Simple estimates for vertical diffusion from sources near the ground

Ulden, A. P. van

doi:10.1016/0004-6981(78)90167-1

Cited by 252 publications

(99 citation statements)

References 8 publications

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“…This ensures that our results are consistent with those obtained in earlier studies (Nieuwstadt and van Ulden 1978;van Ulden 1978). We used a typical value of deposition velocity equal to 0.01 m s −1 in the numerical model for SO 2 dispersion (Gryning et al 1983).…”

Section: Model Performancesupporting

confidence: 79%

“…where L is the Obukhov length defined by L = −T 0 u 3 * /(κgQ 0 ), where Q 0 is the surface kinematic heat flux, u * is the surface friction velocity, g is the acceleration due to gravity, T 0 is a reference temperature, and κ is the Von Karman constant taken to be 0.35 as in van Ulden (1978). Equation 6 is inferred from concentrations measured during the Prairie Grass experiment (Barad 1958).…”

Section: Description Of Modelsmentioning

confidence: 99%

“…This numerical solution provides an excellent description of surface-layer dispersion (e.g. van Ulden 1978), and represents the best available steady-state model. The performance of this model is compared with that of AERMOD (Cimorelli et al 2005), the regulatory model recommended by the U.S. Environmental Protection Agency, which represents the current generation of dispersion models used in regulatory applications.…”

Section: Introductionmentioning

confidence: 99%

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Performance of Steady-State Dispersion Models Under Low Wind-Speed Conditions

Qian

Venkatram

2010

Boundary-Layer Meteorol

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We examine the performance of two steady-state models, a numerical solution of the advection-diffusion equation and the Gaussian plume-model-based AERMOD (the American Meteorological Society/Environmental Protection Agency Regulatory Model), to predict dispersion for surface releases under low wind-speed conditions. A comparison of model estimates with observations from two tracer studies, the Prairie Grass experiment and the Idaho Falls experiment indicates that about 50% of the concentration estimates are within a factor of two of the observations, but the scatter is large: the 95% confidence interval of the ratio of the observed to estimated concentrations is about 4. The model based on the numerical solution of the diffusion equation in combination with the model of Eckman (1994, Atmos Environ 28:265-272) for horizontal spread performs better than AERMOD in explaining the observations. Accounting for meandering of the wind reduces some of the overestimation of concentrations at low wind speeds. The results deteriorate when routine one-level observations are used to construct model inputs. An empirical modification to the similarity estimate of the surface friction velocity reduces the underestimation at low wind speeds.

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Section: Model Performancesupporting

confidence: 79%

Section: Description Of Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Performance of Steady-State Dispersion Models Under Low Wind-Speed Conditions

Qian

Venkatram

2010

Boundary-Layer Meteorol

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“…This analytical solution takes into account atmospheric stability and uses the wind velocity power law above the canopy, allowing it to be applicable to all conditions of atmospheric stability. In SAFE, the flux footprint PDF, f(x,y,z m ) in m −2 , is defined as the product of the crosswind concentration distribution function D y (x,y) in m −1 and the crosswind-integrated footprint, f y (x,z m ) in m −1 (Pasquill and Smith, 1983;van Ulden, 1978;Horst and Weil, 1992). The SAFE model input includes the EC sensor height (h m ), canopy height (h c ), roughness length (z 0 ), friction wind speed threshold for EC flux calculation (u th * ), and several meteorological variables (WD, u, v and u * ) and sensible and latent heat fluxes measured at the EC sensor height.…”

Section: Footprint Pdf Modellingmentioning

confidence: 99%

Assessing eddy-covariance flux tower location bias across the Fluxnet-Canada Research Network based on remote sensing and footprint modelling

Chen

Coops

et al. 2011

Agricultural and Forest Meteorology

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“…Analytical solution for the Eulerian and Lagrangian particle models are usually obtained just for stationary conditions and by making strong assumptions about the wind solutions of the diffusion-advection equation they are assumed constant along the whole Planetary Boundary Layer (PBL) or following a power law (van Ulden, 1978;Pasquill and Smith, 1983;Seinfeld, 1986;Tirabassi et al, 1986;Sharan et al, 1996). In Lagrangian particle models, the solution of the Langevin equation is normally obtained according to the rules is normally obtained according to the rules of the Ito calculus (Rodean, 1996).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of two semi-analytical techniques in air quality applications

Carvalho

Moreira

2007

Rev. bras. meteorol.

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RESUMO: AVALIAÇÃO DE DUAS TÉCNICAS SEMI-ANALÍTICAS EM APLICAÇÕES NA QUALIDADE DO AR.Neste artigo é realizada uma avaliação de duas técnicas semi-analíticas, considerando a qualidade e a exatidão destas técnicas em reproduzir valores de concentração ao nível da superfície de poluentes passivos emitidos a partir de fontes baixas e altas. A primeira técnica é um modelo Euleriano baseado na solução da equação advecção-difusão através da técnica de transformada de Laplace. A segunda é um modelo Lagrangiano baseado na solução da equação de Langevin através do Método Iterativo de Picard. Parâmetros da turbulência são calculados de acordo com uma parametrização capaz de gerar valores contínuos em todas as condições de estabilidade e em todas as alturas na camada limite planetária. Simulações numéricas e comparações mostram uma boa concordância entre valores de concentração previstos e observados. Comparações entre as duas técnicas revelam que o modelo Lagrangiano gera resultados mais precisos, mas o modelo Euleriano exige um menor tempo computacional. Palavras-chave: técnica semi-analítica, modelo Euleriano, modelo Lagrangiano, transformada de Laplace, Método Iterativo de Picard, avaliação de modelos.

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Simple estimates for vertical diffusion from sources near the ground

Cited by 252 publications

References 8 publications

Performance of Steady-State Dispersion Models Under Low Wind-Speed Conditions

Performance of Steady-State Dispersion Models Under Low Wind-Speed Conditions

Assessing eddy-covariance flux tower location bias across the Fluxnet-Canada Research Network based on remote sensing and footprint modelling

Evaluation of two semi-analytical techniques in air quality applications

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