(CMAQ) modeling system Daewon W. · Output meteorological data: MCIP writes the bulk of its two-...

One-atmosphere dynamics description in the

Models-3 Community Multi-scale Air Quality

(CMAQ) modeling system

Daewon W.Atmospheric Sciences Modeling Division, Air Resources Laboratory,National Oceanic and Atmospheric Administration, Research Triangle

f art, NC 27777, L%4

Email: [email protected] assignment to the National Exposure Research Laboratory, U.S.

Environmental Protection Agency

ABSTRACT

This paper proposes a general procedure to link meteorological data with airquality models, such as U.S. EPA's Models-3 Community Multi-scale AirQuality (CMAQ) modeling system. CMAQ is intended to be used for studyingmulti-scale (urban and regional) and multi-pollutant (ozone, aerosol, andacid/nutrient depositions) air quality problems. The Models-3 CMAQ system isexpected to be a common vehicle to advance environmental modeling techniquesfor scientists and the regulatory community. To provide multi-scale capabilityfor meteorological and air quality modeling, a set of governing equations for thefully compressible atmosphere is introduced. By recasting input meteorologicaldata with the variables that satisfy the governing equations in a generalizedcoordinate system, CMAQ can follow the dynamics and thermodynamics of themeteorological model closely. A robust mass conservation scheme is introducedand discussed.

1. Introduction

Eulerian photochemical air quality models have been used to understand airpollution problems, to answer emissions control policy questions, and to projectfuture air quality. Advances in understanding of atmospheric processes and rapiddevelopments in computer technologies require significant and periodic updatesto these models. To promote free exchange of ideas for improving air quality,

Transactions on Ecology and the Environment vol 29 © 1999 WIT Press, www.witpress.com, ISSN 1743-3541

884 Air Pollution

U.S. EPA has developed a new comprehensive and flexible Models-3Community Multi-scale Air Quality (CMAQ) modeling system (Byun et al.fl],[2]). It is intended to be used for studying multi-scale (urban and regional) andmulti-pollutant (ozone, aerosol, and acid/nutrient depositions) air qualityproblems. The system is expected to be a common vehicle to advanceenvironmental modeling techniques for scientists and the regulatory community.We encourage participation of the modeling community for the continuousdevelopment of the physical and chemical process algorithms in CMAQ.

Sources of meteorological data are diverse and many difficulties can arisewhile linking these with air quality models. Meteorological data fromoperational weather forecasting models are expected to be mass consistent.However, due to many reasons including infrequent output intervals and effects cfdata assimilation processes, density and wind fields can be mass inconsistent.This paper proposes a general procedure to conserve mass of trace species in airquality models. To provide an integral view of meteorological and air qualitymodeling, a set of governing equations for the fully compressible atmosphere isintroduced. By recasting input meteorological data with the variables that satisfythe governing equations in a generalized coordinate system, CMAQ follows thedynamics and thermodynamics of the meteorological model closely.

2. One-atmosphere dynamics description

To simulate weather and air quality phenomena realistically, adaptation of a oneatmosphere perspective based mainly on "first principles" description of theatmospheric system is necessary. The perspective emphasizes that the influenceof interactions at different dynamic scales and among multi-pollutants. Forhandling multi-scale atmospheric phenomena, the governing equations for thethermodynamic variables are preferably expressed in the continuity equationform. Following Ooyama[8], prognostic equations for entropy and air densityare employed to ensure the mass conservation characteristics with theatmospheric modeling system. The principle used here is the separation cfdynamic and thermodynamic parameters into their primary roles. An inevitableinteraction between dynamics and thermodynamics occurs in the form of thepressure gradient force. A set of governing equations in a generalized curvilinearcoordinate system for a fully compressible dry adiabatic atmosphere is introducedin the literature, such as Byun[3].

