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Earth Sciences & Geography - Atmospheric Sciences | Data Assimilation and its Applications

Data Assimilation and its Applications

Sharan, Maithili, Issartel, Jean Pierre (Eds.)

2012, VI, 286 p.

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  • About this book

  • Presents an up-to-date and versatile methodology
  • Contains many original findings for atmospheric, oceanic and environmental systems
  • Suitable researchers as well as for graduate level courses

Data assimilation is a novel, versatile methodology for estimating atmospheric and oceanic variables. The estimation of a quantity of interest via data assimilation involves the combination of observational data with the underlying dynamical principles governing the system under observation.

This volume contains many original findings in data assimilation and its applications related to atmospheric, oceanic and environmental systems. This covers various data assimilation techniques with in Bayesian and non-Bayesian framework ranging from Least-Square, nudging, three dimensional variational (3DVAR), four-dimensional variational (4DVAR), Local Ensemble Kalman filter, Genetic algorithm etc. This also covers the applications to extreme weather event, hurricane, Asian summer monsoon, structure of the barrier layer in the equatorial Pacific ocean and identification of emission sources.

This volume will be useful as a reading material in graduate level courses dealing with data assimilation and its application to meteorology, ocean and air quality. The scientific community at large especially younger scientists will find this book a useful addition to their personal and institutional libraries.

Content Level » Research

Keywords » Bayesian and non-Bayesian techniques - application to meteorology, ocean and air quality - genetic algorithm - multidimensional variational methods - novel estimation methods for environmental variables

Related subjects » Birkhäuser Applied Probability and Statistics

Table of contents 

Preface.- A. General Techniques.- B. Data Assimilation with Applications to the Atmosphere.- C. Data Assimilation with Applications to Air Quality.- D. Data Assimilation with Applications to the Ocean.

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