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The Resource Forecast Error Correction using Dynamic Data Assimilation, by Sivaramakrishnan Lakshmivarahan, John M. Lewis, Rafal Jabrzemski, (electronic resource)

Forecast Error Correction using Dynamic Data Assimilation, by Sivaramakrishnan Lakshmivarahan, John M. Lewis, Rafal Jabrzemski, (electronic resource)

Label
Forecast Error Correction using Dynamic Data Assimilation
Title
Forecast Error Correction using Dynamic Data Assimilation
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by Sivaramakrishnan Lakshmivarahan, John M. Lewis, Rafal Jabrzemski
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Language
eng
Summary
This book introduces the reader to a new method of data assimilation with deterministic constraints (exact satisfaction of dynamic constraints){u2014}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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Literary form
non fiction
Series statement
Springer Atmospheric Sciences,
Forecast Error Correction using Dynamic Data Assimilation, by Sivaramakrishnan Lakshmivarahan, John M. Lewis, Rafal Jabrzemski, (electronic resource)
Label
Forecast Error Correction using Dynamic Data Assimilation, by Sivaramakrishnan Lakshmivarahan, John M. Lewis, Rafal Jabrzemski, (electronic resource)
Link
http://libproxy.rpi.edu/login?url=http://dx.doi.org/10.1007/978-3-319-39997-3
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Antecedent source
mixed
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
not applicable
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
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{u2019}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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{'f': 'http://opac.lib.rpi.edu/record=b4258623'}
Extent
XVI, 270 p. 125 illus., 104 illus. in color.
File format
multiple file formats
Form of item
electronic
Isbn
9783319399973
Level of compression
uncompressed
Media category
computer
Media MARC source
rdamedia
Media type code
c
Other physical details
online resource.
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absent
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access
Specific material designation
remote

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