000			03538naaaa2200433uu 4500
001			https://directory.doabooks.org/handle/20.500.12854/81696
005			20220714195339.0
020			_a978-3-030-96709-3
020			_a9783030967093
024	7		_a10.1007/978-3-030-96709-3 _cdoi
041	0		_aEnglish
042			_adc
072		7	_aRB _2bicssc
072		7	_aPBT _2bicssc
072		7	_aPBTB _2bicssc
100	1		_aEvensen, Geir _4auth _91285166
700	1		_aVossepoel, Femke C. _4auth _91285168
700	1		_avan Leeuwen, Peter Jan _4auth _91285169
245	1	0	_aData Assimilation Fundamentals : A Unified Formulation of the State and Parameter Estimation Problem
260			_aCham _bSpringer Nature _c2022
300			_a1 electronic resource (245 p.)
506	0		_aOpen Access _2star _fUnrestricted online access
520			_aThis open-access textbook's significant contribution is the unified derivation of data-assimilation techniques from a common fundamental and optimal starting point, namely Bayes' theorem. Unique for this book is the "top-down" derivation of the assimilation methods. It starts from Bayes theorem and gradually introduces the assumptions and approximations needed to arrive at today's popular data-assimilation methods. This strategy is the opposite of most textbooks and reviews on data assimilation that typically take a bottom-up approach to derive a particular assimilation method. E.g., the derivation of the Kalman Filter from control theory and the derivation of the ensemble Kalman Filter as a low-rank approximation of the standard Kalman Filter. The bottom-up approach derives the assimilation methods from different mathematical principles, making it difficult to compare them. Thus, it is unclear which assumptions are made to derive an assimilation method and sometimes even which problem it aspires to solve. The book's top-down approach allows categorizing data-assimilation methods based on the approximations used. This approach enables the user to choose the most suitable method for a particular problem or application. Have you ever wondered about the difference between the ensemble 4DVar and the "ensemble randomized likelihood" (EnRML) methods? Do you know the differences between the ensemble smoother and the ensemble-Kalman smoother? Would you like to understand how a particle flow is related to a particle filter? In this book, we will provide clear answers to several such questions. The book provides the basis for an advanced course in data assimilation. It focuses on the unified derivation of the methods and illustrates their properties on multiple examples. It is suitable for graduate students, post-docs, scientists, and practitioners working in data assimilation.
540			_aCreative Commons _fby/4.0/ _2cc _4http://creativecommons.org/licenses/by/4.0/
546			_aEnglish
650		7	_aEarth sciences _2bicssc _972931
650		7	_aProbability & statistics _2bicssc _9947505
650		7	_aBayesian inference _2bicssc _91283605
653			_aData Assimilation
653			_aParameter Estimation
653			_aEnsemble Kalman Filter
653			_a4DVar
653			_aRepresenter Method
653			_aEnsemble Methods
653			_aParticle Filter
653			_aParticle Flow
856	4	0	_awww.oapen.org _uhttps://library.oapen.org/bitstream/20.500.12657/54434/1/978-3-030-96709-3.pdf _70 _zDOAB: download the publication
856	4	0	_awww.oapen.org _uhttps://directory.doabooks.org/handle/20.500.12854/81696 _70 _zDOAB: description of the publication
999			_c3022431 _d3022431