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Advanced Data Assimilation for GeosciencesLecture Notes of the Les Houches School of Physics: Special Issue, June 2012$
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Éric Blayo, Marc Bocquet, Emmanuel Cosme, and Leticia F. Cugliandolo

Print publication date: 2014

Print ISBN-13: 9780198723844

Published to Oxford Scholarship Online: March 2015

DOI: 10.1093/acprof:oso/9780198723844.001.0001

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Data assimilation in coastal oceanography

Data assimilation in coastal oceanography

IS4DVAR in the Regional Ocean Modelling System (ROMS)

(p.555) 24 Data assimilation in coastal oceanography
Advanced Data Assimilation for Geosciences

J. Zavala-Garay

J. Wilkin

J. Levin

Oxford University Press

This chapter presents examples of variational data assimilation in coastal oceanography using the Regional Ocean Modeling System (ROMS). Realizing that satellite data is the only source of information in real time in most parts of the world ocean, the Ocean Modeling Group at Rutgers University has developed methodologies to exploit the information content in remotely sensed observations. This chapter evaluates the extent to which incremental, strong constraint, four-dimensional variational data assimilation (IS4DVAR) can improve prediction of mesoscale variability using ROMS. Examples of two applications of IS4DVAR in two very different dynamical regimes are presented: the East Australia Current (EAC) and the Middle Atlantic Bight (MAB). The two main sources of satellite information, namely sea surface temperature (SST) and sea surface height anomaly (SSHA), are found to be complementary, and therefore both need to be assimilated in order to approximate the three-dimensional structure of the ocean.

Keywords:   data assimilation, coastal oceanography, variational data assimilation, mesoscale variability, sea surface temperature, SST, sea surface height anomaly, SSHA

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