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- Author or Editor: Steven G. Perry x
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Abstract
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Abstract
The Complex Terrain Dispersion Model (CTDMPLUS), a point-source, steady-state model for complex-terrain applications, is described. The model simulates the flow and plume distortion near user-selected, three-dimensional terrain features, yet retains simplicity by applying flow-distortion corrections to flat-terrain, Gaussian, and bi-Gaussian pollutant distributions.
The algorithms for stable and near-neutral conditions are based on the demonstrated concept of a dividing streamline. These algorithms were developed using data from three major plume-impaction field studies and a number of fluid-modeling studies. The algorithms for plumes released into convective layers are based on recent understanding of the convective boundary layer through fluid modeling, numerical modeling, and field studies. The non-Gaussian nature of vertical dispersion is accounted for; lateral dispersion is modeled with the aid of convective scaling concepts.
A terrain preprocessor and a meteorological preprocessor, which provide input specifically for the CTDMPLUS model, are described. The model requires a fully three-dimensional description of individual terrain features in order to estimate flow (and plume) distortions. Estimates of surface-layer parameters (friction velocity and Monin-Obukhov length) and depth of the mixed layer are required to define the state of the boundary layer.
Abstract
The Complex Terrain Dispersion Model (CTDMPLUS), a point-source, steady-state model for complex-terrain applications, is described. The model simulates the flow and plume distortion near user-selected, three-dimensional terrain features, yet retains simplicity by applying flow-distortion corrections to flat-terrain, Gaussian, and bi-Gaussian pollutant distributions.
The algorithms for stable and near-neutral conditions are based on the demonstrated concept of a dividing streamline. These algorithms were developed using data from three major plume-impaction field studies and a number of fluid-modeling studies. The algorithms for plumes released into convective layers are based on recent understanding of the convective boundary layer through fluid modeling, numerical modeling, and field studies. The non-Gaussian nature of vertical dispersion is accounted for; lateral dispersion is modeled with the aid of convective scaling concepts.
A terrain preprocessor and a meteorological preprocessor, which provide input specifically for the CTDMPLUS model, are described. The model requires a fully three-dimensional description of individual terrain features in order to estimate flow (and plume) distortions. Estimates of surface-layer parameters (friction velocity and Monin-Obukhov length) and depth of the mixed layer are required to define the state of the boundary layer.
Abstract
The Complex Terrain Dispersion Model (CTDMPLUS), described in Part I of this paper, was evaluated using the SO2 field-study data from the Lovett generating station in southeastern New York state. For perspective, CTDMPLUS estimates were also compared with those from the regulatory version of the Rough Terrain Diffusion Model (RTDM). For comparisons unpaired in space or time, the highest 25 CTDMPLUS model predictions tended to overpredict the highest 25 hourly observations by, on average, about a factor of 2. Similar results were found for 3-h and 24-h avenge predictions. Overpredictions occurred mainly for stable atmospheric conditions.
In contrast, the hourly and 3-h average model concentrations paired in time with observations underpredicted the observations by as much as a factor of 4. CTDMPLUS displayed no strong bias in estimating the 24-b average concentrations.
To understand the performance of CTDMPLUS, the meteorological conditions associated with the highest 25 observed concentrations were examined. This analysis suggests that the most significant factors affecting CTDMPLUS predictions for stable conditions are the height of the plume and its relation to the dividing streamline; in convective conditions, significant factors are the fraction of plume material penetrating the stable layer aloft, lateral plume spread, and wind direction.
Abstract
The Complex Terrain Dispersion Model (CTDMPLUS), described in Part I of this paper, was evaluated using the SO2 field-study data from the Lovett generating station in southeastern New York state. For perspective, CTDMPLUS estimates were also compared with those from the regulatory version of the Rough Terrain Diffusion Model (RTDM). For comparisons unpaired in space or time, the highest 25 CTDMPLUS model predictions tended to overpredict the highest 25 hourly observations by, on average, about a factor of 2. Similar results were found for 3-h and 24-h avenge predictions. Overpredictions occurred mainly for stable atmospheric conditions.
In contrast, the hourly and 3-h average model concentrations paired in time with observations underpredicted the observations by as much as a factor of 4. CTDMPLUS displayed no strong bias in estimating the 24-b average concentrations.
To understand the performance of CTDMPLUS, the meteorological conditions associated with the highest 25 observed concentrations were examined. This analysis suggests that the most significant factors affecting CTDMPLUS predictions for stable conditions are the height of the plume and its relation to the dividing streamline; in convective conditions, significant factors are the fraction of plume material penetrating the stable layer aloft, lateral plume spread, and wind direction.
Abstract
A surface layer experiment is described which includes measurements of turbulent velocities at 2 m above the surface with an army of newly developed drag anemometers. The experiment site is located in central Pennsylvania where mesoscale topographic irregularities exist. The presence of a low mountain ridge near the site affects the estimated lateral scale of turbulence and the fluctuations of the lateral velocity component. A good correlation has been found between the variance spectrum of the lateral (or crosswind) velocity component and an estimate of the lateral Eulerian integral scale of the longitudinal velocity component. This can provide future estimates of the lateral scale from turbulent velocity measurements at a single location.
