| | P1.1 | Value of the NWS/NCEP Short Range Ensemble Forecast (SREF) system for Quantifying Mesoscale Model Error
Jeffery T. McQueen, NOAA/NWS/NCEP, Camp Springs, MD; and J. Du, B. Zhou, G. DiMego, B. Ferrier, G. Manikin, E. Rogers, H. Juang, and Z. Toth |
| | P1.2 | Using numerical continuation to examine the predictability of the atmospheric boundary layer
Richard T. McNider, University of Alabama, Huntsville, AL; and M. Friedman, A. Biazar, and X. Shi |
| | P1.3 | Time-step sensitivity of nonlinear atmospheric models: numerical convergence, truncation error growth and ensemble design
João Teixeira, UCAR Visiting Scientist, NRL, Monterey, CA; and C. A. Reynolds and K. Judd |
| | P1.4 | The influence of rawinsonde observations on 3-7 day weather forecasts
Sharanya J. Majumdar, RSMAS/University of Miami, Miami, FL |
| | P1.5 | Slow manifold and predictability
V. Krishnamurthy, COLA, Calverton, MD |
| | P1.6 | Roots of ensemble forecasting
John M. Lewis, NOAA/NSSL, Reno, NV, NV |
| | P1.7 | Quantifying observation impact with an adjoint-based procedure
Rolf H. Langland, NRL, Monterey, CA |
| | P1.8 | Quantifying and reducing uncertainty by employing model error estimation methods
Dusanka Zupanski, CIRA/Colorado State University, Fort Collins, CO |
| | P1.9 | Predictability as gleaned from recent Eta Model results: can we still significantly increase synoptic-scale NWP skill out to several days?
Fedor Mesinger, NOAA/NWS/NCEP/EMC and UCAR, Camp Springs, MD |
| | P1.10 | Predictability—Who is the Main Player: IC or Model Physics Uncertainty?
Jun Du, NOAA/NWS/NCEP, Camp Springs, MD; and J. McQueen |
| | P1.11 | On the predictability of mesoscale convective systems
David J. Stensrud, NOAA/NSSL, Norman, OK; and L. J. Wicker |
| | P1.12 | Non-Gaussian probability distributions: What are their implications for predictability?
Philip Sura, NOAA-CIRES Climate Diagnostics Center, Boulder, CO; and M. Newman, C. Penland, and P. Sardeshmukh |
| | P1.13 | Model Predictability – From Lorenz System to Operational Ocean/Atmospheric Models
Peter C. Chu, NPS, Monterey, CA; and L. M. Ivanov |
| | P1.14 | Model error, attractors, and predictability
Kevin Judd, University of Western Australia, Perth, Western Australi, Australia; and C. A. Reynolds and T. E. Rosmond |
| | P1.15 | Model diversities and their implication in multi-model ensembles
Dingchen Hou, NOAA/NWS/NCEP/EMC, Camp Springs, MD; and Z. Toth, Y. Zhu, and R. Wobus |
| | P1.16 | Maximum likelihood ensemble filter: exploiting dynamic localization of Lyapunov vectors
Milija Zupanski, Colorado State University, Fort Collins, CO; and S. J. Fletcher, I. M. Navon, and B. Uzunoglu |
|  | P1.17 | Local Lagrangian and Eulerian available energetics in moist atmospheres
Peter R. Bannon, The Pennsylvania State University, University Park, PA |
| | P1.18 | Intercomparison of Global Research and Operational Forecasts
Jennifer C. Roman, AFWA/DNXT, Offutt AFB, NE; and G. Miguez-Macho, L. A. Byerle, and J. Paegle |
| | P1.19 | Initial-time sensitivity of tropical cyclone track forecasts
Melinda S. Peng, NRL, Monterey, CA; and C. A. Reynolds |
| | P1.20 | Flow and Regime dependent mesoscale predictability
Fuqing Zhang, Texas A&M University, College Station, TX; and C. Snyder and R. Rotunno |
| | P1.21 | Extratropical control of subtropical humidity: diagnosis using tracers of last saturation
Joseph Galewsky, Columbia University, New York, NY; and A. H. Sobel and I. M. Held |
| | P1.22 | EOFs –myths, misconceptions and open problems
Ian T Jolliffe, University of Reading, Cowes, Isle of Wight, United Kingdom |
| | P1.23 | Ensemble Data Assimilation with the NCEP GFS
Jeffrey S. Whitaker, NOAA-CIRES/CDC, Boulder, CO; and T. M. Hamill |
| | P1.24 | Energy-conserving and Hamiltonian extensions of the Lorenz model
Alexander Gluhovsky, Purdue University, West Lafayette, IN |
| | P1.25 | Diagnosis of medium-range predictability enhancement associated with anomalous zonal flow over western North America during winter
Lee A. Byerle, AWS, Papillion, NE; and J. Paegle |
| | P1.26 | Off-line sequential bias estimation experiments with a Lorenz model
Joshua P. Hacker, NCAR, Boulder, CO; and C. Snyder |
| | P1.27 | Available potential energy and its relatives
Theodore G. Shepherd, University of Toronto, Toronto, ON, Canada |
| | P1.28 | Assessing Predictability using Linear Inverse Models
Prashant D. Sardeshmukh, NOAA-CIRES Climate Diagnostics Center, Boulder, CO; and M. Newman and C. Penland |
| | P1.29 | Another look at predictability in flows with many scales
Chris Snyder, NCAR, Boulder, CO; and R. Rotunno, F. Zhang, and R. Morss |
| | P1.30 | A simple 2-dimensional chaotic rain gush model
Stanley David Gedzelman, The City College of New York, New York, NY |
| | P1.31 | A priori identification of the inferior solutions in an ensemble of dynamical forecasts
Wilbur Y. Chen, NOAA/NWS/NCEP/CPC, Camp Springs, MD |
| | P1.32 | A dynamical system analysis of Lorenz's low order model of the general circulation
Joy Romanski, Columbia University, New York, NY; and W. B. Rossow |
| | P1.33 | Singular vectors computed with a flow-dependent analysis error covariance norm
Mark Buehner, MSC, Dorval, QC, Canada; and A. Zadra |
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