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Titlebook: Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. II); Seon Ki Park,Liang Xu Book 2013 Springer-Verlag Berlin H

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发表于 2025-3-21 20:03:36 | 显示全部楼层 |阅读模式
书目名称Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. II)
编辑Seon Ki Park,Liang Xu
视频video
概述New theories and methodologies in data assimilation.The most recent studies in data assimilation.Front-edge applications of data assimilation technique to various disciplines in Geosciences.Includes s
图书封面Titlebook: Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. II);  Seon Ki Park,Liang Xu Book 2013 Springer-Verlag Berlin H
描述This book contains the most recent progress in data assimilation in meteorology, oceanography and hydrology including land surface. It spans both theoretical and applicative aspects with various methodologies such as variational, Kalman filter, ensemble, Monte Carlo and artificial intelligence methods. Besides data assimilation, other important topics are also covered including targeting observation, sensitivity analysis, and parameter estimation. The book will be useful to individual researchers as well as graduate students for a reference in the field of data assimilation.
出版日期Book 2013
关键词Data assimilation; Front-edge Applications of Data Assimilation; Technique to Various Disciplines in G
版次1
doihttps://doi.org/10.1007/978-3-642-35088-7
isbn_softcover978-3-662-51023-0
isbn_ebook978-3-642-35088-7
copyrightSpringer-Verlag Berlin Heidelberg 2013
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https://doi.org/10.1007/978-1-4612-1368-0s due to the assimilated observations, and the complementary 82 % is the influence of the prior (background) information, a short-range forecast containing information from earlier assimilated observations. About 20 % of the observational information is currently provided by surface-based observing
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Vahlen Matrices for Non-Definite Metricsl and requires appropriate renormalization by rescaling. The exact computation of the rescaling factors (diagonal elements of .) is a computationally expensive procedure, therefore an efficient numerical approximation is needed. Under the assumption of local homogeneity of ., a heuristic method for
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Rafał Abłamowicz,Pertti Lounestoloops perform similarly to sequentially applied 3D-Var assimilations by overfitting the observations and producing state estimates with poor predictive skill. Evaluating the . error covariances, the analysis error, and minimum cost function illustrate how overfitting degrades the solution. This is a
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Adapting Cities to Climate Changeion. In this paper the development and evaluation of the new oceanographic three-dimensional variational (3DVAR) data assimilation is described. Special emphasis is placed on documenting the capabilities built into the 3DVAR to make the system efficient for use in global HYCOM.
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https://doi.org/10.1007/978-3-319-95885-9mble forecast is set up for a 72 h forecast with a 24 h update cycle for the ocean data assimilation. Results from the atmospheric forcing perturbation and ET ocean ensemble mean are examined and discussed. Measurements of the ability of the ETKF to predict 24–48 h ocean forecast error variance redu
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Green Hydrogen Towards Net Zeroty, the vertical in-phase superimposition between upper and lower mesocyclones, and sudden transition from supercell, mesocyclones totornado. In the application, new variables DZ (temporal difference of radar reflectivity) and DZ. (temporal difference of differential reflectivity) are introduced to
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