Session 4
	Advanced Methods for Data Assimilation
	Chair: Michele M. Rienecker, NASA/GSFC, Greenbelt, MD
	1:30 PM	4.1	Ensemble data assimilation with the NCEP Global Forecast System   Jeffrey S. Whitaker, NOAA/ERL/CDC, Boulder, CO
	1:45 PM	4.2	Using an ensemble data assimilation system for climate model development   Jeffrey Anderson, NCAR, Boulder, CO; and K. Raeder, T. Hoar, and N. Collins
	2:00 PM	4.3	Recent development of global and regional local ensemble transform Kalman filters (LETKF) at JMA   Takemasa Miyoshi, Japan Meteorological Agency, Tokyo, Japan; and S. Yamane, Y. Sato, K. Aranami, and T. Enomoto
	2:15 PM	4.4	The Canadian Meso4dvar continental analysis system: Pre-operational evaluations   Luc Fillion, MSC, Dorval, PQ, Canada; and M. Tanguay, Z. Liu, E. Lapalme, N. Ek, C. Page, and S. Pellerin
	2:30 PM	4.5	Assessing observation impact using the adjoint of the GEOS-5 data assimilation system   Ronald Gelaro, NASA/GSFC, Greenbelt, MD; and Y. Zhu
	2:45 PM	4.6	Issues in Assimilation of Lagrangian Data   Kayo Ide, University of California, Los Angeles, CA
	3:00 PM		Coffee Break in Exhibit Hall
	3:30 PM	4.7	An alternative to bias correction in retrievals and direct radiances assimilation   Steven J. Fletcher, Colorado State University, Fort Collins, CO; and M. Zupanski
	3:45 PM	4.8	Ensemble Kalman filter in the presence of model errors   Hong Li, Univ. of Maryland, College Park, MD; and E. Kalnay, T. Miyoshi, and C. M. Danforth
	4:00 PM	4.9	Comparing Local Ensemble Transform Kalman Filter with 4D-Var in a quasi-geostrophic model   Shu-Chih Yang, Univ. of Maryland, College Park, MD; and E. Kalnay
	4:15 PM	4.10	A hybrid ensemble transform Kalman filter(ETKF)-3DVAR data assimilation scheme for WRF   Xuguang Wang, NOAA/ESRL, Boulder, CO; and D. Barker, C. Snyder, and T. Hamill
	4:30 PM	4.11	Four-dimensional variational assimilation without using the adjoint model   Hyo-Jong Song, Seoul National Univ., Seoul, South Korea; and G. H. Lim and H. S. Lee
	4:45 PM	4.12	Predictor-corrector and morphing ensemble filters for the assimilation of sparse data into high-dimensional nonlinear systems   Jan Mandel, University of Colorado at Denver and Health Sciences Center, and National Center for Atmospheric Research, Denver, CO; and J. D. Beezley
	5:00 PM	4.13	Evaluating the ability of the Ensemble Transform Kalman Filter to predict the influence of observations on 3-6 day forecasts   Kathryn J. Sellwood, Univ. of Miami/RSMAS, Miami, FL; and S. J. Majumdar and D. Hodyss

Tuesday, 16 January 2007: 1:30 PM-5:15 PM, 208

* - Indicates paper has been withdrawn from meeting

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