TY  - BOOK
AU  - Lakshmivarahan,Sivaramakrishnan
AU  - Lewis,John M.
AU  - Jabrzemski,Rafal
ED  - SpringerLink (Online service)
TI  - Forecast Error Correction using Dynamic Data Assimilation
T2  - Springer Atmospheric Sciences,
SN  - 9783319399973
AV  - QA76.9.D343 
U1  - 006.312 23
PY  - 2017///
CY  - Cham
PB  - Springer International Publishing, Imprint: Springer
KW  - Data mining
KW  - Computer simulation
KW  - Computers
KW  - Atmospheric sciences
KW  - Geology—Statistical methods
KW  - Data Mining and Knowledge Discovery
KW  - Simulation and Modeling
KW  - Models and Principles
KW  - Atmospheric Sciences
KW  - Quantitative Geology
N1  - 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.; Available to subscribing member institutions only. Доступно лише організаціям членам підписки
N2  - 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.
UR  - https://doi.org/10.1007/978-3-319-39997-3
ER  -