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Air Quality Model Evaluation
AMAD Research Programs
Model Evaluation
To assure that we provide quality products to regulatory, academic, and other end-users, we conduct extensive evaluation studies to rigorously assess air quality model performance in simulating the spatio-temporal features embedded in the air quality observations. We comprehensively analyze the performance of meteorology, emissions, and chemical transport models to not only characterize model performance, but also identify what model improvements (inputs or processes) are needed. Thus, model evaluation efforts are tied directly with model development.
The Division has developed a framework to classify the different aspects of model evaluation under four general categories: operational, diagnostic, dynamic, and probabilistic. Operational evaluation is a comparison of model predicted and observed concentrations of the end-point pollutant(s) of interest and is a fundamental first phase of any model evaluation study. Diagnostic evaluation investigates the processes and input drivers that affect model performance. Dynamic evaluation focuses on assessing the model’s air quality response to changes in emissions and meteorology, which is central to applications in air quality management. Probabilistic evaluation characterizes the uncertainty of air quality model predictions and is used to provide a credible range of predicted values rather than a single “best-estimate”. Since these four types of model evaluation are not necessarily mutually exclusive, research studies often incorporate aspects from more than one category of evaluation.
Model outputs are compared to observations using various techniques including: (a) time series of daily maximum 8-hour O3 concentrations from a 200-member CMAQ model ensemble at a monitoring site in an urban location; and (b) percent contribution of individual aerosol species comprising the total average regional PM2.5 mass concentrations predicted by CMAQ and measured by the Speciated Trends Network (STN) sites.
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Contacts: Alice B. Gilliland, Sergey L. Napelenok
Relevant Publications & Presentations:
Appel, K.W., P.V. Bhave, A.B. Gilliland, G. Sarwar, S.J. Roselle, Evaluation of the Community Multiscale Air Quality (CMAQ) model version 4.5: Sensitivities impacting model performance; Part II - particulate matter, Atmos. Environ, doi:10.1016/j.atmosenv.2008.03.036, 2008.
Pinder, R.W., R.L. Dennis, P.V. Bhave, Observable indicators of the sensitivity of PM2.5 nitrate to emission reductions—Part I: Derivation of the adjusted gas ratio and applicability at regulatory-relevant time scales. Atmospheric Environment, 42(6), 1275-1286, 2008.
Gilliland, A.B., C. Hogrefe, R.W. Pinder, J.M. Godowitch, K.L. Foley, and S.T. Rao, Dynamic Evaluation of Regional Air Quality Models: Assessing Changes in O3 Stemming from Changes in Emissions and Meteorology, Atmospheric Environment, doi:10.1016/j.atmosenv.2008.02.018, 2008.
Dennis, R.L.; P.V. Bhave; R.W. Pinder, Observable indicators of the sensitivity of PM2.5 nitrate to emission reductions—Part II: Sensitivity to errors in total ammonia and total nitrate of the CMAQ-predicted non-linear effect of SO2 emission reductions. Atmospheric Environment, 42(6), 1287-1300, 2008.
Appel, K.W., A.B. Gilliland, G. Sarwar, and R.Gilliam, Evaluation of the Community Multiscale Air Quality (CMAQ) model version 4.5: Sensitivities impacting model performance; Part I - ozone, Atmospheric Environment, 41, 9603-9615, 2007.
Hogrefe, C., P.S. Porter, E. Gego, A. Gilliland, R. Gilliam, J. Swall, J. Irwin, and S.T. Rao, Temporal features in observed and predicted PM2.5 concentrations over the Eastern U.S., Atmospheric Environment, 5041-5055, 2006.