Forecast Error Correction using Dynamic Data Assimilation

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 as...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριοι συγγραφείς: Lakshmivarahan, Sivaramakrishnan (Συγγραφέας), Lewis, John M. (Συγγραφέας), Jabrzemski, Rafal (Συγγραφέας)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2017.
Σειρά:Springer Atmospheric Sciences,
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
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100 1 |a Lakshmivarahan, Sivaramakrishnan.  |e author. 
245 1 0 |a Forecast Error Correction using Dynamic Data Assimilation  |h [electronic resource] /  |c by Sivaramakrishnan Lakshmivarahan, John M. Lewis, Rafal Jabrzemski. 
264 1 |a Cham :  |b Springer International Publishing :  |b Imprint: Springer,  |c 2017. 
300 |a XVI, 270 p. 125 illus., 104 illus. in color.  |b online resource. 
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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 Computer science. 
650 0 |a Geology  |x Statistical methods. 
650 0 |a Atmospheric sciences. 
650 0 |a Computers. 
650 0 |a Data mining. 
650 0 |a Computer simulation. 
650 1 4 |a Computer Science. 
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650 2 4 |a Simulation and Modeling. 
650 2 4 |a Models and Principles. 
650 2 4 |a Atmospheric Sciences. 
650 2 4 |a Quantitative Geology. 
700 1 |a Lewis, John M.  |e author. 
700 1 |a Jabrzemski, Rafal.  |e author. 
710 2 |a SpringerLink (Online service) 
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776 0 8 |i Printed edition:  |z 9783319399959 
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