DMI-ENVIRO-HIRLAM
An On-Line Coupled Multi-Purpose Environment Model
U. Korsholm*, A. Baklanov, A. Mahura, C. Petersen, K. Lindberg, A. Gross, A. Rasmussen, J.H. Sørensen, J. Chenevez
The Danish Meteorological Institute, Copenhagen, Denmark * [email protected], phone: +45 39157439
ACCENT/GLOREAM Workshop 2006
Purpose
• Plans and current status, main model features
• Preliminary results: aerosol–meteorology feedbacks
Motivation
• Climate: direct, indirect, semi-direct effects, large scale dynamical feedback (Kim & Lee, 2006; Kim et al., 2006)
• Local: direct, indirect, semi-direct effects, local scale feedbacks
• Importance for short range weather forecasting ? (Perez et al. 2006)
Radiation budgets
Temperature profiles
Chemistry/Aerosols
CloudCondensation
Nuclei
Precipitation
Chemistry/Aerosols
Examples of feedbacks
Cloud-radiationinteraction
Temperature profiles
Chemistry/Aerosols
Definitions
off-line models comprise:• Separate CTMs driven by meteorological input data from meteo-
preprocessors, measurements or diagnostic models• Separate CTMs driven by analysed or forecasted meteo-data from
NWP archives or datasets• Separate CTMs reading output-files from operational NWP models
or specific MetMs with limited temporal resolution (e.g. 1, 3, 6 hours)
on-line models comprise: • On-line access models, when meteo-data is available at each time-
step• On-line integration of a CTM into a MetM; feedbacks are possible:
on-line coupled modeling
Advantages of On-line & Off-line modeling
On-line coupling• Only one grid; no interpolation
in space• No time interpolation• Physical parameterizations are
the same; no inconsistencies• Possibility of feedbacks with
meteorology• All 3D meteorological variables
are available at the right time (each time step); no restriction in variability of met. fields
• Does not need meteorological- pre/post-processors
Off-line• Possibility of independent
parameterizations• Low computational cost; more
suitable for ensembles and operational activities
• Independence of meteorological model computations
Future plans
Radiative & optic properties models
General Circulation & Climate models
Cloud condensation nuclei (CCN) model
Aerosol dynamics models
Ecosystem
models
Ocean dynamics
model
Atmospheric chemistry and transport models
Emission databases, models and scenarios
Inverse methods and adjoint models
WP7
Integrated Assessment Model
WP5
Numerical Weather Prediction Model
Why develop an on-line coupled model ?Climate; NWP; Research; Operational; Emergency
1. Model nesting for high resolutions 2. Improved representation of pbl. and sl. 3. ‘Urbanisation’ of the NWP model 4. Improvement of advection schemes 5. Implementation of chemical mechanisms6. Implementation of aerosol dynamics 7. Realisation of feedback mechanisms
8. Assimilation of monitoring data
EmissionTransportDispersionDeposition
Current status
Observational database
ECMWF
Surface analysis Upper air analysis
Boundaries Output
Initialisation
Forecast
DMI-ENVIRO-HIRLAM
Model Description 1
Model identification T15 S05
grid points (mlon) 610 496
grid points (mlat) 568 372
number of vertical levels
40 40
horizontal resolution (deg)
0.15°0.05
°
time step (dynamics) 360s 120s
time step (physics) 360s 120s
host model ECMWF T15
Model Description 2
Transport and dispersion
• Bott advection (Bott, 1989) + Easter update for tracers (Easter, 1993); Semi-Lagrangian for meteorology– Risk of mass-wind
inconsistency• No horizontal diffusion• Vertical diffusion: CBR-
scheme (Cuxart et al., 2000)– Coefficient defined by
mixing length formulation in stable/unstable conditions
Mass conservation test for ETEX release
ETEX 1, 48 hours after start of release
Semi-Lagrangian Bott scheme Bott-Easter sheme
Model Description 3
Model Description 4Deposition• Particle size dependent parameterizations for dry and wet deposition• Resistance approach for dry deposition (Wesley, 1989; Zanetti, 1990) • Terminal settling velocity in different regimes:
– Stokes law– non-stationary turbulence regime– correction for small particles
• Dependent on land use classification• Below-cloud scavenging (washout); precipitation rates (Baklanov & Sorensen,
2001) • Scavenging by snow (Maryon et al., 1996)• Different scavenging of particles and gases
Next step• Rainout into 3D clouds (based on on-line coupling):
– convective precipitation – stratiform precipitation
Preliminary results 1: Deposition
Accumulated dry deposition [kBq/m2]
Chernobyl accident; point source emissions (Devell et al., 1995, persson et al., 1986)
Date: 19860501 18:00 UTC
Accumulated wet deposition [kBq/m2]
Preliminary results 2: Feedback
For water clouds: r³eff = kr³v
r³eff =3L/(4lkN)
(Wyser et al. 1999)
L : Cloud condensate contentN: Number concentration of cloud
droplets
ΔNcont = 108.06 conc0.48
ΔNmarine = 102.24 conc0.26
(Boucher & Lohmann, 1995)
Emission rate: 7.95 gs-1; ETEXDiameter: 1 µm
k N [m-3]
Marine 0.81 108
Cont 0.69 4х108
Urban fractions [%; dark green – dark red]
Preliminary results 2: Feedback
Difference (ref - perturbation) in accumulated dry deposition [ng/m2]
Difference (ref - perturbation) in accumulated wet deposition [ng/m2]
Accumulated (reference) dry deposition [μg/m2] +48 h
Accumulated (reference) wet deposition [μg/m2] +48 h
• DMI is developing an on-line coupled environment model: DMI-ENVIRO-HIRLAM
– emission module, inventories– transport, dispersion, dry and wet deposition– aerosol dynamics – gas-phase and heteorogeneos chemistry– data assimilation– cloud, radiation coupling
• To be used for: research, operational, emergency ? • Main advantages of on-line coupled meso-scale NWP model and CTM
– no restriction in variability of input fields– possibility of feedbacks
• Previously tested for mass conservation• Deposition being tested on Chernobyl accident
– looks promising, local hot-spots• Investigation into cloud-aerosol coupling
– model sensitivity, large changes in deposition
Summary
Acknowledgements
The HIRLAM development program at DMICopenhagen Global Change Initiative (COGCI)
References• Baklanov A. & Sorensen, H., J., 2001, Physics and Chemistry of the Earth,
vol. 26, No. 10, 787-799• Bott, A., 1989, Mon. Wea. Rev., 117, 1006-1015• Boucher, O. & Lohmann, U., 1995, Tellus 47, Ser. B, 281-300• Cuxart, J. et al., 2000, Q.J.R. Meteo. Soc., 126, 1-30• Devell et al., 1995, CSNI report, OECD/NEA, Paris• Easter, C., Mon. Wea. Rev., vol. 121, 297-304• Kim, M., K. et al., J. Clim., 2006, in press• Kim, M., K. & Lee, W., S., GRL, vol. 33, L16704, 2006• Maryon R., H. et al., 1996, Depart. Of Env., UK, Met. Office. DoE Report #
DOE/RAS/96.011• