Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. II)
By: edited by Seon Ki Park, Liang Xu
Contributors: Xu, Liang; SpringerLink (Online service)
Publisher: Springer Berlin Heidelberg; Imprint: Springer
Published: 2013
Place of publication: Berlin, Heidelberg
Language: eng
Format: [electronic resource] /
Number of pages: 730
ISBN-13: 9783642350887
Subjects: Atmospheric physics; Artificial Intelligence (incl. Robotics); Geography; Earth sciences; Artificial intelligence; K-theory
Description: 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.
Table of contents: title: A Review on the Theory and Methodologies of Estimation for Dynamical Systems; title: Nudging Methods: A Critical Overview; title: Markov Chain Monte Carlo Methods: Theory and Applications; title: Information Content in Data Assimilation; title: A Question of Adequacy of Observations in Variational Data Assimilation; title: Quantifying Observation Impact for a Limited Area Atmospheric Forecast Model; title: Skewness of the Prior through Position Errors and Its Impact on Data Assimilation; title: Background Error Correlation Modeling with Diffusion Operator; title: The Adjoint Sensitivity Guidance to Diagnosis and Tuning of Error Covariance Parameters; title: Treating Nonlinearities in Data-Space Variational Assimilation; title: Linearized Physics for Data Assimilation at ECMWF; title: Recent Applications in Representer-Based Variational Data Assimilation. .