Surface processes in the cosmo model Part I : modeling components




Yüklə 94.98 Kb.
tarix24.04.2016
ölçüsü94.98 Kb.
Surface processes in the COSMO model – Part I : modeling components

Modeling component

Current status

Under development

Suitable references

Surface energy balance

Surface temperature is area weighted average of temperature of snow covered and snow free surface fraction

Tile approach with four separate energy budgets (sea/lake/towns/nature)
Provision for mosaic/patch

Ament and Simmer (2006)

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





Heise and Schrodin (2002)

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.

Roughness length for scalars implicitly considered by calculation of an additional transport resistance throughout the turbulent and laminar roughness layer






Raschendorfer (1999)

Mironov and Raschendorfer (2001)



Surface processes in the COSMO model – Part II : physiography

Component

Current status

Under development

Suitable references

Orography

GLOBE database

NOAA/NGDC

resolution 30''








Soil types

FAO Digital Soil Map of the World

resolution 10 km




Harmonized World Soil database




Land Cover

and


Land sea mask

GLC2000;

Lookup tables between land use categories and model parameters









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

http://lakemodel.net

Aerosol Optical Thickness




NASA/GISS (Global Aerosol Climatology Project)


http://gacp.giss.nasa.gov/data_sets/transport/













lower boundary condition for soil temperature

T2m Climatology

From CRU of University of East Anglia










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-2

GLOBE

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-1

GLC2000

(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

Current status

Under development

Suitable references

Soil water content

2-dimensional (vertical and temporal) variational technique

using 2-m temperature analyses at 12 and 15 UTC


Analysed variables: Soil moisture of the top 5 soil layers (0-81cm) at 00 UTC






Schraff and Hess (2002)

Schraff and Hess (2003)



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

Implementation of bulk thermodynamic sea ice model presently applied operational in GME.




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 climatology of ECMWF for permanently glacial covered areas.


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).




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.


Verilənlər bazası müəlliflik hüququ ilə müdafiə olunur ©azrefs.org 2016
rəhbərliyinə müraciət

    Ana səhifə