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PROBABILISTIC CHARACTERIZATION OF ATMOSPHERIC TRANSPORT AND DIFFUSION
Citation:
IRWIN, J. S., W. B. PETERSEN, AND S. C. HOWARD. PROBABILISTIC CHARACTERIZATION OF ATMOSPHERIC TRANSPORT AND DIFFUSION. JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY. American Meteorological Society, Boston, MA, 46(7):980-993, (2007).
Impact/Purpose:
The objective of this task is to thoroughly characterize the performance of the emissions, meteorological and chemical/transport modeling components of the Models-3 system, with an emphasis on the chemical/transport model, CMAQ. Emissions-based models are composed of highly complex scientific hypotheses concerning natural processes that can be evaluated through comparison with observations, but not truly validated. Static and Dynamic Operational, Diagnostic, and ultimately Probablistic evaluation methods are needed to both establish credibility and build confidence within the client and scientific community in the simulations results for policy and scientific applications. The characterization of the performance of Models-3/CMAQ is also a tool for the model developers to identify aspects of the modeling system that require further improvement.
Description:
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 explicitly simulate and variations arising from limitations in our 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 to likely 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.