by replacing this centralized library or new data types can be

handled by adding new routines to the library.

3. METEOROLOGICAL MODELING FOR AIR

QUALITY

Air quality modeling should be viewed as an integral part of

atmospheric modeling. It is imperative that the governing

equations and computational algorithms be consistent and

compatible in each component of the system for a “oneatmosphere”

approach. Historically, many AQMs were

designed with limited atmospheric dynamics assumptions.

In the model development process, simplified sets of

governing equations representing a limited range of the

problem scales were adopted to enable rapid development of

models. However, we believe that decisions on the dynamic

assumptions and choice of coordinates should not precede

the determination of the computational structure of the

modeling system. To provide the scalability in describing

dynamics in the CCTM, which does not solve the dynamics

components by itself, a description of the atmosphere with a

fully compressible governing set of equations in a

generalized coordinate system is beneficial (Byun [28],

[29]). By recasting input meteorological data with the

variables that satisfy the governing equations in a

generalized coordinate system, CMAQ can follow the

dynamics and thermodynamics of the meteorological model

closely, regardless of the meteorological model or its native

coordinate structure.

3.1. Governing Equations for the Fully Compressible

Atmosphere

In most numerical weather prediction models, temperature,

pressure, and moisture variables represent the

thermodynamics of the system, and their dynamic equations

are often expressed in the advective form. The density is

diagnosed as a byproduct of the simulation, usually through

the ideal gas law. For multiscale air quality applications

where strict mass conservation is required, prognostic

equations for the thermodynamic variables are preferably

expressed in a conservative form similar to the continuity

equation. Ooyama [30] proposed the use of prognostic

equations for entropy and air density in atmospheric

simulations by highlighting the thermodynamic nature of

pressure. Entropy is a well-defined state function of the

thermodynamic variables such as pressure, temperature, and

density. Therefore, entropy is a field variable that depends

only on the state of the fluid. Ooyama separates dynamic

and thermodynamic parameters into their primary roles. An

inevitable interaction between dynamics and

 

 

 

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