Statistical Corrections to Numerical Prediction Equations

Alan J. Faller Institute for Fluid Dynamics and Applied Mathematics and The Graduate Program for Meteorology, University of Maryland, College Park 20742

Search for other papers by Alan J. Faller in
Current site
Google Scholar
PubMed
Close
and
David K. Lee Institute for Fluid Dynamics and Applied Mathematics and The Graduate Program for Meteorology, University of Maryland, College Park 20742

Search for other papers by David K. Lee in
Current site
Google Scholar
PubMed
Close
Full access

Abstract

A procedure for statistical correction of numerical prediction equations at the end of each predictive time step is described and tested with a one-dimensional prediction model. The model equation is a modified Burgers equation that allows the formation of shocks, analogous to atmospheric fronts, and which contains a space and velocity-dependent source of energy to maintain the flow against dissipation. The detailed flow is calculated from a fine-grid numerical integration. Coarse-grid values, for testing a coarse-grid prediction scheme, are obtained by space-time averages.

Since the coarse-grid prediction equation cannot represent the sub-grid-scale motions, statistical corrections are added in the form of parametric terms as a pragmatic substitute for the missing sub-grid-scale effects. Tests with different versions of the model show that substantial improvement over the straight- forward coarse-grid prediction can he obtained when the coefficients of appropriate parametric terms are determined by a multiple regression procedure.

Abstract

A procedure for statistical correction of numerical prediction equations at the end of each predictive time step is described and tested with a one-dimensional prediction model. The model equation is a modified Burgers equation that allows the formation of shocks, analogous to atmospheric fronts, and which contains a space and velocity-dependent source of energy to maintain the flow against dissipation. The detailed flow is calculated from a fine-grid numerical integration. Coarse-grid values, for testing a coarse-grid prediction scheme, are obtained by space-time averages.

Since the coarse-grid prediction equation cannot represent the sub-grid-scale motions, statistical corrections are added in the form of parametric terms as a pragmatic substitute for the missing sub-grid-scale effects. Tests with different versions of the model show that substantial improvement over the straight- forward coarse-grid prediction can he obtained when the coefficients of appropriate parametric terms are determined by a multiple regression procedure.

Save