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Ensemble Configuration
The assumption behind the dispersion ensemble is that errors in the downwind plume position are primarily a function of the accumulation of initial errors in the particle trajectories. These errors are especially significant just downwind of the source, when the size of the pollutant plume is much smaller than the meteorological grid size. This is analogous to the basis of most meteorological ensemble forecasts, where differences in initial conditions may lead to quite different results. Each ensemble member of the dispersion forecast is computed from the same pollutant source location, but for calculational purposes the meteorological grid is offset by ± one or more meteorological grid points in X,Y,Z, thereby accounting for 27 members of the ensemble. In the future, multiple meteorological models may be included as well. The rationale of shifting the meteorological grid to determine initial transport errors is that the meteorological data field, limited in spatial and temporal resolution, is only an approximation of the true flow field, which is continuous in space and time. The meteorological model's velocity fields can only resolve features that are larger than about four times the grid spacing.
In the current test configuration, the ensemble is composed of 27 members (nine horizontal variations at a one grid point offset at three different levels using a constant emission rate over a six hour temporal aggregation period. Dispersion model concentrations are output hourly and hence each ensemble is composed of all the 27 spatial members times six for a total of 162 elements. Equal probabilities are assigned to all members and probabilities are calculated independently at each horizontal grid point from all the elements after a cumulative concentration sort. At each grid location various percentile concentrations are identified as well as the probability that a specific concentration level will be exceeded. A graphic is also produced to show the spatial distribution of the number of ensemble elements
The assumption behind the dispersion ensemble is that errors in the downwind plume position are primarily a function of the accumulation of initial errors in the particle trajectories. These errors are especially significant just downwind of the source, when the size of the pollutant plume is much smaller than the meteorological grid size. This is analogous to the basis of most meteorological ensemble forecasts, where differences in initial conditions may lead to quite different results. Each ensemble member of the dispersion forecast is computed from the same pollutant source location, but for calculational purposes the meteorological grid is offset by ± one or more meteorological grid points in X,Y,Z, thereby accounting for 27 members of the ensemble. In the future, multiple meteorological models may be included as well. The rationale of shifting the meteorological grid to determine initial transport errors is that the meteorological data field, limited in spatial and temporal resolution, is only an approximation of the true flow field, which is continuous in space and time. The meteorological model's velocity fields can only resolve features that are larger than about four times the grid spacing.
In the current test configuration, the ensemble is composed of 27 members (nine horizontal variations at a one grid point offset at three different levels using a constant emission rate over a six hour temporal aggregation period. Dispersion model concentrations are output hourly and hence each ensemble is composed of all the 27 spatial members times six for a total of 162 elements. Equal probabilities are assigned to all members and probabilities are calculated independently at each horizontal grid point from all the elements after a cumulative concentration sort. At each grid location various percentile concentrations are identified as well as the probability that a specific concentration level will be exceeded. A graphic is also produced to show the spatial distribution of the number of ensemble elements