Poster Session 6 |
| | Poster Session - Ensembles—with Coffee Break |
| | Organizer: John Gyakum, McGill Univ., Montreal, PQ Canada
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| | P6.1 | Paper P6.1 has been moved to the Mesoscale Processes Program, Session 9, Paper number 9.6a
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| | P6.2 | Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter 
Thomas M. Hamill, NOAA/CIRES/CDC, Boulder, CO; and J. S. Whitaker and C. Snyder |
| | P6.3 | Kalman filter error statistics and the global meteorological observing network
Carolyn A. Reynolds, NRL, Monterey, CA; and C. H. Bishop |
| | P6.4 | A simplified model for predicting forecast error variances
Yong Li, NASA/GSFC, Greenbelt, MD; and S. E. Cohn, L. P. Riishojgaard, J. Guo, and Z. Toth |
| | P6.5 | A bayesian technique for estimating covariance parameters in large scale statistical objective analysis
David F. Parrish, NOAA/NWS/NCEP/EMC, Camp Springs, MD; and R. J. Purser |
| | P6.6 | The exact error covariances of an autonomous optimal data assimilation cycle: Implications for ensembles and the global observing network
Craig H. Bishop, Penn State Univ., University Park, PA |
| | P6.7 | Wavelet Approximation in the Computation of Error Covariance Evolution
Andrew V. Tangborn, JCET/Univ. of Maryland Baltimore County, Greenbelt, MD |
| | P6.8 | A Real-Time Ensemble for the Prediction of Hurricane Tracks in the Atlantic Basin
Sim D. Aberson, NOAA/AOML/HRD, Miami, FL; and S. J. Majumdar and C. H. Bishop |
| | P6.9 | Ensemble methods applied to hurricane track forecasting
Brian F. Jewett, Univ. of Illinois, Urbana, IL; and M. K. Ramamurthy and H. Liu |
|  | P6.10 | Probabilistic forecasts and optimal perturbation ensembles
Xuguang Wang, Penn State Univ., University Park, PA; and C. H. Bishop |
| | P6.11 | Estimation of uncertainties in atmospheric data assimilation using singular vectors
Hyun Mee Kim, Univ. of Wisconsin, Madison, WI; and M. C. Morgan and R. E. Morss |
| | P6.12 | Unified treatment of measurement bias and correlation in variational analysis with consideration of the preconditioning problem
R. James Purser, General Sciences Corp., Beltsville, MD; and J. C. Derber |
| | P6.13 | A Comparison of Different Ensemble Generation Techniques
Brian J. Etherton, Penn State Univ., University Park, PA; and C. H. Bishop |
| | P6.14 | Can we predict the reduction in forecast error variance produced by targeted observations?
Sharanya J. Majumdar, Penn State Univ., University Park, PA; and C. H. Bishop, I. Szunyogh, and Z. Toth |
| | P6.15 | Ensemble forecasts using perturbed physics in a multidimensional parameter space
Raymond W. Arritt, Iowa State University, Ames, IA; and C. J. Anderson and W. A. Gallus |
| | P6.16 | Using parallel data assimilation cycles to estimate analysis uncertainity
Jeffrey S. Whitaker, NOAA/CIRES/CDC, Boulder, CO; and T. Hamill |
| | P6.17 | Dynamic Ensemble MOS
Peter P. Neilley, WSI, Inc., Billerica, MA; and W. Myers and G. Young |
| | P6.18 | MM5 adjoint development using TAMC: Experiences with an automatic code generator
Thomas Nehrkorn, AER, Inc., Lexington, MA; and G. D. Modica, M. Cerniglia, F. H. Ruggiero, J. G. Michalakes, and X. Zou |
| | P6.19 | The role of the momentum divergence equation ellipticity in the numerical model solutions
Ireneusz A. Winnicki, Military University of Technology, Warsaw, Poland; and K. Kroszczynski |