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.

This paper discusses the need for critically evaluating regional-scale (∼200-2,000 km) three-dimensional numerical photochemical air quality modeling systems to establish a model's credibility in simulating the spatio-temporal features embedded in the observations. Because of limitations of currently used approaches for evaluating regional air quality models, a framework for model evaluation is introduced here for determining the suitability of a modeling system for a given application, distinguishing the performance between different models through confidence-testing of model results, guiding model development, and analyzing the impacts of regulatory policy options. The framework identifies operational, diagnostic, dynamic, and probabilistic types of model evaluation. Operational evaluation techniques include statistical and graphical analyses aimed at determining whether model estimates are in agreement with the observations in an overall sense. Diagnostic evaluation focuses on process-oriented analyses to determine whether the individual processes and components of the model system are working correctly, both independently and in combination. Dynamic evaluation assesses the ability of the air quality model to simulate changes in air quality stemming from changes in source emissions and/or meteorology, the principal forces that drive the air quality model. Probabilistic evaluation attempts to assess the confidence that can be placed in model predictions using techniques such as ensemble modeling and Bayesian model averaging. The advantages of these types of model evaluation approaches are discussed in this paper.

[1] Gas-phase mechanisms provide important oxidant and gaseous precursors for secondary aerosol formation. Different gas-phase mechanisms may lead to different predictions of gases, aerosols, and aerosol direct and indirect effects. In this study, WRF/Chem-MADRID simulations are conducted over the continental United States for July 2001, with three different gas-phase mechanisms, a default one (i.e., CBM-Z) and two newly implemented ones (i.e., CB05 and SAPRC-99). Simulation results are evaluated against available surface observations, satellite data, and reanalysis data. The model with these three gas-phase mechanisms gives similar predictions of most meteorological variables in terms of spatial distribution and statistics, but large differences exist in shortwave radiation and temperature and relative humidity at 2 m at individual sites under cloudy conditions, indicating the importance of aerosol semi-direct and indirect effects on these variables. Large biases exist in the simulated wind speed at 10 m, cloud water path, cloud optical thickness, and precipitation, due to uncertainties in current cloud microphysics and surface layer parameterizations. Simulations with all three gas-phase mechanisms well reproduce surface concentrations of O 3 , CO, NO 2 , and PM 2.5 , and column NO 2 . Larger biases exist in the surface concentrations of nitrate and organic matter (OM) and in the spatial distribution of column CO, tropospheric ozone residual, and aerosol optical depth, due to uncertainties in primary OM emissions, limitations in model representations of chemical transport, and radiative processes. Different gas-phase mechanisms lead to different predictions of mass concentrations of O 3 (up to 5 ppb), PM 2.5 (up to 0.5 mg m À3 ), secondary inorganic PM 2.5 species (up to 1.1 mg m À3 ), organic PM (up to 1.8 mg m À3), and number concentration of PM 2.5 (up to 2 Â 10 4 cm À3 ). Differences in aerosol mass and number concentrations further lead to sizeable differences in simulated cloud condensation nuclei (CCN) and cloud droplet number concentration (CDNC) due to the feedback mechanisms among H 2 SO 4 vapor, PM 2.5 number, CCN, and CDNC through gas-phase chemistry, new particle formation via homogeneous nucleation, aerosol growth, and aerosol activation by cloud droplets. This study illustrates the important impact of gas-phase mechanisms on chemical and aerosol predictions, their subsequent effects on meteorological predictions, and a need for an accurate representation of such feedbacks through various atmospheric processes in the model. The online-coupled models that simulate feedbacks between meteorological variables and chemical species may provide more accurate representations of the real atmosphere for regulatory applications and can be applied to simulate chemistry-climate feedbacks over a longer period of time.

NOAA and the U.S. Environmental Protection Agency (EPA) have developed a national air quality forecasting (AQF) system that is based on numerical models for meteorology, emissions, and chemistry. The AQF system generates gridded model forecasts of ground-level ozone (O3) that can help air quality forecasters to predict and alert the public of the onset, severity, and duration of poor air quality conditions. Although AQF efforts have existed in metropolitan centers for many years, this AQF system provides a national numerical guidance product and the first-ever air quality forecasts for many (predominantly rural) areas of the United States. The AQF system is currently based on NCEP’s Eta Model and the EPA’s Community Multiscale Air Quality (CMAQ) modeling system. The AQF system, which was implemented into operations at the National Weather Service in September of 2004, currently generates twice-daily forecasts of O3 for the northeastern United States at 12-km horizontal grid spacing. Preoperational testing to support the 2003 and 2004 O3 forecast seasons showed that the AQF system provided valuable guidance that could be used in the air quality forecast process. The AQF system will be expanded over the next several years to include a nationwide domain, a capability for forecasting fine particle pollution, and a longer forecast period. State and local agencies will now issue air quality forecasts that are based, in part, on guidance from the AQF system. This note describes the process and software components used to link the Eta Model and CMAQ for the national AQF system, discusses several technical and logistical issues that were considered, and provides examples of O3 forecasts from the AQF system.