9 th Int. Conf. on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes - 287 - 4.10 CHARACTERIZING UNCERTAINTY IN PLUME DISPERSION MODELS John S. Irwin *1 and Steven R. Hanna 2 1 NOAA Atmospheric Sciences Modeling Division, RTP, NC 27711 USA 2 Hanna Consultants, Kennebunkport, ME, 04046 USA INTRODUCTION In a discussion on predictability, H. Tennekes stated that , “Because the atmosphere is a chaotic system in which all forecasts are divergent, particularly as far as the smaller scales of motion are concerned, we must insist that no forecast is complete without a preceding assessment of forecast skill. In the same spirit, no observation is complete without an appropriately sampled estimate of the variance of the properties observed, and no model calculation is complete without a calculation of the variance of the calculation.”(see Hooke et al., 1990). In the current paper, we provide quantitative estimates of some of the major sources of uncertainty in plume dispersion modeling (e.g., variance of the model predictions) and then provide a preliminary assessment of their effects. The specific sources of uncertainty that are investigated are stochastic effects in the crosswind concentration profile, plume dispersion parameters, plume rise, and transport wind direction and speed. DISCUSSION Crosswind concentration profile variations The widely-used Gaussian approximation for characterizing the crosswind distribution of mass of a dispersing plume as it is carried downwind provides a smoothed viewed of what is really seen in the world. Irwin and Lee (1996) analyzed the Prairie Grass data, as well as additional tracer data from the Kincaid power plant, which had a 183-m stack with a typical buoyant plume rise on the order of 200 m. They concluded that the scatter in the concentration values about the ensemble average Gaussian lateral profile can be characterized for both experimental data sets as having a log-normal distribution with a geometric standard deviation (GeoSD) on the order of 2. The SCIPUFF model (Sykes et al., 1998) is one widely-used plume model that explicitly solves for the fluctuations in concentration internal to the plume as described above. Typically, the relative fluctuation (standard deviation divided by the mean) is simulated to be about 2 on the plume centerline, and is larger towards the edges of the plume. The SCIPUFF estimates of uncertainties are consistent with what has been independently found by the authors, as discussed in the previous paragraph. Note, SCIPUFF simulates additional sources of uncertainty which we have not investigated (e.g., uncertainty due to mesoscale wind fluctuations using inputs of wind speed variance and Lagrangian mesoscale integral scale, and uncertainty if the plume is located far from the wind observation site). Dispersion parameter uncertainty Irwin (1984) calculated the bias in the dispersion parameter estimates, and observed that the bias varied from one site to the next, and also calculated the random errors about the systematic bias at each site. To further explore these uncertainties, an analysis was conducted of the field experiments from 26 different sites listed and discussed in Irwin (1983). Nine of these sites involved elevated releases and the other 17 sites involved near-surface releases. The data were divided into four (4) groups: 1) elevated vertical dispersion values (5 sites), 2) * On assignment to the Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency