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  • 1
    UID:
    almatuudk_9922390626302884
    Umfang: 1 online resource (251 p.)
    ISBN: 3-030-96709-3
    Serie: Springer Textbooks in Earth Sciences, Geography and Environment
    Inhalt: This 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.
    Anmerkung: Description based upon print version of record. , English
    Weitere Ausg.: ISBN 3-030-96708-5
    Sprache: Englisch
    Schlagwort(e): Electronic books.
    URL: Volltext  (kostenfrei)
    URL: Volltext  (kostenfrei)
    URL: Volltext  (kostenfrei)
    URL: Volltext  (kostenfrei)
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  • 2
    UID:
    almafu_BV043528242
    Umfang: xii, 118 Seiten : , Diagramme.
    ISBN: 978-3-319-18346-6
    Serie: Frontiers in applied dynamical systems: reviews and tutorials 2
    Weitere Ausg.: Erscheint auch als Online-Ausgabe ISBN 978-3-319-18347-3
    Sprache: Englisch
    Fachgebiete: Mathematik
    RVK:
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  • 3
    Online-Ressource
    Online-Ressource
    Cham [u.a.] :Springer,
    Dazugehörige Titel
    UID:
    almatuudk_BV042730341
    Umfang: 1 Online-Ressource (XII, 118 S.) : , 19 illus., 15 illus. in color.
    ISBN: 978-3-319-18347-3
    Serie: Frontiers in applied dynamical systems: reviews and tutorials 2
    Weitere Ausg.: Erscheint auch als Druckausgabe ISBN 978-3-319-18346-6
    Sprache: Englisch
    Fachgebiete: Mathematik
    RVK:
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  • 4
    UID:
    almahu_9949286419502882
    Umfang: XIX, 245 p. 63 illus., 62 illus. in color. , online resource.
    Ausgabe: 1st ed. 2022.
    ISBN: 9783030967093
    Serie: Springer Textbooks in Earth Sciences, Geography and Environment,
    Inhalt: This 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.
    Anmerkung: Introduction -- 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. .
    In: Springer Nature eBook
    Weitere Ausg.: Printed edition: ISBN 9783030967086
    Weitere Ausg.: Printed edition: ISBN 9783030967109
    Weitere Ausg.: Printed edition: ISBN 9783030967116
    Sprache: Englisch
    Schlagwort(e): Lehrbuch
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
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  • 5
    UID:
    edoccha_BV048214581
    Umfang: 1 Online-Ressource.
    ISBN: 978-3-030-96709-3
    Serie: Springer textbooks in earth sciences, geography and environment
    Weitere Ausg.: Erscheint auch als Druck-Ausgabe, Hardcover ISBN 978-3-030-96708-6
    Sprache: Englisch
    URL: Volltext  (kostenfrei)
    URL: Volltext  (kostenfrei)
    URL: Volltext  (kostenfrei)
    URL: Volltext  (kostenfrei)
    Mehr zum Autor: Evensen, Geir
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
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  • 6
    Online-Ressource
    Online-Ressource
    Cham : Springer International Publishing
    UID:
    gbv_1657534413
    Umfang: Online-Ressource (XII, 118 p. 19 illus., 15 illus. in color, online resource)
    Ausgabe: 1st ed. 2015
    ISBN: 9783319183473
    Serie: Frontiers in Applied Dynamical Systems: Reviews and Tutorials 2
    Inhalt: This book contains two review articles on nonlinear data assimilation that deal with closely related topics but were written and can be read independently. Both contributions focus on so-called particle filters. The first contribution by Jan van Leeuwen focuses on the potential of proposal densities. It discusses the issues with present-day particle filters and explorers new ideas for proposal densities to solve them, converging to particle filters that work well in systems of any dimension, closing the contribution with a high-dimensional example. The second contribution by Cheng and Reich discusses a unified framework for ensemble-transform particle filters. This allows one to bridge successful ensemble Kalman filters with fully nonlinear particle filters, and allows a proper introduction of localization in particle filters, which has been lacking up to now
