Data Assimilation

A Mathematical Introduction

  • Kody Law
  • Andrew Stuart
  • Konstantinos Zygalakis

Part of the Texts in Applied Mathematics book series (TAM, volume 62)

Table of contents

  1. Front Matter
    Pages i-xviii
  2. Kody Law, Andrew Stuart, Konstantinos Zygalakis
    Pages 1-23
  3. Kody Law, Andrew Stuart, Konstantinos Zygalakis
    Pages 25-52
  4. Kody Law, Andrew Stuart, Konstantinos Zygalakis
    Pages 53-77
  5. Kody Law, Andrew Stuart, Konstantinos Zygalakis
    Pages 79-114
  6. Kody Law, Andrew Stuart, Konstantinos Zygalakis
    Pages 115-149
  7. Kody Law, Andrew Stuart, Konstantinos Zygalakis
    Pages 151-174
  8. Kody Law, Andrew Stuart, Konstantinos Zygalakis
    Pages 175-185
  9. Kody Law, Andrew Stuart, Konstantinos Zygalakis
    Pages 187-206
  10. Kody Law, Andrew Stuart, Konstantinos Zygalakis
    Pages 207-231
  11. Back Matter
    Pages 233-242

About this book

Introduction

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 mathema

tics, either through a lecture course, or through self-study.   

Keywords

Bayesian Statistics Data Assimilation Dynamical Systems Filtering Optimization

Authors and affiliations

  • Kody Law
    • 1
  • Andrew Stuart
    • 2
  • Konstantinos Zygalakis
    • 3
  1. 1.Oak Ridge National LaboratoryComputer Science and Mathematics DivisionOak RidgeUSA
  2. 2.Mathematics InstituteUniversity of WarwickCoventryUnited Kingdom
  3. 3.Department of MathematicsUniversity of SouthamptonSouthamptonUnited Kingdom

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-20325-6
  • Copyright Information Springer International Publishing Switzerland 2015
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-319-20324-9
  • Online ISBN 978-3-319-20325-6
  • Series Print ISSN 0939-2475
  • Series Online ISSN 2196-9949
  • About this book
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