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- Author or Editor: A. Venkatram x
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Abstract
A two-stream solar radiation model was combined with a mixed-layer model to study the effects of absorbing aerosols on the thermal structure of the daytime convective boundary layer. A number of simulations were conducted with the model. The results showed that the criterion used in climatic models to determine the cooling or warming effect of aerosols was not readily applicable to micrometeorological scales. It was also found that soil-interface properties were at least as important as aerosol properties in determining aerosol-induced effects. Any conclusions about aerosol effects on the PBL have to be qualified by statements about surface parameters to be meaningful.
An average of the PBL and surface temperatures (θ a ) is suggested as a physically meaningful indicator of aerosol effects. The results show that aerosols increase θ a over surfaces which are relatively wet or have high reflectances; dry, low-albedo surfaces, however, are associated with a decrease in θ a in the presence of aerosols.
Another important conclusion of the study is that the soil-PBL system has a built-in mechanism to regulate aerosol-induced heating of the PBL and cooling of the surface. The degree of regulation is dependent on soil-PBL interface properties.
Abstract
A two-stream solar radiation model was combined with a mixed-layer model to study the effects of absorbing aerosols on the thermal structure of the daytime convective boundary layer. A number of simulations were conducted with the model. The results showed that the criterion used in climatic models to determine the cooling or warming effect of aerosols was not readily applicable to micrometeorological scales. It was also found that soil-interface properties were at least as important as aerosol properties in determining aerosol-induced effects. Any conclusions about aerosol effects on the PBL have to be qualified by statements about surface parameters to be meaningful.
An average of the PBL and surface temperatures (θ a ) is suggested as a physically meaningful indicator of aerosol effects. The results show that aerosols increase θ a over surfaces which are relatively wet or have high reflectances; dry, low-albedo surfaces, however, are associated with a decrease in θ a in the presence of aerosols.
Another important conclusion of the study is that the soil-PBL system has a built-in mechanism to regulate aerosol-induced heating of the PBL and cooling of the surface. The degree of regulation is dependent on soil-PBL interface properties.
Abstract
A one-dimensional transport model was developed to study the effects of radiative participation of elevated pollutant layers. Special features of the model include a turbulent kinetic energy model and a two-stream solar radiation model. Pollutants were assumed to consist of aerosols and pollutant gases. Aerosols were allowed to scatter and absorb energy in the solar spectrum while pollutant gases were assumed to interact only with thermal radiation.
The results of the simulations conducted with the model showed that elevated layers of pollutants could control mixed-layer expansion by modifying the stability of the capping stable layer. Cooling associated with gaseous pollutants generally helped the growth of the mixed layer, while solar heating induced by pollutants hindered mixed-layer growth by creating sharp inversions.
By affecting mixed-layer growth, radiative participation by pollutants also modified pollutant dispersal from the elevated pollutant layer. These results have important implications from the point of air pollution meteorology (especially fumigation) in which it is generally assumed that pollutants are passive.
Abstract
A one-dimensional transport model was developed to study the effects of radiative participation of elevated pollutant layers. Special features of the model include a turbulent kinetic energy model and a two-stream solar radiation model. Pollutants were assumed to consist of aerosols and pollutant gases. Aerosols were allowed to scatter and absorb energy in the solar spectrum while pollutant gases were assumed to interact only with thermal radiation.
The results of the simulations conducted with the model showed that elevated layers of pollutants could control mixed-layer expansion by modifying the stability of the capping stable layer. Cooling associated with gaseous pollutants generally helped the growth of the mixed layer, while solar heating induced by pollutants hindered mixed-layer growth by creating sharp inversions.
By affecting mixed-layer growth, radiative participation by pollutants also modified pollutant dispersal from the elevated pollutant layer. These results have important implications from the point of air pollution meteorology (especially fumigation) in which it is generally assumed that pollutants are passive.
Abstract
Over the past decade, much attention has been devoted to the evaluation of air-quality models with emphasis on model performance in predicting the high concentrations that are important in air-quality regulations. This paper stems from our belief that this practice needs to be expanded to 1) evaluate model physics and 2) deal with the large natural or stochastic variability in concentration. The variability is represented by the root-mean- square fluctuating concentration (σ c about the mean concentration (C) over an ensemble—a given set of meteorological, source, etc. conditions. Most air-quality models used in applications predict C, whereas observations are individual realizations drawn from an ensemble. For σ c ∼C large residuals exist between predicted and observed concentrations, which confuse model evaluations.
This paper addresses ways of evaluating model physics in light of the large σ c the focus is on elevated point-source models. Evaluation of model physics requires the separation of the mean model error-the difference between the predicted and observed C—from the natural variability. A residual analysis is shown to be an elective way of doing this. Several examples demonstrate the usefulness of residuals as well as correlation analyses and laboratory data in judging model physics.
In general, σ c models and predictions of the probability distribution of the fluctuating concentration (c′), Ω(c′, are in the developmental stage, with laboratory data playing an important role. Laboratory data from point-source plumes in a convection tank show that Ω(c′ approximates a self-similar distribution along the plume center plane, a useful result in a residual analysis. At pmsent,there is one model—ARAP—that predicts C, σ c , and &Omega(c for point-source plumes. This model is more computationally demanding than other dispersion models (for C only) and must be demonstrated as a practical tool. However, it predicts an important quantity for applications— the uncertainty in the very high and infrequent concentrations. The uncertainty is large and is needed in evaluating operational performance and in predicting the attainment of air-quality standards.
Abstract
Over the past decade, much attention has been devoted to the evaluation of air-quality models with emphasis on model performance in predicting the high concentrations that are important in air-quality regulations. This paper stems from our belief that this practice needs to be expanded to 1) evaluate model physics and 2) deal with the large natural or stochastic variability in concentration. The variability is represented by the root-mean- square fluctuating concentration (σ c about the mean concentration (C) over an ensemble—a given set of meteorological, source, etc. conditions. Most air-quality models used in applications predict C, whereas observations are individual realizations drawn from an ensemble. For σ c ∼C large residuals exist between predicted and observed concentrations, which confuse model evaluations.
This paper addresses ways of evaluating model physics in light of the large σ c the focus is on elevated point-source models. Evaluation of model physics requires the separation of the mean model error-the difference between the predicted and observed C—from the natural variability. A residual analysis is shown to be an elective way of doing this. Several examples demonstrate the usefulness of residuals as well as correlation analyses and laboratory data in judging model physics.
In general, σ c models and predictions of the probability distribution of the fluctuating concentration (c′), Ω(c′, are in the developmental stage, with laboratory data playing an important role. Laboratory data from point-source plumes in a convection tank show that Ω(c′ approximates a self-similar distribution along the plume center plane, a useful result in a residual analysis. At pmsent,there is one model—ARAP—that predicts C, σ c , and &Omega(c for point-source plumes. This model is more computationally demanding than other dispersion models (for C only) and must be demonstrated as a practical tool. However, it predicts an important quantity for applications— the uncertainty in the very high and infrequent concentrations. The uncertainty is large and is needed in evaluating operational performance and in predicting the attainment of air-quality standards.
