ADPIC—A Three-Dimensional Particle-in-Cell Model for the Dispersal of Atmospheric Pollutants and its Comparison to Regional Tracer Studies

Länge, R.

doi:10.1175/1520-0450(1978)017<0320:atdpic>2.0.co;2

Cited by 129 publications

(55 citation statements)

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“…The first appears to have been MASCON, a two-dimensional flux model developed by Dickerson (1978) at the Lawrence Livermore National Laboratory to supply meteorological inputs to air pollution models of the San Francisco Bay area. This was soon followed by MATHEW (Sherman, (1978)), which extended the variational formalism to three dimensions, and provided inputs to the ADPIC pollutant transport model (Lange, (1978)). The development below most closely parallels that of Sherman (1978), and fills in some details that have been glossed over in the literature.…”

Section: Variational Calculusmentioning

confidence: 99%

Three-Dimensional Wind Field Modeling: A Review

Homicz

2002

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Over the past several decades, the development of computer models to predict the atmospheric transport of hazardous material across a local (on the order of 10s of km) to mesoscale (on the order of 100s of km) region has received considerable attention, for both regulatory purposes, and to guide emergency response teams. Wind inputs to these models cover a spectrum of sophistication and required resources. At one end is the interpolation/ extrapolation of available observations, which can be done rapidly, but at the risk of missing important local phenomena. Such a model can also only describe the wind at the time the observations were made. At the other end are sophisticated numerical solutions based on socalled Primitive Equation models. These prognostic models, so-called because in principle they can forecast future conditions, contain the most physics, but can easily consume tens of hours, if not days, of computer time. They may also require orders of magnitude more effort to set up, as both boundary and initial conditions on all the relevant variables must be supplied. The subject of this report is two classes of models intermediate in sophistication between the interpolated and prognostic ends of the spectrum. The first, known as mass-consistent (sometimes referred to as diagnostic) models, attempt to strike a compromise between simple interpolation and the complexity of the Primitive Equation models by satisfying only the conservation of mass (continuity) equation. The second class considered here consists of the so-called linear models, which purport to satisfy both mass and momentum balances. A review of the published literature on these models over the past few decades was performed. Though diagnostic models use a variety of approaches, they tend to fall into a relatively few welldefined categories. Linear models, on the other hand, follow a more uniform methodology, though they differ in detail. The discussion considers the theoretical underpinnings of each category of the diagnostic models, and the linear models, in order to assess the advantages and disadvantages of each. It is concluded that diagnostic models are the better suited of the two for predicting the atmospheric dispersion of hazardous materials in emergency response scenarios, as the linear models are only able to accommodate gently-sloping terrain, and are predicated on several simplifying approximations which can be difficult to justify a priori. Of the various approaches used in diagnostic modeling, that based on the calculus of variations appears to be the most objective, in that it introduces the fewest number of arbitrary parameters. The strengths and weaknesses of models in this category, as they relate to the activities of Sandia's Nuclear Emergency Support Team (NEST), are further highlighted.

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Section: Variational Calculusmentioning

confidence: 99%

Three-Dimensional Wind Field Modeling: A Review

Homicz

2002

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“…MODELING SYSTEM ARAC's emergency response computer system is built around the MATHEW (mass-consistent three-dimensional wind field) and ADPIC (atmospheric dispersion particle-in-cell) diagnostic models (Sherman, 1978, Lange, 1978. These models are run on a three-dimensional Eulerian grid typically with 40 x 40 x 14 uniform rectangular cells scaled to encompass the desired domain Wind speed and direction from up to 50 surface stations and 15 upper-air profiles within and surrounding the domain are used to initialize MATHEW.…”

Section: Mathew/adpicmentioning

confidence: 99%

Evaluation of the Atmospheric Release Advisory Capability Emergency Response Model for Explosive Sources

Baskett¹,

Freis²,

Nasstrom³

1994

Air Pollution Modeling and Its Application X

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“…Therefore, the entire atmospheric dynamical system is not simulated in the Lagrangian frame. For instance, Lange (1978) presented a three-dimensional particle-in-cell model in which the concentration field was advected by a wind field supplied by an accompanying non-Lagrangian code called the mass-adjusted three-dimensional wind field (MATHEW; Sherman 1978). Further examples in-clude such commonly used models as the Stochastic Time Inverted Lagrangian Transport model (STILT; Lin et al 2003), "FLEXPART" (Stohl et al 1998) Ryall and Maryon 1998).…”

Section: Introductionmentioning

confidence: 99%

Toward a Fully Lagrangian Atmospheric Modeling System

Alam

Lin

2008

Monthly Weather Review

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An improved treatment of advection is essential for atmospheric transport and chemistry models. Eulerian treatments are generally plagued with instabilities, unrealistic negative constituent values, diffusion, and dispersion errors. A higher-order Eulerian model improves one error at significant cost but magnifies another error. The cost of semi-Lagrangian models is too high for many applications. Furthermore, traditional trajectory "Lagrangian" models do not solve both the dynamical and tracer equations simultaneously in the Lagrangian frame. A fully Lagrangian numerical model is, therefore, presented for calculating atmospheric flows. The model employs a Lagrangian mesh of particles to approximate the nonlinear advection processes for all dependent variables simultaneously. Verification results for simulating seabreeze circulations in a dry atmosphere are presented. Comparison with Defant's analytical solution for the sea-breeze system enabled quantitative assessment of the model's convergence and stability. An average of 20 particles in each cell of an 11 ϫ 20 staggered grid system are required to predict the two-dimensional sea-breeze circulation, which accounts for a total of about 4400 particles in the Lagrangian mesh. Comparison with Eulerian and semi-Lagrangian models shows that the proposed fully Lagrangian model is more accurate for the sea-breeze circulation problem. Furthermore, the Lagrangian model is about 20 times as fast as the semi-Lagrangian model and about 2 times as fast as the Eulerian model. These results point toward the value of constructing an atmospheric model based on the fully Lagrangian approach.

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ADPIC—A Three-Dimensional Particle-in-Cell Model for the Dispersal of Atmospheric Pollutants and its Comparison to Regional Tracer Studies

Cited by 129 publications

References 0 publications

Three-Dimensional Wind Field Modeling: A Review

Three-Dimensional Wind Field Modeling: A Review

Evaluation of the Atmospheric Release Advisory Capability Emergency Response Model for Explosive Sources

Toward a Fully Lagrangian Atmospheric Modeling System

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