Theoretical Modeling for Numerical Weather Prediction

The goal is to utilize predictability theory and numerical experimentation to identify and understand some of the dynamical processes which must be modeled more realistically if large-scale numerical weather predictions are to be improved. The emphasis is on the use of relatively simple models to exlore the properties of physically comprehensive general circulation models (GCM's). A global linear quasi-geostrophic model and the Goddard Laboratory for Atmospheric Sciences (GLAS) GCM were used to investigate several mechanisms which are responsible for the decay of large-scale forecast skill in mid-latitude numerical weather predictions. Five-day forecasts for an ensemble of cases were made using First GARP Global Experiment data. It was found that forecast skill depends crucially on the specification of the stationary forcing. A lack of stationary forcing leads to spurious westwad propagation of the ultralong waves. Forecasts made with stationary forcings derived from climatological data are superior to those using forcings inferred from observations immediately preceding the forecast period. Interhemispheric forecast differences were analyzed, and the model errors were compared to errors of a simple persistence-damped-to-climatology scheme and to errors of the GLAS GCM.