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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.

CHIMERE (ARPA-IT): CHIMERE (included in NINFA modelling system)

General information

Model name and version

short nameCHIMERE (ARPA-IT)
full nameCHIMERE (included in NINFA modelling system)
revision
date
last change

Responsible for this information

nameMarco Deserti
instituteARPA-SIM
addressviale Silvani, 6 Bologna
zip47100
cityBologna
countryItaly
phone+39051525915
fax+39051
e-mailmdeserti(belongs-to)arpa.emr.it

Additional information on the model

Contact person for model code

same as person above
nameLaurent Menut
instituteLaboratoire de Meteorologie Dynamique IPSL
divisionsEcole Polytechnique
street
zip91128
cityPalaiseau Cedex
countryFrance
phone
email
fax

Model developer and model user

developer and userdeveloper: Robert Vautard

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://euler.lmd.polytechnique.fr/chimere/

Minimum computer resources required

typePC linux
time needed for run60 minutes for 1 simulated day with aerosols
storage

Further information

documentationhttp://euler.lmd.polytechnique.fr/chimere/
model referencesSchmidt H., C. Derognat, R.Vautard and M.Beekmann, 'A comparison of simulated and observed ozone mixing ratios for the summer of 1998 in western Europe', Atmospheric Environment, 2001 Blond, N., Bel, L. and Vautard, R., Three-dimensional ozone data analysis with an air quality model over the Paris area, J. Geophys. Res., 108(D23), 4744, doi:10.1029/2003JD003679, 2003. Hodzic A., Vautard, R., Bessagnet, B., Lattuati, M., Moreto, F., Long-term urban aerosol simulation versus routine particulate matter observations. Atmospheric Environment, 39, 5851-5864, 2005
webpagehttp://www.arpa.emr.it/sim/archivio/downloads/generale/descrizione_ninfa_gb.pdf
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 ratePhotolysis rates are calculated under clear sky conditions as a function of height using the TUV model (Madronich, S., McKenzie, R. E., Bjorn, L. O., and Caldwell, M. M. (1998). Changes in biologically active ultraviolet radiation reaching the earth’s surface. J. Photochem. Photobiol. B:Biology, 46:5–19.). Then clouds are taken into account in a highly parameterized fashion. For all photolysed species, clear sky photolysis rates Jc(z) are multiplied throughout model columns by an attenuation coefficient A(d) depending on the total cloud optical depth (COD) d. Using the TUV model, and a large set of CODs for clouds at various altitudes, the attenuation relative to the clear-sky case has been fitted as a function of COD.
dry depositionThe aerodynamic resistance Ra is calculated as the integral of the inverse of the diffusivity coefficient K(z) up to the middle of the model surface layer, which can be estimated using the analytical formulae of the surface-layer similarity profiles for K ([Seinfeld and Pandis, 1997]). Dry deposition for aerosols also makes use of a resistance scheme.
wet depositionFor gases in clouds: Nitric acid, ammonia in the gas phase are scavenged by cloud droplets. This process is assumed to be revertible. Moreover, for in-cloud scavenging, dissolved gases in a non precipitating cloud can reappear in the gas phase due to cloud dissipation. Equilibrium between dissolved gases concentration and gas-phase concentrations. • For gases in rain droplets below the clouds: Dissolution of gases in precipitating drops is assumed to be irrevertible, both for HNO3 and NH3. In the model, sulfur dioxide and hydrogen peroxide are also scavenged by precipitation. • For particles in clouds : Particles can be scavenged either by coagulation with cloud droplets or by precipitating drops. Particles also act as cloud condensation nuclei to form new droplets. • For particles in rain droplets below the clouds : Particles are scavenged by raining drops.
remarks

