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Titlebook: Data Assimilation; Making Sense of Obse William Lahoz,Boris Khattatov,Richard Menard Book 2010 Springer-Verlag Berlin Heidelberg 2010 Atmos

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發(fā)表于 2025-3-21 16:23:34 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Data Assimilation
副標(biāo)題Making Sense of Obse
編輯William Lahoz,Boris Khattatov,Richard Menard
視頻videohttp://file.papertrans.cn/263/262725/262725.mp4
概述Includes supplementary material:
圖書封面Titlebook: Data Assimilation; Making Sense of Obse William Lahoz,Boris Khattatov,Richard Menard Book 2010 Springer-Verlag Berlin Heidelberg 2010 Atmos
描述.Data assimilation methods were largely developed for operational weather forecasting, but in recent years have been applied to an increasing range of earth science disciplines. This book will set out the theoretical basis of data assimilation with contributions by top international experts in the field. Various aspects of data assimilation are discussed including: theory; observations; models; numerical weather prediction; evaluation of observations and models; assessment of future satellite missions; application to components of the Earth System. References are made to recent developments in data assimilation theory (e.g. Ensemble Kalman filter), and to novel applications of the data assimilation method (e.g. ionosphere, Mars data assimilation)..
出版日期Book 2010
關(guān)鍵詞Atmospheric chemistry; Earth System; Meteorology; Ocean; Weather forecasting; algorithm; algorithms; chemis
版次1
doihttps://doi.org/10.1007/978-3-540-74703-1
isbn_softcover978-3-642-42273-7
isbn_ebook978-3-540-74703-1
copyrightSpringer-Verlag Berlin Heidelberg 2010
The information of publication is updating

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Assimilation of Operational Dataradiation from satellite instruments. Other, recent examples include ground-based GPS (Global Positioning Satellites) and radio-occultation data. The related issues of quality control and data thinning are also covered. Assimilation of time-sequences of observations is discussed. This chapter comple
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Variational Assimilation function, called the ., that measures the misfit to the available data. In particular, ., usually abbreviated as ., minimizes the misfit between a temporal sequence of model states and the observations that are available over a given assimilation window. As such, and contrary to the standard Kalman
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Ensemble Kalman Filter: Current Status and Potential representative prototype of these methods, and several examples of how advanced properties and applications that have been developed and explored for 4D-Var (four-dimensional variational assimilation) can be adapted to the LETKF without requiring an adjoint model. Although the Ensemble Kalman filte
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The Principle of Energetic Consistency in Data Assimilationns requires all the sources of uncertainty – in the initial conditions, the dynamics, and the observations – to be identified and accounted for properly in the data assimilation process. This task is complicated by the fact that the non-linear dynamical system actually being observed is typically an
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The Global Observing Systemferent techniques to observe the atmosphere, the ocean and land surfaces. It should be stressed that the various observing systems generally tend to be complementary to one another, and that redundancy where it exists is valuable as it enables cross checking and inter-comparison of data.
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