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Community Multiscale Air Quality Modeling System (CMAQ)

CMAQ Models

About CMAQ

CMAQ's Purpose

It is important to characterize the exposure of people and ecosystems to pollutants. Measurements are one way of gathering this information, but a more complete picture in space and time is often needed. Air quality models can provide this information for the past, present or future depending on how they are used. 

Numerical air quality models simulate the emissions, chemistry and physics of the atmosphere. The Community Multiscale Air Quality (CMAQ) model is a numerical air quality model that relies on scientific first principlesHelpfirst principlesThe fundamental concepts or assumptions on which a theory, system, or method is based. to predict the concentration of airborne gases and particles, and the deposition of these pollutants back to Earth’s surface. Because it includes information about the emissions and properties of compounds and classes of compounds, CMAQ can also inform users about the chemical composition of a mixture of pollutants. This is particularly useful when measurements only give insight into aggregate details, like total particulate mass.

The purpose of CMAQ is to provide fast, technically sound estimates of ozone, particulates, toxics, and acid deposition.  CMAQ is designed to meet the needs of the scientific community and concerned community leaders by combining current knowledge in atmospheric science and air quality modeling, multi-processor computing techniques, and an open-source framework into a single modeling system. 

The two panels illustrate the added value of using chemical transport models. The left panel shows the spatial coverage of ozone monitoring sites in the US. The right panel shows the spatial field created by the CMAQ simulation.(Left) Spatial map of ozone monitoring sites in the U.S. with colors indicating the max-8hr ozone concentration. (Right) Spatial map of CMAQ output of max-8hr ozone concentrations.

Capabilities of the CMAQ Model Family

CMAQ allows users to explore different kinds of air pollution scenarios. For example, CMAQ is often used to test the impact of future emission regulations. The interaction of meteorology and air quality, e.g. the effects of particles on solar radiation, can be explored with the two-way WRF-CMAQ system, which couples the Weather Research and Forecasting (WRF) meteorological model with the CMAQ air quality model. The Direct-Decoupled Method (DDM) can be used in CMAQ-DDM to quantify the sensitivity of air pollution predictions to model input values like emissions or reaction rates. Often, people want know more about which individual emission sources or groups of sources are contributing the most to the air pollution at a site. This can be explored using the Integrated Source-Apportionment Method (ISAM) in the CMAQ-ISAM model.

​Components of the Modeling System

The CMAQ system is a suite of software programs that work in concert to estimate ozone, particulate matter, toxic compounds, and acid deposition throughout the troposphere.  As a framework for simulating the interactions of multiple complex atmospheric processes, CMAQ requires two primary types of inputs: meteorological information, and emission rates from sources of emissions that affect air quality.  

Weather conditions such as the changes in temperature, winds, cloud formation, and precipitation rates are the primary physical driving forces in the atmosphere.  These conditions are represented in air quality model simulations using output from regional-scale numerical meteorology models, such as WRF.  To obtain inputs on emissions, CMAQ relies on the open-source Sparse Matrix Operator Kernel Emissions (SMOKE) model to estimate the magnitude and location of pollution sources.

Figure 2-1

CMAQ History

Since 1998, when the first version was released, CMAQ has been used to evaluate potential air quality policy management decisions. The model provides reliable information for decision makers about the estimated impacts of different air quality policies.  CMAQ’s generalized and flexible formulation has enabled incorporation of alternate process algorithms and numerical solution methods. This has allowed inclusion of new science in the model to address increasingly complex air pollution issues.

CMAQ release timeline for v4.0 through v5.3

Specific model updates and new features available with recent model releases:

  • CMAQv5.3 and v5.3.1

    August 2019

    System Updates

    • Incorporated updated instrumented models: Sulfur Tacking, Integrated Source Apportionment (ISAM) consistent with science process updates. Improvements in computational efficiency of these instrumented techniques have led to substantially faster run times to support their practical applications.
    • A new emissions interface allows for substantial flexibility in the way emissions are mapped, scaled, and checked for quality and can greatly simplify the task of assessing air quality improvements resulting from emission changes.
    • Incorporated updates (new data sources, updated vertical coordinate system) to CMAQ and the Meteorology-Chemistry Interface Processor (MCIPv5.0) to increase scientific consistency between the atmospheric dynamics and chemistry calculations.

    New Features and Processes

    • Updated marine chemistry to represent impacts of (1) halogen chemistry on ozone depletion and sulfate formation and (2) dimethyl sulfide on aerosol sulfate.
    • Expanded the representation of secondary pollutant formation in clouds (AQCHEM-KMT2).
    • Updated aerosol module (AERO7) that explicitly tracks 84 particulate species .
    • Updated pathways for secondary organic aerosol formation from biogenic VOCs .
    • Harmonized treatment of water uptake to aerosol organic phase to improve representation of aerosol chemistry, mixing, state and optical properties.
    • Improved the representation of bi-directional exchange of ammonia at the surface Improved representation of O3 dry deposition to snow.
    • Incorporated a new deposition module – the Surface Tiled Aerosol and Gaseous Exchange (STAGE) model to estimate land-use specific deposition.

