Editorial Type:
Article
Article Type:
Research Article

Statistical Correction of Dynamical Prognoses: The Decision Problem

A. F. Bennett Department of Mathematics, Monash University, Clayton, Victoria, Australia 3168

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L. M. Leslie Australian Numerical Meteorology Research Centre, Melbourne, Victoria, Australia 3001

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Print Publication:
01 Feb 1983
DOI:
https://doi.org/10.1175/1520-0493(1983)111<0343:SCODPT>2.0.CO;2
Page(s):
343–352
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Abstract

A statistical correction scheme significantly reduces the mean S1 skill score of the Australian Region Primitive Equation Model. However, on a number of days, the statistically predicted corrections degrade the dynamical prognoses. Empirical Bayes’ methods are used here to construct decision rules which reject a priori most of the deleterious corrections, at the expense of rejecting some of the beneficial corrections.

Abstract

A statistical correction scheme significantly reduces the mean S1 skill score of the Australian Region Primitive Equation Model. However, on a number of days, the statistically predicted corrections degrade the dynamical prognoses. Empirical Bayes’ methods are used here to construct decision rules which reject a priori most of the deleterious corrections, at the expense of rejecting some of the beneficial corrections.

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