It is becoming increasingly important to have methods that not only accurately predict the transport and dispersion of chemical and biological contaminants, but to also be able to compute the uncertainty in the modeled concentrations. To that end, modern atmospheric transport and dispersion (AT&D) models like SCIPUFF are often driven by numerical weather prediction (NWP) ensemble output. Meteorological uncertainty expressed in the ensemble statistics, particularly in the wind direction, can have a substantial impact on the hazard area in AT&D predictions. It is important to attempt to construct the ensemble in such a way that it encompasses almost all possible realizations of the future state of the atmosphere. This is necessary in order to provide an upper bound on meteorological uncertainty and thus a conservative estimate of the potential hazard. There are several ways that NWP ensembles can be constructed, including varying the initial conditions (ICs), boundary conditions (BCs), physics parameterization schemes, data assimilation schemes, the NWP model itself, or some combination thereof. This study compares the performance of two types of NWP ensembles that are used to drive SCIPUFF in modeling the observed plume during the 1983 Cross Appalachian Tracer Experiment (CAPTEX-83). One ensemble varies physics parameterization and data assimilation schemes, and the other incorporates IC, BC and physics parameterization variability. Recommendations are made as to the type of NWP ensemble that should be used to drive AT&D models.