|
|
|
|
| LEADER |
02737nmm a2200385 u 4500 |
| 001 |
EB001266163 |
| 003 |
EBX01000000000000000880747 |
| 005 |
00000000000000.0 |
| 007 |
cr||||||||||||||||||||| |
| 008 |
161103 ||| eng |
| 020 |
|
|
|a 9783319399973
|
| 100 |
1 |
|
|a Lakshmivarahan, Sivaramakrishnan
|
| 245 |
0 |
0 |
|a Forecast Error Correction using Dynamic Data Assimilation
|h Elektronische Ressource
|c by Sivaramakrishnan Lakshmivarahan, John M. Lewis, Rafal Jabrzemski
|
| 250 |
|
|
|a 1st ed. 2017
|
| 260 |
|
|
|a Cham
|b Springer International Publishing
|c 2017, 2017
|
| 300 |
|
|
|a XVI, 270 p. 125 illus., 104 illus. in color
|b online resource
|
| 505 |
0 |
|
|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.
|
| 653 |
|
|
|a Geology
|
| 653 |
|
|
|a Models of Computation
|
| 653 |
|
|
|a Computer science
|
| 653 |
|
|
|a Computer simulation
|
| 653 |
|
|
|a Computer Modelling
|
| 653 |
|
|
|a Data mining
|
| 653 |
|
|
|a Data Mining and Knowledge Discovery
|
| 653 |
|
|
|a Atmospheric Science
|
| 653 |
|
|
|a Atmospheric science
|
| 700 |
1 |
|
|a Lewis, John M.
|e [author]
|
| 700 |
1 |
|
|a Jabrzemski, Rafal
|e [author]
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
|
| 989 |
|
|
|b Springer
|a Springer eBooks 2005-
|
| 490 |
0 |
|
|a Springer Atmospheric Sciences
|
| 028 |
5 |
0 |
|a 10.1007/978-3-319-39997-3
|
| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-3-319-39997-3?nosfx=y
|x Verlag
|3 Volltext
|
| 082 |
0 |
|
|a 006.312
|
| 520 |
|
|
|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.
|