    Release notes for CMAQv5.3 EXIT


    CMAQv5.3.1 - December 2019

    System Updates

    CMAQ model version 5.3.1 is a minor update to CMAQv5.3 that includes multiple bug fixes to both CMAQ and MCIP (released as MCIP version 5.1), as well as a feature addition to the Detailed Emission Scaling Isolation and Diagnostic (DESID) module to allow for the definition of chemical, region and stream families.

    Release notes for CMAQv5.3.1 EXIT

  • CMAQv5.2 and v5.2.1

    June 2017

    System Updates

    • Incorporated updates to representation of organic nitrate lifetimes and halogen chemistry for better representation of tropospheric chemistry on hemispheric scales.
    • Added new sources of secondary organic aerosols (heterogeneous uptake of glyoxal and methylglyoxal in CB6) and updated properties of traditional secondary organic aerosol. The volatility of both secondary and primary organic compounds are now treated consistently with each other.
    • Incorporated updated instrumented models: DDM-3D, Sulfur Tacking, Integrated Source Apportionment (ISAM) consistent with science process updates.

    New Features and Processes

    • Incorporated the CB6 chemical mechanism to incorporate new information on gas-phase kinetics.
    • Incorporated representation of stratosphere-troposphere exchange process on three-dimensional O3 distributions, using a space and time varying potential vorticity scaling approach.
    • Incorporated a new physics based windblown dust emission model that yields better agreement with observations of fine and coarse PM and constituents.
    • Added a new model species to account for the organic aerosol compounds resulting from combustion processes.

    Release notes for CMAQv5.2 EXIT

    CMAQv5.2.1 - March 2018

    System Updates

    • The CMAQ model has been updated with both important and minor patches to several modules including chemistry, aerosols, transport and emissions.  In addition, issues that were identified with the default compilation and execution workflow have been resolved.

    New Features and Processes

    •  With this model release, the CMAQ team included a new Developers' Guide to support CMAQ users who want to contribute new features to the code-base. 

     Release Note for CMAQ5.2.1 EXIT 

  • CMAQv5.1

    December 2015

    System Updates

    • Improved consistency in representation of radiation attenuation by clouds between WRF and photolysis module in CMAQ.
    • Included the Rodas3 Rosenbrock solver to solve cloud chemistry, kinetic mass transfer, ionic dissociation, and wet deposition.
    • Improvements to the land-surface model and ACM mixing scheme to enable finer-scale applications.
    • Improvements in representation of aerosol mixing state and optical properties for 2-way coupled WRF-CMAQ configurations.

    New Features and Processes

    • Incorporated the RACM2 chemical mechanism.
    • Included detailed representation of impacts of halogen chemistry on O3 in marine environments. Improved representation of O3 in coastal regions as well as representation of O3 loss in air masses transported intercontinentally across vast oceans.
    • New secondary organic aerosol (SOA) sources from isoprene, alkanes, and polyaromatic hydrocarbons (PAHs).
    • Incorporation of new binary nucleation and updates to PM2.5 emission size distribution to improve aerosol size distribution simulation.
    • Included gravitational settling for coarse aerosols.

    Release notes for CMAQv5.1 Exit

  • CMAQv5.0 and v5.0.2

    CMAQv5.0 - February 2012

    System Updates

    • Incorporated ISORROPIAv2 to explicitly represent Ca2+, K+, and Mg2+, species abundant in sea-salt and soil dust.
    • Incorporated the “mosaic approach”, an option to output the dry deposition flux for the different land-use categories within a grid cell.
    • Two-way coupling to enable feedback of aerosol direct radiative effects on WRF simulated dynamics and subsequent impacts on simulated air quality through modification of atmospheric ventilation, changes in rainfall, thermal reactions, and temperature and wind-speed dependent emission rates of primary species.
    • Introduced the namelist option to manage model species and centralize the specification of various attributes of modeled chemical species.

    New Features and Processes

    • Incorporated in-line photolysis module to represent effects of scattering and absorbing aerosols on photolysis rate modulation and atmospheric photochemistry regulating the formation of secondary air pollutants.
    • Incorporated impacts of varying surface albedo (function of wavelength, land-use, and time) on photolysis rates. Enabled representation of effects of reflection from snow surface and consequently improved applicability of the model for winter-time conditions simulation of winter-time O3 in the Western States.
    • Speciated PMother into primary ammonium (NH4+), sodium (Na+), chloride (Cl-), selected trace elements (Mg, Al, Si, K, Ca, Ti, Mn, and Fe), and non-carbon organic mass (NCOM). This speciation allowed for detailed characterization of the species, processes, and emission sector contributions to the model bias in primary and consequently total PM.
    • Incorporated representation of NOx emissions from lightning.
    • Incorporated detailed representation of bi-directional exchange of NH3 and Hg. Inclusion of bidirectional exchange significantly reduced bias in annual NHx wet deposition.
    • First implementation of on-line coupling between CMAQ and WRF enabling dynamical and chemical calculations at finer time-steps that are needed for the application of CMAQ at smaller horizontal grid cells.

    Release notes for CMAQv5.0 Exit

    CMAQv5.0.2 - May 2014

    New Features and Processes

    • First public release of the CMAQ Integrated Source Apportionment Method (CMAQ ISAM) to track contributions from source groups and/or regions to ambient levels and deposited amounts of ozone and inorganic PM2.5.