A model for the decay of horizontal coherence which accounts for the stability, roughness and instrument separation has been suggested in a previous paper by Panofsky and Mizuno. The present data compare favorably with this model. The effect of stability on coherence decay is found to have a definite site dependence.
Abstract
A surface layer experiment is described which includes measurements of turbulent velocities at 2 m above the surface with an army of newly developed drag anemometers. The experiment site is located in central Pennsylvania where mesoscale topographic irregularities exist. The presence of a low mountain ridge near the site affects the estimated lateral scale of turbulence and the fluctuations of the lateral velocity component. A good correlation has been found between the variance spectrum of the lateral (or crosswind) velocity component and an estimate of the lateral Eulerian integral scale of the longitudinal velocity component. This can provide future estimates of the lateral scale from turbulent velocity measurements at a single location.
A model for the decay of horizontal coherence which accounts for the stability, roughness and instrument separation has been suggested in a previous paper by Panofsky and Mizuno. The present data compare favorably with this model. The effect of stability on coherence decay is found to have a definite site dependence.
Abstract
The formulation of the American Meteorological Society (AMS) and U.S. Environmental Protection Agency (EPA) Regulatory Model (AERMOD) Improvement Committee’s applied air dispersion model is described. This is the first of two articles describing the model and its performance. Part I includes AERMOD’s characterization of the boundary layer with computation of the Monin–Obukhov length, surface friction velocity, surface roughness length, sensible heat flux, convective scaling velocity, and both the shear- and convection-driven mixing heights. These parameters are used in conjunction with meteorological measurements to characterize the vertical structure of the wind, temperature, and turbulence. AERMOD’s method for considering both the vertical inhomogeneity of the meteorological characteristics and the influence of terrain are explained. The model’s concentration estimates are based on a steady-state plume approach with significant improvements over commonly applied regulatory dispersion models. Complex terrain influences are provided by combining a horizontal plume state and a terrain-following state. Dispersion algorithms are specified for convective and stable conditions, urban and rural areas, and in the influence of buildings and other structures. Part II goes on to describe the performance of AERMOD against 17 field study databases.
Abstract
The formulation of the American Meteorological Society (AMS) and U.S. Environmental Protection Agency (EPA) Regulatory Model (AERMOD) Improvement Committee’s applied air dispersion model is described. This is the first of two articles describing the model and its performance. Part I includes AERMOD’s characterization of the boundary layer with computation of the Monin–Obukhov length, surface friction velocity, surface roughness length, sensible heat flux, convective scaling velocity, and both the shear- and convection-driven mixing heights. These parameters are used in conjunction with meteorological measurements to characterize the vertical structure of the wind, temperature, and turbulence. AERMOD’s method for considering both the vertical inhomogeneity of the meteorological characteristics and the influence of terrain are explained. The model’s concentration estimates are based on a steady-state plume approach with significant improvements over commonly applied regulatory dispersion models. Complex terrain influences are provided by combining a horizontal plume state and a terrain-following state. Dispersion algorithms are specified for convective and stable conditions, urban and rural areas, and in the influence of buildings and other structures. Part II goes on to describe the performance of AERMOD against 17 field study databases.
Abstract
The performance of the American Meteorological Society (AMS) and U.S. Environmental Protection Agency (EPA) Regulatory Model (AERMOD) Improvement Committee’s applied air dispersion model against 17 field study databases is described. AERMOD is a steady-state plume model with significant improvements over commonly applied regulatory models. The databases are characterized, and the performance measures are described. Emphasis is placed on statistics that demonstrate the model’s abilities to reproduce the upper end of the concentration distribution. This is most important for applied regulatory modeling. The field measurements are characterized by flat and complex terrain, urban and rural conditions, and elevated and surface releases with and without building wake effects. As is indicated by comparisons of modeled and observed concentration distributions, with few exceptions AERMOD’s performance is superior to that of the other applied models tested. This is the second of two articles, with the first describing the model formulations.
Abstract
The performance of the American Meteorological Society (AMS) and U.S. Environmental Protection Agency (EPA) Regulatory Model (AERMOD) Improvement Committee’s applied air dispersion model against 17 field study databases is described. AERMOD is a steady-state plume model with significant improvements over commonly applied regulatory models. The databases are characterized, and the performance measures are described. Emphasis is placed on statistics that demonstrate the model’s abilities to reproduce the upper end of the concentration distribution. This is most important for applied regulatory modeling. The field measurements are characterized by flat and complex terrain, urban and rural conditions, and elevated and surface releases with and without building wake effects. As is indicated by comparisons of modeled and observed concentration distributions, with few exceptions AERMOD’s performance is superior to that of the other applied models tested. This is the second of two articles, with the first describing the model formulations.
