Those implementing the modeling/analysis protocol should describe the rationale for

distinguishing among episodes which are modeled. The selection may reflect a number of areaspecific

considerations. Qualitative procedures such as reviewing surface and aloft weather

maps, and observed or modeled wind patterns may suffice for distinguishing episodes with

distinctively different meteorological conditions. More quantitative procedures, such as a

Classification and Regression Tree (CART) analysis or a principal component analysis (PCA), to

identify distinctive groupings of meteorological/air quality parameters corresponding with high

daily maxima or averages, may sometimes be desirable. An example of a CART analysis

applied to select episodes is described by Deuel (1998). LADCO used CART to rank historical

years for Midwestern cities by their conduciveness to ozone formation (Kenski, 2004). A PCA

may also be used to characterize predominant meteorological conditions and relate those

conditions to ozone concentrations (Battelle, 2004). This information can be used to quantify the

relative "ozone forming potential" of different days, regimes, and years.

The interpretation of results of a wind rose analysis or a statistical analysis such as PCA

or CART should focus on episodic time periods, rather than individual days. The winds may be

blowing from different directions on consecutive days, but that does not necessarily mean that

those days represent different meteorological regimes. Preference should be given to modeling

episodic cycles.

Additionally, statistical analyses such as PCA normally limit the number of identified

meteorological regimes to a relatively small number of generalized patterns. The analysis may

indicate that only one or two of these patterns are responsible for most or all of the exceedance

days in an area. But no two days and no two episodes are exactly the same. Further analysis

should be performed on potential episode periods to differentiate subtle, but often important,

differences between episodes. For this reason, it may be beneficial to model more than one

episode from the most frequently occurring meteorological regimes which lead to ozone

exceedances. Modeling a continuous time period which encompasses several episodes or a full

ozone season will make it easier to adequately account for all of the potential meteorological


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