Law, Kody and Stuart, Andrew and Zygalakis, Konstantinos (2015) Data Assimilation: A Mathematical Introduction. Texts in Applied Mathematics. Vol.62. Springer , Cham, Switerland. ISBN 978-3-319-20325-6 http://resolver.caltech.edu/CaltechAUTHORS:20161110-161028249
Full text is not posted in this repository. Consult Related URLs below.
Use this Persistent URL to link to this item: http://resolver.caltech.edu/CaltechAUTHORS:20161110-161028249
This book provides a systematic treatment of the mathematical underpinnings of work in data assimilation, covering both theoretical and computational approaches. Specifically the authors develop a unified mathematical framework in which a Bayesian formulation of the problem provides the bedrock for the derivation, development and analysis of algorithms; the many examples used in the text, together with the algorithms which are introduced and discussed, are all illustrated by the MATLAB software detailed in the book and made freely available online. The book is organized into nine chapters: the first contains a brief introduction to the mathematical tools around which the material is organized; the next four are concerned with discrete time dynamical systems and discrete time data; the last four are concerned with continuous time dynamical systems and continuous time data and are organized analogously to the corresponding discrete time chapters. This book is aimed at mathematical researchers interested in a systematic development of this interdisciplinary field, and at researchers from the geosciences, and a variety of other scientific fields, who use tools from data assimilation to combine data with time-dependent models. The numerous examples and illustrations make understanding of the theoretical underpinnings of data assimilation accessible. Furthermore, the examples, exercises and MATLAB software, make the book suitable for students in applied mathematics, either through a lecture course, or through self-study.
|Additional Information:||© 2015 Sprinter International Publishing Switzerland. Some of the research described in the book has emerged from collaborations with others, and the authors are indebted to all of their coauthors on collaborative work cited in the bibliography sections. The authors are very grateful to Sergios Agapiou, Daniel Sanz-Alonso, and Yuan-Xiang Zhang for help in typesetting parts of this book, and for input regarding the presentation; furthermore, Ian Hawke and Gregory Ashton provided help with the preparation of the figures, while Daniel Sanz-Alonso road-tested many of the exercises. The authors also wish to thank Mel Ades and Håkon Hoel for useful feedback, which helped to improve the presentation. The anonymous reviewers chosen by Springer and editors at Springer also gave very useful feedback, and Achi Dosanjh from Springer provided very helpful overall guidance and support throughout the publication process. And finally, the authors thank the student audiences at Peking University (2012), KAUST (2014), the University of Warwick (2014), and Fudan University (2015), whose interest helped to shape the notes. AMS is grateful to EPSRC, ERC, ESA, and ONR for financial support for research whose development suggested the need for a mathematization of the subject of data assimilation. KJHL was supported as a member of the SRI-UQ Center at KAUST while much of the writing of this book was undertaken.|
|Usage Policy:||No commercial reproduction, distribution, display or performance rights in this work are provided.|
|Deposited By:||Linda Taddeo|
|Deposited On:||16 Nov 2016 00:23|
|Last Modified:||16 Nov 2016 00:23|
Repository Staff Only: item control page