Dissertation/Thesis Abstract

Practical optimization algorithms in the data assimilation of large-scale systems with non-linear and non-smooth observation operators
by Steward, Jeffrey L., Ph.D., The Florida State University, 2012, 132; 3519374
Abstract (Summary)

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.

Indexing (document details)
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
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
Copyright © 2021 ProQuest LLC. All rights reserved. Terms and Conditions Privacy Policy Cookie Policy