    Release notes for CMAQv5.0.2 Exit

  • CMAQv4.7 and v4.7.1

    CMAQ4.7 - September 2008

    System Updates

    • Significantly expanded SOA formation pathways to include isoprene SOA, sesquiterpene SOA, polymerization, acid-catalyzed SOA, cloud-SOA.
    • New parameterization for heterogeneous N2O5 hydrolysis.
    • Research version of inline photolysis module to represent effects of aerosol scattering and absorption on photolysis rates.

    New Features and Processes

    • Chemically-active coarse mode to allow condensation and evaporation of semivolatile inorganic components (NO3-, CL-, NH4+) from coarse model.
    • In-lined dry deposition to enable representation of bi-directional pollutant fluxes at the earth’s surface In-lined meteorology dependent emission components (BEIS, plume-rise).
    • First public release of multi-pollutant capability incorporating HAPs and Hg in a single platform.

    CMAQv4.7.1 - June 2010

    System Updates

    • Improved time-step calculation schemes internal to the advection and aqueous chemistry modules

    New Features and Processes

    • Incorporated instrumented models: DDM-3D, Sulfur Tacking, Primary Carbon Apportionment based on CMAQv4.7 science updates
  • CMAQ4.6

    September 2006

    System Updates

    • Updated representation of inorganic gas-aerosol partitioning using ISORROPIAv1.7.
    • Revised representation of homogeneous and heterogeneous N2O5 hydrolysis to improve representation of aerosol NO3-.
    • Added a CGRID restart file (used to initialize next day’s simulation). This enabled flexibility in the number of species and layers in the time dependent 3D concentration output (CONC) file.

    New Features and Processes

    • Incorporated the CB05 gas-phase chemical mechanism
  • CMAQv4.5

    September 2005

    System Updates

    • Incorporated a new scheme to ensure mass-consistent three-dimensional pollutant advection.
    • Included a new sub-grid cloud mixing scheme (ACM-cloud) to correct unrealistic above-cloud entrainment in previous sub-grid cloud schemes.
    • Expanded to CB4 gas-phase chemical mechanism to included representation of chlorine chemistry to improve ozone predictions.
    • Included diagnostic capability for primary carbon source apportionment.

    New Features and Processes

    • Included representation of sea-salt aerosols to improve PM predictions in coastal environments
    • Incorporated a parametrization based on urban fraction in a grid cell to begin representing heat-island effects on pollutant mixing in urban areas

Community Based Development

CMAQ incorporates input from a large, world-wide user community. To support the CMAQ user community, EPA and the University of North Carolina at Chapel Hill host the Community Modeling and Analysis System (CMAS) Center, which distributes CMAQ software, hosts user email exchanges, and provides new user training on the CMAQ modeling system.

This growing community, which includes thousands of users in more than 50 countries, has helped assess and improve the model’s functionality. Users include scientists, researchers and air quality modelers, as well as governmental air quality managers who apply the modeling system in their environmental management programs. Their input has helped EPA scientists prioritize modeling research to improve CMAQ’s capabilities.

Visit the CMAS Center.EXIT

Please refer to our page on how to cite CMAQ if you plan on using the modeling system in your own research.

 Latest Version: CMAQv5.3.1 

The CMAQ model is continually updated to reflect the most recent available scientific information. EPA scientists review the scientific literature and analyze field study data to develop and update the modeling approaches used in CMAQ. The model code is maintained in a GitHub repository to allow the development team to collaborate better and to test research versions of the model.

Periodically, a version is tagged for release with a version number, like v5.3, and made available to the public. A change in the number before the decimal point reflects a major release, while changes to numbers after the decimal point reflect minor releases. If you are running CMAQ, it is critical to note the number of the model version being used, since sometimes input data requirements differ among the versions. If you are using  CMAQ output data, it is important to note the version number since science processes are different between the versions.

Updates in CMAQv5.3.1 (Released December, 2019)

CMAQ model version 5.3.1 is a minor update to CMAQv5.3 that includes multiple bug fixes to both CMAQ and MCIP (released as MCIP version 5.1), as well as a feature addition to the Detailed Emission Scaling Isolation and Diagnostic (DESID) module to allow for the definition of chemical, region and stream families.

Webinars on CMAQv5.3

New Features in CMAQv5.3 (Released August, 2019)

CMAQv5.3, the latest version of the Community Multiscale Air Quality modeling system, includes the following new features:

