Forecast error correction using dynamic data assimilation / Sivaramakrishnan Lakshmivarahan, John M. Lewis, Rafal Jabrzemski.
Lakshmivarahan, S., author.; Lewis, John M., author.; Jabrzemski, Rafal, author.
2017
QA614.8 .L35 2017eb
Available Online

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Title Forecast error correction using dynamic data assimilation / Sivaramakrishnan Lakshmivarahan, John M. Lewis, Rafal Jabrzemski.
Author Lakshmivarahan, S., author.
ISBN 9783319399973 (electronic book)
3319399977 (electronic book)
9783319399959
3319399950
Published Switzerland : Springer, [2017]
Language English
Description 1 online resource (xvi, 270 pages)
Call Number QA614.8 .L35 2017eb
Dewey Decimal Classification 515/.352
Summary 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. .
Bibliography, etc. Note Includes bibliographical references (pages 259-263) and index.
Access Note Access limited to authorized users.
Source of Description Description based on print version record.
Added Author Lewis, John M., author.
Jabrzemski, Rafal, author.
Series Springer atmospheric sciences.
Available in Other Form Forecast Error Correction Using Dynamic Data Assimilation.
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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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