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- Author or Editor: D. Y. C. Leung x
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Abstract
A three-dimensional second-order closure meteorological and pollutant dispersion model is developed, and the computed results are evaluated. A finite-element method is used to solve the governing equations because of its versatility in handling variable-resolution meshes and complex geometries. The one-dimensional version of this model is used to simulate a 24-h diurnal cycle for a horizontally homogeneous atmospheric boundary layer in neutral, stable, and unstable stratifications. The simulated turbulence fields under a convective boundary layer act as the background turbulence for simulating cases of three-dimensional pollutant dispersion from elevated point sources. The simulated turbulence and pollutant distribution compared well with experimental observations and with other numerical models, ensuring the validity of the adopted mathematical formulation as well as the developed model. The computed results provide an overview of turbulence structures in different atmospheric stabilities and are helpful to enhance understanding of the characteristics of air pollutant dispersion, such as plume rise and descent in a convective boundary layer. The current study suggests the need for an insightful and practical numerical model to perform air-quality analysis, one that is capable of overcoming the weaknesses of traditional Gaussian plume and k-theory dispersion models.
Abstract
A three-dimensional second-order closure meteorological and pollutant dispersion model is developed, and the computed results are evaluated. A finite-element method is used to solve the governing equations because of its versatility in handling variable-resolution meshes and complex geometries. The one-dimensional version of this model is used to simulate a 24-h diurnal cycle for a horizontally homogeneous atmospheric boundary layer in neutral, stable, and unstable stratifications. The simulated turbulence fields under a convective boundary layer act as the background turbulence for simulating cases of three-dimensional pollutant dispersion from elevated point sources. The simulated turbulence and pollutant distribution compared well with experimental observations and with other numerical models, ensuring the validity of the adopted mathematical formulation as well as the developed model. The computed results provide an overview of turbulence structures in different atmospheric stabilities and are helpful to enhance understanding of the characteristics of air pollutant dispersion, such as plume rise and descent in a convective boundary layer. The current study suggests the need for an insightful and practical numerical model to perform air-quality analysis, one that is capable of overcoming the weaknesses of traditional Gaussian plume and k-theory dispersion models.
Abstract
A three-dimensional mesoscale meteorological model was developed based on second-moment closure equations that were solved by the finite-element method. This paper aims to evaluate the performance of the model under flat terrain and horizontally homogeneous atmospheric boundary layer conditions. The one-dimensional version of this model was tested against field measurements, a water tank experiment, and another numerical model. It showed several interesting behaviors of the atmospheric boundary layer under stable and unstable flows that are of primary interest for environmental studies.
Abstract
A three-dimensional mesoscale meteorological model was developed based on second-moment closure equations that were solved by the finite-element method. This paper aims to evaluate the performance of the model under flat terrain and horizontally homogeneous atmospheric boundary layer conditions. The one-dimensional version of this model was tested against field measurements, a water tank experiment, and another numerical model. It showed several interesting behaviors of the atmospheric boundary layer under stable and unstable flows that are of primary interest for environmental studies.
ABSTRACT
Regional climate modeling addresses our need to understand and simulate climatic processes and phenomena unresolved in global models. This paper highlights examples of current approaches to and innovative uses of regional climate modeling that deepen understanding of the climate system. High-resolution models are generally more skillful in simulating extremes, such as heavy precipitation, strong winds, and severe storms. In addition, research has shown that fine-scale features such as mountains, coastlines, lakes, irrigation, land use, and urban heat islands can substantially influence a region’s climate and its response to changing forcings. Regional climate simulations explicitly simulating convection are now being performed, providing an opportunity to illuminate new physical behavior that previously was represented by parameterizations with large uncertainties. Regional and global models are both advancing toward higher resolution, as computational capacity increases. However, the resolution and ensemble size necessary to produce a sufficient statistical sample of these processes in global models has proven too costly for contemporary supercomputing systems. Regional climate models are thus indispensable tools that complement global models for understanding physical processes governing regional climate variability and change. The deeper understanding of regional climate processes also benefits stakeholders and policymakers who need physically robust, high-resolution climate information to guide societal responses to changing climate. Key scientific questions that will continue to require regional climate models, and opportunities are emerging for addressing those questions.
ABSTRACT
Regional climate modeling addresses our need to understand and simulate climatic processes and phenomena unresolved in global models. This paper highlights examples of current approaches to and innovative uses of regional climate modeling that deepen understanding of the climate system. High-resolution models are generally more skillful in simulating extremes, such as heavy precipitation, strong winds, and severe storms. In addition, research has shown that fine-scale features such as mountains, coastlines, lakes, irrigation, land use, and urban heat islands can substantially influence a region’s climate and its response to changing forcings. Regional climate simulations explicitly simulating convection are now being performed, providing an opportunity to illuminate new physical behavior that previously was represented by parameterizations with large uncertainties. Regional and global models are both advancing toward higher resolution, as computational capacity increases. However, the resolution and ensemble size necessary to produce a sufficient statistical sample of these processes in global models has proven too costly for contemporary supercomputing systems. Regional climate models are thus indispensable tools that complement global models for understanding physical processes governing regional climate variability and change. The deeper understanding of regional climate processes also benefits stakeholders and policymakers who need physically robust, high-resolution climate information to guide societal responses to changing climate. Key scientific questions that will continue to require regional climate models, and opportunities are emerging for addressing those questions.
