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COMPARISON OF SPATIAL PATTERNS OF POLLUTANT DISTRIBUTION WITH CMAQ PREDICTIONS
Citation:
PHILLIPS, S. AND P. L. FINKELSTEIN. COMPARISON OF SPATIAL PATTERNS OF POLLUTANT DISTRIBUTION WITH CMAQ PREDICTIONS. ATMOSPHERIC ENVIRONMENT. Elsevier Science Ltd, New York, NY, 40(26):4999-5009, (2006).
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:
To evaluate the Models-3/Community Multiscale Air Quality (CMAQ) modeling system in reproducing the spatial patterns of aerosol concentrations over the country on timescales of months and years, the spatial patterns of model output are compared with those derived from observational data. Simple spatial interpolation procedures were applied to data from the Clean Air Status and Trends Network (CASTNet) and Speciation Trends Network (STN) monitoring networks. Species included sulfate PM, total nitrate (NO3 + HNO3), and ammonium PM. Comparisons were made for the annual average concentrations for 2001, and for one lunar month (four weeks), where the month chosen for each species represents the highest concentrations of the year. Comparisons between the modeled and interpolated spatial patterns show very good agreement in the location and magnitude of the maxima and minima, as well as the gradients between them. Some persistent biases are identified and noted. Limitations on our ability to describe the spatial pattern from sparse data as well as the limitations of the networks are briefly discussed.