On the Evaluation of Predictions from a Gaussian Plume Model

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Abstract
The performance of a CRSTER equivalent Gaussian plume model (CEQM) was examined, using data from the EPRI Plume Model Validation study at the Kincaid, Illinois [USA] site. Four-way comparisons were made on the ordered statistics or the cumulative frequency distribution (CFD) of maximum hourly observed and predicted concentrations. Using the uniform random distribution and the lognormal random distribution as simple predictive schemes without any physical context, it was found that the CEQM predicted a concentration CFD which matched the observed CFD significantly closer than the CFD predicted by the uniform random distribution. The 2-parameter lognormal random distribution predicted the concentration CFD better than the CEQM over all concentration ranges; the CEQM fit the upper range of the concentration distribution better than the lognormal random distribution, despite the fact that the predictions were generated using dispersion conditions entirely different from those of the observations. The nature of this ergodicity of distribution was probed by exercising CEQM, using randomized input based on the observed frequency distributions of the input parameters instead of feeding the hour-by-hour model input matched by time into CEQM, as was customarily done. The exercise of the model by uncoupling the time linkage in model input had no systematic effect on the predicted cumulative frequency distribution of concentrations. Only at the highest concentration range (.gtoreq.99.5%) did the 2 sets of predictions begin to diverge.

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