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REVIEW ARTICLES

Review of the Governing Equations, Computational Algorithms, and Other Components of the Models-3 Community Multiscale Air Quality (CMAQ) Modeling System

[+] Author and Article Information
Daewon Byun1

Atmospheric Sciences Modeling Division, Air Resources Laboratory, National Oceanic and Atmospheric Administration, Research Triangle Park, NC 27711

Kenneth L. Schere2

Atmospheric Sciences Modeling Division, Air Resources Laboratory, National Oceanic and Atmospheric Administration, Research Triangle Park, NC 27711

1

Present affiliation: University of Houston, 4800 Calhoun, Houston, TX 77204-5007.

2

On assignment to the National Exposure Research Laboratory, U.S. EPA, Research Triangle Park, NC.

Appl. Mech. Rev 59(2), 51-77 (Mar 01, 2006) (27 pages) doi:10.1115/1.2128636 History:

This article describes the governing equations, computational algorithms, and other components entering into the Community Multiscale Air Quality (CMAQ) modeling system. This system has been designed to approach air quality as a whole by including state-of-the-science capabilities for modeling multiple air quality issues, including tropospheric ozone, fine particles, acid deposition, and visibility degradation. CMAQ was also designed to have multiscale capabilities so that separate models were not needed for urban and regional scale air quality modeling. By making CMAQ a modeling system that addresses multiple pollutants and different spatial scales, it has a “one-atmosphere” perspective that combines the efforts of the scientific community. To implement multiscale capabilities in CMAQ, several issues (such as scalable atmospheric dynamics and generalized coordinates), which depend on the desired model resolution, are addressed. A set of governing equations for compressible nonhydrostatic atmospheres is available to better resolve atmospheric dynamics at smaller scales. Because CMAQ is designed to handle scale-dependent meteorological formulations and a large amount of flexibility, its governing equations are expressed in a generalized coordinate system. This approach ensures consistency between CMAQ and the meteorological modeling system. The generalized coordinate system determines the necessary grid and coordinate transformations, and it can accommodate various vertical coordinates and map projections. The CMAQ modeling system simulates various chemical and physical processes that are thought to be important for understanding atmospheric trace gas transformations and distributions. The modeling system contains three types of modeling components (Models-3): a meteorological modeling system for the description of atmospheric states and motions, emission models for man-made and natural emissions that are injected into the atmosphere, and a chemistry-transport modeling system for simulation of the chemical transformation and fate. The chemical transport model includes the following process modules: horizontal advection, vertical advection, mass conservation adjustments for advection processes, horizontal diffusion, vertical diffusion, gas-phase chemical reactions and solvers, photolytic rate computation, aqueous-phase reactions and cloud mixing, aerosol dynamics, size distributions and chemistry, plume chemistry effects, and gas and aerosol deposition velocity estimation. This paper describes the Models-3 CMAQ system, its governing equations, important science algorithms, and a few application examples. This review article cites 114 references.

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Copyright © 2006 by American Society of Mechanical Engineers
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References

Figures

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Figure 1

Science process modules in Models-3 CMAQ. Independent processors are represented with round rectangles and interface processes are shown with rectangular boxes. Typical science process modules (in hexagon boxes) update the concentration field directly and the data-provider modules (e.g., Photolysis routine in a pentagon box) include routines to feed appropriate environmental input data to the science process modules.

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Figure 2

Driver module and its science process call sequence

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Figure 3

Grid-size-dependent horizontal diffusivity used in CMAQ (estimated for different magnitude of deformation, from 10−6to10−3s−1)

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Figure 4

Evolution of aerosol size distributions for the clear, urban, and hazy cases. Initial conditions of Seigneur (95) are used.

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Figure 5

Comparison of PinG modeled species concentrations for ozone (solid line), NOy (thick solid line) and SO2 (long dash) versus observed plume concentrations obtained from a horizontal aircraft traverse intercepting multiple point source plumes from Johnsonville (extreme left plume) and the Cumberland plume (in the middle) near Nashville, 18:45 UTC, 7 July, 1995

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Figure 6

Scatterplot of daily maximum 8hr average ozone concentrations (ppb) from continental U.S. AIRS stations for the period June 15–July 16, 1999 versus comparable CMAQ model estimates. Dotted line is best fit to data.

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Figure 7

Scatterplot of daily average PM2.5 concentrations (μgm−3) from continental U.S. monitoring stations for the period June 15–July 16, 1999 versus comparable CMAQ model estimates. Solid lines are 1:1 as well as 2:1 and 1:2.

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Figure 8

Scatterplot of daily average PM2.5 concentrations (μgm−3) from continental U.S. monitoring stations for the period January 4–February 19, 2002 versus comparable CMAQ model estimates. Solid lines are 1:1 as well as 2:1 and 1:2.

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Figure 9

Daily maximum modeled ozone concentrations (ppm) with modified VOC emissions data

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Figure 10

Scatter diagram comparing CAMS and modeled ozone concentrations (ppb) with modified VOC emissions data

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Figure 11

Time series of CAMS and modeled ozone concentrations (ppb) with modified VOC emissions data for nonindustrial sites

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