Description
Computational Methods for Data Evaluation and Assimilation
Authors: Cacuci Dan Gabriel, Navon Ionel Michael, Ionescu-Bujor Mihaela
Language: EnglishSubject for Computational Methods for Data Evaluation and Assimilation:
Keywords
NWP; Background Error Covariance; Variational Data Assimilation; Data Assimilation; Background Error Covariance Matrix; Assimilation Window; Relative Standard Deviation; Cost Functional; Random Variable; Observation Error; Analysis Error Covariance Matrix; LMQN Method; Adjoint Code; Adjoint Model; Var Procedure; Tangent Linear Model; Nudging Method; Truncated Newton Method; SQP Method; Trust Region Methods; Error Covariance; Unrecognized Errors; SA Algorithm; Maximum Entropy Principle; Augmented Lagrangian Methods
Approximative price 74.82 €
In Print (Delivery period: 14 days).
Add to cart the book of Cacuci Dan Gabriel, Navon Ionel Michael, Ionescu-Bujor MihaelaPublication date: 09-2019
· 15.6x23.4 cm · Paperback
Approximative price 208.65 €
In Print (Delivery period: 15 days).
Add to cart the book of Cacuci Dan Gabriel, Navon Ionel Michael, Ionescu-Bujor MihaelaPublication date: 09-2013
480 p. · 15.6x23.4 cm · Hardback
Description
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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.
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.