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001431364 001__ 1431364
001431364 003__ OCoLC
001431364 005__ 20230308003232.0
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001431364 020__ $$a9783030967093$$q(electronic bk.)
001431364 020__ $$a3030967093$$q(electronic bk.)
001431364 020__ $$z9783030967086$$q(print)
001431364 0247_ $$a10.1007/978-3-030-96709-3$$2doi
001431364 035__ $$aSP(OCoLC)1312727657
001431364 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dEBLCP$$dOCLCO$$dOCLCF$$dOCLCQ
001431364 049__ $$aISEA
001431364 050_4 $$aQA276.8
001431364 08204 $$a519.5/44$$223/eng/20220427
001431364 1001_ $$aEvensen, Geir,$$eauthor.
001431364 24510 $$aData assimilation fundamentals :$$ba unified formulation of the state and parameter estimation problem /$$cGeir Evensen, Femke C. Vossepoel, Peter Jan van Leeuwen.
001431364 264_1 $$aCham, Switzerland :$$bSpringer,$$c2022.
001431364 300__ $$a1 online resource (xix, 245 pages) :$$billustrations (some color).
001431364 336__ $$atext$$btxt$$2rdacontent
001431364 337__ $$acomputer$$bc$$2rdamedia
001431364 338__ $$aonline resource$$bcr$$2rdacarrier
001431364 4901_ $$aSpringer textbooks in earth sciences, geography and environment,$$x2510-1315
001431364 504__ $$aIncludes bibliographical references and indexes.
001431364 5050_ $$aIntroduction -- Part I Mathematical Formulation: Problem formulation -- Maximum a posteriori solution -- Strong-constraint 4DVar -- Weak constraint 4DVar -- Kalman filters and 3DVar -- Randomized-maximum-likelihood sampling -- Low-rank ensemble methods -- Fully nonlinear data assimilation -- Localization and inflation -- Methods’ summary -- Part II Examples and Applications: A Kalman filter with the Roessler model -- Linear EnKF update -- EnKF for an advection equation -- EnKF with the Lorenz equations -- 3Dvar and SC-4DVar for the Lorenz 63 model -- Representer method with an Ekman-flow model -- Comparison of methods on a scalar model -- Particle filter for seismic-cycle estimation -- Particle flow for a quasi-geostrophic model -- EnRML for history matching petroleum models -- ESMDA with a SARS-COV-2 pandemic model -- Final summary -- References -- Index.
001431364 5060_ $$aOpen access.$$5GW5XE
001431364 520__ $$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.
001431364 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed April 27, 2022).
001431364 650_0 $$aEstimation theory.
001431364 650_0 $$aKalman filtering.
001431364 650_6 $$aThéorie de l'estimation.
001431364 650_6 $$aFiltre de Kalman.
001431364 655_0 $$aElectronic books.
001431364 7001_ $$aVossepoel, Femke Cathelijne,$$d1971-$$eauthor.
001431364 7001_ $$aLeeuwen, Peter Jan van,$$eauthor.
001431364 830_0 $$aSpringer textbooks in earth sciences, geography and environment,$$x2510-1315
001431364 852__ $$bebk
001431364 85640 $$3Springer Nature$$uhttps://link.springer.com/10.1007/978-3-030-96709-3$$zOnline Access$$91397441.2
001431364 909CO $$ooai:library.usi.edu:1431364$$pGLOBAL_SET
001431364 980__ $$aBIB
001431364 980__ $$aEBOOK
001431364 982__ $$aEbook
001431364 983__ $$aOnline
001431364 994__ $$a92$$bISE