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Titlebook: Data Assimilation; The Ensemble Kalman Geir Evensen Book 20071st edition Springer-Verlag Berlin Heidelberg 2007 Data assimilation.Derivati

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發(fā)表于 2025-3-21 16:26:49 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Data Assimilation
副標題The Ensemble Kalman
編輯Geir Evensen
視頻videohttp://file.papertrans.cn/263/262722/262722.mp4
概述Comprehensively covers both data assimilation and inverse methods.Presents the mathematical framework and derivations in a way which is common for any discipline where dynamics is merged with measurem
圖書封面Titlebook: Data Assimilation; The Ensemble Kalman  Geir Evensen Book 20071st edition Springer-Verlag Berlin Heidelberg 2007 Data assimilation.Derivati
描述.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...Rather than emphasize a particular discipline such as oceanography or meteorology, 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. In particular, this webpage conta
出版日期Book 20071st edition
關鍵詞Data assimilation; Derivation; Ensemble Kalman Filter; Ensemble Kalman Smoother; algorithm; algorithms; ca
版次1
doihttps://doi.org/10.1007/978-3-540-38301-7
isbn_ebook978-3-540-38301-7
copyrightSpringer-Verlag Berlin Heidelberg 2007
The information of publication is updating

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發(fā)表于 2025-3-21 20:47:03 | 只看該作者
https://doi.org/10.1007/978-981-99-2544-5mulation as given by either (7.12) or (7.13) to derive the generalized variational inverse formulation for the combined parameter and state estimation problem..Then in Chap. 9 the Ensemble Smoother (ES) is derived from the standard Bayesian formulation while the recursive form of Bayes’ theorem, giv
板凳
發(fā)表于 2025-3-22 03:40:26 | 只看該作者
Advances in‘Material Research and Technology variational problem would then become the prior for the next. This could be a sensible approach except that the variational methods, such as the representer and adjoint methods, do not easily provide statistical information about the errors of the estimate, which is needed when the estimate is used
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Probabilistic formulation,mulation as given by either (7.12) or (7.13) to derive the generalized variational inverse formulation for the combined parameter and state estimation problem..Then in Chap. 9 the Ensemble Smoother (ES) is derived from the standard Bayesian formulation while the recursive form of Bayes’ theorem, giv
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發(fā)表于 2025-3-22 21:51:56 | 只看該作者
Generalized Inverse, variational problem would then become the prior for the next. This could be a sensible approach except that the variational methods, such as the representer and adjoint methods, do not easily provide statistical information about the errors of the estimate, which is needed when the estimate is used
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發(fā)表于 2025-3-23 02:17:23 | 只看該作者
An ocean prediction system,cies and among ocean researchers, on the need for development of operational ocean prediction systems. It is expected that several such systems will be established in the near future, covering the global ocean and providing valueable information about the state of the ocean both to commercial users
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Estimation in an oil reservoir simulator,ave been observed in many other methods. This must be attributed to the sequential processing of observations, but also the fact that the EnKF also allows for model errors in addition to errors in the estimated parameters. Furthermore, the solution is searched for in the space spanned by the ensembl
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