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.
General
Imprint: |
Springer Nature Switzerland AG
|
Country of origin: |
Switzerland |
Series: |
Springer Textbooks in Earth Sciences, Geography and Environment |
Release date: |
April 2023 |
First published: |
2022 |
Authors: |
Geir Evensen
• Femke C. Vossepoel
• Peter Jan Van Leeuwen
|
Dimensions: |
235 x 155mm (L x W) |
Pages: |
245 |
Edition: |
1st ed. 2022 |
ISBN-13: |
978-3-03-096711-6 |
Categories: |
Books
|
LSN: |
3-03-096711-5 |
Barcode: |
9783030967116 |
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