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
49Quantile-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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