Generic Guidance and Optimum Model Settings for the CALPUFF ...

4. DISCUSSION ON THE APPROPRIATE PROCEDURES FOR
EVALUATING CALMET AND CALPUFF MODELLING RESULTS
4.1 Model Output: Uncertainty
Accuracy of model predictions is often a source of debate. Measurements and model predictions
can be compared in a variety of ways, each providing a different perspective on model
performance. A model may show good competency in certain predictions (e.g., maximum
concentrations) but poor in others (e.g. the frequency of concentrations above a certain threshold).
Usually the reasons for poor model performance is due to uncertainties in the input values for
example, poor quality or unrepresentative meteorological, geophysical and source emission data,
or, lack of modeling expertise, or, incorrect and unsuitable model depending on the application.
The sources of uncertainty in model predictions can be significantly reduced by collecting the
proper input data, preparing the input files correctly, checking and re-checking for errors,
correcting for ‘odd’ model behaviour, insuring that errors in the measured data are minimized and
applying the correct model to suit each application. As well as user ‘error’ inputs there is some
‘inherent uncertainty’ in model predictions which occurs in all dispersion models’ due to the
uncertainty of atmospheric behaviour.
Consider the following general statements on model performance which have been derived from
the EPA 2003 and are to be considered in their totality, i.e., altogether.
� Models are more reliable for estimating longer time averaged concentrations than for
estimating short-term concentrations at specific locations
� Estimates of concentrations that occur at a specific time and site are poorly correlated
with actual observed concentrations (paired in space and time) and are less reliable
(mostly due to reducible uncertainty such as error in plume location due to a wind
direction error).
� Models are reasonably reliable in estimating the highest concentrations occurring
sometime, somewhere in an area. Model certainty is expected to be in the range of a
factor of 2.
Further it is important to note that model performance will vary depending on the application. For
example, in some cases models will overpredict and in other cases underpredict. Further, under
some conditions CALPUFF will produce higher concentrations than AUSPLUME and in other
applications it may do the opposite. It is important to note that there are many differences that
can occur between AUSPLUME and CALPUFF especially under complex meteorological and
terrain conditions. But, generally under near field flat terrain and over a full year, the results of
AUSPLUME are likely to be similar to that of CALPUFF.
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