EAN: 9783030967116

TEXTBOOKS IN EARTH SCIENCES GEOGRAPHY AND ENVIR...

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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