    Anmerkung: Description based upon print version of record , Preface to the Series; Preface; Contents; 1 Nonlinear Data Assimilation for high-dimensional systems; 1 Introduction; 1.1 What is data assimilation?; 1.2 How do inverse methods fit in?; 1.3 Issues in geophysical systems and popular present-day data-assimilation methods; 1.4 Potential nonlinear data-assimilation methods for geophysical systems; 1.5 Organisation of this paper; 2 Nonlinear data-assimilation methods; 2.1 The Gibbs sampler; 2.2 Metropolis-Hastings sampling; 2.2.1 Crank-Nicolson Metropolis Hastings; 2.3 Hybrid Monte-Carlo Sampling; 2.3.1 Dynamical systems; 2.3.2 Hybrid Monte-Carlo , 2.4 Langevin Monte-Carlo Sampling2.5 Discussion and preview; 3 A simple Particle filter based on Importance Sampling; 3.1 Importance Sampling; 3.2 Basic Importance Sampling; 4 Reducing the variance in the weights; 4.1 Resampling; 4.2 The Auxiliary Particle Filter; 4.3 Localisation in particle filters; 5 Proposal densities; 5.1 Proposal densities: theory; 5.2 Moving particles at observation time; 5.2.1 The Ensemble Kalman Filter; 5.2.2 The Ensemble Kalman Filter as proposal density; 6 Changing the model equations; 6.1 The `Optimal' proposal density; 6.2 The Implicit Particle Filter , 6.3 Variational methods as proposal densities6.3.1 4DVar as stand-alone method; 6.3.2 What does 4Dvar actually calculate?; 6.3.3 4DVar in a proposal density; 6.4 The Equivalent-Weights Particle Filter; 6.4.1 Convergence of the EWPF; 6.4.2 Simple implementations for high-dimensional systems; 6.4.3 Comparison of nonlinear data assimilation methods; 7 Conclusions; References; 2 Assimilating data into scientific models: An optimal coupling perspective; 1 Introduction; 2 Data assimilation and Feynman-Kac formula; 3 Monte Carlo methods in path space; 3.1 Ensemble prediction and importance sampling , 3.2 Markov chain Monte Carlo (MCMC) methods4 McKean optimal transportation approach; 5 Linear ensemble transform methods; 5.1 Sequential Monte Carlo methods (SMCMs); 5.2 Ensemble Kalman filter (EnKF); 5.3 Ensemble transform particle filter (ETPF); 5.4 Quasi-Monte Carlo (QMC) convergence; 6 Spatially extended dynamical systems and localization; 7 Applications; 7.1 Lorenz-63 model; 7.2 Lorenz-96 model; 8 Historical comments; 9 Summary and Outlook; References
    Weitere Ausg.: ISBN 9783319183466
    Weitere Ausg.: Erscheint auch als Druck-Ausgabe Van Leeuwen, Peter Jan Nonlinear data assimilation Cham : Springer, 2015 ISBN 9783319183466
    Sprache: Englisch
    URL: Volltext  (lizenzpflichtig)
    URL: Cover
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
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  • 7
    UID:
    edoccha_9960720964902883
    Umfang: 1 online resource (251 p.)
    ISBN: 3-030-96709-3
    Serie: Springer Textbooks in Earth Sciences, Geography and Environment
    Inhalt: This 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.
    Anmerkung: Description based upon print version of record. , English
    Weitere Ausg.: ISBN 3-030-96708-5
    Sprache: Englisch
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
    BibTip Andere fanden auch interessant ...
  • 8
    UID:
    edocfu_9960720964902883
    Umfang: 1 online resource (251 p.)
    ISBN: 3-030-96709-3
    Serie: Springer Textbooks in Earth Sciences, Geography and Environment
    Inhalt: This 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.
    Anmerkung: Description based upon print version of record. , English
    Weitere Ausg.: ISBN 3-030-96708-5
    Sprache: Englisch
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
    BibTip Andere fanden auch interessant ...
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