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04270nam a22005535i 4500 |
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|a 9783540383017
|9 978-3-540-38301-7
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|a 10.1007/978-3-540-38301-7
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|a Evensen, Geir.
|e author.
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
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|a Data Assimilation
|h [electronic resource] :
|b The Ensemble Kalman Filter /
|c by Geir Evensen.
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| 250 |
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|a 1st ed. 2007.
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|a Berlin, Heidelberg :
|b Springer Berlin Heidelberg :
|b Imprint: Springer,
|c 2007.
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|a XXII, 280 p. 63 illus., 52 illus. in color.
|b online resource.
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|a text
|b txt
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|a computer
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|a online resource
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|a text file
|b PDF
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|a Statistical definitions -- Analysis scheme -- Sequential data assimilation -- Variational inverse problems -- Nonlinear variational inverse problems -- Probabilistic formulation -- Generalized Inverse -- Ensemble methods -- Statistical optimization -- Sampling strategies for the EnKF -- Model errors -- Square Root Analysis schemes -- Rank issues -- An ocean prediction system -- Estimation in an oil reservoir simulator.
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|a Data Assimilation comprehensively covers data assimilation and inverse methods, including both traditional state estimation and parameter estimation. This text and reference focuses on various popular data assimilation methods, such as weak and strong constraint variational methods and ensemble filters and smoothers. It is demonstrated how the different methods can be derived from a common theoretical basis, as well as how they differ and/or are related to each other, and which properties characterize them, using several examples. It presents the mathematical framework and derivations in a way which is common for any discipline where dynamics is merged with measurements. The mathematics level is modest, although it requires knowledge of basic spatial statistics, Bayesian statistics, and calculus of variations. Readers will also appreciate the introduction to the mathematical methods used and detailed derivations, which should be easy to follow, are given throughout the book. The codes used in several of the data assimilation experiments are available on a web page. The focus on ensemble methods, such as the ensemble Kalman filter and smoother, also makes it a solid reference to the derivation, implementation and application of such techniques. Much new material, in particular related to the formulation and solution of combined parameter and state estimation problems and the general properties of the ensemble algorithms, is available here for the first time.
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|a Earth sciences.
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|a Probabilities.
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|a Mathematical physics.
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|a Mathematical models.
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|a Applied mathematics.
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|a Engineering mathematics.
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|a Earth Sciences, general.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/G00002
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|a Probability Theory and Stochastic Processes.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/M27004
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|a Theoretical, Mathematical and Computational Physics.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/P19005
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|a Mathematical Modeling and Industrial Mathematics.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/M14068
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|a Mathematical and Computational Engineering.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/T11006
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| 710 |
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|a SpringerLink (Online service)
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| 773 |
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|t Springer Nature eBook
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| 776 |
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|i Printed edition:
|z 9783540828402
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|i Printed edition:
|z 9783540383000
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|u https://doi.org/10.1007/978-3-540-38301-7
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|a ZDB-2-SXEE
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|a Earth and Environmental Science (SpringerNature-11646)
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|a Earth and Environmental Science (R0) (SpringerNature-43711)
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