Treatment of Interfaces in Random Walk Dispersion Models

Thomson, David J.; Physick, W. L.; Maryon, R. H.

doi:10.1175/1520-0450(1997)036<1284:toiirw>2.0.co;2

Cited by 42 publications

(29 citation statements)

References 13 publications

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“…But when more complicated profiles of T Lw and s w were prescribed, particles accumulated in low-turbulence regions. Thomson et al [1997] outlined a reflection/transmission scheme for Gaussian turbulence that preserves wellmixed distributions for particles moving across interfaces between step changes in turbulence parameters. We adopted this approach to satisfy the well-mixed criterion in the strongly inhomogeneous environment of the PBL, where the simple drift correction does not work.…”

Section: Model Description: Stilt Modelmentioning

confidence: 99%

“…A typical configuration in STILT has 17 layers below the altitude of 10 km, with denser coverage near the ground (five layers below 1 km). A particle with vertical velocity w i and approaching from below an interface between lower (z iÀ ) and upper (z i+ ) layers is randomly transmitted with probability a and velocity w t or otherwise reflected with Àw i [Thomson et al, 1997], where…”

Section: Model Description: Stilt Modelmentioning

confidence: 99%

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A near‐field tool for simulating the upstream influence of atmospheric observations: The Stochastic Time‐Inverted Lagrangian Transport (STILT) model

et al. 2003

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[1] We introduce a tool to determine surface fluxes from atmospheric concentration data in the midst of distributed sources or sinks over land, the Stochastic Time-Inverted Lagrangian Transport (STILT) model, and illustrate the use of the tool with CO 2 data over North America. Anthropogenic and biogenic emissions of trace gases at the surface cause large variations of atmospheric concentrations in the planetary boundary layer (PBL) from the ''near field,'' where upstream sources and sinks have strong influence on observations. Transport in the near field often takes place on scales not resolved by typical grid sizes in transport models. STILT provides the capability to represent near-field influences, transforming this noise to signal useful in diagnosing surface emissions. The model simulates transport by following the time evolution of a particle ensemble, interpolating meteorological fields to the subgrid scale location of each particle. Turbulent motions are represented by a Markov chain process. Significant computational savings are realized because the influence of upstream emissions at different times is modeled using a single particle simulation backward in time, starting at the receptor and sampling only the portion of the domain that influences the observations. We assess in detail the physical and numerical requirements of STILT and other particle models necessary to avoid inconsistencies and to preserve time symmetry (reversibility). We show that source regions derived from backward and forward time simulations in STILT are similar, and we show that deviations may be attributed to violation of mass conservation in currently available analyzed meterological fields. Using concepts from information theory, we show that the particle approach can provide significant gains in information compared to conventional gridcell models, principally during the first hours of transport backward in time, when PBL observations are strongly affected by surface sources and sinks.

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Section: Model Description: Stilt Modelmentioning

confidence: 99%

Section: Model Description: Stilt Modelmentioning

confidence: 99%

A near‐field tool for simulating the upstream influence of atmospheric observations: The Stochastic Time‐Inverted Lagrangian Transport (STILT) model

et al. 2003

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“…Those methods depend on the physical and numerical factors considered in the simulation [25]. In present DTPM simulations, the reflection boundary condition at the airway wall is satisfied by a repositioning numerical treatment if points are out of the flow region.…”

Section: A Widiatmojo Et Almentioning

confidence: 99%

Discrete Tracer Point Method to Evaluate Turbulent Diffusion in Circular Pipe Flow

Widiatmojo¹,

Sasaki²,

Widodo³

et al. 2013

JFCMV

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Diffusion of a solute in turbulent flows through a circular pipe or tunnel is an important aspect of environmental safety. In this study, the diffusion coefficient of turbulent flow in circular pipe has been simulated by the Discrete Tracer Point Method (DTPM). The DTPM is a Lagrangian numerical method by a number of imaginary point displacement which satisfy turbulent mixing by velocity fluctuations, Reynolds stress, average velocity profile and a turbulent stochastic model. Numerical simulation results of points' distribution by DTPM have been compared with the analytical solution for turbulent plug-flow. For the case of turbulent circular pipe flow, the appropriate DTPM calculation time step has been investigated using a constant β, which represents the ratio between average mixing lengths over diameter of circular pipe. The evaluated values of diffusion coefficient by DTPM have been found to be in good agreement with Taylor's analytical equation for turbulent circular pipe flow by giving β = 0.04 to 0.045. Further, history matching of experimental tracer gas measurement through turbulent smooth-straight pipe flow has been presented and the results showed good agreement.

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“…The random walk scheme in NAME calculates turbulent velocity components using either an inhomogeneous velocity memory turbulence scheme, with a small model time-step and turbulence parameters which vary with height, or a homogeneous diffusive scheme (no velocity memory), with a larger time-step and turbulence parameters which are constant with height. Particles are reflected at the ground and, for the homogeneous scheme, an entrainment scheme is used to deal with the discontinuity in the turbulence parameters at the boundary layer top (Thomson et al, 1997). Results, obtained using the inhomogeneous turbulence scheme, are good for a range of deposition heights (Figure 1).…”

Section: Varying the Deposition Heightmentioning

confidence: 98%

Dry deposition modelling in a Lagrangian dispersion model

Webster

Thomson

2011

IJEP

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Revisions to the dry deposition scheme in the Lagrangian dispersion model, NAME are described. Improvements are made for situations where material is not well mixed within the boundary layer and for sedimenting heavy particles. The revised scheme overcomes computational noise, common in Lagrangian dispersion models, with a manageable or no increase to the computational cost. The result is a scheme which is well suited to all situations and which has better interaction between the advection (mean and turbulent), deposition and sedimentation.

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Treatment of Interfaces in Random Walk Dispersion Models

Cited by 42 publications

References 13 publications

A near‐field tool for simulating the upstream influence of atmospheric observations: The Stochastic Time‐Inverted Lagrangian Transport (STILT) model

A near‐field tool for simulating the upstream influence of atmospheric observations: The Stochastic Time‐Inverted Lagrangian Transport (STILT) model

Discrete Tracer Point Method to Evaluate Turbulent Diffusion in Circular Pipe Flow

Dry deposition modelling in a Lagrangian dispersion model

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