Keywords:
Bayesian and non-Bayesian techniques
;
application to meteorology, ocean and air quality
;
genetic algorithm
;
multidimensional variational methods
;
novel estimation methods for environmental variables
Description / Table of Contents:
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.
Pages:
Online-Ressource (VI, 286 Seiten)
ISBN:
9783034804417
URL:
http://link.springer.com/journal/24/169/3/
Language:
English
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