A Bayesian technique for estimating continuously varying statistical parameters of a variational assimilation

This note addresses the challenging problem of inferring, from observed meteorological data, a set of continuous parameters defining the error covariances used to analyze these data in a variational assimilation scheme. The method we propose is a Bayesian extension of the "maximum-likelihood" technique, which means that prior information about the parameters is brought into play. The method uses a stochastic approximation in the computation of some of the required terms, which are difficult and costly to evaluate by other, more standard methods. One important advantage of the proposed Bayesian approach is that it makes it possible to estimate objectively a spatially dependent but smoothly varying set of parameters in a consistent manner, provided the scale over which the variations occur are sufficiently large. This ability is illustrated in th idealized tests presented here. R. James Purser, David F. Parrish. "May 2000." System requirements: Adobe Acrobat Reader. Includes bibliographical references (pages 22-23). 2000 NWS (National Weather Service) NCEP (National Centers for Environmental Prediction) Library Public Domain 2285