Governing equations for momentum components (not presented here)are expressed in terms of contravariant wind components defined as;

+mVj (la)

^ ds ds . \ -v* = — = — + ( — V »

dr A gwhere, V, is the horizontal wind vector, m is the map scale factor, v' is for each

contravariant wind component, s is the generalized vertical coordinate, <E> isgeopotential height, and U and V are horizontal wind components in Cartesiancoordinates which are rotated from true east-west and north-south directions, wrepresents vertical wind component. The conservation equations for air density,entropy density, and trace species concentrations are found to be:


Air Pollution 885

., (2)

= , (4)) °*

where <p. represents trace species concentration, j^ is the Jacobian of verticalcoordinate transformation. £ is entropy per unit volume (entropy density),defined as

where T is temperature, 7^ and p^ are temperature and density of the referenceatmosphere, respectively, at pressure p^ = 1000 mb = 10* Pascal, c^ is thespecific heat capacity at constant volume, and &/ is the gas constant for dry air.The g-terms represent sources and sinks of each conservative property.Although the source term for air density, Q , should be zero in an ideal case, itis retained here to capture possible density error originating from numericalprocedures in meteorological models or interpolation methods used in chemistry-transport models (CTMs). It is important to understand how this error terminfluences computations of other parameters such as vertical velocitycomponents. To close the system we need to utilize the ideal gas law and thethermodynamic relations for temperature, entropy, pressure gradients, anddensity. Here, atmospheric pressure is treated as a thermodynamic variable thatis fully defined by the density and entropy of the atmosphere. Then, pressuregradient terms can be computed using the thermodynamic relations with densityand entropy.

Benefits of the one-atmosphere description can be summarized as: (1)Entropy and density are better conserving quantities than temperature andpressure; (2) Mass conservation is treated identically both in the meteorologicaland chemistry-transport models; (3) Pressure is treated as one of thethermodynamic quantities that are determined by the properties of fluid; (4)Pressure and temperature estimation methods are not constrained by theassumptions on the atmospheric dynamics, and finally; (5) It allows simulationof the atmosphere for fully compressible, or incompressible; and hydrostatic, ornonhydrostatic descriptions using the same fundamental set of governingequations.

3. Meteorology-Chemistry Interface Processor (MCIP)

The meteorology-chemistry interface processor (MCIP) is the key processorallowing the consistent linkage between meteorological models and CMAQ.Currently, different meteorological models are used by different atmosphericmodeling groups forming their own respective user communities. This is


886 Air Pollution

because various meteorological models are applicable for limited ranges of spatialand temporal scales. They use different vertical coordinates and dynamicassumptions. For example, MM5 (Mesoscale Modeling System Generation 5)(Grell et al.[7]), uses a terrain-influenced reference pressure coordinate (<j Jsystem for nonhydrostatic studies and a normalized dynamic pressure (<?_)coordinate system for hydrostatic applications. RAMS (Regional AtmosphericModeling System) (Pielke et al.[9]) uses an anelastic atmosphere assumption andARPS (Advanced Regional Prediction System) (Xue et al.[ll]) utilizes a set offormulas for a compressible atmosphere, both with the terrain-influencednormalized height coordinate (o\) system. To expand the user base of theModels-3 CMAQ system and to promote the one-atmosphere communitymodeling paradigm, it is essential to continuously develop MCIP modules forseveral popular mesoscale meteorological models such as RAMS, ARPS, andothers.

Major MCIP functions can be categorized into three processing phases:(1) input phase for data extraction and interpolation, (2) processing phase for theestimation of diagnostic parameters and generalization of dynamics, and (3)output phase for the creation of CMAQ meteorological files. Figure 1 shows thethree processing phases and key functions of MCIP are summarized below.

Data extraction and interpolation: MCIP reads in meteorological model output

files and stores the information in the memory for further processing. Essentialheader information is passed to the MCIP output file header. Because CMAQuses a smaller computational domain than meteorological models to removeboundary effects, MCIP extracts data for the CMAQ window domain. Also,MCIP interpolates coarse meteorological model output for finer grid resolution,if needed, and collapses meteorological profiles when coarse vertical resolutiondata are requested.

Estimation of diagnostic parameters: Because meteorological models do notprovide all the needed parameters for CMAQ, MCIP diagnoses planetaryboundary layer (PBL), cloud, and radiation parameters using mean wind,temperature, and humidity profiles, surface data, and detailed land use informationas available. Depending on the user options, MCIP can pass through surface andPBL parameters simulated by the meteorology model directly. In addition, MCIPcomputes dry deposition velocities for important gaseous species.

