Extended Abstract (360K)5.4
Improved analyses and forecasts with AIRS retrievals using the local ensemble transform Kalman filter
Hong Li, Univ. of Maryland, College Park, MD; and J. Liu, E. Fertig, E. Kalnay, J. Aravequia, I. Szunyogh, E. J. Kostelich, and R. Todling
The Local Ensemble Transform Kalman Filter (LETKF, Hunt et al. 2006) has been implemented to assimilate simulated observations in the NCEP GFS model (Szunyogh et al. 2005), and in the NASA fvGCM model (Liu et al. 2006). The results from LETKF are much better than those from 3DVAR in a perfect model scenario. With real data, LETKF has been shown to be superior to the NCEP SSI (operational 3DVAR) by Szunyogh et al 2006 when applied on the NCEP GFS model at T62L28 resolution, and verified against the NCEP T254/L64 analysis using all available operational observations.
The Atmospheric Infrared Sounder (AIRS) was lunched on EOS Aqua in 2002. Some positive impacts on global analysis and forecast have been found in 3DVAR (Marshall et al. 2006, Chahine et al. 2006). Since the LETKF was shown to be better than 3DVAR when using all operational observations except radiances,in this study we assess the impact of adding AIRS data on the LETKF. Our preliminary results show that the AIRS temperature retrievals have consistent positive impact on both analysis and forecast, found not only in the temperature field but also in the other variables. This positive impact is biggest in Southern Hemisphere but still significant in Northern Hemisphere.
Session 5, Data Impact Tests and Observing System Simulation Experiments (OSSE)
Wednesday, 17 January 2007, 8:30 AM-5:00 PM, 212B
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