Figure 18.2. Scatter plot analysis for a 12-km model vs. observed 2001 case. The scatter plot

shows a performance analysis for daily 8-hour maxima ozone pairs for the month of July.

• Daily tile plots of predicted ozone across the modeling domain with the actual

observations as an overlay. Plots should be completed for both daily 1-hour maxima and

daily 8-hour maxima. These plots can reveal locations where the model performs poorly.

Superimposing observed hourly or daily maximum concentrations on the predicted

isopleths reveals useful information on the spatial alignment of predicted and observed


49 Quantile-quantile (Q-Q) plots may also provide additional information with regards to

the distribution of the observations vs. predictions. But due to the fact that Q-Q plots are not

paired in time, they may not always provide useful information. Care should be taken in

interpreting the results.

Animations of predicted hourly ozone concentrations for all episode days or for certain

periods of interest. Animations are useful for examining the timing and location of ozone

formation. Animations may also reveal transport patterns (especially when looking at

ozone aloft). Animations can also be used to qualitatively compare model outputs with

the conceptual model of particular ozone episodes.

18.4.2 PM/RH Operational Evaluation

An operational evaluation for PM2.5 and regional haze is similar to that for ozone. Some

important differences are that PM2.5 consists of many components and is typically measured with

a 24-hour averaging time. The individual components of PM2.5 should be evaluated individually.

In fact, it is more important to evaluate the components of PM2.5 than to evaluate total PM2.5 itself.

Apparent “good performance” for total PM2.5 does not indicate whether modeled PM2.5 is

predicted for “the right reasons” (the proper mix of components). If performance of the major

components is good, then performance for total PM2.5 should also be good.

This section contains some additional recommended statistics that have typically not been

calculated for ozone performance, but have been found to be particularly useful for PM analyses

(such as fractional bias and error). We also show examples of some new types of display plots

such as “soccer plots” and “bugle plots”. Soccer plots provide a convenient way to display a

summary of model performance (including bias and error at the same time). Bugle plots have

variable bias and error goals, based on ambient concentrations. This allows for a higher

percentage error and bias at very low concentrations. This recognizes the fact that models often

have difficulty in accurately predicting near background concentrations50.


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