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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.

MATCH: Multi-scale Atmospheric Transport and Chemistry

General information

Model name and version

short nameMATCH
full nameMulti-scale Atmospheric Transport and Chemistry
revisionversion 4.4
date2006-01-09
last change

Responsible for this information

nameValentin Foltescu
instituteSMHI
addressFolkborgsvägen 1
zip601 76
cityNorrköping
countrySweden
phone+46 11 495 8661
fax+46 11 495 8001
e-mailvalentin.foltescu(belongs-to)smhi.se

Additional information on the model

Contact person for model code

same as person above
nameValentin Foltescu
instituteSMHI
divisionsFolkborgsvägen 1
street
zip601 76
cityNorrköping
countrySweden
phone+46 11 495 8661
emailvalentin.foltescu(belongs-to)smhi.se
fax+46 11 495 8001

Model developer and model user

developer and userMATCH development team: Lennart Robertson, Magnuz Engardt, Robert Bergström, Joakim Langner, Valentin Foltescu, Cecilia Bennet, Camilla Andersson, Thomas Klein, Michael Kahnert, Christer Persson MATCH users includes: the development team, other groups at SMHI, Swedish universities, groups in Chile and Estonia

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 detailsUpon request and license agreement

Minimum computer resources required

typeLinux PC or Unix work station
time needed for runapplication dependent from minutes to weeks
storageapplication dependent - could rise to hundreds of GBytes

Further information

documentationAutomatically generated html-documentation included in model installation. General information also available on the web-page.
model referencesRobertson, L., Langner, J. and Engardt, M. 1999. An Eulerian limited-area atmospheric transport model. J. Appl. Meteor. 38. Foltescu, V.L., Pryor, S.C. and Bennet C. 2005. Sea salt generation, dispersion and removal on the regional scale Atmos. Environ. 39. Gidhagen, L., Johansson, C., Langner J. and, Foltescu, V.L. 2005. Urban scale modeling of particle number concentration in Stockholm. Atmos. Environ. 39.
webpagehttp://www.smhi.se/sgn0106/if/FoUl/en/index.html
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 radioactivityRn-222
2nd radioactivityCs-137
3rd radioactivityI-131
remarksThe photochemistry version includes ca 60 species including radicals.

Approximations

Boussinesq
anelastic
hydrostatic
flat earth
remarksMATCH relies on meteorological input data on hybrid and sigma vertical coordinates, and as such terrain following.

Parametrizations

Chemistry & transport

photolysis rateFor the cloud-free sky the photolytic rates are pre-calculated for solar zenith angles between 0 and 97 degrees. Photolysis parameters are tabulated for four seasons, for different surface albedo, different (fixed) ozone columns and different levels with 1 km vertical resolution. The effect of clouds on photolysis rates are accounted for by scaling clear sky photolytic rates with the ratio of the actual global radiation (corrected for clouds) to the clear sky global radiation.
dry depositionDry deposition is modelled using a resistance approach. Deposition schemes with different degrees of sophistication are available.
wet depositionFor most gas phase species wet scavenging is assumed to be proportional to the precipitation intensity, using species-specific (and sometimes altitude dependent) scavenging coefficients. In the photochemistry version SO2, O3 and H2O2 in-cloud scavenging is calculated by assuming Henry’s law equilibrium within the clouds; sub-cloud scavenging is neglected for these three species. For particles, several different wet deposition schemes are available, e.g., adapted versions of the schemes in Berge (1993, Tellus, 45B, 1-22) and Zhang et al. (2001, Atm. Environ., 35, 549-560)
remarks

Chemical reactions

Gas & wet phase chemistry

chemical transformations calculated
chemical transformations neglected
other
gas phase chemistry (give details)Several different chemistry schemes are available. At present, the most sophisticated photochemistry scheme is based on the EMEP MSC-W model chemistry (Simpson et al., 1993 EMEP MSC-W Note 2/93). The main difference is that for the isoprene chemistry an adapted version of the Carter 1-product mechanism (Carter, 1996, Atmos. Environ. 30, 4275-4290) is used instead of the EMEP mechanism. In addition to the photochemistry the reduced nitrogen chemistry from Hov et al. (1994, J. Geophys. Res. 99, D9, 18735-18748) is included (as well as aqueous-phase oxidation of SO2 by ozone and H2O2 in cloud water). The total chemical scheme includes 110 thermal, 28 photochemical, 2 aqueous-phase, 5 aerosol reactions and 4 gas-phase aqueous-phase and aerosol equlibria between 60 chemical components.
wet phase chemistry (give details)Usually only aqueous-phase oxidation of SO2 by ozone and H2O2 in cloud water are included.
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 detailsThe particle sizes are represented by a discrete sectional scheme of a user-defined number of bins. The size classes are treated as individual chemical tracers. Particle dynamics are not treated in the current model formulation.