Diagnosing of cloud parameters: When important parameters needed forprocessing cloud effects in the CMAQ CTMs are not provided by themeteorological model, MCIP diagnoses them (i.e., cloud top, base, liquid watercontent, and coverage) using a simple convective parameterization. They are usedin the CTM to process aqueous-phase chemistry and cloud mixing as well as tomodulate photolysis rates that reflect the effects of cloud.

Generalization of dynamics: MCIP generates coordinate dependent meteorologicaldata for the generalized coordinate CTM simulation. Many of the coordinate-


Air Pollution 887

related functions traditionally treated in a CTM have been incorporated as a partof the MCIP functions. This enhances modularity of the CMAQ CTMregardless of the coordinates used and eliminates many coordinate-dependentprocessor modules in the CTM. MCIP transforms momentum components intothe contravariant form, and recasts thermodynamic state variables into entropyand densities (air and water vapor). By incorporating such interpolation methodsthat preserve characteristics of the continuity equations, the dynamic andthermodynamic consistencies among the meteorological data are maintained evenafter the temporal interpolations in the CMAQ CTM.

Output meteorological data: MCIP writes the bulk of its two- and three-dimensional meteorological and geophysical output data in a transportablenetCDF-based binary format using the Models- 3 input/output applicationsprogram interface (I/O API) library.

Meteorological data generated by MCIP are used in several Models-3CMAQ processors and in CMAQ chemistry transport models. Surfacetemperature and photosynthetically active radiation (PAR) components are usedto estimate biogenic emissions from trees and vegetation, and temperature at thestreet level is used for computing mobile emission factors in emissionsprocessing. Atmospheric turbulence parameters and temperature and windprofiles are used to estimate plume rise height and turbulence dispersion ofemissions from stacks. Cloud information is used to modify the photolyticrates, which are critical in determining photochemical reaction rates and toaccount for the effects of cloud mixing and aqueous-phase chemistry on pollutantconcentrations. Wind components and density distributions are used for theadvection of pollutants. Finally, boundary layer height, atmospheric stabilityinformation, and deposition velocities are used to compute effects of turbulencemixing and deposition of pollutants.

Coordinate/Grid

Dynamics (17, V, HOThermodynamics

Surface Parameters

2D/3D Pass-Through

JacobianC^.)Layer Height

PBL Parametersurface Fluxes

Dep. Velocities

Phase

Figure 1. Processing phases in MCIP. Dynamic and thermodynamic parametersfrom different meteorological models are recast into the parameters used in the

fully compressible set of governing equations for CMAQ CTM.


888 Air Pollution

Many of these computations are performed in the generic coordinates ofthe respective physical processes. For example, to compute cloud effects, apressure coordinate is used while to estimate turbulence mixing coefficients, ageometric height coordinate is used. By relying on the information captured inthe Jacobian, entropy, and air density, the necessary parameters such as pressureand temperature can be obtained in a dynamically consistent manner even aftertemporal interpolations. This further enhances modularity of the CMAQ CTMeliminating the need for many coordinate-dependent processor modules in theCTM. Consistencies in dynamic descriptions of the atmosphere, physicalparameterizations, and numerical algorithms, where applicable, in meteorologicaland chemistry-transport models are critical in determining the quality of airpollutant simulations. This issue becomes more critical with high resolution airquality studies where nonhydrostatic meteorological models must be used.

4. Demonstration of mass correction scheme

Eulerian air quality models heavily depend on the mass conservationcharacteristics of the system. On the other hand, limited-area (urban andregional) meteorological models sometimes suffer mass conservation problemsdue to many reasons. Refer to Byun[4] for the details. Under this condition,one cannot expect exact mass conservation of pollutants in air quality models(AQMs). Instead, some AQMs rely on the conservation of mixing ratio tominimize the side effects of the variability of atmospheric situations and massinconsistent meteorological inputs in predicting pollutant concentrations.