  • A more detailed representation of the characteristics of PM: CMAQ 5.3 improves modeling of PM composition, size distributions, and optical properties (Pye et al., 2017). It also enhances the simulation of human-influenced secondary organic aerosols by considering newer laboratory and observational data (Pye et al., 2019).  Aerosols diagram
  • Expanded chemistry for ozone and PM formation from global-to-local scales: CMAQ 5.3 updates the science behind the interactions of chemicals in the air and clouds (Fahey et al., 2017; Luecken et al., 2019; Sarwar et al., 2019). These advances reflect the state of the science and are more inclusive of chemical processes not just within the U.S., but under different climatic conditions across the globe. Marine Chemistry Figure
  • More complex land and atmosphere interactions to support both air quality and ecosystems applications: CMAQ 5.3 includes two new options for simulating the exchange of pollutants between the land and the atmosphere, improving linkages of CMAQ for ecological applications (Bash et al., 2018;Pleim et al., 2019). Air-surface exchange
  • Increased emphasis on pollutants originating outside the U.S.: While air quality has improved through EPA regulations under the Clean Air Act, understanding the fate of air pollutants originating in other countries remains critical for addressing air quality in the U.S. CMAQ 5.3 better captures the influences of reactive chemical species originating from the oceans, and it increases the emphasis on more accurate characterization of pollutants transported through the air from distant sources (Mathur et al., 2017; Hogrefe et al., 2018). lateral boundary conditions on ozone bias
  • Increased scientific consistency between meteorology and chemistry models: As the state of the science in the meteorology model evolves, changes are introduced into CMAQ to represent the atmospheric processes as consistently as possible between these models. The meteorology model used by CMAQ was updated by adding scientific complexity, incorporating new data sources, and changing the representation of the atmosphere above the troposphere—that is, above where most of the weather occurs. This change is important because intermittent infusions of high concentrations of ozone into the lower atmosphere occur through physical processes at the top of this layer -- the tropopause. 
  • Greater flexibility to support increasingly diverse uses of CMAQ: Some of the software in the CMAQ modeling system has been restructured so that both users and developers can more readily extend CMAQ to meet their needs. For example, a new emissions interface allows for substantial flexibility in the way emissions are mapped, scaled, and checked for quality and can greatly simplify the task of assessing air quality improvements resulting from emission changes. Emission in CMAQ diagram
  • Improved efficiency for CMAQ-ISAM for isolating source contributions to air quality: The science algorithms used by the Integrated Source Apportionment Method (CMAQ-ISAM) to track contributions from different emission sources to ozone and PM have been updated. Code improvements have led to substantially faster run times to support its practical applications. 
  • Fully revised documentation to better reflect CMAQ’s current structure and capabilities: A new comprehensive user’s guide provides instructions on setting up and running the model, including guidance on what model options are recommended for different types of modeling applications.



Bash, J. O., D. Schwede, P. Campbell, T. Spero, W. Appel, and R. Pinder (2018).  Introducing the Surface Tiled Aerosol and Gaseous Exchange (STAGE) dry deposition option in CMAQ v5.3.  Presented at 17th Annual CMAS Conference, 22–24 October 2018, Chapel Hill, NC.

Fahey, K. M., Carlton, A. G., Pye, H. O. T., Baek, J., Hutzell, W. T., Stanier, C. O., Baker, K. R., Appel, K. W., Jaoui, M., and Offenberg, J. H. (2017). A framework for expanding aqueous chemistry in the Community Multiscale Air Quality (CMAQ) model version 5.1, Geosci. Model Dev., 10, 1587-1605,

Hogrefe, C., Liu, P., Pouliot, G., Mathur, R., Roselle, S., Flemming, J., Lin, M., and Park, R. J. (2018). Impacts of different characterizations of large-scale background on simulated regional-scale ozone over the continental United States, Atmos. Chem. Phys., 18, 3839-3864,

Luecken, D. J., G. Yarwood, and W. T. Hutzell (2019). Multipollutant modeling of ozone, reactive nitrogen and HAPs across the continental US with CMAQ-CB6,  Atmospheric Environment, 201, 62-72,

Mathur, R., Xing, J., Gilliam, R., Sarwar, G., Hogrefe, C., Pleim, J., Pouliot, G., Roselle, S., Spero, T. L., Wong, D. C., and Young, J. (2017). Extending the Community Multiscale Air Quality (CMAQ) modeling system to hemispheric scales: overview of process considerations and initial applications, Atmos. Chem. Phys., 17, 12449-12474,

Pleim, J. E., Ran, L., Appel, W., Shephard, M. W., & Cady‐Pereira, K. (2019). New bidirectional ammonia flux model in an air quality model coupled with an agricultural model. Journal of Advances in Modeling Earth Systems, 11.

Pye, H. O. T.; Murphy, B. N.; Xu, L.; Ng, N. L.; Carlton, A. G.; Guo, H. Y.; Weber, R.; Vasilakos, P.; Appel, K. W.; Budisulistiorini, S. H.; Surratt, J. D.; Nenes, A.; Hu, W. W.; Jimenez, J. L.; Isaacman-VanWertz, G.; Misztal, P. K.; Goldstein, A. H. (2017). On the implications of aerosol liquid water and phase separation for organic aerosol mass. Atmos Chem Phys, 17 (1), 343-369.

Pye, H. O. T., D’Ambro, E., Lee, B., Schobesberger, S., Takeuchi, M., Zhao, Y., Lopez-Hilfiker, F., Liu, J., Shilling, J., Xing, J., Mathur, R., Middlebrook, A., Liao, J., Welti,A., Graus, M., Warneke, C., de Gouw, J., Holloway, J., Ryerson, T., Pollack, I., Thornton, J. A. (2019). Anthropogenic enhancements to production of highly oxygenated molecules from autoxidation. P Natl Acad Sci USA.

