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|
|School Location:||United States -- Florida|
|Source:||DAI-B 73/12(E), Dissertation Abstracts International|
|Subjects:||Applied Mathematics, Meteorology, Remote sensing|
|Keywords:||Clouds, Data assimilation, Infrared satellites, Inverse problem, Limited memory bundle method, Non-differentiable|
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