Data assimilation fundamentals : a unified formulation of the state and parameter estimation problem / Geir Evensen, Femke C. Vossepoel, Peter Jan van Leeuwen.
This open-access textbook's significant contribution is the unified derivation of data-assimilation techniques from a common fundamental and optimal starting point, namely Bayes' theorem. Unique for this book is the "top-down" derivation of the assimilation methods. It starts fro...
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| Main Authors: | , , |
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| Format: | Ebook |
| Language: | English |
| Published: |
Cham, Switzerland :
Springer Nature,
[2022]
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| Subjects: | |
| Online Access: | Springer eBooks Access via Directory of Open Access Books |
| Summary: | This open-access textbook's significant contribution is the unified derivation of data-assimilation techniques from a common fundamental and optimal starting point, namely Bayes' theorem. Unique for this book is the "top-down" derivation of the assimilation methods. It starts from Bayes theorem and gradually introduces the assumptions and approximations needed to arrive at today's popular data-assimilation methods. This strategy is the opposite of most textbooks and reviews on data assimilation that typically take a bottom-up approach to derive a particular assimilation method. E.g., the derivation of the Kalman Filter from control theory and the derivation of the ensemble Kalman Filter as a low-rank approximation of the standard Kalman Filter. The bottom-up approach derives the assimilation methods from different mathematical principles, making it difficult to compare them. Thus, it is unclear which assumptions are made to derive an assimilation method and sometimes even which problem it aspires to solve. The book's top-down approach allows categorizing data-assimilation methods based on the approximations used. This approach enables the user to choose the most suitable method for a particular problem or application. The book provides the basis for an advanced course in data assimilation. It focuses on the unified derivation of the methods and illustrates their properties on multiple examples. It is suitable for graduate students, post-docs, scientists, and practitioners working in data assimilation. |
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| Physical Description: | 1 online resource (xix, 246 pages) : illustrations |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: | 3030967085 9783030967086 3030967093 9783030967093 |