Model assessment and verification - Air Pollution Modeling and Its Application - page 339

Community Multiscale Air Quality (CMAQ) model to improve model predictions
of aerosol nitrate, NO 3 (Gilliland et al., 2003).
Fig. 2. CMAQ model ambient PM 2.5 nitrate estimates and CASTNET observations before (left)
and after (right) top-down estimates of NH 3 emissions
Dynamic Evaluation characterizes how well the model captures observed
changes in air quality induced by changes in emissions and/or meteorology. Some
of the challenges involved in dynamic evaluation include distinguishing between
meteorological and emissions signals in the model results and determining the
relevant space/time scales on which to conduct these evaluations. Assessing the
emissions signal is particularly challenging, as anthropogenic source emissions
tend to change slowly over time (years), except for weekday/weekend activity
differences, and emissions controls implemented over a short period of time.
Probabilistic Evaluation transforms deterministic model predictions into
probabilistic form, helping to build confidence in the use of air quality models in
policy setting. This approach, for example, may characterize the probability of
success of an emissions control option in meeting a given air quality objective.
One key issue is determining the role of ensemble modeling in probabilistic model
use and evaluation (Pinder et al., 2009).
3. Air Quality Model Evaluation International Initiative
(AQMEII)
Inspired by the emergence of the model evaluation framework and the discussions
held at the August 2007 EPA/AMS Workshop, the Air Quality Model Evaluation
International Initiative (AQMEII) is now proposed. To start this new collaborative
project, a Workshop was held during 27-29 April 2009 in Stresa, Italy, hosted by
the European Commission's Joint Research Centre and attended by 50 scientists
from North America and Europe. Workshop discussions covered the types of
model evaluations contained in the framework and were motivated by key questions
(see Fig. 3) . The goals of the Workshop included exchanging expert knowledge in
regional air quality modeling, identifying knowledge gaps in the science, establishing