| 书目名称 | Data Assimilation | | 副标题 | The Ensemble Kalman | | 编辑 | Geir Evensen | | 视频video | http://file.papertrans.cn/263/262721/262721.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 | | 图书封面 |  | | 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 2009Latest edition | | 关键词 | Data assimilation; Ensemble Kalman Filter; Ensemble Kalman Smoother; Measure; bayesian statistics; invers | | 版次 | 2 | | doi | https://doi.org/10.1007/978-3-642-03711-5 | | isbn_softcover | 978-3-642-42476-2 | | isbn_ebook | 978-3-642-03711-5 | | copyright | Springer-Verlag Berlin Heidelberg 2009 |
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