In cases where the air density and wind fields are not consistent, i.e.,when j Q in Eq. (2) is nonzero, the continuity equation must be modified atleast to conserve pollutant mixing ratio (Byun[4]):

dt * ^ m* J ds * ' p '^'

However, because the total mass of a pollutant in the domain should beconserved when there are no emissions and deposition (i.e., Q - 0), Eq. (6) can

conserve the total mass only with the additional condition

where d£2 represents the boundary of a computational domain. The condition issatisfied only when either there is no mass inconsistency in air flow (i.e., Q^ - 0)at the boundary, or the tracer concentration is zero at the domain boundary. Forthe internal domain, the mass conservation error caused by theshould be corrected with

pwhere superscripts T and 'cor' represent values after transport (advection) andafter correction, respectively. Table 1 summarizes three correction methodscompared. A correction scheme should conserve the total tracer mass underdynamically consistent wind and density fields. Further, it should at least


Air Pollution 889

conserve mixing ratio even in the event that wind and density fields are not massconsistent.

Table 1. Mass correction schemes tested.

Correction Method Air quality model using the Mass correction algorithmcorrection scheme

Advection of Urban Airshed Model (UAM) ^ _ q>* ~ _ j Q-calibration gas (Scheffe and Morris 1993) ^ ~9/^

Advection of air SARMAP Air Quality Model „,, _ %/ in,density (SAQM) (Chang et al., 1997) ' " p

Advection of pj Community Multi-scale Airwith two-step time Quality (CMAQ) modelsplitting

The CMAQ correction scheme is expected to maintain conservation ofmixing ratio to the machine precision as long as the numerical advectionschemes themselves are mass conservative. The other two correction schemesignore effects of variation in density field or coordinate/grid structures during thecorrection process. For Cartesian coordinates where the Jacobian is uniform andconstant with time, the SAQM scheme is equivalent to the CMAQ scheme. Allthe correction schemes become identical for the atmosphere with uniform(spatially) and constant (temporally) density distribution and nondivergent flowsin a Cartesian coordinate system.

Here, we demonstrate performance of the CMAQ correction scheme withan idealized two-dimensional flow. A more complete description of the studywill be presented in another paper, Byun and Lee[5]. The schemes were testedwith a distortional flow, a combination of rotational and stretching flow. Thedensity field consists of a non-uniform, but mass-consistent distribution with anadditional mass-inconsistent component. Plate (a) of Figure 2 reflects the effectof this mass-inconsistency on the transport, demonstrating creation of tracermass. On the other hand, transport with CMAQ adjustment removes theproblem as shown in Plate (b). Here, not only the mixing ratio, but also thetotal tracer mass is conserved because the boundary condition Eq. (7) is satisfied.For a limited-area air quality simulation, operational meteorological input andpollutant distributions cannot guarantee the boundary condition for maintainingmass conservation and thus, in all practical purposes, only conservation ofmixing ratio should be expected.


890 Air Pollution

(a) Without Adjustmen

(b) With CMAQ Adjustment

'0,'Ob,

Figure 2. Transformation of Witch of Agnesi mountain shape under acombination of rotational and stretching flows with a mass-inconsistent densityfield. Plate (a) shows result without the mixing ratio adjustment scheme and

(b) with the CMAQ adjustment scheme.


Air Pollution 891

5. Conclusion

In this paper, I have studied a few issues involved with linking CMAQ withdifferent dynamic meteorological models, especially focusing on thecompatibility of the governing set of equations and state variables used. Otherkey issues that must be considered before linking a meteorological model withCMAQ are scale limitations in subgrid scale parameterizations such as cloud,turbulence, and surface exchange processes, and consistency in numericalalgorithms and discretization methods.

The current CMAQ version is intended to be used for studying urbanand regional air quality problems. To extend its capability to neighborhoodscales for providing necessary air quality input for human exposure studies and toglobal scales for establishing linkage with the global tropospheric chemistry andclimate issues, additional development efforts are needed. Most modelingdifficulties for meso-gamma (tens of kilometer domain with about 1 km gridresolution) scale air quality problems are concerned with whether we can providereliable meteorological and emissions inputs to CMAQ. For example,representation of the urban canopy in meteorology models and in CTMs must beimproved and a very high resolution emissions inventory must be established.To move CMAQ to the global scales, additional numerical modules must beadded and some improvement of the Models-3 I/O API is required because thespectral method used in general circulation models such as CCM3 (CommunityClimate Model Generation 3) is fundamentally different from the CMAQ's finite-difference discretization method. Also, efforts for harmonizing the urban/regionalchemical mechanisms of CMAQ with those of global scale models are essential.

