Extended Abstract (1.2M)J1.6
An analysis of the impact of observational data on ETKF-based ensemble perturbations
Mozheng Wei, NOAA/NWS/NCEP, UCAR Visiting Scientist, Camp Springs, MD; and Z. Toth, R. Wobus, and Y. Zhu
Ensemble Transform Kalman Filter (ETKF), which belongs to Kalman Square-root filters, is used to generate initial perturbations in experimental ensemble forecasts generated with the NCEP GFS forecast model.
The ETKF scheme uses real-time information on the location of observations (and the associated observational error variance) to produce initial ensemble perturbations. The initial forecast results based on the ETKF ensemble are comparable with those with the current bred-vector based NCEP operational ensemble forecasts. In addition, it appears that even with only 10 ensemble members the method is capable of representing day to day variations in analysis uncertainty due to variations in global data coverage. The results indicate that the ensemble based Kalman square-root filters may also offer a potentially useful tool for data assimilation.
Joint Session 1, Data Assimilation and Observational Network Design. Part I (Joint between the Symposium on Forecasting the Weather and Climate of the Atmosphere and Ocean and the 20th Conference on Weather Analysis and Forecasting/16th Conference on Numerical Weather Prediction) (ROOM 6A)
Monday, 12 January 2004, 9:00 AM-12:15 PM, Room 6A
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