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Springer Atmospheric Sciences Ser.: Forecast Error Correction Using Dynamic Data Assimilation by John M. Lewis, Sivaramakrishnan Lakshmivarahan and Rafal Jabrzemski (2016, Hardcover)
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About this product
Product Identifiers
PublisherSpringer International Publishing A&G
ISBN-103319399950
ISBN-139783319399959
eBay Product ID (ePID)224112023
Product Key Features
Number of PagesXvi, 270 Pages
LanguageEnglish
Publication NameForecast Error Correction Using Dynamic Data Assimilation
Publication Year2016
SubjectComputer Simulation, Mechanics / Dynamics, Probability & Statistics / General, Databases / Data Mining
TypeTextbook
AuthorJohn M. Lewis, Sivaramakrishnan Lakshmivarahan, Rafal Jabrzemski
Subject AreaMathematics, Computers, Science
SeriesSpringer Atmospheric Sciences Ser.
FormatHardcover
Dimensions
Item Weight196.2 Oz
Item Length9.3 in
Item Width6.1 in
Additional Product Features
Dewey Edition23
Number of Volumes1 vol.
IllustratedYes
Dewey Decimal551.63
Table Of ContentPart 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.
SynopsisThis 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., 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.
LC Classification NumberQA76.9.D343
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