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COSMO-MUSCAT: Model of the Consortium for Small Scale Modeling and Multi-Scale Atmospheric Transport Model

General information

Model name and version

short nameCOSMO-MUSCAT
full nameModel of the Consortium for Small Scale Modeling and Multi-Scale Atmospheric Transport Model
revisionCOSMO version 4.13
date31.05.2010
last change18.06.2012

Responsible for this information

nameOswald Knoth
instituteLeibniz-Institut für Troposphärenforschung
addressPermoserstr. 15
zip04318
cityLeipzig
countryGermany
phone++49 341 2352147
fax++49 341 2352139
e-mailknoth(belongs-to)tropos.de

Additional information on the model

Contact person for model code

same as person above
nameRalf Wolke
instituteLeibniz-Institut für Troposphärenforschung
divisions
streetPermoserstr. 15
zip04318
cityLeipzig
countryGermany
phone++49 341 2352860
emailwolke@tropos.de
fax++49 341 2352139

Model developer and model user

developer and userModel COSMO (former LM): Consortium for Small-Scale Modeling (German Weather Service et al.) Model MUSCAT: Leibniz Institute for Tropospheric Research, Leipzig, Germany (Ralf Wolke and Oswald Knoth)

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 detailsCOSMO: DWD, 63067 Offenbach. MUSCAT: IfT, 04318 Leipzig

Minimum computer resources required

typeMulti-processor computer
time needed for runUp to several hours per 1 day forecast
storageAbout 1 GB per 1 day forecast

Further information

documentationCOSMO: http://www.cosmo-model.org
model referencesRalf Wolke, Oswald Knoth, Olaf Hellmuth, Wolfram Schröder, Eberhard Renner, 2004. The Parallel Model System LM-MUSCAT for Chemistry-Transport Simulations: Coupling Scheme, Parallelization and Applications. In Parallel Computing, pages 363–370. Elsevier. ---- Ralf Wolke, Wolfram Schroeder, Roland Schroedner, Eberhard Renner, 2012. Influence of grid resolution and meteorological forcing on simulated European air quality: A sensitivity study with the modeling system COSMO-MUSCAT. Atmos. Environ., 53, 1904-1912.
webpagehttp://www.cosmo-model.org
additional information

Model properties

Model type

2D
3D
meteorology
chemistry & transport

Model scale

microscale
mesoscale
macroscale
short term
long term

Meteorological variables

PrognosticDiagnostic
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 gasesOH, HO2, H2O2, HNO2, HNO4, NO3, N2O5, H2SO4, ISO, PAN, TOL, MGLY,...
1st radioactivity
2nd radioactivity
3rd radioactivity
Cd
Pb
other heavymetals
pesticides
1st radioactivity
2nd radioactivity
3rd radioactivity
remarks

Approximations

Boussinesq
anelastic
hydrostatic
flat earth
remarksNon-hydrostatic, compressible, surface heterogeneity (orography, land use)

Parametrizations

Meteorology

turbulence schemeBased on prognostic turbulent kinetic energy and mixing length; considering e.g. vertical wind shear and thermal stability
deep convectionTiedtke mass-flux scheme with equilibrium closure
surface exchangeDrag-law formulation with Louis transfer coefficients; considering resistances in the turbulent, viscous, and surface sublayers
surface temperatureEnergy budget considering vertical heat fluxes in atmosphere and soil as well as melting and freezing (snow, ice, water) on surface; with prognostic multi-layer soil model
surface humidityHumidity budget considering vertical water fluxes, horizontal runoff, and plant transpiration in atmosphere and soil; with prognostic multi-layer soil model
radiationTwo-stream transfer equations for 8 spectral intervals; shading by clouds
unresolved orographic drag
radiation in vegetation
radiation between obstacles
treatment of obstacles
clouds / rainClouds: Kessler bulk scheme with saturation adjustment. -- Rain: autoconversion of cloud water, accretion of cloud water by rain drops, evaporation, and sedimentation.
remarks

Chemistry & transport

photolysis rateDepending on solar angle and species (MCM = The Master Chemical Mechanism, see http://chmlin9.leeds.ac.uk/MCM/); shading by cloud cover
dry depositionResistance model including plant and surface processes, sedimentation, and diffusion in the turbulent and viscous sublayers; (or constant species dependent deposition velocities)
wet depositionIn-cloud and sub-cloud scavanging (similar to EMEP)
remarks

