Neutral density estimation from multiple equivalent platforms
Abstract/Contents
- Abstract
- Accurate modeling of atmospheric neutral density variations has been a challenge to the atmospheric science and space community for the past 50 years. The study of this topic gives insights to the dynamical processes active within our atmosphere, as well as enabling accurate prediction of the motion of objects within this region. Atmospheric density itself is a complex phenomenon that varies spatially and temporally, and is inherently linked to the behavior of the sun. Although multiple methods and models have been utilized to measure and predict neutral densities in the past, the lower thermosphere is particularly difficult to probe. This is due to the fact in this region, satellites that can provide direct measurements of density quickly deorbit and in situ instrumentation missions are infrequent due to cost and operational issues. In addition, models usually exhibit a 15\% error in their estimated densities, which can increase to beyond 50\% during periods of high solar activity and active geomagnetic conditions. With the drastic increase of small satellite constellations and abundance of meteoroid observations in recent years, new opportunities have arisen for atmospheric science, unprecedented in coverage and scope. This thesis presents a new methodology for estimating neutral densities using large quantities of measurements that are becoming increasingly available. The focus is on the concept of equivalent platforms, and approaches the problem from a stochastic viewpoint. By utilizing order statistics in combination with physical laws, the probability distribution of the variations between platforms can be inferred. The method does not depend upon prior models of the atmosphere, and is a novel way to derive neutral densities. It also is able to provide a new framework in which uncertainty across platforms may be combined with uncertainty inherent in physical models. The neutral density estimation methodology was applied to two particular scenarios: a constellation of low Earth orbit CubeSats and meteoroid observations as measured by a high power large aperture radar. Results show that this estimation scheme is capable of predicting trends as seen by accepted models, but is also able to derive densities not otherwise predicted. This is due to the neutral density estimates being directly data-based, where models will often make predictions based solely upon a few preselected parameters. In the case for meteoroids, a new partitioned approach is able to predict densities per a specific layer of the atmosphere. Estimated standard deviations can be decreased to less than 5\% and 12\% for satellite and meteoroid derived densities under idealized scenarios, respectively. In the event that the measurements are noisy, the standard deviations will increase, to approximately 10\% and 16\%, respectively. Moreover, the method is able to observe trends not otherwise reported by official models. As increasing numbers of satellite constellations are launched and highly sensitive radars are built in the future, the topics covered in this thesis will aid neutral density estimation within the least explored region of the atmosphere.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2016 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Li, Alan | |
|---|---|---|
| Associated with | Stanford University, Department of Aeronautics and Astronautics. | |
| Primary advisor | Close, Sigrid, 1971- | |
| Thesis advisor | Close, Sigrid, 1971- | |
| Thesis advisor | D'Amico, Simone | |
| Thesis advisor | Rock, Stephen | |
| Advisor | D'Amico, Simone | |
| Advisor | Rock, Stephen |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Alan Li. |
|---|---|
| Note | Submitted to the Department of Aeronautics and Astronautics. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2016. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2016 by Alan Sheng Xi Li
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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