Development of a prognostic forecast error variance model for data assimilation

The forecast error variances used in the Goddard Earth Observing System (GEOS) global data assimilation system are currently estimated on-line by updating the time-mean statistics of observed-minus-forecast residuals. This ensures that the prescribed variances are consistent with the actual data residuals, but only on time scales longer than a week or so. To represent error variability on synoptic scales ("errors of the day"), we are developing a flow-dependent prognostic forecast error variance model.

This model describes the evolution of potential temperature error variances. It involves advection by the forecast model winds, along with parameterized growth due to model dynamics and physical forcing. The geopotential height error variances required by the analysis system can then be obtained from the potential temperature error variances and from assumptions about the sea level pressure error variances.

In this presentation we describe results of comparing this model with the ensemble spread implied by NCEP ensemble forecast products.