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• W2014805809 abstract "The main theme of this article is the central role that dynamics plays in estimating the state of the atmosphere and of the ocean from incomplete and noisy data. The evolution of meteorological data usage in estimation and prediction is reviewed, from manual synoptic analysis through objective analysis to four-dimensional assimilation into a prediction model. 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 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. A simple barotropic model of geophysical flow is used to illustrate the basic concepts. This model's results with simulated data are compared with the accumulated experience from full-scale meteorological models, assimilating real data. The so-called initialization problem of eliminating fast, inertia-gravity waves in meteorological data assimilation is described within the context of sequential estimation. Improvements to operational schemes of data assimilation in current meteorological use are suggested, with a view to ocean applications." @default.
• W2014805809 created "2016-06-24" @default.
• W2014805809 creator A5045772011 @default.
• W2014805809 date "1989-06-01" @default.
• W2014805809 modified "2023-10-10" @default.
• W2014805809 title "Meteorological data assimilation for oceanographers. Part I: Description and theoretical framework" @default.
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• W2014805809 doi "https://doi.org/10.1016/0377-0265(89)90040-7" @default.
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