As mentioned above in the ozone operational evaluation, graphics are a
useful means for understandinghow predictions and observations differ. For PM evaluations we
recommend creating time series plots, tile plots, and scatter plots. Time series plots tell whether
there is any particular time of day, day(s) of the week, or months (seasons) of the year when the
model performs poorly. Tile plots reveal geographic locations where the model performs poorly.
Information from tile plots and time series may provide clues about where to focus quality
assurance efforts for model inputs. Scatter plots show whether there is any part of the distribution
of observations for which the model performs poorly. These plots are also useful for helping to
interpret calculations of bias between observations and predictions. For example, they could
show large differences between component PM2.5 observations and predictions which just happen
to balance, producing low estimated aggregated bias. As mentioned above, since the NAAQS for
PM2.5 and the regional haze goals will likely require modeling different times of year, seasonspecific
graphic displays are helpful for evaluating and diagnosing model performance.
These above mentioned graphical plots have been provided as examples in the ozone
operational evaluation section. Other graphical analysis displays can also be developed to better
inform a model performance evaluation. Additional types of graphical plots are shown below.
These plots were generated using the AMET tool.
Figure 18.4 shows an example of a “soccer plot” and “bugle plots” (Boylan, 2006)
(Tesche, 2006). The soccer plot is so named because the dotted lines resemble a soccer goal. The
plot is a convenient way to visualize model performance, as measures of both bias and error are
shown on a single plot. As bias and error approach zero, the points are plotted closer to or within
the “goal”, represented here by the dashed boxes. The “bugle plot”, named for the shape formed
by the criteria and goal lines, is another plot available for model performance evaluation. The
bugle plots are shaped as such because the goal and criteria lines are adjusted based on the
average concentration of the observed species. As the average concentration becomes smaller,
the criteria and goal lines become larger to adjust for the model’s ability to predict at low
concentrations. We recommend allowing for larger bias and error when the ambient
concentration falls below 2 ug/m3.
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