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Forecast error correction using dyna...

Jabrzemski, Rafal.

Forecast error correction using dynamic data assimilation

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Forecast error correction using dynamic data assimilationby Sivaramakrishnan Lakshmivarahan, John M. Lewis, Rafal Jabrzemski.
作者:	Lakshmivarahan, Sivaramakrishnan.
其他作者:	Lewis, John M.
出版者:	Cham :Springer International Publishing :2017.
面頁冊數:	xvi, 270 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	ForecastingMathematical models.
電子資源:	http://dx.doi.org/10.1007/978-3-319-39997-3
ISBN:	9783319399973$q(electronic bk.)

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$q(electronic bk.)

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

206969
Forecasting
--Mathematical models.

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

Dewey Class. No.: 515.352

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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.
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