Initialization & boundary treatment

Initialization

chemistry & transportInitial state can be taken from other model runs (at any model resolution) or based on the boundary concentrations specified by the user (interpolated to fill the model volume).
meteorology

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

orographyfrom the meteorological files
land usefrom meteorological files or from external land survey
obstacles
vegetation
meteorologyHIRLAM, ALADIN, ECMWF-operational, ERA-40, regional climate model runs In addition to upper level data from the above sources meso-scale analysis may be used for near surface parameters
concentrationsMeasured data, e.g. from EMEP stations and/or data from large scale models
emissionsEMEP, GEIA, SEI, Streets et al., Local emission inventories
remarks

Data assimilation

Chemistry & transport
nudging technique
adjoint model
3D-VAR
4D-VAR
OI
detailsA full data assimilation scheme is not yet implemented. The adjoint transport scheme is though available. In order to provide boundary conditions a 2D variational scheme is used for analysis of e.g. O3. A procedure for local adaptations by Kalman-filter is available.

Boundary conditions

Chemistry & transport
surfaceInitial concentrations may be assigned complete domain, trough specfying, (i) constant values, (ii) vertical profiles, or (iii) three-dimensonal fields.
topThe user may select 'open' (or 'closed') boundary. Thereby tracer may (may not) flow in and across the top boundary. See details under 'lateral inflow/outflow'.
lateral inflowOccurs if the user set boundary concentration. Initial and time varying boundary conditions are read from three-dimensional data in GRIB format. The updating frequency of boundary-conditions may be set individually for each component. The user may also specify constant concentratins on boundaries, which may be advected into the modelling domain.
lateral outflowOccurs. Tracer leaving domain will not return.

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 exchangeuser-defined, typically 1-3 h
explain methodInitial state and time dependent boundary conditions are read from data in GRIB format. The update frequency of boundaries may be set individually for each component. The one-way nesting is straight forward as output from MATCH is produced in GRIB format.
variables nestedmost chemical components
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
remarksThe model accounts for up to 6 different projections on input and output. The grid could either be defined by the input weather data, or be specifically set up. For the latter interpolation of weather data (if needed) is made on the fly. The model is mainly adapted to weather data on so called hybrid vertical coordinates, and the vertical coordinates of weather data define the model vertical coordinates.

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
othersemi-implicit for vertical diffusion and deposition

Spatial discretisation

scalar quantitiesBott-type advection on non-uniform grids
additional informationA Lagrangian particle model could be switched on as an interface between point sources and the Eulerian grid. The 'particles' are normally transported during some hours before entering the grid. Chemistry is not applied in the Lagrangian model.
other
chemistry solverA two-stage Rosenbrock solver is used. The solver is implemented using the KPP (Kinetic PreProcessor) by Damian et al. (2002, Computers and Chemical Engineering, 26, 1567-1579).

Model resolution

Chemistry & transport

HorizontalVertical
max1501000
min0.510

Domain size

Chemistry & transport

HorizontalVertical
max500020000
min203500

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 examplesWarner, S., Platt, N. and Heagy, J.F. 2005. Comparisons of transport and dispersion model predictions of the european tracer experiment: area- and population-based user-oriented measures of effectiveness. Atmos. Environ. 39, 4425-4437. Langner, J., Bergström, R. and Foltescu, V. 2005. Impact of climate change on surface ozone and deposition of sulphur and nitrogen in Europe. Atmos. Environ. 39, 1129-1141. Hedberg, E., Gidhagen, L. and Johansson, C. 2005. Source contributions to PM10 and arsenic concentrations in Central Chile using positive matrix factorization. Atmos. Environ. 39, 549561. Engardt, M., Siniarovina, U., Khairul, N.I. and Leong, C.P. 2005. Country to country transport of anthropogenic sulphur in Southeast Asia. Atmos. Environ. 39, 51375148. Solberg, S., Bergström, R., Langner, J., Laurila, T. and Lindskog, A. 2005. Changes in Nordic surface ozone episodes due to European emission reductions in the 1990s. Atmos. Environ. 39, 179-192.

Participation in specific model evaluation exercises

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