Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. II)

Author:   Seon Ki Park ,  Liang Xu
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Edition:   2013 ed.
ISBN:  

9783642350870


Pages:   730
Publication Date:   03 June 2013
Format:   Hardback
Availability:   Manufactured on demand   Availability explained
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Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. II)


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Overview

This book contains the most recent progress in data assimilation in meteorology, oceanography and hydrology including land surface. It spans both theoretical and applicative aspects with various methodologies such as variational, Kalman filter, ensemble, Monte Carlo and artificial intelligence methods. Besides data assimilation, other important topics are also covered including targeting observation, sensitivity analysis, and parameter estimation. The book will be useful to individual researchers as well as graduate students for a reference in the field of data assimilation.

Full Product Details

Author:   Seon Ki Park ,  Liang Xu
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Imprint:   Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Edition:   2013 ed.
Dimensions:   Width: 15.50cm , Height: 4.30cm , Length: 23.50cm
Weight:   1.404kg
ISBN:  

9783642350870


ISBN 10:   3642350879
Pages:   730
Publication Date:   03 June 2013
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

A Review on the Theory and Methodologies of Estimation for Dynamical Systems.- Nudging Methods: A Critical Overview.- Markov Chain Monte Carlo Methods: Theory and Applications.- Information Content in Data Assimilation.- A Question of Adequacy of Observations in Variational Data Assimilation.- Quantifying Observation Impact for a Limited Area Atmospheric Forecast Model.- Skewness of the Prior through Position Errors and Its Impact on Data Assimilation.- Background Error Correlation Modeling with Diffusion Operator.- The Adjoint Sensitivity Guidance to Diagnosis and Tuning of Error Covariance Parameters.- Treating Nonlinearities in Data-Space Variational Assimilation.- Linearized Physics for Data Assimilation at ECMWF.- Recent Applications in Representer-Based Variational Data Assimilation.

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