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Forecast error correction using dyna...

Lakshmivarahan, Sivaramakrishnan.

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Forecast error correction using dynamic data assimilation

Record Type:	Electronic resources : Monograph/item
Title/Author:	Forecast error correction using dynamic data assimilation/ by Sivaramakrishnan Lakshmivarahan, John M. Lewis, Rafal Jabrzemski.
Author:	Lakshmivarahan, Sivaramakrishnan.
other author:	Lewis, John M.
Published:	Cham :Springer International Publishing : : 2017.,
Description:	xvi, 270 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer eBooks
Subject:	Forecasting - Mathematical models. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-39997-3
ISBN:	9783319399973

Forecast error correction using dynamic data assimilation
Lakshmivarahan, Sivaramakrishnan.

Forecast error correction using dynamic data assimilation[electronic resource] /by Sivaramakrishnan Lakshmivarahan, John M. Lewis, Rafal Jabrzemski. - Cham :Springer International Publishing :2017. - xvi, 270 p. :ill., digital ;24 cm. - Springer atmospheric sciences,2194-5217. - Springer atmospheric sciences..

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.

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.

ISBN: 9783319399973

Standard No.: 10.1007/978-3-319-39997-3doiSubjects--Topical Terms:

682004
Forecasting
--Mathematical models.

LC Class. No.: QA614.8 / .L35 2017

Dewey Class. No.: 515.352

Forecast error correction using dynamic data assimilation
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100 1 $a Lakshmivarahan, Sivaramakrishnan. $3 3217322
245 1 0 $a Forecast error correction using dynamic data assimilation $h [electronic resource] / $c by Sivaramakrishnan Lakshmivarahan, John M. Lewis, Rafal Jabrzemski.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2017.
300 $a xvi, 270 p. : $b ill., digital ; $c 24 cm.
490 1 $a Springer atmospheric sciences, $x 2194-5217
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.
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.
650 0 $a Forecasting $x Mathematical models. $3 682004
650 0 $a Differentiable dynamical systems. $3 524351
650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
650 2 4 $a Simulation and Modeling. $3 890873
650 2 4 $a Models and Principles. $3 891221
650 2 4 $a Atmospheric Sciences. $3 1019179
650 2 4 $a Quantitative Geology. $3 908377
650 1 4 $a Computer Science. $3 626642
700 1 $a Lewis, John M. $3 3217323
700 1 $a Jabrzemski, Rafal. $3 3217324
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856 4 0 $u http://dx.doi.org/10.1007/978-3-319-39997-3
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