000			03590nam a22003858i 4500
001			CR9780511526480
003			UkCbUP
005			20200124160238.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			090407s2006||||enk o ||1 0|eng|d
020			_a9780511526480 (ebook)
020			_z9780521851558 (hardback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	0	_aQA401 _b.L475 2006
082	0	4	_a511.8 _222
100	1		_aLewis, J. M., _eauthor.
245	1	0	_aDynamic data assimilation : _ba least squares approach / _cJohn M. Lewis, S. Lakshmivarahan, Sudarshan Dhall.
264		1	_aCambridge : _bCambridge University Press, _c2006.
300			_a1 online resource (xxii, 654 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
490	1		_aEncyclopedia of mathematics and its applications ; _vvolume 104
500			_aTitle from publisher's bibliographic system (viewed on 05 Oct 2015).
505	0	0	_g1. _tSynopsis -- _g2. _tPathways into data assimilation : illustrative examples -- _g3. _tApplications -- _g4. _tBrief history of data assimilation -- _g5. _tLinear least squares estimation : method of normal equations -- _g6. _tA geometric view : projection and invariance -- _g7. _tNonlinear least squares estimation -- _g8. _tRecursive least squares estimation -- _g9. _tMatrix methods -- _g10. _tOptimization : steepest descent method -- _g11. _tConjugate direction/gradient methods -- _g12. _tNewton and quasi-Newton methods -- _g13. _tPrinciples of statistical estimation -- _g14. _tStatistical least squares estimation -- _g15. _tMaximum likelihood method -- _g16. _tBayesian estimation method -- _g17. _tFrom Gauss to Kalman : sequential, linear minimum variance estimation.
520			_aDynamic data assimilation is the assessment, combination and synthesis of observational data, scientific laws and mathematical models to determine the state of a complex physical system, for instance as a preliminary step in making predictions about the system's behaviour. The topic has assumed increasing importance in fields such as numerical weather prediction where conscientious efforts are being made to extend the term of reliable weather forecasts beyond the few days that are presently feasible. 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. Thus Part I is a broad introduction to the history, development and philosophy of data assimilation, illustrated by examples; Part II considers the classical, static approaches, both linear and nonlinear; and Part III describes computational techniques. Parts IV to VII are concerned with how statistical and dynamic ideas can be incorporated into the classical framework. Key themes covered here include estimation theory, stochastic and dynamic models, and sequential filtering. The final part addresses the predictability of dynamical systems. Chapters end with a section that provides pointers to the literature, and a set of exercises with instructive hints.
650		0	_aSimulation methods.
650		0	_aMathematical models.
700	1		_aLakshmivarahan, S., _eauthor.
700	1		_aDhall, Sudarshan Kumar, _d1937- _eauthor.
776	0	8	_iPrint version: _z9780521851558
830		0	_aEncyclopedia of mathematics and its applications ; _vv. 104.
856	4	0	_uhttps://doi.org/10.1017/CBO9780511526480
999			_c518303 _d518301