Forecast Error Correction using Dynamic Data Assimilation

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

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...

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Bibliographic Details
Main Authors: Lakshmivarahan, Sivaramakrishnan (Author), Lewis, John M. (Author), Jabrzemski, Rafal (Author)
Corporate Author: SpringerLink (Online service)
Format: eBook
Language:English
Published: Cham : Springer International Publishing : Imprint: Springer, 2017.
Edition:1st ed. 2017.
Series:Springer Atmospheric Sciences,
Springer eBook Collection.
Subjects:
Data mining.
Computer simulation.
Computers.
Atmospheric sciences.
Geology—Statistical methods.
Electronic resources (E-books)
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
Table of 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’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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