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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.

RAMS: REGIONAL ATMOSPHERIC MODELING SYSTEM

General information

Model name and version

short nameRAMS
full nameREGIONAL ATMOSPHERIC MODELING SYSTEM
revision
dateOctober 2003 / v 4.4.0
last change

Responsible for this information

nameMillan
instituteFundacion CEAM
addressParque Tecnologico. C/Charles R. Darwin-14
zip46980
cityPaterna (Valencia)
countrySpain
phone+34961318227
fax+34961318190
e-mailpilarz(belongs-to)ceam.es

Additional information on the model

Contact person for model code

same as person above
nameGorka Pérez-Landa
instituteFundación CEAM
divisionsMeteorological Modeling
streetParque Tecnologico. C/Charles R. Darwin-14
zip46980
cityPaterna (Valencia)
countrySpain
phone+34961318227
emailgorkapl@confluencia.biz
fax+34961318190

Model developer and model user

developer and userRAMS, the Regional Atmospheric Modeling System, is a highly versatile numerical code developed by scientists at Colorado State University and the *ASTER division of Mission Research Corporation (http://www.atmet.com).

Level of Knowledge needed to operate model

basic
intermediate
advanced
remarksRAMS is a very useful modelling tool, but deep knowledge in mesoscale processes is needed to operate the model, as the simulated results highly depend on the configuration established.

Model use at your institution

operational
for research
other use

Model code available?

is available?yes
more details

Minimum computer resources required

typeLinux PC-Cluster
time needed for run4 days
storage800 Gb

Further information

documentationhttp://www.atmet.com/html/docs/rams/rams_techman.pdf
model referencesTremback, C.J., G.J. Tripoli, and W.R. Cotton, 1985: A regional scale atmospheric numerical model including explicit moist physics and a hydrostatic time-split scheme. Preprints, 7 th Conference on Numerical Weather Prediction, June 17-20, 1985, Montreal, Quebec, AMS. Pielke, R. E., Cotton, W. R., Walko, R. L., Tremback, C. J., Lyons, W. A., Grasso, L. D., Nicholls, M. E., Moran, M. D., Wesley, D. A., Lee, T. J., and Copeland, J. H., 1992: A Comprehensive Meteorological Modeling System - RAMS. Metero. Atmos. Phys.. 49, 69-91. Walko, R. L., Tremback, C. J., 1991: RAMS - The Regional Atmospheric odeling System Version 2C: User's guide. Published by ASTeR, Inc., P.O. Box 466, Fort ollins, Colorado. 86pp.
webpagehttp://www.atmet.com/
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

Approximations

Boussinesq
anelastic
hydrostatic
flat earth
remarks

Parametrizations

Meteorology

turbulence schemeMellor and Yamada level 2.5 scheme with prognostic turbulent kinetic energy.
deep convectionA modification of the generalized form of the Kuo parameterization described by Molinari.
surface exchangeSurface fluxes momentum, heat and water vapour are computed from similarity theory of Louis.
surface temperatureSVAT model (LEAF-2)
surface humiditySVAT model (LEAF-2)
radiationChen and Cotton.
unresolved orographic drag
radiation in vegetation
radiation between obstacles
treatment of obstacles
clouds / rainThe representation of cloud and precipitation microphysics in RAMS includes the treatment of each water species (cloud water, rain, pristine ice, snow, aggregates, graupel, hail) as a generalized Gamma distribution.
remarks

Initialization & boundary treatment

Initialization

chemistry & transport
meteorologySpatial interpolation of Global Gridded analysis.

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

orographyUSGS
land useCORINE; PELCOM
obstacles
vegetation
meteorologyECMWF, GFS, NCAR Reanalysis
concentrations
emissions
remarks

Data assimilation

Meteorology
nudging technique
adjoint model
3D-VAR
4D-VAR
OI
detailsNudging type of four-dimensional data assimilation scheme with observational data. The technique combines the analysis nudging and the observational nudging schemes. Nudging based on 3D fields produced by RAMS Isentropic Analysis (ISAN) pre-processor.

Boundary conditions

Meteorology
surfaceSVAT model (LEAF-2)
topRayleigh friction absorbing layer.
lateral inflowKlemp and Wilhelmson scheme.
lateral outflowKlemp and Wilhelmson

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 exchangeeach model domain corresponding time step
explain methodCommunication from the parent to the nested gris is accomplished immediatelly following a timestep on the parentgrid, which updates the prognostic fields.
variables nestedAll
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 vertical structure of the grid uses the sigma-z terrain-following coordinate system (Gal-Chen and Somerville, 1975; Clark, 1977; Tripoli and Cotton, 1982).

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
other

Spatial discretisation

momentum equationsSee: http://www.atmet.com/html/docs/rams/rams_techman.pdf
scalar quantitiesSee: http://www.atmet.com/html/docs/rams/rams_techman.pdf
additional informationSee: http://www.atmet.com/html/docs/rams/rams_techman.pdf
other

Model resolution

Meteorology

HorizontalVertical
max901000
min1.3315

Domain size

Meteorology

HorizontalVertical
max500017000
min10017000

Model Validation and Application

Validation & evaluation

Used validation & evaluation methods

analytic solutions
evaluated reference dataset
model intercomparison
additional validation & evaluation efforts

Evaluated reference dataset

Meteorology

u
v
w
T
qv
qlc
qsc
qlr
zi
other
testcase description
testcase references
used data set
reference for evaluation
remarks

Model intercomparison

Meteorology

u
v
w
T
qv
qlc
qsc
qlr
zi
other
testcase description
testcase references
used data set
reference for evaluation
remarks
remarks

Application examples

application examplesPalau, J.L.; Pérez-Landa, G.; Dieguez, J.J.; Monter, C. and Millán, M.M. (2005): The importance of meteorological scales to forecast air pollution scenarios on coastal complex terrain. Atmos. Chem. Phys., 5, 2771-2785. Fay B., Neunhäuserer L., Baklanov A., Kukkonen J., Oedegaard V., Palau J.L., Perez-Landa G., Rantamäki M., Rasmussen A., Valkama I. (2004): Evaluating and inter-comparing NWP and mesoscale models for forecasting urban air pollution episodes in FUMAPEX. Fourth Annual Meeting of the European Meteorological Society. EMS Annual meeting Abstracts, Vol I, 00401.

Participation in specific model evaluation exercises

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