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Data Assimilation Fundamentals - A Unified Formulation of the State and Parameter Estimation Problem (Hardcover, 1st ed. 2022)
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Data Assimilation Fundamentals - A Unified Formulation of the State and Parameter Estimation Problem (Hardcover, 1st ed. 2022)
Series: Springer Textbooks in Earth Sciences, Geography and Environment
Expected to ship within 9 - 15 working days
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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. Have you ever wondered about the difference between
the ensemble 4DVar and the "ensemble randomized likelihood" (EnRML)
methods? Do you know the differences between the ensemble smoother
and the ensemble-Kalman smoother? Would you like to understand how
a particle flow is related to a particle filter? In this book, we
will provide clear answers to several such questions. 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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