The CCTM uses data from other models and CMAQ subprograms as input for model simulations (Figure 2-2). Detailed information about the meteorological model in the context of preparing inputs for CMAQ can be found in Chapter 4. Detailed information about the emissions models for preparing CMAQ input can be found in Chapter 5.

Figure 2.2. Chemical Transport Model (CCTM) and preprocessors

The input data for the CCTM are developed using the five processors shown in Figure 2-2. CMAQ uses the MCIP processor to prepare the meteorological fields for the CCTM. The ICON and BCON processors generate the initial and boundary conditions for a CCTM simulation. JPROC computes the photolysis rates that will be used when simulating photochemical reactions in the CCTM. The PDM generates plume information for emissions sources that use a subgrid Plume-In-Grid (PinG) treatment to characterize their emissions. Emissions for CMAQ must be prepared with a modeling system that generates emissions for direct input to the CCTM. Brief descriptions of the different CMAQ input processors are presented in this section.

2.2.1. MCIP: Meteorology Chemistry Interface Processor

The flow chart in Figure 2-3 illustrates the role of MCIP and its relationship to the CMAQ chemical transport model. In addition to the files shown in this Figure, GRIDDESC and GRID_CRO_3D provide necessary grid definition information for CMAQ execution.

Figure 2.3. Meteorology preprocessing for CMAQ with MCIP

Using output fields from the meteorology model, MCIP performs the following functions:

• Extracts meteorological model output for the CCTM horizontal grid domain. Important considerations here that CCTM uses a smaller computational domain than meteorological model, and the lateral boundaries from the meteorological model are generally not used by the CCTM.
• Processes all required meteorological fields for the CCTM and the emissions model.
• “Collapses” meteorological model fields, if coarser vertical resolution data are desired for the CCTM. MCIP uses mass-weighted averaging on higher vertical-resolution meteorological model output.
• If selected by the user, computes surface and planetary boundary layer (PBL) fields using output from the meteorological model.
• Computes dry-deposition velocities for important gaseous species using the surface and PBL parameters also generated by MCIP. Dry deposition is the deposition of pollutants from the air onto the surface of the earth. The rate of this removal is determined by various chemical, physical, and biological factors. In addition, dry deposition depends on the type of pollutant, nature and type of surface, and the amount of turbulence or mixing in the atmosphere. A measurement used in simulating the dry deposition of pollutants is the dry deposition velocity. The dry deposition velocity is analogous to the settling velocity of particles due to gravity, and it is useful in determining surface fluxes. MCIP can compute dry deposition using two methods:
  • The RADM dry deposition method (Wesely, 1989) calculates deposition velocities of 13 chemical species using friction velocities and aerodynamic resistances. Inputs required for this method include temperature, humidity, and horizontal wind component profiles.
  • The surface exchange aerodynamic method (Pleim et al., 2001) uses surface resistance, canopy resistance, and stomatal resistance to compute dry deposition velocities.
• Computes cloud top, cloud base, liquid water content, and cloud coverage for cumuliform clouds using simple convective schemes. The cloud parameters influence CCTM aqueous phase chemistry and cloud mixing as discussed in Section 2.2.5 (Walcek and Taylor, 1986; Chang et al., 1987). First, the cloud base is determined as the lifting condensation level computed from the highest saturated equivalent potential temperature below 700 mb. Then, the cloud top is computed by following a saturated adiabatic lapse rate from cloud base until it reaches a temperature five degrees cooler than the surrounding environment. Once the top and bottom of the cloud are determined, a vertical profile of the adiabatic liquid water mixing ratio can be constructed as the difference between the saturated mixing ratio at each level and the source level mixing ratio. By iteratively solving the equations governing the conservation of total water mass, energy conservation for cloud top mixing, along with the temperature profile, the cloud coverage fraction can be obtained.
• Outputs meteorological/geophysical files in the I/O API format, which is standard within the Models-3 framework.