Chemical reactions

Gas & wet phase chemistry

chemical transformations calculated
chemical transformations neglected
other
gas phase chemistry (give details)CHIMERE offers the option to include different gas phase chemical mechanisms. The original, complete scheme ([Lattuati, 1997]), hereafter called MELCHIOR1, describes more than 300 reactions of 80 gaseous species. The hydrocarbon degradation is fairly similar to the EMEP gas phase mechanism ([Simpson, 1992]). Adaptations are made in particular for low NOx conditions and NOx-nitrate chemistry. All rate constants are updated according to [Atkinson et al., 1997] and [De Moore et al., 1994]. Heterogeneous formation of HONO from deposition of NO2 on wet surfaces is now considered, using the formulation of [Aumont et al., 2003]. In order to reduce the computing time a reduced mechanism with 44 species and about 120 reactions is derived from MELCHIOR ([Derognat et al., 2003]), following the concept of 'chemical operators' ([Carter, 1990]). This reduced mechanism is called MELCHIOR2 hereafter.
wet phase chemistry (give details)SO2, H2O2 and O3 in the aqueous phase are in equilibrium with the concentrations in the gas phase. Moreover, aqueous SO2 is dissociated into HSO−3 and SO2−3 . Catalyzed oxidation reactions of sulfur dioxide in aqueous droplets with iron and manganese are considered, following [Hoffman and Calvert, 1985] among others. Henry’s law coefficient and other aqueous equilibrium constants are used ([Seinfeld and Pandis, 1997]). Sulfur chemistry is very pH sensitive. pH is kept bound between 4.5 and 6.0.
more informationhttp://euler.lmd.polytechnique.fr/chimere/

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 detailsNitric acid is produced onto existing particles and fog droplets. Although aerosol particles and cloud droplets occupy a very small fraction of the atmosphere, it is now well established that reactions involving gas species onto their surfaces may significantly contribute to atmospheric chemistry cycles. [Jacob, 2000] recommends for ozone model to include a minimal set of reactions with associated uptake coefficients given by [Harrison and Kito, 1990. precursor volatile organic compounds able to form secondary aerosol species are high chain alkanes, aromatics and monoterpenes. ASOA and BSOA are artitioned between gas and aerosol phases. Mass transfer is not only driven by the gas phase diffusion but also by the thermodynamic equilibrium through a temperature dependent partition coefficient ([Pankow, 1994]).

Initialization & boundary treatment

Initialization

chemistry & transportThere are two possible initialization modes: • Initialization by reading initial concentrations in a restart file. • Initialisation by interpolating boundary conditions. This is the case when no initial file is available.
meteorology

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

orographyGLCF data base
land useCORINE2000 + JRC GLC2000
obstacles
vegetation
meteorologyIn the ARPA-IT implementation, 3D meteorological fields are provided by the LAMI meteorological model (reanalisys or forecasts). Some variables are calculated by DIAGMET meteorological module, as a post-processing of the LAMI output.
concentrationsBoundary conditions: provided by the european scale implementation of Chimere run by Ineris (50km horiz.resol.; prevair.org) Initial conditions: interpolated from the boundary conditions.
emissionsEmission input data are based on the Italian National Inventory of the year 2000 (5km horiz.resol.) Outside Italy: EMEP 2000 (50km horiz.resol.)
remarks

Data assimilation

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

Boundary conditions

Chemistry & transport
surface-
topBoundary conditions can be either 'external' or given by a coarse-resolution CHIMERE simulation. In the first case and in the present release, a choice of monthly climatologies is offered between the MOZART second-generation global chemistry-transport model (MOZART2) [Horowitz et al., 2003], or the LMDZ-INCA2 chemistry-transport model. The concentration of a few species are considered as constant (but depending on month) throughout the simulation. The MOZART climatologies have been calculated and are kindly provided here by the Max-Planck Institut, Hamburg, by M. Schultz, G. Brasseur, C. Granier and D. Niehl. In the second case, a first coarse run must have been carried out.
lateral inflowidem
lateral outflowidem

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 methodModel simulations can be nested in a one-way mode. That is, a coarse CHIMERE simulation can give boundary conditions to a high-resolution simulation. Nested simulations must be performed one after the other. There is no need for any correspondence or relationship between the coarse and the fine horizontal grids. The vertical grids are not required to match : A nested grid may have a higher vertical resolution. This allows for performing a coarse simulation wiith moderate vertical resolution and a high-resolution simulation with accuracy in all three directions.
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
remarkshybrid sigma-p coordinate

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 solverSecond order TWOSTEP algorithm originally proposed by Verwer, 1994.

Model resolution

Chemistry & transport

HorizontalVertical
max10*10~2000 (top)
min5*540 (bottom)

Domain size

Chemistry & transport

HorizontalVertical
max650*500 (Northern Italy)5000
min300*200 (Emilia Romagna)5000

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- daily 72h forecasts - long term air quality assessment (10km Northern Italy; 5km Emilia Romagna region) - long term emission reduction scenarios (10km Northern Italy; 5km Emilia Romagna region; CLE2010, local reduction actions)

Participation in specific model evaluation exercises

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