Surface processes in the COSMO model – Part I : modeling components
Modeling component
Surface energy
balance
Current status
Surface temperature is area
weighted average of
temperature of snow covered
and snow free surface
fraction
Under development
Suitable
references
Tile approach with four Ament and
separate energy
Simmer (2006)
budgets
(sea/lake/towns/nature)
Provision for
mosaic/patch
Coupling with the
atmosphere
Explicit coupling
Soil transfers
7-layer soil model
Layer-depth between 1 cm
and 14.58 m
Solution of the heat
conduction equation
Frozen soils
Temperature and soil type
dependent computation of
fractional freezing/melting of
total soil water content in 6
active soil layers
Vegetation
One-layer –
Evapotranspiration after
Dickinson (1984) –
interception reservoir
Snow model
One layer – prognostic
variables : snow temperature,
snow water equivalent, snow
density, snow albedo
Multi-layer – liquid
water in snow pack as
an additional
prognostic variable
Lake model
Prescribed surface
temperature (analysis)
Lake model (Flake)
Mironov et al.
(2008)
Sea-ice
Prescribed surface
temperature (analysis)
Sea-ice model
Mironov and
Ritter (2003)
Ocean model
Prescribed surface
temperature (analysis)
Charnock formulation for
roughness length
Urban areas
Modified surface roughness,
leaf area index, plant
coverage
Multi-layer urban
canopy
parametrization.
Martilli et
al.(2002)
Chemistry module
None
COSMO-ART :
Aerosols and dust
emission
Vogel et al.
(2008)
Surface boundary layer Application of the turbulence
scheme at the lower model
boundary and iterative
interpolation.
Heise and
Schrodin (2002)
Raschendorfer
(1999)
Mironov and
Raschendorfer
Roughness length for scalars
implicitly considered by
calculation of an additional
transport resistance
throughout the turbulent and
laminar roughness layer
(2001)
Surface processes in the COSMO model – Part II : physiography
Component
Current status
Under
development
Orography
GLOBE
database
NOAA/NGDC
resolution 30''
Soil types
FAO Digital Soil Harmonized
Map of the World World Soil
resolution 10 km database
Land Cover
and
Land sea
mask
GLC2000;
Lookup tables
between land
use categories
and model
parameters
Suitable references
Seasonal
variability of
plant fraction
NDVI
Climatology,
NASA/GSFC
based on
Monthly mean
data from
SEAWiFS,
resolution 2.5 '
Lake
properties
specific lake
database
(location +
depth) from
DWD for
FLAKE
Aerosol
Optical
Thickness
NASA/GISS
http://gacp.giss.nasa.gov/data_sets/transport/
(Global Aerosol
Climatology
Project)
lower
boundary
condition for
soil
temperature
T2m Climatology
From CRU of
University of
East Anglia
http://lakemodel.net
List of model external parameter fields
External parameter
Short Name
Unit
Used raw
dataset
geometrical height of earths surface
HSURF
m
GLOBE
Geopotential of earths surface
FIS
m2s-
GLOBE
2
land cover
FR_LAND
1
GLC2000
(GLOBE,
DSMW)
standard deviation of subgrid scale orographic height
SSO_STDH
m
GLOBE
anisotropy of topography
SSO_GAMMA
1
GLOBE
angle between principal axis of orography and global E
SSO_THETA
1
GLOBE
mean slope of subgrid scale orography
SSO_SIGMA
1
GLOBE
surface roughness
Z0
m
GLC2000,
GLOBE
soil texture
SOILTYP
1
DSMW
long wave surface emissivity
EMIS_RAD
1
GLC2000
Plant root depth
ROOTDP
m
GLC2000
ground fraction covered by plants (vegetation period)
PLCOV_MX
1
GLC2000
ground fraction covered by evergreen forest
FOR_E
1
GLC2000
ground fraction covered by deciduous forest
FOR_D
1
GLC2000
leaf area index (vegetation period)
LAI_MX
1
GLC2000
Minimum plant stomata resistance
RS_MIN
s m-
GLC2000
1
(monthly mean) normalized differential vegetation index
NDVI
1
SEAWIFS
Annual maximum of normalized differential vegetation
index
NDVI_MAX
1
SEAWIFS
ratio of actual montly value/annual maximum normalized
differential vegetation index
NDVI_RATIO
1
SEAWIFS
(monthly) optical thickness from black carbon aerosol
AER_BC
1
GACP
(monthly) optical thickness from dust aerosol
AER_DUST
1
GACP
(monthly) optical thickness from organic aerosol
AER_ORG
1
GACP
(monthly) optical thickness from SO4 aerosol
AER_SO4
1
GACP
(monthly) optical thickness from sea salt aerosol
AER_SS
1
GACP
Near surface temperature (climatological mean)
T_2M_CL
K
CRU
Lake Depth
DEPTH_LK
m
DWD
Lake Fraction
FR_LAKE
1
DWD
Surface processes in the COSMO model – Part III : assimilation and analysis
Model parameters
Soil water content
Current status
Under development
2-dimensional (vertical
and
temporal)
variational technique
using 2-m temperature
analyses at 12 and 15
UTC
Suitable references
Schraff and Hess
(2002)
Schraff and Hess
(2003)
Analysed
variables:
Soil moisture of the top
5 soil layers (0-81cm)
at 00 UTC
Sea surface
temperature
Correction method
Background field from
GME SST analysis
using NCEP 0.5° x 0.5°
SST analysis that
includes satellite data.
