Probabilistic Characterization of Atmospheric Transport and Diffusion

Abstract

Abstract The observed scatter of observations about air quality model predictions stems from a combination of naturally occurring stochastic variations that are impossible for any model to simulate explicitly and variations arising from limitations in knowledge and from imperfect input data. In this paper, historical tracer experiments of atmospheric dispersion were analyzed to develop algorithms to characterize the observed stochastic variability in the ground-level crosswind concentration profile. The algorithms were incorporated into a Lagrangian puff model (“INPUFF”) so that the consequences of variability in the dispersion could be simulated using Monte Carlo methods. The variability in the plume trajectory was investigated in a preliminary sense by tracking the divergence in trajectories from releases adjacent to the actual release location. The variability in the near-centerline concentration values not described by the Gaussian crosswind profile was determined to be on the order of a factor of 2. The variability in the trajectory was determined as likely to be larger than the plume width, even with local wind observations for use in characterizing the transport. Two examples are provided to illustrate how estimates of variability 1) can provide useful information to inform decisions for emergency response and 2) can provide a basis for sound statistical designs for model performance assessments.