Sarwar, G., B. Gantt, K. Foley, K. Fahey, T. L. Spero, D. Kang, R. Mathur, H. Foroutan, J. Xing, T. Sherwen, and A. Saiz-Lopez (2019).  Influence of bromine and iodine chemistry on annual, seasonal, diurnal, and background ozone: CMAQ simulations over the Northern Hemisphere, Atmospheric Environment, 213, 395-404,

    Additional Information

    WRF-CMAQ Coupled Model


    Air quality models are typically run in two different ways:

    1. Standalone – Archived output from a meteorological model is used to drive the air quality model.
    2. Coupled – The air quality and meteorological models are run simultaneously and the chemistry can impact the weather.

    The latter “coupled” method is beneficial for studying important interactions between chemistry and weather. For example, aerosols can affect the amount of sunlight that reaches the surface, thus impacting temperature. Aerosols also have important impacts on cloud formation and cloud albedoHelpalbedoThe proportion of the incident light or radiation that is reflected by a surface, like a forest, desert, city, or ocean. It can also refer to the light reflected by a cloud.HelpalbedoThe amount of solar radiation reflected from an object or surface, often expressed as a percentage.. CMAQ has been coupled to the Weather Research and Forecasting (WRF) model for this purpose. 

    In the WRF-CMAQ two-way coupled model (Wong et al., 2012), WRF and CMAQ are simultaneously integrated and information from CMAQ, like aerosol concentration, is passed into WRF so that the chemistry can impact the weather. Specifically, the CMAQv5.2 two-way model gives users the options to pass aerosol optical properties to the radiation modules in WRF (aerosol direct radiative effects).  The ability to pass aerosol information into the cloud microphysics routines (aerosol indirect effects; Yu et al, 2014) is currently under development and will be available in a future release.   

    Schematic of information flow between the meteorology and chemistry modules in the coupled WRF-CMAQ model.The WRF-CMAQ model passes vital atmospheric state and pollutant information back and forth between two interdependent modules.

    Aerosol Direct Radiative Feedback Effects

    Aerosol information from CMAQ is transferred to the meteorological model, WRF.  Wavelength dependent aerosol optical properties (extinction, single scattering albedo, and asymmetry factor) are estimated using aerosol composition and size distribution information simulated by CMAQ in conjunction with an algorithm based on Mie theory.  Black carbon is treated by the core-shell approach developed by Frank Binkowski based on Bohren and Huffman (1983). This has been implemented in the shortwave Rapid Radiative Transfer Model for General Circulation Models (RRTMG) radiation scheme in WRF, where aerosol optical properties are calculated for 14 wavelength bands (Clough et al. 2005). The aerosol optics calculations in the WRF-CMAQ model were assessed through comparison to measured optical properties of ambient aerosols made during the Carbonaceous Aerosol and Radiation Effects Study (CARES) as detailed by Gan et al. (2015a).

    Application and Evaluation

    The dynamics of the planetary boundary layer are complex and have profound impacts on pollutant concentrationsThe impacts of enhanced aerosol concentrations on surface-level meteorology are depicted here. There is a positive feedback of pollutant levels when concentrations are high enough and conditions are favorable.The ability of the coupled WRF-CMAQ system to reproduce historical trends in the tropospheric aerosol burden, aerosol optical depths, and clear-sky short wave radiation across the northern hemisphere and the U.S., has recently been assessed through extensive comparisons of long-term simulations of these quantities with observation-derived records from 1990 to 2010 (Xing et al. 2015a,b; Gan et al., 2015b). The model captured declining Aerosol Optical Depth (AOD) trends along with the corresponding decreased top-of-atmosphere (TOA) short-wave radiation (SWR), or  upwellingHelpupwellingThe upward movement of an air mass in the atmosphere., and increased surface SWR, or downwellingHelpdownwellingThe downwelling movement of an air mass in the atmosphere., in the eastern US, Europe and the northern Atlantic for the period of 2000–2010. Estimates of the aerosol direct radiative effects (ADE) at TOA were comparable with those derived from measurements and, compared to general circulation models, the model exhibited better estimates of surface-aerosol direct radiative efficiency (Eτ) (Xing et al., 2015b).

    Additionally, top-of-atmosphere clear-sky shortwave radiation during 2000-2010, inferred from the NASA Cloud and Earth’s Radiant Energy System (CERES) satellite retrievals show decreasing trends in the eastern U.S. and increasing trends in eastern China. The inclusion of ADE in WRF-CMAQ yielded better agreement with these contrasting trends suggesting that the trends in clear-sky radiation are influenced by trends in the tropospheric aerosol burden.

    The concentrations of particles in the most polluted regions of the world.The aerosol direct effect contributes to strong build-ups of pollutant concentrations in cities with the highest concentrations already. This map shows northern hemispheric distribution with heavy concentrations in polluted cities.Impacts of aerosol cooling are not limited to changes in surface temperature, since variation in atmospheric dynamics caused by the increased stability can worsen local air quality and impact human health.

    Hemispheric WRF-CMAQ model simulation over two decades (1990−2010) shows enhanced surface PM2.5 concentrations in the most polluted regions of the world due to the aerosol direct effect.

    History and Latest Version

    The first WRF-CMAQ coupled system was released in 2012 and used WRFv3.3 and CMAQv5.0. With each new CMAQ release, a new two-way system is developed based on the latest available WRF version. The most current version uses WRFv4.1.1 and CMAQv5.3. Instructions for compiling and running the two-way system are also included with each release.


