J5.1
Descriptions of sub-grid model variability and methods for incorporation in advanced applications of CMAQ
Jason K. Ching, USEPA/ORD/NERL/AMD, Research Triangle Park, NC NC; and V. Isakov and M. A. Majeed
Air quality grid models simulate only a portion of the spatial and temporal variabilities in the concentration fields. The variability at sub-grid (SGV) scales is typically not explicitly represented nor assessed in grid models. Outputs of grid simulations is single valued and lacks spatial texture, thus, there will always be differences between what is simulated and what is measured. In this paper, we suggest that explicit representation of the SGV has merit as a weight of evidence factors to air quality model attainment demonstrations, for model evaluation studies, and for its utility in human air pollution exposure assessments. For weight of evidence analyses, the SGV distributions can provide qualifying concentration bounds for the model grid simulations. Similarly, for model evaluation, knowing the magnitude and characteristics of the SGV provides a basis for constructing a valid statistical design for comparing what is simulated with what is observed. For human population exposure to air pollution assessments, the introduction of sub-grid variability to the gridded concentration fields provides a more complete description of the total variability (distribution of possible outcomes).
In this analyses we describe and discuss parameterizations of SGV based on each grids concentration probability density functions (PDFs). These distributions are derived for 4 and 12 km grid simulations from local scale dispersion, fine scale CMAQ and photochemical dynamical modeling contributions. In this presentation, CMAQ was run at multi-scales, (12, 4, and 1 km grid sizes) for July 2001 for a domain centered over Delaware. An initial formulation applies gridded distributions to a base 4 km simulation where we assume the coefficient of variability (COV) as a reasonable parameter for weight-of-evidence allowances. On the other hand, the use of peak-to-mean ratios or quartiles or deciles might be more suitable CMAQ complement for improved human population exposure assessments when additional exposure factors such as proximity of population residence to roadway sources, within-cell density of road links and other surrogates for exposure are introduced.
Recorded presentationJoint Session 5, Photochemical Modeling and Monitoring-II
Wednesday, 23 January 2008, 4:00 PM-5:30 PM, 220
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