Observations from
Synop-Ship and buoy,
sea ice cover analysis
from BSH for the Baltic
Sea .
Wergen and Buchhold
(2002)
Schraff and Hess
(2003).
Sea-ice extent
Sea ice cover analysis
from BSH (German
Institute for shipping
and hydrology) for the
Baltic Sea , resolution
lon/lat: 0.167 x 0.1
degrees., NCEP
analysis in other areas
Wergen and Buchhold
(2002),
Schraff and Hess
(2003).
Sea ice temperature
Interpolated from
monthly ECMWF
climatology
Sea-ice concentration
None
Snow depth
Correction method
Used
Data:
Background
values
from COSMO model,
Snow
depth
observations
from
synop stations, present
and
past
synop
weather, precipitation
amount,
2-m
temperature analysis
(plus model prediction).
Monthly snow depth
Implementation of bulk
thermodynamic sea ice
model presently
applied operational in
GME.
Major revision of snow
model within COSMO
project COLOBOC at
Meteo Suisse.
Modifications in
calculation of
rho_snow, t_snow,
additional use of snow
observations in the
alpine region.
Wergen and Buchhold
(2002),
Schraff and Hess
(2003).
climatology of ECMWF
for permanently glacial
covered areas.
Lake
Closest point from SST
analysis, adapted to
model terrain height.
Climatological lake
temperature for
Bodensee and Lake
Geneva.
Vegetation
None
References:
Ament, F. and Simmer, C., 2006: Improved Representation of Land-Surface Heterogeneity in a
Non-Hydrostatic Numerical Weather Prediction Model, Boundary-Layer Meteorology, 121, 153-174
Heise E., Schrodin R. Aspects of snow and soil modelling in the operational short range weather
prediction models of the German weather service // Computational technologies. 2002. V. 7.
Special issue. P. 121-140
Martilli, A., Clappier, A., Rotach, M. W.: 2002, ’An urban surfaces exchange
parameterisation for mesoscale models’, Bound.-Layer Meteor., 104, 261-304.
Mironov D. and Matthias Raschendorfer (2001): Evaluation of Empirical Parameters of the New
LM Surface-Layer Parameterization. Scheme. COSMO Technical Report, No. 1, Deutscher
Wetterdienst, Offenbach am Main, Germany
Mironov, D., and B. Ritter, 2003: A first version of the ice model for the global NWP system GME of
the German Weather Service. Research Activities in Atmospheric and Oceanic Modelling, J. Cote,
Ed., Report No. 33, April 2003, WMO/TD, 4.13-4.14.
Mironov, D. , 2008: Parameterization of lakes in numerical weather prediction. Description of a lake
model. COSMO Technical Report, No. 11, Deutscher Wetterdienst, Offenbach am Main, Germany.
Raschendorfer, M. (1999): The new turbulence parameterization of LM, Quarterly Report of the.
Operational NWP-Models of the DWD, No 19, 3-12, May 1999
Schraff, C. and R. Hess (2002): Datenassimilation für das LM, Promet Jahrgang 27, Heft 3/4, p
156-163.
Schraff, C. and R. Hess (2003): A Description of the Nonhydrostatic Regional Model LM Part III :
Data Assimilation. Available from DWD.
Vogel, H., Pauling, A., Vogel, B. (2008), Numerical simulation of birch pollen dispersion with an
operational weather forecast system, Int J Biometeorol. 2008 Nov;52(8):805-814,
doi:10.1007/s00484-008-0174-3.
Wergen, W. and M. Buchhold (2002): Datenassimilation für das Globalmodell GME, Promet
Jahrgang 27, Heft 3/4, p 149-155.