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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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Combining models and data in large-scale oceanography

Combining models and data in large-scale oceanography

examples from the consortium for Estimating the Circulation and Climate of the Ocean (ECCO)

Chapter:
(p.535) 23 Combining models and data in large-scale oceanography
Source:
Advanced Data Assimilation for Geosciences
Author(s):

I. Fukumori

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780198723844.003.0023

This chapter describes examples of combining observations with numerical models in the context of studying ocean circulation. Model–data syntheses provide complete descriptions of the ocean, including elements not directly measured, and are conducive to understanding mechanisms of ocean circulation. The estimates, in conjunction with the models and their associated tools, such as the models’ adjoints, allow quantitative analyses of processes and causal mechanisms. Salient differences exist between model–data syntheses intended for studying processes and those meant for numerical forecasting. While the latter focus on obtaining optimal estimates at discrete instances (i.e. successive initial conditions), the former require explicit physical accounting of the system’s entire temporal evolution. This chapter also describes some of the practical techniques for implementing advanced estimation methods with large models, including Kalman filters, related smoothers, and adjoint methods, as well as ways to estimate and prescribe prior uncertainties, including their covariance.

Keywords:   Kalman filter, smoother, adjoint method, ocean circulation, model–data synthesis, prior uncertainties

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