    Clough, S.A., Shephard, M. W., Mlawer, E. J., Delamere, J. S., Iacono, M. J., Cady-Pereira, K., Boukabara, S., & Brown, P. D. (2005). Atmospheric radiative transfer modeling: a summary of the AER codes. J. Quant. Spectrosc. Ra., 91, 233–244.

    Gan, C., Binkowski, F., Pleim, J., Xing, J., Wong, D-C., Mathur, R., & Gilliam, R. (2015a). Assessment of the Aerosol Optics Component of the Coupled WRF-CMAQ Model using CARES Field Campaign data and a Single Column Model. Atmospheric Environment, 115, 670-682. doi: 10.1016/j.atmosenv.2014.11.028 Exit

    Gan, C., Pleim, J., Mathur, R., Hogrefe, C., Long, C., Xing, J., Wong, D-C., Gilliam, R., & Wei, C. (2015b). Assessment of long-term WRF–CMAQ simulations for understanding direct aerosol effects on radiation "brightening" in the United States. Atmospheric Chemistry and Physics, 15, 12193-12209. doi: 10.5194/acp-15-12193-2015 Exit

    Mathur, R., Pleim, J., Wong, D., Otte, T., Gilliam, R., Roselle, S., Young, J. (2011). Overview of the Two-way Coupled WRF-CMAQ Modeling System. 2011 CMAS Conference, Chapel Hill, NC. Presentation available from the CMAS conference website. Exit 

    Wong, D.C., Pleim, J., Mathur, R., Binkowski, F., Otte, T., Gilliam, R., Pouliot, G., Xiu, A., and Kang, D. (2012). WRF-CMAQ two-way coupled system with aerosol feedback: software development and preliminary results. Geosci. Model Dev., 5, 299-312. doi: 10.5194/gmd-5-299-2012Exit

    Yu, S., Mathur, R., Pleim, J., Wong, D., Gilliam, R., Alapaty, K., Zhao, C., Liu, X. (2014). Aerosol indirect effect on the grid-scale clouds in the two-way coupled WRF-CMAQ: model description, development, evaluation and regional analysis.  Atmos. Chem. Phys.14, 11247–11285. doi: 10.5194/acp-14-11247-2014Exit

    The Decoupled Direct Method in Three Dimensions (CMAQ-DDM-3D)

    The Decoupled Direct Method in Three Dimensions (DDM-3D) provides CMAQ concentration and deposition sensitivity information for user specified model parameters.

    In air quality modeling, sensitivities measure the response of a model output to a change in one or several predefined model parameters. In policy applications, the parameters of interest are usually emissions and the output of interest is pollutant concentrations. We may be interested in emissions from a particular geographical region, like an urban area, a group of states, or a country, and/or emissions from a particular source, such as wildfires, electricity generating units (EGUs), or light duty diesel trucks.

    This plot shows how the direct decoupled method works. Sensitivities are vital to Air Quality model analysis.A hypothetical relationship between emissions of SO2 and sulfate concentrations.  The green tangent line illustrates the sensitivity of sulfate concentration to emissions of SO2.Emissions sensitivities can be calculated by simply running the air quality model twice – once with standard emissions inputs, and once with the emissions of interest adjusted in some way. The difference in outputs between the two runs in relation to the size of the adjustment then becomes the model sensitivity. While this process is fairly easy to implement and interpret, it quickly becomes computationally complex as the number of desired sensitivities increases. For example, calculating sensitivity to EGU emissions from 10 southeastern states in the U.S. would require 11 separate air quality model simulations. 

    An alternative approach to calculate sensitivities is available with the CMAQ model – CMAQ-DDM-3D. CMAQ-DDM-3D is a separately downloadable version of the CMAQ model that allows for sensitivity calculation simultaneously with the standard concentrations and deposition fields. This is done by altering the existing model algorithms to allow for sensitivity propagation through every science module in CMAQ. While CMAQ-DDM-3D does require more computational resources than standard CMAQ, it scales much more favorably with the number of desired calculations.

    Besides emissions, sensitivities to other model parameters can also be calculated. Currently, CMAQ-DDM-3D can be used for sensitivity to emission rates, boundary conditions, initial conditions, reaction rates, potential vorticity, or any combination of these parameters. Second order sensitivity calculations, or sensitivity of sensitivity, are also available.


    Cohan, D.S., & Napelenok, S.L. (2011). Air Quality Response Modeling for Decision Support. Atmosphere, 2(3), 407-425. doi: 10.3390/atmos2030407Exit

    Napelenok, S.L., Cohan, D.S., Odman, M.T., & Tonse, S. (2008). Extension and evaluation of sensitivity analysis capabilities in a photochemical model. Environmental Modelling & Software, 23(8), 994-999. doi: 10.1016/j.envsoft.2007.11.004Exit

    Napelenok, S.L., Cohan, D.S., Hu, Y.T., & Russell, A.G. (2006). Decoupled direct 3D sensitivity analysis for particulate matter (DDM-3D/PM). Atmospheric Environment, 40(32), 6112-6121. doi: 10.1016/j.atmosenv.2006.05.039Exit

    The Integrated Source Apportionment Method (CMAQ-ISAM)

    The Integrated Source Apportionment Method (ISAM) calculates source attribution information for user specified ozone and particulate matter precursors within the CMAQ model.

