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Dynamic Data Assimilation A Least Squares Approach

ISBN-10: 0521851556

ISBN-13: 9780521851558

Edition: 2006

Authors: Sudarshan Dhall, S. Lakshmivarahan, John M. Lewis, B. Doran, P. Flajolet

List price: $244.00
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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. This book is designed to be a basic one-stop reference for graduate students and researchers. It is based on graduate courses taught over a decade to mathematicians, scientists, and engineers, and its modular structure accommodates the various audience requirements. Chapters end with a section that provides pointers to the literature, and a set of exercises with instructive hints. Computation is encouraged: algorithms are liberally scattered throughout the text. Accompanying refresher material - in many areas of mathematics including vector spaces, optimization and probability theory - will be available from The book ends with a comprehensive bibliography.
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Book details

List price: $244.00
Copyright year: 2006
Publisher: Cambridge University Press
Publication date: 8/3/2006
Binding: Hardcover
Pages: 680
Size: 6.50" wide x 9.00" long x 1.50" tall
Weight: 2.640
Language: English

Sudarshan K. Dhall is a Professor at the School of Computer Science, University of Oklahoma.

Pathways into data assimilation: illustrative examples
Brief history of data assimilation
Linear least squares estimation: method of normal equations
A geometric view: projection and invariance
Nonlinear least squares estimation
Recursive least squares estimation
Matrix methods
Optimisation: steepest descent method
Conjugate direction/gradient methods
Newton and quasi-Newton methods
Principles of statistical estimation
Statistical least squares estimation
Maximum likelihood method
Bayesian estimation method
From Gauss to Kalman: sequential, linear minimum variance estimation
Data assimilation-static models: concepts and formulation
Classical algorithms for data assimilation
3DVAR - a Bayesian formulation
Spatial digital filters
Dynamical data assimilation: the straight line problem
First-order adjoint method: linear dynamics
First-order adjoint method: nonlinear dynamics
Second-order adjoint method
The ADVAR problem: a statistical and a recursive view
Linear filtering - Part I: Kalman filter
Linear filtering-part II
Nonlinear filtering
Reduced rank filters
Predictability: a stochastic view
Predictability: a deterministic view