Computational Methods for Data Evaluation and Assimilation

Dan Gabriel Cacuci, Ionel Michael Navon, Mihaela Ionescu-Bujor

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August 21, 2013 by Chapman and Hall/CRC
Reference - 373 Pages - 1 B/W Illustrations
ISBN 9781584887355 - CAT# C7354

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Features

  • Illustrates concepts using examples from diverse areas, including geophysical science and nuclear physics
  • Discusses the use of group theory and entropy maximization for assigning priors
  • Explains how to evaluate means and covariances
  • Presents popular techniques for performing nonlinear constrained optimization, including the penalty method, barrier methods, augmented Lagrangian methods, and sequential quadratic programming methods
  • Explores current research on the 4D VAR algorithm
  • Includes the requisite probability theory, functional analysis, and parameter identification and estimation concepts in the appendices

Summary

Data evaluation and data combination require the use of a wide range of probability theory concepts and tools, from deductive statistics mainly concerning frequencies and sample tallies to inductive inference for assimilating non-frequency data and a priori knowledge. Computational Methods for Data Evaluation and Assimilation presents interdisciplinary methods for integrating experimental and computational information. This self-contained book shows how the methods can be applied in many scientific and engineering areas.

After presenting the fundamentals underlying the evaluation of experimental data, the book explains how to estimate covariances and confidence intervals from experimental data. It then describes algorithms for both unconstrained and constrained minimization of large-scale systems, such as time-dependent variational data assimilation in weather prediction and similar applications in the geophysical sciences. The book also discusses several basic principles of four-dimensional variational assimilation (4D VAR) and highlights specific difficulties in applying 4D VAR to large-scale operational numerical weather prediction models.