    The CMAQ model provides users the concentration and deposition fields of many pollutant species. These species are usually combinations of different types of primary emissions and secondary formation that have been physically and chemically transformed in the model. However, sometimes it's desirable to know specific source attribution information for the model outputs. For example, how much of the ozone in an urban area was formed due to nitrogen oxides emitted from motor vehicles in a neighboring state?

    Answering this type of question often requires running an air quality model twice, once with the standard emissions scenario and once with the source of interest completely removed. The difference between the two runs is then assumed to be attributed to the removed source.  While this approach is reasonably straightforward to implement, it has some drawbacks.  For example, removing a large source from the system in a highly nonlinear chemical mixture can lead to some errors. Also, calculating source attribution of many sources can be logistically and computationally complex. 

    Alternatively, the Integrated Source Apportionment Method (ISAM) is available as a separately downloadable version of CMAQ that can calculate source attribution of a large number of sources directly by the model in one simulation.

    This image links to an interactive map that demonstrates the functionality of CMAQ-ISAM


    Cohan, D.S., & Napelenok, S.L. (2011). Air Quality Response Modeling for Decision Support. Atmosphere, 2(3), 407-425. doi: 10.3390/atmos2030407EXIT

    Kwok, R.H.F., Baker, K.R., Napelenok, S.L., & Tonnesen, G.S. (2015). Photochemical grid model implementation and application of VOC, NOx, and O-3 source apportionment. Geoscientific Model Development, 8(1), 99-114. doi: 10.5194/gmd-8-99-2015Exit

    Kwok, R.H.F., Napelenok, S.L., & Baker, K.R. (2013). Implementation and evaluation of PM2.5 source contribution analysis in a photochemical model. Atmospheric Environment, 80, 398-407. doi: 10.1016/j.atmosenv.2013.08.017Exit

    Example ISAM Application

    Simon, et al. (2018). Characterizing CO and NOy Sources and Relative Ambient Ratios in the Baltimore Area Using Ambient Measurements and Source Attribution Modeling. Journal of Geophysical Research - Atmospheres, 123(6), 3304-3320. doi: 10.1002/2017JD027688 EXIT

    The Next-Generation Air Quality Model

    Motivation for Improving Combined Meteorological and Air Quality Models

    Air quality modelers need an efficient multiscale global system to account for the effects of air pollution from overseas while resolving detailed air quality impacts in the U.S. The current two-way Weather Research and Forecasting (WRF)-CMAQ system requires a series of nested domainsHelpdomainsThe area in space or time period over which a prediction is made. with increasing resolution to span hemispheric (~100 km) to local scales (~1 km). This protocol has been useful in the past but is fundamentally inefficient and has been shown to result in interpolation errors and discontinuities when transitioning from larger domains to smaller ones. The traditional approach does not account for the effects of small-scale processes on large-scale phenomena.
    From left to right this figure shows typical US EPA WRF/CMAQ model domains from the 108 km hemispheric scale, 12 km continental United States, regional 4 km and local 1 km over the Washington DC-Baltimore area.From left to right this figure shows typical US EPA WRF/CMAQ model domains from the 108 km hemispheric scale, 12 km continental scale United States, regional 4 km to a local 1 km domain over the Washington DC-Baltimore area.

    Learn more about the Model for Prediction Across Scales (MPAS).EXIT

    Approach for the Next-Generation Air Quality Model

    New meteorological models have been developed that include seamless mesh refinement from global to local scales. These are the future of multi-scale global air quality models. Such mesh structures are ideal for air quality modeling since they cover the entire globe with a coarse mesh, but can resolve areas of interest like the United States with a much finer mesh. Or, further refinement down to a region of the United States, a specific city or even many large urban centers where air quality is still a health problem. For our Next Generation Air Quality Model, we have chosen the Model for Prediction Across Scales (MPAS) to provide the computational grid mesh and the meteorology. The air quality component is in the process of redesign from the current 3D CMAQ model to a 1D column model (vertical dimension only) where the horizontal transport of chemical concentrations will be handled by MPAS. This design will result in flexibility, efficiency and consistency. 

    Figure showing the transition between large-scale grid cells and very fine, local grid cellsThe transition from large grid cells at the global scale to fine cells at the near-urban and urban scales is seamless (left). The size of the grid cells can change dramatically, albeit smoothly, over hundreds of kilometers (middle). The angles, side lengths and even the number of sides of each grid cell is variable throughout the model grid.

    Performance of MPAS for an Annual Simulation

    After the first series of modification to the MPAS model to produce more accurate retrospective simulations, a full annual simulation for 2016 was conducted (previous testing simulated only one month). The evaluation is currently being detailed in a forth coming journal article. Among the features added to MPAS were four-dimensional data assimilation, the Pleim-Xiu land surface model (P-X LSM), the asymmetric convective planetary boundary layer model 2 (ACM2), an updated Kain-Fritsch convection parameterization with feedback to the radiation schemes and a dynamic convective time scale, and processing for soil nudging inputs (see references below for details). Furthermore, we have updated our tools for data visualization (i.e.; VERDI) and model evaluation to work with this new global model data.

