18th Conference on Weather and Forecasting, 14th Conference on Numerical Weather Prediction, and Ninth Conference on Mesoscale Processes

Poster Session 6: Poster Session - Ensembles—with Coffee Break

Thursday, 2 August 2001: 2:30 PM-4:00 PM
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Organizer:  John Gyakum, McGill Univ.
Papers:
 
P6.1
Paper P6.1 has been moved to the Mesoscale Processes Program, Session 9, Paper number 9.6a

 
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

Poster PDF (174.6 kB)
 
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

Poster PDF (764.9 kB)
 
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

Poster PDF (46.6 kB)
 
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

 
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

Poster PDF (189.1 kB)
 
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

Poster PDF (906.1 kB)
 
P6.13
A Comparison of Different Ensemble Generation Techniques

 
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

Poster PDF (361.3 kB)
 
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 Jr.

Poster PDF (45.2 kB)
 
P6.16
Using parallel data assimilation cycles to estimate analysis uncertainity

 
P6.17
Dynamic Ensemble MOS

 
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

Poster PDF (106.0 kB)
 
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

Poster PDF (36.9 kB)
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