Assessing a local ensemble Kalman filter: Perfect model experiments with the NCEP global model

The accuracy and computational e ciency of the recently proposed Local Ensemble Kalman Filter (LEKF) data assimilation scheme is investigated on a state-of-the-art operational numerical weather prediction model using simulated observations. The model selected for this purpose is the T-62 horizontal- and 28-level vertical-resolution version of the Global Forecast System (GFS) of the National Centers for Environmental Prediction (NCEP). The performance of the data assimilation system is assessed for di erent configurations of the LEKF scheme. It is shown that a modest size (40-member) ensemble is su cient to track the evolution of the atmospheric state with high accuracy. (For this ensemble size the computational time per analysis is less than 9 minutes on a cluster of PCs). The analyses are extremely accurate in the mid-latitude storm track regions. The largest analysis errors, which are typically much smaller than the observational errors, occur where parameterized physical processes play important roles. Since these are also the regions where model errors are expected to be the largest, limitations of a real-data implementation of the ensemble based Kalman filter may be easily mistaken for model errors. In light of these results, the importance of testing the ensemble based Kalman filter data assimilation systems on simulated observations is stressed.