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Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications, Volume 2

New theories and methodologies in data assimilation
The most recent studies in data assimilation
Front-edge applications of data assimilation technique to various disciplines in Geosciences

By: Seon Ki Park (Editor), Liang Xu (Editor)

749 pages, 170 colour & 99 b/w illustrations

Springer-Verlag

Hardback | May 2013 | #218645 | ISBN-13: 9783642350870
Availability: Usually dispatched within 1-2 weeks Details
NHBS Price: £138.00 $174/€164 approx

About this book

Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications, Volume 2 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. Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications, Volume 2 will be useful to individual researchers as well as graduate students for a reference in the field of data assimilation.


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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