Evaluation of eight short-term long-range transport models using field data

Abstract

Eight short-term long-range transport models (MESOPUFF, MESOPLUME, MSPUFF, MESOPUFF II, MTDDIS, ARRPA, RADM and RTM-II) have been tested with field data from two data bases involving tracer releases. The Oklahoma data base involved two separate experiments with measurements taken at 100 and 600 km arcs downwind of a 3-h perfluorocarbon release. The Savannah River Plant data base encompassed 15 experiments with measurements taken over 2–5 days at distances of 28–144 km downwind from a 62 m stack release of Kr-85 gas.

Application of the American Meteorological Society statistics to the model/data comparisons showed that six of the eight models predicted within a factor of two of the observed concentrations for all of the following: points paired in space and time, points paired in space only, and for points unpaired in space and time. However, the ratio of the standard deviation of residuals to the average observed value showed improvement as more unpairing was done in the comparison of the models with the data. The statistical comparisons reveal a definite tendency of the models to overpredict plume concentrations. Supplemental graphical comparisons showed that plume concentration overprediction is accompanied with an underprediction of plume spreading, and that a definite time lag is often observed between the time of arrival of the observed plume and the time of arrival of the predicted plume.

The causes of model/data discrepancies can be largely traced to inadequate wind field modeling that leads to an incorrect temporal and spatial positioning of the plume, and the use of the Turner [Workbook of atmospheric dispersion estimates. U.S. Dept of H.E.W. Publication 999-AP-26 (1970)] curves to downwind distances beyond which they can accurately represent the scales of atmospheric turbulence. The use of multilayer wind field models and the use of the Heffter [J. appl. Met.4, 153–156 (1965)] formula for lateral plume dispersion close to the source appear to improve model accuracies.