Atmospheric Transport Models for Complex Terrain

Davis, C. G.; Bunker, S.; Mutschlecner, J. P.

doi:10.1175/1520-0450(1984)023<0235:atmfct>2.0.co;2

Cited by 33 publications

(15 citation statements)

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“…Previous model evaluations using the Ar-41 dataset include RIMPUFF with two different wind models: the wind field model LINCOM (LINearized COMputation), supplied by Riso (Troen, I. and A.F. de Bass, 1986) and also with the wind field model NUATMOS (New version of the ATMOS1 model; Davis et al, 1984), developed at Monash University (Ross et al, 1988). Both are diagnostic wind field models for complex terrain and LINCOM/RIMPUFF is the predecessor of LSMC/RIMPUFF.…”

Section: Atmospheric Dispersion Modelsmentioning

confidence: 99%

Evaluating emergency response models of radiological dispersion in complex terrain

Dyer

2011

IJEP

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Operational airborne releases of trace quantities of the radioactive noble gas Ar-41 from the HIFAR Nuclear Research Reactor located in Sydney, Australia are valuable for evaluating emergency response models incorporating radiological dispersion. The Australian Nuclear Science and Technology Organisation (ANSTO), where the reactor is located, has a network of meteorological stations and GR-150 environmental gamma dose detectors placed in complex terrain within a 5km radius of the site. The current version of ANSTO's Emergency Response System feeds real-time meteorological data from this network into the wind field model NUATMOS, which is then subsequently used, together with known source emissions of Ar-41 from HIFAR, to drive the Lagrangian mesoscale atmospheric dispersion puff model RIMPUFF. Output from RIMPUFF is compared with data from the gamma dose monitoring network in order to assess the overall performance of the system. An updated model combination LSMC/RIMPUFF has recently been evaluated using Ar-41 peak observations from the detector network during 2002-03 under a range of atmospheric stability conditions. Sensitivity tests of the new model version were performed by perturbing the gridded meteorological data within the model domain, which incorporates complex terrain. Results from statistical analyses of the model output are reported, assessing the suitability of LSMC/RIMPUFF to replace the current models in the Emergency Response System. The LSMC/RIMPUFF evaluation results are compared with two previous evaluations carried out in 2004, of LSMC's predecessor LINCOM/RIMPUFF and the NUATMOS/RIMPUFF combination currently used at ANSTO. Results from these tests have been favourable for LSMC/RIMPUFF, which is consequently now likely to replace the older models within ANSTO's Emergency Response System. The decision support system ARGOS, which also incorporates the LSMC/RIMPUFF model, is currently being evaluated for potential use by a number of Organisations within Australia including ANSTO. Some results of this initial evaluation will also be included here.

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Section: Atmospheric Dispersion Modelsmentioning

confidence: 99%

Evaluating emergency response models of radiological dispersion in complex terrain

Dyer

2011

IJEP

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“…The value of α governs the adjustment made in the vertical component relative to the horizontal components. In early mass-consistent models, the value was assumed to be a small constant over the entire domain (Sherman, 1978;Davis et al, 1984) and the value was determined subjectively. Snyder, Thompson, Eskridge, Lawson, Castro, Lee, Hunt, and Ogawa (1985) and Ross, Smith, Manins, and Fox (1988) have related the α value with a hill Froude number to account for the atmospheric stability.…”

Section: Partitioning the Flow Using The Richardson Numbermentioning

confidence: 99%

“…The partition of flow into both the vertical and horizontal directions is based on a simple empirical coefficient. Development of other such models by Davis, Bunker, and Mutschlecner (1984) and Ross, Smith, Manins, and Fox (1988) employs a terrain-following sigma z coordinate system. Connell (1988) has done extensive testing on Ross's model using observational data, and her results conclude that the model performed adequately for the flow over a single mountain.…”

Section: Introductionmentioning

confidence: 99%

A High Resolution, Three-Dimensional, Computationally Efficient, Diagnostic Wind Model: Initial Development Report

Wang¹,

Mercurio²,

Williamson³

et al. 2003

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Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. NOTICES DisclaimersThe findings in this report are not to be construed as an official Department of the Army position unless so designated by other authorized documents.Citation of manufacturer's or trade names does not constitute an official endorsement or approval of the use thereof.Destroy this report when it is no longer needed. Do not return it to the originator. ii Army Research Laboratory REPORT DOCUMENTATION PAGE Form Approved OMB No. 0704-0188Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing the burden, to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. REPORT DATE (DD-MM-YYYY)October 2003 DISTRIBUTION/AVAILABILITY STATEMENTApproved for public release; distribution unlimited. SUPPLEMENTARY NOTES ABSTRACTThis document describes a three-dimensional, high resolution, computationally efficient, diagnostic model for flow over complex terrain using a mass consistent approach. The differences between the current model and similar approaches are (a) the lower boundary conditions and (b) an efficient numerical method. The model includes the effects of topography and small surface features such as forest areas and buildings on the overall flow. The numerical implementation takes advantage of a multigrid method that greatly improves the computation speed. The framework of the model and associated implementations are described here. Several preliminary test cases for the model are also given in this documentation.

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“…ATMOS1 is a diagnostic model developed by Davis, et al (1984) to provide meteorological inputs to ATMOS2, a pollutant advection/diffusion code. It is similar to MATHEW and NOABL, in that it solves a single PDE, Eq.…”

Section: Variational Calculusmentioning

confidence: 99%

“…Like the earlier ATMOS1 (Davis, et al (1984)) model on which it is based, NUATMOS employs TFC and variable grid spacing in the vertical coordinate. What most distinguishes it from earlier models is the authors' attempt to choose α based on objective criteria related to atmospheric stability.…”

Section: If the Interpolated Wind Field Has Not Been Sampled At Pointmentioning

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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Atmospheric Transport Models for Complex Terrain

Cited by 33 publications

References 0 publications

Evaluating emergency response models of radiological dispersion in complex terrain

Evaluating emergency response models of radiological dispersion in complex terrain

A High Resolution, Three-Dimensional, Computationally Efficient, Diagnostic Wind Model: Initial Development Report

Three-Dimensional Wind Field Modeling: A Review

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