Overview
Comprehensively covers both data assimilation and inverse methods
Presents the mathematical framework and derivations in a way which is common for any discipline where dynamics is merged with measurements
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Table of contents (16 chapters)
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Front Matter
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Back Matter
Keywords
About this book
Data Assimilation comprehensively covers data assimilation and inverse methods, including both traditional state estimation and parameter estimation. This text and reference focuses on various popular data assimilation methods, such as weak and strong constraint variational methods and ensemble filters and smoothers. It is demonstrated how the different methods can be derived from a common theoretical basis, as well as how they differ and/or are related to each other, and which properties characterize them, using several examples.
Rather than emphasize a particular discipline such as oceanography or meteorology, it presents the mathematical framework and derivations in a way which is common for any discipline where dynamics is merged with measurements. The mathematics level is modest, although it requires knowledge of basic spatial statistics, Bayesian statistics, and calculus of variations. Readers will also appreciate the introduction to the mathematical methods used and detailed derivations, which should be easy to follow, are given throughout the book. The codes used in several of the data assimilation experiments are available on a web page. In particular, this webpage contains a complete ensemble Kalman filter assimilation system, which forms an ideal starting point for a user who wants to implement the ensemble Kalman filter with his/her own dynamical model.
The focus on ensemble methods, such as the ensemble Kalman filter and smoother, also makes it a solid reference to the derivation, implementation and application of such techniques. Much new material, in particular related to the formulation and solution of combined parameter and state estimation problems and the general properties of the ensemble algorithms, is available here for the first time.
Reviews
From the reviews:
"It serves as a textbook for students as well as a reference book for researchers in the field as well as for those approching data assimilation from a field other than applied mathematics. The availability of code used in several of the data assimilation experiments make reading the book even more attractive. The book contains … an appendix, a large number of illustrating plots (often in color), and a very up-to-date list of references." (Nina Kirchner, Zentralblatt MATH, Vol. 1157, 2009)
Authors and Affiliations
About the author
Geir Evensen obtained his Ph.D. in applied mathematics at the University in Bergen in 1992. Thereafter he has worked as a Research Director at the Nansen Environmental and Remote Sensing Center/Mohn-Sverdrup Center, as Prof. II at the Department of Mathematics at the University in Bergen, and as a Principal Engineer at the Hydro Research Center in Bergen. He is author or coauthor of more that 40 refereed publications related to modelling and data assimilation, and he has been the coordinator of international research projects on the development of data assimilation methodologies and systems.
Bibliographic Information
Book Title: Data Assimilation
Book Subtitle: The Ensemble Kalman Filter
Authors: Geir Evensen
DOI: https://doi.org/10.1007/978-3-540-38301-7
Publisher: Springer Berlin, Heidelberg
eBook Packages: Earth and Environmental Science, Earth and Environmental Science (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2007
eBook ISBN: 978-3-540-38301-7Published: 22 December 2006
Edition Number: 1
Number of Pages: XXII, 280
Number of Illustrations: 11 b/w illustrations, 52 illustrations in colour
Topics: Earth Sciences, general, Probability Theory and Stochastic Processes, Theoretical, Mathematical and Computational Physics, Mathematical Modeling and Industrial Mathematics, Mathematical and Computational Engineering