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Sequential estimation and satellite data assimilation in meteorology and oceanographyThe central theme of this review article is the role that dynamics plays in estimating the state of the atmosphere and of the ocean from incomplete and noisy data. Objective analysis and inverse methods represent an attempt at relying mostly on the data and minimizing the role of dynamics in the estimation. Four-dimensional data assimilation tries to balance properly the roles of dynamical and observational information. Sequential estimation is presented as the proper framework for understanding this balance, and the Kalman filter as the ideal, optimal procedure for data assimilation. The optimal filter computes forecast error covariances of a given atmospheric or oceanic model exactly, and hence data assimilation should be closely connected with predictability studies. This connection is described, and consequences drawn for currently active areas of the atmospheric and oceanic sciences, namely, mesoscale meteorology, medium and long-range forecasting, and upper-ocean dynamics.
Document ID
19870053376
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Ghil, M.
(California, University Los Angeles; New York University, NY, United States)
Date Acquired
August 13, 2013
Publication Date
January 1, 1986
Subject Category
Oceanography
Accession Number
87A40650
Funding Number(s)
CONTRACT_GRANT: NSG-5130
CONTRACT_GRANT: NAG5-713
Distribution Limits
Public
Copyright
Other

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