empirical, physical, and data assimilation models and techniques are used to obtain the mass density estimates. Observations to validate the upper atmospheric models are both temporally and spatially limited and sparse. Physical/numerical models in general offer not only great potential for validating observations and forecasting the transient response of the thermosphere but also are excellent tools for understanding the driving mechanisms of various thermospheric trends and features. This work uses empirical, physical, and data assimilation models to investigate the thermospheric structure and dynamics.

One key contribution of this thesis is the first systematic comparison between Swarm-C accelerometer-derived thermospheric mass density and physical and empirical model estimates. This comparison provides key insights into the data and model performance, the strengths and weaknesses of the various model performance metrics, and visualisations of the statistics. The comparison at Swarm-C's temporal resolution provides a useful evaluation of the models' fidelity for orbit prediction and related space weather forecasting applications. The results show that the physical model better captured the short-timescale variations observed by Swarm-C during periods of high-solar and high-geomagnetic activities than the empirical models.

Another important contribution of this thesis is the numerical demonstration of the relationship between solar and geomagnetic activities, and mass density-temperature (p-T) synchrony in the thermosphere across multiple seasons. The physical relationship between mass density and temperature is critical for thermospheric forecasting with numerical models that assume hydrostatic and diffusive equilibriums. This work uses a physical model to isolate the dependency of the p-T synchrony features on the season, altitude, space weather conditions, high latitude electrodynamics, and lower atmospheric tides. The work also includes a comprehensive description of the p-T synchrony in the thermosphere. The results demonstrate that the p-T synchrony begins around 300-km (350-km) altitude at the equator (high latitudes). The study attributes the large p-T phase lag in the high latitudes to ion drag and temperature fluctuations via soft particle precipitation. The study provides physical insights into how the winds contribute to the p-T synchrony. In addition, the results show that geomagnetic activity contributes significantly to the p-T synchrony; the underlying mechanism may be related to temperature enhancements in the high latitudes via Joule heating and associated nonlinear interactions.

This thesis also puts the evolution of the thermosphere state into context by conducting time integrated data assimilation experiments. A global physical model actively constrained by electron density and temperature data using a variant of the Kalman filter technique shows promising results for thermosphere forecasts. The experiments focus on assimilation accuracy during different solar activity periods. The results show that improvement gained in the overall forecasted thermosphere state is better during solar minimum compared to solar maximum. The results also provide insights into biases inherent in the model - particularly along thermospheric features with sharp spatial gradients. The results find that adjustment to the electron density state via data assimilation is fugacious and the ionosphere has a tendency to relax toward climatology in a relatively short time of the order of hours. The experiment with assimilating temperature data showed more promise over assimilating radio occultation data in terms of estimating mass density along two polar orbiting satellites.

The work adds new insights to our understanding of the Earth's upper atmosphere through the use of state-of-the-art models and both space- and ground-based observations.
Degree Doctor of Philosophy (PhD)
Institution RMIT University
School, Department or Centre Science
Subjects Mesospheric, Ionospheric and Magnetospheric Physics
Geodesy
Atmospheric Dynamics
Space and Solar Physics
Keyword(s) upper atmospheric physics
thermosphere
ionosphere
space weather
data assimilation
space situational awareness
high-performance computing
machine learning
Kalman filter
numerical simulations
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Created: Wed, 19 Jun 2019, 16:49:01 EST by Keely Chapman
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