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This dissertation compares and contrasts large-scale optimization algorithms in the use of variational and sequential data assimilation on two novel problems chosen to highlight the challenges in non-linear and non-smooth data assimilation. The first problem explores the impact of a highly non-linear observation operator and highlights the importance of background information on the data assimilation problem. The second problem tackles large-scale data assimilation with a non-smooth observation operator. Together, these two cases show both the importance of choosing an appropriate data assimilation method and, when a variational or variationally-inspired method is chosen, the importance of choosing the right optimization algorithm for the problem at hand.
| Advisor: | Navon, Ionel M. |
| Commitee: | Erlebacher, Gordon, Gunzburger, Max, Liu, Guosheng |
| School: | The Florida State University |
| Department: | Computational Science |
| School Location: | United States -- Florida |
| Source: | DAI-B 73/12(E), Dissertation Abstracts International |
| Source Type: | DISSERTATION |
| Subjects: | Applied Mathematics, Meteorology, Remote sensing |
| Keywords: | Clouds, Data assimilation, Infrared satellites, Inverse problem, Limited memory bundle method, Non-differentiable |
| Publication Number: | 3519374 |
| ISBN: | 978-1-267-52258-0 |
