MASCON—A Mass Consistent Atmospheric Flux Model for Regions with Complex Terrain

Dickerson, M.H.

doi:10.1175/1520-0450(1978)017<0241:mmcafm>2.0.co;2

Cited by 86 publications

(42 citation statements)

References 7 publications

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“…On average, there were original 550 wind data points at the lower level and 370 data points at the upper level. The interpolation formula was based on Dickerson (1978). The interpolated fields of upper wind vectors were directly used for the analysis.…”

Section: Data and Analysismentioning

confidence: 99%

“…The interpolated fields of upper wind vectors were directly used for the analysis. However the lower level wind fields were readjusted using the MASCON model (Dickerson, 1978). This model produces an adjusted wind field in such a way that the mass is conserved and the observational data are changed in a minimal way, using spatially sparse wind data interpolated and/or extrapolated to the grid point.…”

Section: Data and Analysismentioning

confidence: 99%

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The Origin of Tropical Disturbances in the Equatorial Pacific

Heta

1991

Journal of the Meteorological Society of Japan

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Section: Data and Analysismentioning

confidence: 99%

Section: Data and Analysismentioning

confidence: 99%

The Origin of Tropical Disturbances in the Equatorial Pacific

Heta

1991

Journal of the Meteorological Society of Japan

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“…The discussion will alert the reader to cases where the nomenclature differs from that used in the original source. † Licensed access to SciSearch at LANL® through the Sandia Restricted Network (SRN) is available at the following URL: http://scisearch2.lanl.gov/sandia/sci.html Reynolds, et al (1973) Endlich (1967; SST Goodin, et al (1980); TFC b Sasaki (1958Sasaki ( , 1970 Peters & Jouvanis (1979) Liu & Goodin (1976); SST Giarola, et al (1995); SST a Dickerson (1978); 2D, SST a Carmichael & Peters (1980) Endlich ( Sontowski, et al(1994Sontowski, et al( ,1995; part of HPAC; TFC b Barnard, et al (1987) Ross, et al (1988 ext. of ATMOS1; TFC b CONDOR, REDBL Moussiopoulos, et al (1986) TFC b Mathur and Peters (1990) WIND04 Venkatesan et al (1996) Montero, et al (1998) unstructured, FE sol'n.…”

Section: Diagnostic Modelsmentioning

confidence: 99%

“…Though he considered some simple examples to illustrate the method, it wasn't until the late 1970s that the formalism was embodied in practical computer programs that automated the process. 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)).…”

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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“…In it, he points out that a mass consistent model can provide a performance similar to that of a full dynamical simulation model in some specific applications. Sherman (1978) and Dickerson (1978) have applied mass conservation to an analysis of atmospheric flows over complex terrain. Their model uses a local Cartesian coordinate, and the earth's surface is approximated with a constant step in the vertical direction.…”

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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MASCON—A Mass Consistent Atmospheric Flux Model for Regions with Complex Terrain

Cited by 86 publications

References 7 publications

The Origin of Tropical Disturbances in the Equatorial Pacific

The Origin of Tropical Disturbances in the Equatorial Pacific

Three-Dimensional Wind Field Modeling: A Review

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

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