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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.

MM5(GKSS-D): Fifth Generation PSU/NCAR Mesoscale Model

General information

Model name and version

short nameMM5(GKSS-D)
full nameFifth Generation PSU/NCAR Mesoscale Model
revision3.7.3
date2005/11/22
last change

Responsible for this information

nameVolker Matthias
instituteGKSS Research Center
addressMax-Planck-Strasse 1
zip21502
cityGeesthacht
countryGermany
phone+49-4152-872346
fax+49-4152-872332
e-mailvolker.matthias(belongs-to)gkss.de

Additional information on the model

Contact person for model code

same as person above
name
institute
divisions
street
zip
city
country
phone
emailmesouser@ucar.edu
fax

Model developer and model user

developer and userPenn State University and NCAR, USA. The model is widely used in the US and worldwide as a community model. Contributions can be made by all users.

Level of Knowledge needed to operate model

basic
intermediate
advanced
remarksThe model can be operated using many different physical parameterisation schemes. It needs some experience to identify which combination of paramerisations serves the users needs best.

Model use at your institution

operational
for research
other use

Model code available?

is available?yes
more detailsCan be downloaded together with an online tutorial through the MM5 web page (www.mmm.ucar.edu/mm5/mm5-home.html)

Minimum computer resources required

typelinux PC
time needed for rundepends on domain size, in our configuration typ. 40 min/day on a PC
storagefew hundred GB for extensive runs (0.5 GB per day for 54/18 km domains)

Further information

documentationGrell, G., Dudhia, J., and Stauffer, D.R., 1995. A Description of the Fifth-Generation Penn State/NCAR Mesoscale Model (MM5), NCAR Technical Note 398, NCAR, Boulder, Colorado, USA.
model referencessee publications list at www.mmm.ucar.edu/mm5/mm5-home.html
webpagewww.mmm.ucar.edu/mm5/mm5-home.html
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 ipressure perturbation
other variables ii
other variables iii

Approximations

Boussinesq
anelastic
hydrostatic
flat earth
remarks

Parametrizations

Meteorology

turbulence scheme7 different schemes can be used. AT GKSS the MRF scheme, based on Troen and Mahrt countergradient term and K-profile in the well mixed PBL. Details given by Hong and Pan (Mon. Wea. Rev., 1996).
deep convection
surface exchangeBulk aerodynamic parameterisation
surface temperature
surface humidityBulk aerodynamic parameterisation
radiationshortwave and longwave broadband schemes considering clouds
unresolved orographic drag
radiation in vegetation
radiation between obstacles
treatment of obstacles
clouds / rain7 different schemes are available. At GKSS the Reisner scheme inculding rain, ice, snow and graupel is used
remarks

Initialization & boundary treatment

Initialization

chemistry & transport
meteorologyInitial conditions are generated from analysis fields (at GKSS: ERA40) on prescribed sigma levels. Pressure, temperature, wind and humidity are interpolated to the specified grid. Surface data and soil information can also be used.

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

orographyUSGS
land useUSGS
obstacles
vegetation
meteorologyERA40 other can be used, e.g. NCEP, ERA15
concentrations
emissions
remarks

Data assimilation

Meteorology
nudging technique
adjoint model
3D-VAR
4D-VAR
OI
detailsDynamic assimilation every time step. Either observations, analysis fields or both can be used.

Boundary conditions

Meteorology
surface4 options avalaible. AT GKSS NOAH land surface model with 4 layers is used.
topradiative boundary conditions
lateral inflowtime dependent values in one outer row and column for the coarse mesh. Two outer rows and columns are used for nests. Several outer meteorology data sets can be used for lateral boundary conditions. At GKSS, ERA40 data is used.
lateral outflowDepends on variables prescribed at the boundaries. Variables which are not specified by the outer meteorology fields are zero on inflow and zero grdaient on outflow.

Nesting

Meteorology
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 methodOne way nesting: initial and boundary conditions for the nest are calculated from the coarse grid (whose horizontal resolution is typically 3 times the nests resolution) for each timestep of the coarse grid. Two way nesting: new fields calculated in the nest feed back into the coarse grid.
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
remarksOther types of map projections can be chosen: Mercartor or polar stereographic.

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
othersecond order leap frog. Time splitting for fast terms.

Spatial discretisation

momentum equations
scalar quantities
additional information
other

Model resolution

Meteorology

HorizontalVertical
max54300
min337

Domain size

Meteorology

HorizontalVertical
max486015000
min12015000

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 examplesNicole P.M. van Lipzig, Marc Schröder, Susanne Crewell, Felix Ament, Jean-Pierre Chaboureau, Ulrich Löhnert, Volker Matthias, Erik van Meijgaard, Markus Quantee, Ulrika Willén, Wenchieh Yen: Comparison of model predicted low-level cloud parameters with observations from the BALTEX Bridge Campaigns, Atmospheric Research, in press, 2006 Marc Schröder, Nicole van Lipzig, Felix Ament, Jean-Pierre Chaboureau, Susanne Crewell, Jürgen Fischer, Volker Matthias, Erik van Meijgaard, Andi Walther, Ulrika Willén: Comparison of model predicted low-level cloud parameters with satellite remote sensing observations during the BALTEX Bridge Campaigns, Atmospheric Research, in press 2006

Participation in specific model evaluation exercises

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