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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.

CALGRID: California Grid Model

General information

Model name and version

short nameCALGRID
full nameCalifornia Grid Model
revision
date
last change

Responsible for this information

nameClaudio Carnevale
instituteUniversity of Brescia
addressVia Branze, 38
zip25123
cityBrescia
countryItaly
phone+39 0303715449
fax+39 030380014
e-mailcarneval(belongs-to)ing.unibs.it

Additional information on the model

Contact person for model code

same as person above
nameDr. Robert J. Yamartino and Mr. Joseph S. Scire
instituteEarth Tech
divisions
street196 Baker Avenue
zip01742-2167
cityConcord, MA
countryUSA
phone978-371-4265
emailrjy(at)src.com, jss(at)src.com
fax 978-371-2468

Model developer and model user

developer and userAtmospheric Studies Group, Earth Tech., Concord MA 01742, USA (formerly Sigma Research Corporation)

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 detailshttp://www.src.com/calgrid/sourcecode.htm

Minimum computer resources required

typeExtensively used on a Pentium II PC (LINUX) and on various workstation platforms (mainly IBM/RISC, DEC Alpha and IBM SP2).
time needed for runOn a DIGITAL Alpha 69600 gridpoints: with SAPRC-90 12 times faster than reality.
storageSame machine, same case about 30 Mbytes RAM. Disk space: 200 Mbytes needed for input-output files according to output required.

Further information

documentationManuals available in English
model referencesYamartino, R.J., 1993: Nonnegative, conserved scalar transport using grid-cell-centered, spectrally constrained Blackman cubics for applications on a variable-thickness mesh. Mon. Wea. Rev., 121, 753-763. Yamartino R.J., Scire J.S., Carmichael G.R., and Chang Y.S., (1992) The CALGRID mesoscale photochemical grid model-I: Model formulation. Atmospheric Environment 26A, p.1493-1512 Silibello C., Calori G., Brusasca G., Catenacci G., Finzi G. (1998): Application of a photochemical grid model to Milan metropolitan area. Atmospheric Environment 32, 2025-2038.
webpage
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 rateDepending on zenith angle.
dry depositionThree layer, full-resistance model.
wet deposition
remarks

Chemical reactions

Gas & wet phase chemistry

chemical transformations calculated
chemical transformations neglected
other
gas phase chemistry (give details)Gas Chemical mechanisms: SAPRC90, CBIV90. Solver: fully explicit Quasi Steady State Approximation (QSSA) solver.
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 & transportThe model uses the 3D concentration fields for each species, coming either from measurement or coarser model grid results.
meteorology24h pre-run

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

orographyOrography height for each grid location
land useNone -used by CALMET meteorological preprocessor
obstacles
vegetation
meteorologyHourly 3D wind gridded fields of wind components, air temperature and liquid water content. Hourly 2D fields of mixing height, Monin-Obukhov lenght (L), PGT class, friction velocity (u*), convective scale velocity (w*).
concentrationsInitial conditions: 3D concentration fields for eahc species, coming either from measurement or coarser model grid results. Boundary conditions: Time varying concentrations at lateral boundaries, coming either from measurement or coarser model grid results.
emissionsHourly NOx, NMVOC, NH3, CO, SO2, PM gridded emission fields. NMVOC and PM are split and lumped on the basis of the selected chemical gas and aerosol mechanism. The emission fields are computed by POEM-PM emission pre-processor.
remarksAll the input files are in binary format

Data assimilation

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

Boundary conditions

Chemistry & transport
surface
topTime varying concentrations at lateral boundaries, coming either from measurement or coarser model grid results.
lateral inflowTime varying concentrations at lateral boundaries, coming either from measurement or coarser model grid results.
lateral outflow

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 exchange
explain method
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
remarksTerrain following co-ordinate system.

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
otherMarchuk Operator Splitting Technique. Transport module uses explicit/semi-implicit numerical scheme based on Chapeau function. Chemistry solver: see below

Spatial discretisation

scalar quantities
additional information
other
chemistry solverThe model uses the fully explicit QSSA solver, based on the quasi steady state approximation. In this approach, the production and loss term for all reacting species are constant during an integration step. In this way, the chemistry ODE system became linear with constant coefficents and an analytical solution can be computed.

Model resolution

Chemistry & transport

HorizontalVertical
max202000
min0,520

Domain size

Chemistry & transport

HorizontalVertical
max100010000
min201

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 examples[1] V. Gabusi, M. Volta (2005). Seasonal modelling assessment of ozone sensitivity to precursors in Northern Italy. Atmospheric Environment, Vol. 39, Issue 15, pp. 2795-2804, <a href=http://dx.doi.org/10.1016/j.atmosenv.2004.07.041>doi:10.1016/j.atmosenv.2004.07.04</a>. [2] V. Gabusi, C. Pertot, G. Finzi (2003). Performance assessment of long-term photochemical modelling system. International Journal of Environment and Pollution, Vol. 20, pp. 64-74. [3] M. Bedogni, C. Carnevale, V. Gabusi, E. Minguzzi, C. Pertot, G. Pirovano, M. Volta (2005). Ozone seasonal assessment of emission reduction scenarios over Northern Italy, Proc. 5th International conference on Urban Air Quality, Cdrom, (R.S. Sokhi, M.M. Millan and N. Moussiopulos editors), ISBN I-898543-92-5, University of Hertfordshire. [4] M. Bedogni, C. Carnevale, C. Pertot, M. Volta (2004). A four modelling inter-comparison concerning chemical mechanisms and numerical integration methods, Proc. 9th International Conference on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes (P. Suppan Ed.), Vol. 1, pp 28-32, ISBN 3-923704-44-5, Forschungszentrum Karlsruhe (D). [5] M. Bedogni, C. Carnevale, E. Minguzzi, G. Pirovano (2005). Sentitivity of CTM simulations to meteorological input, International Journal of Environment and Pollution.

Participation in specific model evaluation exercises

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