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 principlesfirst 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.
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.
- Please refer to CMAQ's User Guide for information on all of the components of the modeling system.
- A flow diagram showing the dependencies between the different CMAQ programs is available as one of the CMAQ Tutorials.
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.
Specific model updates and new features available with recent model releases:
- CMAQv5.2 and v5.2.1
- 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.
CMAQv5.2.1 - March 2018
- 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.
- 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.
- CMAQv5.0 and v5.0.2
CMAQv5.0 - February 2012
- 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.
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.
- CMAQv4.7 and v4.7.1
CMAQ4.7 - September 2008
- 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
- 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
- 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
- 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.
Latest Version: CMAQv5.2.1
Release Date: March 16, 2018
The CMAQ model has been updated with both important and minor patches to several modules including chemistry, aerosols, transport and emissions. With these solutions in place, users are provided with an even more robust modeling platform than was released with CMAQv5.2. In addition, issues that were identified with the default compilation and execution workflow have been resolved. You can view the details of these changes in the CMAQv5.2.1 Release Notes. Exit With this model release, the CMAQ team is also making public for the first time a Developers' Guide to support CMAQ users who want to contribute new features to the code-base. To download CMAQv5.2.1 and documentation, visit the CMAQ GitHub repository Exit
CMAv5.2 (Released June 30, 2017)
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 v5.2, 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.
CMAQv5.2, the latest version of the Community Multiscale Air Quality modeling system (release date June 2017), includes the following new features:
- Foroutan et al., 2017). The new model has been evaluated and shown to compare much better with observations than previous dust emission components in CMAQ. New windblown dust emission model: This is a physics-based model that incorporates an innovative dynamic relationship for the surface roughness length relevant to small-scale dust generation processes (
- New gas-phase photochemistry mechanism: Updated Carbon Bond chemical mechanism (CB6r3) added to CMAQv5.2 includes a better treatment of rural and remote chemistry, which is particularly important for modeling compliance strategies for a lowered National Ambient Air Quality Standard (NAAQS). CMAQv5.2 also includes an extension to the CB05 version of the Carbon Bond mechanism for modeling oceanic halogen chemistry in hemispheric simulations.
- (Pye et al., 2017). The volatility of both secondary and primary organic compounds are now treated consistently with each other and the available literature. A new model speciesspeciesAn individual molecule or chemical compound. has been added to account for the organic aerosol compounds resulting from combustion processes, which are missed by the former parameterizations. This new species is constrained with available intensive and routine field observation data (Murphy et al., 2017). New pathways to organic aerosol: New sources of secondary organic aerosol have been added to the model (heterogeneous uptake of glyoxal and methylglyoxal in the Carbon Bond chemical mechanism version 6, referred to as CB6) and properties of traditional secondary organic aerosol have been updated
- Instrumented diagnostic capabilities: Several diagnostic capabilities that allow users to probe source-receptor relationships have been updated with this model version. The tools will be released in September 2017 and include:
- Decoupled Direct Method in Three Dimensions (DDM3D) for calculating sensitivity coefficients for user defined parameters;
- Integrated Source Apportionment Method (ISAM) for tracking contributions from sources and regions; and
- Sulfur Tracking Model (STM) for tracking sulfate production pathway contributions. The instrumented models are useful tools for a variety of decision support applications.
Foroutan, H., Young, J., Napelenok, S., Ran, L., Appel, K.W., Gilliam, R.C., & Pleim, J.E. (2017). Development and evaluation of a physics-based windblown dust emission scheme implemented in the CMAQ modeling system, Journal of Advances in Modeling Earth Systems, doi: 10.1002/2016MS000823. Exit
Murphy, B. N., Woody, M. C., Jimenez, J. L., Carlton, A. M. G., Hayes, P. L., Liu, S., Ng, N. L., Russell, L. M., Setyan, A., Xu, L., Young, J., Zaveri, R. A., Zhang, Q., and Pye, H. O. T.: Semivolatile POA and parameterized total combustion SOA in CMAQv5.2: impacts on source strength and partitioning, Atmos. Chem. Phys. Discuss., doi: 10.5194/acp-2017-193 EXIT, in review, 2017.
Pye, H.O.T., Murphy, B.N., Xu, L., Ng, N.L., Carlton, A.G., Guo, H., Weber, R., Vasilakos, P., Appel, K.W., Budisulistiorini, S.H., Surratt, J.D., Nenes, A., Hu, W., Jimenez, J.L., Isaacman-VanWertz, G., Misztal, P.K., and Goldstein, A.H. (2017). On the implications of aerosol liquid water and phase separation for organic aerosol mass, Atmos. Chem. Phys., doi: 10.5194/acp-17-343-2017.Exit
WRF-CMAQ Coupled Model
Air quality models are typically run in two different ways:
- Standalone – Archived output from a meteorological model is used to drive the air quality model.
- 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 albedoalbedoThe 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.albedoThe 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.
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
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 upwellingupwellingThe upward movement of an air mass in the atmosphere., and increased surface SWR, or downwellingdownwellingThe 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).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 (
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.
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 WRFv3.8 and CMAQv5.2. 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.
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.
- CMAQ-DDM-3D is downloadable with the standard release of the model.
- For documentation please refer to the CMAQ-DDM-3D User's Guide. Exit
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.
- CMAQ-ISAM is downloadable with the standard release of the model.
- For documentation please refer to the ISAM instruction manual. Exit
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
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 series of nested domainsdomainsThe 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 processes occurring at smaller scales on large-scale phenomena.
Purpose of 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.
Performance of a 2016 MPAS Simulation
After the first series of modification to MPAS model designed for more accurate retrospective simulations, a full annual simulation for 2016 was conducted as previous testing was only done for one month. The evaluation is currently being detailed in a journal article that will be published as a product soon. Among the features added to MPAS were four-dimensional data assimilation, Pleim-Xiu land surface model (P-X LSM), asymmetric convective planetary boundary layer model 2 (ACM2) 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 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 surface meteorology, above surface meteorology 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, or better than WRF.
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. The figure shows MPAS precipitation is consistent with the PRISM observation-based dataset in terms of annual precipitation. Furthermore, MPAS actually has a slightly lower Mean Absolute Error (MAE) and correlation (COR) than WRF.
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 accuracy of a model 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.
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. We have made updates since inlcuding a CMAQ5.3, and on the verge of running all of 2016, so these results are more of a "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 simulated better, this early version of MPAS-CMAQ is in the ballpark in most areas of the world. A key with this early simulations is the lack of model spin-up. Global models require a long spin-up cycle, especially for ozone that is not only formed from primary emission sources near the surface, but exists in the upper atmosphere in very high concentrations that is transported down to the surface via tropspheric-stratospheric exchance 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.
Potential Air Quality Model Configurations
The Next Generation Air Quality Model will be a 1-dimensional (vertical column) air quality model coupled with a meteorology model. We envision three configurations of flexible systems:
- Online 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 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 but 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, https://doi.org/10.5194/gmd-11-2897-2018, 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.