Forecast Error Correction using Dynamic Data Assimilation

✍ Scribed by Sivaramakrishnan Lakshmivarahan, John M. Lewis, Rafal Jabrzemski (auth.)

• Publisher: Springer International Publishing
• Year: 2017
• Tongue: English
• Leaves: 278
• Series: Springer Atmospheric Sciences
• Edition: 1
• Category: Library

✦ Synopsis

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

Front Matter....Pages i-xvi
Front Matter....Pages 1-1
Introduction....Pages 3-18
Forward Sensitivity Method: Scalar Case....Pages 19-56
On the Relation Between Adjoint and Forward Sensitivity....Pages 57-91
Forward Sensitivity Method: General Case....Pages 93-106
Forecast Error Correction Using Optimal Tracking....Pages 107-146
Front Matter....Pages 147-147
The Gulf of Mexico Problem: Return Flow Analysis....Pages 149-205
Lagrangian Tracer Dynamics....Pages 207-253
Back Matter....Pages 255-270

✦ Subjects

Data Mining and Knowledge Discovery;Simulation and Modeling;Models and Principles;Atmospheric Sciences;Quantitative Geology