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Computational Methods for Data Evaluation and Assimilation

Autor Dan Gabriel Cacuci, Ionel Michael Navon, Mihaela Ionescu-Bujor
en Limba Engleză Hardback – 21 aug 2013
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.
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Specificații

ISBN-13: 9781584887355
ISBN-10: 1584887354
Pagini: 374
Ilustrații: 1 b/w image and 6 tables
Dimensiuni: 156 x 234 x 25 mm
Greutate: 0.66 kg
Ediția:New.
Editura: CRC Press
Colecția Chapman and Hall/CRC

Public țintă

Academic and Professional Practice & Development

Cuprins

Experimental Data Evaluation: Basic Concepts. Computation of Means and Variances from Measurements. Optimization Methods for Large-Scale Data Assimilation. Basic Principles of 4D VAR. 4D VAR in Numerical Weather Prediction Models. Appendices. Bibliography. Index.

Notă biografică

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

Descriere

This self-contained book presents interdisciplinary methods for integrating experimental and computational information in many scientific and engineering areas. It 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. 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.