TY - JOUR
T1 - Assessing a local ensemble Kalman filter
T2 - Perfect model experiments with the National Centers For Environmental Prediction global model
AU - Szunyogh, Istvan
AU - Kostelich, Eric
AU - Gyarmati, G.
AU - Patil, D. J.
AU - Hunt, Brian R.
AU - Kalnay, Eugenia
AU - Ott, Edward
AU - Yorke, James A.
PY - 2005/8
Y1 - 2005/8
N2 - The accuracy and computational efficiency 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 T62 horizontal- and 28-level vertical-resolution version of the Global Forecast System (GFS) of the National Center for Environmental Prediction. 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. For this ensemble size, the computational time per analysis is less than 9 min 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 parametrized physical processes play important roles. Because 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.
AB - The accuracy and computational efficiency 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 T62 horizontal- and 28-level vertical-resolution version of the Global Forecast System (GFS) of the National Center for Environmental Prediction. 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. For this ensemble size, the computational time per analysis is less than 9 min 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 parametrized physical processes play important roles. Because 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.
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U2 - 10.1111/j.1600-0870.2005.00136.x
DO - 10.1111/j.1600-0870.2005.00136.x
M3 - Review article
AN - SCOPUS:27744434698
SN - 0280-6495
VL - 57
SP - 528
EP - 545
JO - Tellus, Series A: Dynamic Meteorology and Oceanography
JF - Tellus, Series A: Dynamic Meteorology and Oceanography
IS - 4
ER -