Illustrated model performance metrics, available PM2.5 and O3 data, and evaluation
software tool. Suggested that air quality modeling should include model evaluation as
part of the system.
• Comparing performance metrics may not be enough since performance metrics often
show mixed response and it is possible for a better model to have poorer metrics (e.g.,
bias in met & emissions inputs). Diagnostic evaluation is needed to judge finer grid
performance since coarser grid may have compensating errors. But should we assume
that finer grid modeling always gives better simulations/physics?
• An example of VISTAS modeling (July 1999) showed differences of hourly sulfate and
its wet deposition between CMAQ results using 12-km and 36-km grids, possible due
to regional transport (wind speed & direction), precipitations/clouds, and numerical
• Recommended bias factor as the best metric for evaluating haze.
11. Wang, Z., et al.,” Comparison and Diagnostic Evaluation of Air Quality Models for
Particulate Matter: CAMx, CMAQ, CMAQ-MADRID”, National RPO Modeling Meeting,
• Conducted model evaluation based on 1999 SOS episode (6/29-7/10) EPRI modeling
• CAMx has higher positive bias than CMAQ and CMAQ-Madrid in predicting sulfate;
CMAQ underestimated nitrate and CAMx and CMAQ-MADRID overestimated
• All three models underestimated OM. However, there was no clear winner in model
• The three models responded differently to a 50% increase in ammonia emissions,
indicating a need to further look at the models’ responsiveness to changes in emissions.
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