John M. Lewis, Sudarshan Dhall, S. Lakshmivarahan

Dynamic Data Assimilation

A Least Squares Approach

• Gebundenes Buch

Produktbeschreibung

Dynamic data assimilation is the assessment, combination and synthesis of observational data, scientific laws and mathematical models to make predictions about how a complex physical system will behave. Designed as a basic one-stop reference for graduate students and researchers, the book is based on graduate courses taught over a decade to mathematicians, scientists, and engineers. Chapters end with a section that provides pointers to the literature, and a set of exercises with instructive hints. Accompanying refresher material - in many areas of mathematics - is available from www.cambridge.org/0521851556.

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Produktdetails

• Verlag: Cambridge University Press
• Seitenzahl: 680
• Erscheinungstermin: 1. November 2008
• Englisch
• Abmessung: 235mm x 157mm x 44mm
• Gewicht: 1238g
• ISBN-13: 9780521851558
• ISBN-10: 0521851556
• Artikelnr.: 21756906

Autorenporträt

John M. Lewis is a Research Scientist at the National Severe Storms Laboratory in Oklahoma, and the Desert Research Institute in Nevada.

Inhaltsangabe

1. Synopsis
2. Pathways into data assimilation: illustrative examples
3. Applications
4. Brief history of data assimilation
5. Linear least squares estimation: method of normal equations
6. A geometric view: projection and invariance
7. Nonlinear least squares estimation
8. Recursive least squares estimation
9. Matrix methods
10. Optimisation: steepest descent method
11. Conjugate direction/gradient methods
12. Newton and quasi-Newton methods
13. Principles of statistical estimation
14. Statistical least squares estimation
15. Maximum likelihood method
16. Bayesian estimation method
17. From Gauss to Kalman: sequential, linear minimum variance estimation
18. Data assimilation-static models: concepts and formulation
19. Classical algorithms for data assimilation
20. 3DVAR - a Bayesian formulation
21. Spatial digital filters
22. Dynamical data assimilation: the straight line problem
23. First-order adjoint method: linear dynamics
24. First-order adjoint method: nonlinear dynamics
25. Second-order adjoint method
26. The ADVAR problem: a statistical and a recursive view
27. Linear filtering - Part I: Kalman filter
28. Linear filtering-part II
29. Nonlinear filtering
30. Reduced rank filters
31. Predictability: a stochastic view
32. Predictability: a deterministic view
Bibliography
Index.