This thesis presents a series of studies investigating the Earth's thermosphere, ionosphere, and plasmasphere using continuous wavelet analysis techniques, multi-layer feedforward neural networks, and Kalman filters, along with extensive datasets of satellite and ground-based observations collected during the solar cycle 23.
It is widely accepted that the overall ionospheric and thermospheric variability is primarily influenced by the solar activity, geomagnetic activity, and meteorological processes originating at lower atmospheric layers. While the solar activity influences mostly the long-term variability of the thermosphere-ionosphere system, the geomagnetic activity and meteorological processes tipically induce oscillations with periods ranging from several days to minutes and even seconds. Stemming from the need to understand and predict the behavior of the thermosphere-ionosphere system and its deviations from normal climatological patterns, during both quiet and disturbed geomagnetic conditions, recent modeling and experimental studies have shown an increased interest in the short-period (minutes-to-hours) and day-to-day variability, or weather aspects of the Earth's upper atmosphere and ionosphere. In this context, one of the main objectives of our current studies is to investigate the geomagnetically forced multi-day periodic variations in the thermosphere-ionosphere system, especially those associated with the recurrent geomagnetic activity at the declining and minimum phases of the solar cycle 23. For this purpose, several ionospheric, thermospheric, and solar wind parameters, along with different geomagnetic and solar activity indices are analyzed using a wide spectrum of wavelet-based analysis techniques.
A large part of this thesis is also dedicated to presenting a variety of neural network-based models developed (1) for estimating the daytime equatorial zonal electric fields using magnetometer observations, (2) for quantifying the relationships between the interplanetary electric field and the daytime penetration electric fields at equatorial latitudes, and (3) for investigating the shielding effect of the ring current at different longitude sectors. In addition, we also examine the periodic variations in the magnetometer-inferred equatorial ionospheric electric fields using wavelet analysis, and relate these variations to periodic fluctuations in the dawn-to-dusk component of the interplanetary electric field, showing that the geomagnetic activity is an important source of periodic oscillations in the ionosphere.
A significant part of this thesis is also dedicated to presenting a Kalman filter-based data assimilation algorithm developed for the near-real time estimation of both the ionospheric and plasmaspheric contributions to the GPS measurements of total electron content (TEC), by combining GPS-TEC observations with background information from an ionospheric model and from a plasmaspheric model. It is shown that the newly-developed algorithm represents a valuable remote sensing technique for investigating both the ionosphere and plasmasphere in near-real time.
|Advisor:||Gasiewski, Albin J.|
|Commitee:||Born, George H., Gasiewski, Albin J., Mullis, Clifford T., Richmond, Arthur D., Smith, Dean F., Westwater, Ed R., Zabotin, Nikolay A.|
|School:||University of Colorado at Boulder|
|School Location:||United States -- Colorado|
|Source:||DAI-B 70/07, Dissertation Abstracts International|
|Subjects:||Atmospheric sciences, Remote sensing|
|Keywords:||Ionosphere, Kalman filters, Neural networks, Plasmasphere, Thermosphere, Wavelet analysis|
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