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

diffusion.

• 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,

2004.

• Conducted model evaluation based on 1999 SOS episode (6/29-7/10) EPRI modeling

study,

• CAMx has higher positive bias than CMAQ and CMAQ-Madrid in predicting sulfate;

CMAQ underestimated nitrate and CAMx and CMAQ-MADRID overestimated

nitrate.

• All three models underestimated OM. However, there was no clear winner in model

performance.

• 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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