Chemical reactions

Gas & wet phase chemistry

chemical transformations calculated
chemical transformations neglected
other
gas phase chemistry (give details)RACM-MIM2: 95 species; 248 chemical and 25 photolytic reactions
wet phase chemistry (give details)Sulfur chemistry; temperature dependent equilibrium between aqueous and gas phase for NH3 and HNO3
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 detailsextended M7 (see Vignati et al., 2004; Wolke et al., 2012)

Initialization & boundary treatment

Initialization

chemistry & transportClimatological background profiles (or zero) or global data for outermost-nest model as initialisation and boundary values
meteorologyInterpolated reanalysis data of global model GME or COSMO-DE (DWD, Offenbach, Germany) as initialisation and boundary values

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

orographyDWD, Offenbach, Germany
land useCORINE data set
obstacles
vegetation
meteorologyInterpolated reanalysis data of global model GME or operational COSMO_DE runs serve as lateral boundary conditions
concentrationsClimatological background profiles or global data for outermost-nest model
emissionsEMEP (http://www.EMEP.int), TNO, alternatively registers from several German states
remarks

Data assimilation

MeteorologyChemistry & transport
nudging technique
adjoint model
3D-VAR
4D-VAR
OI
detailsInterpolated reanalysis data of global model GME serve as lateral boundary conditions at least for the outermost-nest model

Boundary conditions

MeteorologyChemistry & transport
surfaceFriction boundary conditions for constant-flux layer with surface budget Gradient zero or given deposition velocity
topFree slip (vanishing vertical velocity and gradients)Gradient zero or constant background values or input from outer-nest model
lateral inflowRelaxation conditions forcing adaption to profiles of outer-nest model or to reanalysis data of global model GME (within a zone of few cells at each lateral boundary)Gradient zero, constant background values or input from outer-nest model runs
lateral outflowSame as lateral inflowGradient zero

Nesting

MeteorologyChemistry & 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 (e.g., 1 hour)Online nesting with internal time step
explain methodRelaxation conditions forcing adaption to profiles of outer-nest model or to reanalysis data of global model GME (within a zone of few cells at each lateral boundary)Alternative or additional possibility: one-way nesting with interpolated input from outer-nest model
variables nestedAll prognostic
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
remarksVertical hybrid grid for Met and CT: terrain-following coordinates in lower, horizontal coordinates in upper atmosphere (based either on height or reference pressure). -- Horizontal grid: Uniform with nested sub-domains of raised spatial resolution (for CT only).

Numeric

Meteorology

Grid

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

Time integration

explicit
split-explicit
semi-implicit
otherLeapfrog method

Spatial discretisation

momentum equationsSecond-order centered finite differences
scalar quantitiesSecond-order centered finite differences
additional information
other

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
otherImplicit-explicit Runge-Kutta scheme with time step control

Spatial discretisation

scalar quantitiesThird-order upwind-biased discretisation
additional information
other
chemistry solverImplicit together with vertical advection, diffusion, and deposition

Model resolution

Meteorology

HorizontalVertical
max1001000
min0.120

Chemistry & transport

HorizontalVertical
max1001000
min0.120

Domain size

Meteorology

HorizontalVertical
max1000020000
min105000

Chemistry & transport

HorizontalVertical
max1000010000
min103000

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) Heinold, B., Helmert, J., Hellmuth, O., et al., 2007. Regional modeling of Saharan dust events using LM-MUSCAT: Model description and case studies, J. Geophys. Res., 112, D11204, doi:10.1029/2006JD007443. -- (2) R. Stern, P. Builtjes, M. Schaap, R. Timmermans, R. Vautard, A. Hodzic, M. Memmesheimer, H. Feldmann, E. Renner, R. Wolke, A. Kerschbaumer, 2008. A model intercomparison study focussing on episodes with elevated PM10 concentrations. Atmos. Environ., 42:4567–4588. -- (3) D. Hinneburg, E. Renner, R. Wolke, 2009. Formation of secondary inorganic aerosols by power plant emissions exhausted through cooling towers in Saxony. Environ Sci. Pollut Res., 16:25-35, doi:10.1007/s11356-008-0081-5. -- (4) E. Renner, R. Wolke, 2010. \\\\\\\"Modelling the formation and atmospheric transport of secondary inorganic aerosols with special attention to regions with high ammonia emissions.\\\\\\\" Atmos. Environ. 44(15): 1904-1912.

Participation in specific model evaluation exercises

AQMEII
List experiments (AQMEII)EU 2006
Cost728
List experiments (COST728)Winter 2003, Spring 2006
HTAP
List experiments (HTAP)
MEGAPOLI
List experiments (MEGAPOLI)