The accuracy 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 and real 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 different configurations of the LEKF scheme.
It is shown that a modest size (40-member) ensemble is sufficient to track the evolution of the atmospheric state with high accuracy. 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.
In collaboration with E. J. Kostelich, G. Gyarmati, D. J. Patil, B. R.
Hunt, E. Kalnay, E. Ott, and J. Yorke
Copyright. All Rights Reserved.