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Simplification of the Kalman filter for meteorological data assimilationThe paper proposes a new statistical method of data assimilation that is based on a simplification of the Kalman filter equations. The forecast error covariance evolution is approximated simply by advecting the mass-error covariance field, deriving the remaining covariances geostrophically, and accounting for external model-error forcing only at the end of each forecast cycle. This greatly reduces the cost of computation of the forecast error covariance. In simulations with a linear, one-dimensional shallow-water model and data generated artificially, the performance of the simplified filter is compared with that of the Kalman filter and the optimal interpolation (OI) method. The simplified filter produces analyses that are nearly optimal, and represents a significant improvement over OI.
Document ID
19910058220
Acquisition Source
Legacy CDMS
Document Type
Reprint (Version printed in journal)
External Source(s)
Authors
Dee, Dick P.
(Delft Hydraulics Emmeloord, Netherlands)
Date Acquired
August 15, 2013
Publication Date
January 1, 1991
Publication Information
Publication: Royal Meteorological Society, Quarterly Journal
Volume: 117
ISSN: 0035-9009
Subject Category
Cybernetics
Accession Number
91A42843
Funding Number(s)
CONTRACT_GRANT: NAG5-820
CONTRACT_GRANT: NAG5-341
Distribution Limits
Public
Copyright
Other

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