Four dimensional ensemble-variational hybrid data assimilation system (4DEnVar) based on the gridpoint statistical interpolation (GSI) system was developed and is under pre-implementation test for the Global Forecast System (GFS) at the National Center for Environmental Prediction (NCEP). Experiments have shown a significant improvement of the 4DEnVar over the 3DEnVar for the GFS (Wang and Lei, 2014; Kleist and Ide, 2015).

A recent study by Lei and Wang (2015) using 3DEnVar suggested that increasing the ensemble size may further improve the performance of the 4DEnVar system. A direct increase of the ensemble size, however, will require significant increase of the computational resources. In this study a time-lagged ensemble method was proposed to increase the ensemble size by directly introducing the GFS control forecasts and the Global Ensemble Forecast System (GEFS) 20-member forecasts. This method will incur minimum extra costs due to the freely available lagged forecasts in the real time.

The GFS 4DEnVar system with the lagged ensemble was developed. Initial tests have demonstrated the potential of using the lagged ensemble in improving 4DEnVar performance especially when proper weighting is assigned. More results will be presented at the workshop.

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*email: bo.huang@ou.edu
*Preference: Oral