Disclaimer

This paper has been reviewed in accordance with the U.S. EnvironmentalProtection Agency's peer and administrative review policies and approved forpresentation and publication. Mention of trade names or commercial productsdoes not constitute endorsement or recommendation for use.

References

1. Byun D, A. Hanna, Coats C., and D. Hwang., Models-3 air quality modelprototype science and computational concept development. Transactions ofAir & Waste Management Association Specialty Conference on RegionalPhotochemical Measurement and Modeling Studies, Nov. 8-12, SanDiego, CA. 1993, pp. 197-212, 1995.

2. Byun, D.W., J. Young., G. Gipson., J. Godowitch., F. Binkowsk., S.Roselle, B. Benjey, J. Pleim, J. Ching., J. Novak, C. Coats, T. Odman,A. Hanna, K. Alapaty, R. Mathur, J. McHenry, U. Shankar, S. Fine, A.Xiu, and C. Jang, Description of the Models-3 Community Multiscale AirQuality (CMAQ) model. Proceedings of the American MeteorologicalSociety 78th Annual Meeting, Phoenix, AZ, Jan. 11-16, pp. 264-268,1998.


892 Air Pollution

3. Byun, D.W., Dynamically consistent formulations in meteorological andair quality models for multi-scale atmospheric applications: I. GoverningEquations in Generalized Coordinate System. J. Atmos. Sci., 1999a. (inprint)

4. Byun, D.W., Dynamically consistent formulations in meteorological andair quality models for multi-scale atmospheric applications: II. Massconservation issues. J. Atmos. Sci., 1999b. (in print)

5. Byun, D.W. and S.-M. Lee, Mass conservative numerical integration oftrace species conservation equation: I Experiment with idealized two-dimensional flows. 1999: (in preparation)

6. Chang J. S., S. Jin, Y. Li, M. Beauharnois, C.-H. Lu, H.-C. Huang, S.Tanrikulu and J. DaMassa, The SARMAP air quality model. Final Report,SJVAQS/AUSPEX Regional Modeling Adaptation Project, 53 pp. 1997.[Available from California Air Resources Board, 2020 L Street,Sacramento, California 95814.]

7. Grell, G.A., J. Dudhia, and D.R. Stauffer, A description of the fifthgeneration Penn State/NCAR mesoscale model (MM5). NCAR Tech.Note NCAR/TN-398+STR, 117 pp. 1994. [Available from the NationalCenter for Atmospheric Research. P.O. Box 3000, Boulder, CO 80307.]

8. Ooyama, K. V., A thermodynamic foundation for modeling the moistatmosphere. J. Atmos. ScL, 47, 2, pp. 580-2593, 1990.

9. Pielke, R. A., W. R. Cotton, R. L. Walko, C. J. Tremback, M. E.Nicholls, M. D. Moran, D. A. Wesley., T. J. Lee and J. H. Copeland, Acomprehensive meteorological modeling system-RAMS. Meteor. Atmos.Phys., 49, pp. 69-91, 1992.

10. Scheffe, R. D. and R. E. Morris, A review of the development andapplication of the Urban Airshed Model. Atmos. Environ., 27B, pp. 23-39,1993.

11. Xue, M., K. Droegemeier, V. Vong, A. Shapiro and K. Brewster, ARPSVersion 4.0 User's Guide. Center for the Analysis and Prediction ofStorms, Univ. of Oklahoma, 380 pp, 1995. [Available from Center for theAnalysis and Prediction of Storms, Univ. of Oklahoma, Norman, OK73019].


(CMAQ) modeling system Daewon W. · Output meteorological data: MCIP writes the bulk of its two-...

Documents

Transcript of (CMAQ) modeling system Daewon W. · Output meteorological data: MCIP writes the bulk of its two-...