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Lakshmivarahan S., Lewis J.M., Jabrzemski R. Forecast Error Correction using Dynamic Data Assimilation

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Lakshmivarahan S., Lewis J.M., Jabrzemski R. Forecast Error Correction using Dynamic Data Assimilation
Springer, 2017. — 278 p. — (Springer Atmospheric Sciences). — ISBN: 9783319399973, 9783319399959
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.
Contents:
Introduction
Forward Sensitivity Method: Scalar Case
On the Relation Between Adjoint and Forward Sensitivity
Forward Sensitivity Method: General Case
Forecast Error Correction Using Optimal Tracking
The Gulf of Mexico Problem: Return Flow Analysis
Lagrangian Tracer Dynamics
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