The impact of a digital filter finalization technique in a global data assimilation system

The ability of a digital filter technique to control high-frequency gravity wave noise in numerical weather forecasts based on the primitive equations is examined in the context of a global data assimilation system. The method uses a 12-h forward integration of the complete model to generate a time series that is filtered to give a balanced model state valid 6 h into the integration. This state is free of high-frequency noise and serves as a background field for the next analysis. The technique is referred to as digital filter finalization. The technique is first applied to a long model run in order to identify the impact of the chosen cutoff period in the design of the filter on a properly balanced model state. The robustness of the technique to typical imbalances between the mass and wind fields produced by an operational statistical interpolation procedure is also examined. Results of data assimilation experiments performed with the digital filter finalization and with the currently-operational adiabatic nonlinear normal mode initialization scheme are compared. The digital filter finalization technique examined here is shown to be an accurate, consistent and very simple way to remove the undesirable high-frequency noise from a global model forecast.