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Systematic estimation of forecast and observation error covariances in four-dimensional data assimilationA two-part algorithm is presented for reliably computing weather forecast model and observational error covariances during data assimilation. Data errors arise from instrumental inaccuracies and sub-grid scale variability, whereas forecast errors occur because of modeling errors and the propagation of previous analysis errors. A Kalman filter is defined as the primary algorithm for estimating the forecast and analysis error convariance matrices. A second algorithm is described for quantifying the noise covariance matrices of any degree to obtain accurate values for the observational error covariances. Numerical results are provided from a linearized one-dimensional shallow-water model. The results cover observational noise covariances, initial instrumental errors and erroneous model values.
Document ID
19870024404
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Dee, D. P.
(New York Univ. New York, NY, United States)
Cohn, S. E.
(New York Univ. New York, NY, United States)
Ghil, M.
(New York University NY, United States)
Date Acquired
August 13, 2013
Publication Date
January 1, 1985
Subject Category
Meteorology And Climatology
Accession Number
87A11678
Funding Number(s)
CONTRACT_GRANT: NSF INT-83-14934
CONTRACT_GRANT: FINEP-4-3-82-017900
CONTRACT_GRANT: NSG-5130
CONTRACT_GRANT: CNPQ-1,01,10,021/83
CONTRACT_GRANT: NAG5-341
Distribution Limits
Public
Copyright
Other

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