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Note:
More information on some input arrays can be found when moving the cursor above the corresponding field in the questionnaire. Those fields are also explained in the glossary.

CMAQ: Community Multiscale Air Quality Model

General information

Model name and version

short nameCMAQ
full nameCommunity Multiscale Air Quality Model
revision
date
last change

Responsible for this information

nameNutthida Kitwiroon
institute
addressAtmospheric Science Research Group, University of Hertfordshire, Hatfield, Hertfordshire, AL10 9AB, United Kingdom
zip
city
countryUnited Kingdom
phone+44(0)1707286143
fax
e-mailn.kitwiroon(belongs-to)herts.ac.uk

Additional information on the model

Contact person for model code

same as person above
name
institute
divisions
street
zip
city
country
phone
email
fax

Model developer and model user

developer and userDeveloper: USEPA User: Worldwide

Level of Knowledge needed to operate model

basic
intermediate
advanced
remarks

Model use at your institution

operational
for research
other use

Model code available?

is available?yes
more detailsSource code are available at http://www.cmascenter.org

Minimum computer resources required

typePC Pentium IV 2.4 GHz, 1GB RAM
time needed for runApproximately 25 min. for 7 days run, 6 vertical layers, 35x35 cells, CB-IV chemical mechanism
storage60 GB Hard drive

Further information

documentationByun, D.W. and Ching, J. K. S. 1999. Science Algorithms of the EPA Models-3 Community Multiscale Air Quality (CMAQ) Modelling System, EPA/600/R-99/030, USEPA, Washington, DC.
model referencesSokhi, R.S., San José R., Kitwiroon, N., Fragkou, E., Pérez, J.L. and Middleton, D.R. 2005. Prediction of ozone levels in London using the MM5–CMAQ modelling system. Environmental Modelling & Software, In Press, Corrected Proof, Available online 12 February 2005.
webpagehttp://www.cmascenter.org
additional information

Model properties

Model type

2D
3D
meteorology
chemistry & transport

Model scale

microscale
mesoscale
macroscale
short term
long term

Meteorological variables

Input data
u
v
w
ζ
pv
T
θ
θl
p
Gph
ρ
qv
qt
qlc
qf
qsc
qlr
qsh
qsg
qss
N
E
ε
K
zi
other variables i
other variables ii
other variables iii

Chemical substances

PrognosticDiagnosticDry depositionWet depositionInput data
SO2
NO
NO2
NOX
NH3
HNO3
O3
CH4
DMS
H2O2
VOC
C6H6
HCHO
CO
CO2
POP
PM 10
PM 2.5
PPM10
PM 0.1
PM 1
NH4
SO4
dust
sea salt
BC
POM
SOA
NO3
Other gases
1st radioactivity
2nd radioactivity
3rd radioactivity
Cd
Pb
other heavymetals
pesticides
1st radioactivity
2nd radioactivity
3rd radioactivity
remarks

Approximations

Boussinesq
anelastic
hydrostatic
flat earth
remarks

Parametrizations

Chemistry & transport

photolysis rate
dry deposition
wet deposition
remarks

Chemical reactions

Gas & wet phase chemistry

chemical transformations calculated
chemical transformations neglected
other
gas phase chemistry (give details)
wet phase chemistry (give details)
more information

Aerosol chemistry

passive aerosol
dry aerosol
wet aerosol
sectional approach
modal approach
other
nucleation
coagulation
condensation
aerosol mixing
aerosol ageing
primary aerosol formation
aerosol-gas phase interactions
optical properties
give details

Initialization & boundary treatment

Initialization

chemistry & transport
meteorology

Input data (name sources for data, e.g. website)

orography
land useOptional used to diagnose missing PBL parameters and deposit velocity
obstacles
vegetation
meteorology2D and 3D time dependent variabls and 2D and 3D time independent variables calculated from MM5
concentrationsInitial concentrations and boundary conditions of model species derived from either observation or modelling
emissionsGridded hourly emissions of model species required by chemical mechanism used
remarks

Data assimilation

Chemistry & transport
nudging technique
adjoint model
3D-VAR
4D-VAR
OI
details

Boundary conditions

Chemistry & transport
surface
top
lateral inflowzero-flux divergence condition
lateral outflowzero-flux divergence condition

Nesting

Chemistry & transport
one way
two way
other
variables nested
nesting online
nesting offline
data exchange by array
data exchange by file
time step for data exchangeevery time step
explain methodStatic grid nesting: finer grids (FGs) are placed (i.e., nested) inside coarser grids (CGs). The resolution and the extent of each grid are determined a priori and remain fixed throughout the CTM simulation.
variables nested
other

Solution technique

Coordinate system and projection

Horizontal

cartesian
Lambert conformal
latitude / longitude
rotated lat. / long.

Vertical

z coordinate
surface fitted grid
pressurecoordinate
sigma coordinate
remarks

Numeric

Chemistry & transport

Grid

Arakawa A
Arakawa B
Arakawa C
Arakawa D
Arakawa E
uniform grid
nonuniform grid
Euler
Lagrange
Gauss

Time integration

explicit
split-explicit
semi-implicit
time step same as meteorology
other

Spatial discretisation

scalar quantities
additional information
other
chemistry solver

Model resolution

Chemistry & transport

HorizontalVertical
max
min

Domain size

Chemistry & transport

HorizontalVertical
max
min

Model Validation and Application

Validation & evaluation

Used validation & evaluation methods

analytic solutions
evaluated reference dataset
model intercomparison
additional validation & evaluation efforts
remarks

Application examples

application examplestropospheric ozone prediction, acid deposition (Eutrophication), fine particulates, visibility, and others key air pollutants (e.g., CO, SO2, NO, NO2) predictions.

Participation in specific model evaluation exercises

AQMEII
List experiments (AQMEII)
Cost728
List experiments (COST728)
HTAP
List experiments (HTAP)
MEGAPOLI
List experiments (MEGAPOLI)