    When we evaluate a meteorology model, high-quality observations are essential. We use an internally developed Atmospheric Model Evaluation Tool (AMET) that employs observations from the National Oceanic and Atmospheric Administration (NOAA) Meteorological Assimilation Data Ingest System (MADIS). Common aspects of the meteorology that are evaluated are near-surface meteorology, meteorology aloft, and precipitation. Below is a comparison of WRF and MPAS daily errors of modeled near surface temperature (T2) and moisture (Q2). Except for summer moisture, on most days MPAS performs as well as, or better than WRF.

    Figure shows the daily errors of MPAS and WRF for temperature and moisture. On most days MPAS temperature and moisture is about the same or lower than WRF.Daily errors of MPAS and WRF for temperature and moisture during the winter and summer of 2016.

    The ability to reproduce precipitation is one of the most important aspects of any meteorological model simulation. AMET allows for a daily or monthly evaluation of precipitation using PRISM Climate Group datasets. The figure below shows monthly precipitation data accumulated for all of 2016, showing that MPAS precipitation is consistent with the PRISM observation-based datasetfor the annual total. Furthermore, MPAS actually has a slightly lower mean absolute error (MAE) and a higher correlation (COR) than WRF.

    Figure shows the observed precipitation for 2016 over the United States. Also shown is the equivalent modeled precipitation from the MPAS and WRF models. A table of error statistics shows MPAS has lower error and higher correlation.Observed (PRISM) and modeled (MPAS and WRF) precipitation for 2016. Percent difference between MPAS and PRISM is also provided along with mean absolute error (MAE) and correlation (COR). 

    Many model evaluation studies are limited to surface meteorology. AMET allows direct comparison of MPAS and WRF with global balloon soundings of the atmosphere to understand the model accuracy throughout the layer of the atmosphere where weather occurs (troposphere). The figure below shows errors at all global balloon sounding sites valid for Jan-Dec 2016. Modeled temperature errors are generally 1 deg Celsius/Kelvin or less (1.8 deg F) and wind speed errors around 2 meter per second (~4.5 mph). This level or error is considered low in weather modeling literature. The figure also demonstrates our ability to evaluate global models using AMET.

    Figure shows the error of temperature and wind speed throughout the layer in the atmosphere where weather occurs. Error levels are considered very low for a meteorology model.Figure shows the error of temperature and wind speed throughout the layer in the atmosphere where weather occurs. Error levels are considered very low for a meteorology model.

    Performance of a Prototype MPAS-CMAQ

    While development is in the early stages, a version of the coupled MPAS-CMAQ model has been tested for the limited period of July 2013. Additional updates have been made since then, such as including CMAQ5.3, with plans for simulating all of 2016, so these initial results below are more of an "in the ballpark test" at this stage. Below is a figure that shows the mean of the daily eight-hour maximum ozone that was observed globally along with the attempted reproduction by the MPAS-CMAQ model. While there are certainly differences that our longstanding WRF-CMAQ would simulate better, this early version of MPAS-CMAQ is reasonable in most areas of the world. A key problem with these early simulations is the lack of model spin-up. Global models require a long spin-up period, especially for ozone, which is not only formed from primary emission sources near the surface, but also exists in the upper atmosphere in very high concentrations that are transported down to the surface via tropospheric-stratospheric exchange and vertical transport processes. There is no set spinup guideline, but it does take a month to months or even longer for proper spinup of global models. For our first annual 2016 MPAS-CMAQ simulations we plan a three month spinup.

    Figure shows observed and simulated global ozone. MPAS-CMAQ is still in development, but early results are promising.Observed and simulated MPAS-CMAQ ozone. Values are average daily eight-hour maximum ozone for July 11-31, 2013 period.

    Animation of global surface ozone from the prototype MPAS-CMAQ simulationAnimation of global surface ozone from the prototype MPAS-CMAQ simulation on October 29, 2016.

    Potential Air Quality Model Configurations

    The Next Generation Air Quality Model will be a one-dimensional (vertical column) air quality model coupled with a 3D meteorology model. We envision three configurations of flexible systems:

    • Online-coupled global model with seamless grid refinement
      • 2-way coupling between MPAS and AQ column model working simultaneously to simulate meteorological and air-quality processes around the globe
    • Online-coupled regional model
      • 2-way coupling between WRF and AQ column model working simultaneously to simulate meteorological and air-quality processes over a limited area.
      • If MPAS is eventually adapted for limited-area use, it could be used in place of WRF to simulate meteorological processes in the combined system
    • Offline regional
      • 1-way coupling (sequential) of meteorology (WRF) and the same AQ column model but with additional components for horizontal transport to simulate air-quality without feedbacks to the meteorology


    Bullock Jr., O. R., Foroutan, H., Gilliam, R. C., and Herwehe, J. A.: Adding four-dimensional data assimilation by analysis nudging to the Model for Prediction Across Scales – Atmosphere (version 4.0), Geosci. Model Dev., 11, 2897-2922,, 2018.

    Pleim J., Wong D., Gilliam R., Herwehe J., Bullock R., Hogrefe C., Pouliot G., Ran Limei, Murphy B., Kang D., Appel W., Mathur R., and Hubal E., The New Generation of Air Quality Modeling Systems., EM, Oct 2018.