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Estimation of mean square prediction error of wind components is required in the optimal interpolation (OI) process in numerical prediction of atmospheric variables. Previous work has suggested that statistical models with log-linear scale parameters which include covariates can be used to predict mean square prediction errors. However, the parameters of the statistical relationships appear to change over time. A procedure is described to recursively update the estimated parameters. Data from July of 1991 are used to fit the model parameters and to study the predictive ability of the recursive procedure. This preliminary investigation indicates that observational and first guess wind components can be helpful in predicting mean square prediction error for wind components.... Hierarchical model, Gaussian model with log-linear scale parameters.
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Bayesian prediction of mean square errors with covariates
1992, Naval Postgraduate School, Available from National Technical Information Service
in English
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Book Details
Edition Notes
Cover title.
"NPS-OR-93-004."
"November 1992."
AD A259 585.
Includes bibliographical references (p.16-17).
aq/aq cc:9116 07/08/97
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