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Lakshmivarahan / Lewis / Jabrzemski

Forecast Error Correction using Dynamic Data Assimilation

Medium: Buch
ISBN: 978-3-319-82010-1
Verlag: Palgrave Macmillan
Erscheinungstermin: 23.06.2018
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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.


Produkteigenschaften


  • Artikelnummer: 9783319820101
  • Medium: Buch
  • ISBN: 978-3-319-82010-1
  • Verlag: Palgrave Macmillan
  • Erscheinungstermin: 23.06.2018
  • Sprache(n): Englisch
  • Auflage: Softcover Nachdruck of the original 1. Auflage 2017
  • Serie: Springer Atmospheric Sciences
  • Produktform: Kartoniert, Paperback
  • Gewicht: 4394 g
  • Seiten: 270
  • Format (B x H x T): 155 x 235 x 16 mm
  • Ausgabetyp: Kein, Unbekannt
Autoren/Hrsg.

Autoren

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.