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161103 ||| eng |
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|a 9783319399973
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|a Lakshmivarahan, Sivaramakrishnan
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245 |
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|a Forecast Error Correction using Dynamic Data Assimilation
|h Elektronische Ressource
|c by Sivaramakrishnan Lakshmivarahan, John M. Lewis, Rafal Jabrzemski
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250 |
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|a 1st ed. 2017
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260 |
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|a Cham
|b Springer International Publishing
|c 2017, 2017
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300 |
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|a XVI, 270 p. 125 illus., 104 illus. in color
|b online resource
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505 |
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|a Part I Theory -- Introduction -- Dynamics of evolution of first- and second-order forward sensitivity: discrete time and continuous time -- Estimation of control errors using forward sensitivities: FSM with single and multiple observations -- Relation to adjoint sensitivity and impact of observation -- Estimation of model errors using Pontryagin’s Maximum Principle- its relation to 4-D VAR and hence FSM -- FSM and predictability - Lyapunov index -- Part II Applications -- Mixed-layer model - the Gulf of Mexico problem -- Lagrangian data assimilation -- Conclusions -- Appendix -- Index.
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653 |
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|a Geology
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653 |
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|a Models of Computation
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653 |
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|a Computer science
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653 |
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|a Computer simulation
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653 |
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|a Computer Modelling
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653 |
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|a Data mining
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653 |
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|a Data Mining and Knowledge Discovery
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653 |
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|a Atmospheric Science
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653 |
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|a Atmospheric science
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700 |
1 |
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|a Lewis, John M.
|e [author]
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700 |
1 |
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|a Jabrzemski, Rafal
|e [author]
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041 |
0 |
7 |
|a eng
|2 ISO 639-2
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989 |
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|b Springer
|a Springer eBooks 2005-
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490 |
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|a Springer Atmospheric Sciences
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028 |
5 |
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|a 10.1007/978-3-319-39997-3
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856 |
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|u https://doi.org/10.1007/978-3-319-39997-3?nosfx=y
|x Verlag
|3 Volltext
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082 |
0 |
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|a 006.312
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520 |
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|a This book introduces the reader to a new method of data assimilation with deterministic constraints (exact satisfaction of dynamic constraints)—an optimal assimilation strategy called Forecast Sensitivity Method (FSM), as an alternative to the well-known four-dimensional variational (4D-Var) data assimilation method. 4D-Var works with a forward in time prediction model and a backward in time tangent linear model (TLM). The equivalence of data assimilation via 4D-Var and FSM is proven and problems using low-order dynamics clarify the process of data assimilation by the two methods. The problem of return flow over the Gulf of Mexico that includes upper-air observations and realistic dynamical constraints gives the reader a good idea of how the FSM can be implemented in a real-world situation.
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