Dynamic Data Assimilation: A Least Squares Approach: Encyclopedia of Mathematics and its Applications, cartea 104
Autor John M. Lewis, S. Lakshmivarahan, Sudarshan Dhallen Limba Engleză Hardback – 2 aug 2006
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Specificații
ISBN-13: 9780521851558
ISBN-10: 0521851556
Pagini: 680
Ilustrații: 29 tables 208 exercises
Dimensiuni: 164 x 244 x 39 mm
Greutate: 1.1 kg
Editura: Cambridge University Press
Colecția Cambridge University Press
Seria Encyclopedia of Mathematics and its Applications
Locul publicării:Cambridge, United Kingdom
ISBN-10: 0521851556
Pagini: 680
Ilustrații: 29 tables 208 exercises
Dimensiuni: 164 x 244 x 39 mm
Greutate: 1.1 kg
Editura: Cambridge University Press
Colecția Cambridge University Press
Seria Encyclopedia of Mathematics and its Applications
Locul publicării:Cambridge, United Kingdom
Cuprins
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.
Recenzii
"I find a lot of detail that the readers will appreciate, and I like the way the book is structured -- from 'simple' estimation methods to the Kalman filter and variational methods."
Professor Martin Ehrendorfer, University of Vienna
"This book on data assimilation covers essentially all that we know about state estimation for dynamically evolving systems -- a grand effort on a much-needed textbook."
Professor Tomi Vukicevic, Colorado State University
"I think the book will be very useful, giving derivations of key results at a level that my staff will find appropriate."
Dr. Andrew Lorenc, Head, Data Assimilation Section, British Meteorological Office
"It was enjoyable to see so many ideas so nicely set out -- a treasure and wonderful resource for students."
Dr. James Purser, Research Meteorologist, National Center for Environmental Prediction, USA
"... The book is pleasant to read... The book is of interest to meteorologists, geologists, and other geoscientists, but also to statisticians and applied mathematicians."
Stephan Morgenthaler, Mathematical Reviews
"This book provides readers with a good mathematical framework for data assimilation, with all important proofs and deviations. I recommend this book for data assimilation system developers and colleagues who work with data assimilation research and applications." - Ziang-Yu Huang, Bulletin of the American Meteorological Society
Professor Martin Ehrendorfer, University of Vienna
"This book on data assimilation covers essentially all that we know about state estimation for dynamically evolving systems -- a grand effort on a much-needed textbook."
Professor Tomi Vukicevic, Colorado State University
"I think the book will be very useful, giving derivations of key results at a level that my staff will find appropriate."
Dr. Andrew Lorenc, Head, Data Assimilation Section, British Meteorological Office
"It was enjoyable to see so many ideas so nicely set out -- a treasure and wonderful resource for students."
Dr. James Purser, Research Meteorologist, National Center for Environmental Prediction, USA
"... The book is pleasant to read... The book is of interest to meteorologists, geologists, and other geoscientists, but also to statisticians and applied mathematicians."
Stephan Morgenthaler, Mathematical Reviews
"This book provides readers with a good mathematical framework for data assimilation, with all important proofs and deviations. I recommend this book for data assimilation system developers and colleagues who work with data assimilation research and applications." - Ziang-Yu Huang, Bulletin of the American Meteorological Society
Notă biografică
Descriere
A basic one-stop reference for graduate students and researchers.