Forecast Error Correction using Dynamic Data Assimilation 1st Edition by Sivaramakrishnan Lakshmivarahan,John M Lewis,Rafal Jabrzemski ISBN 3319399950 978-3319399959

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Product details:

ISBN 10: 3319399950 

ISBN 13: 978-3319399959

Author: 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 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.

Table of contents:

Part I: Theory

1. Introduction
2. Dynamics of Evolution of First- and Second-Order Forward Sensitivity: Discrete Time and Continuous Time
3. Estimation of Control Errors Using Forward Sensitivities: FSM with Single and Multiple Observations
4. Relation to Adjoint Sensitivity and Impact of Observation
5. Estimation of Model Errors Using Pontryagin’s Maximum Principle – Its Relation to 4-D VAR and Hence FSM
6. FSM and Predictability
7. Lyapunov Index

Part II: Applications

8. Mixed-Layer Model
9. The Gulf of Mexico Problem
10. Lagrangian Data Assimilation
11. Conclusions

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Tags: Sivaramakrishnan Lakshmivarahan, John M Lewis, Rafal Jabrzemski, Forecast Error, Correction using Dynamic, Data Assimilation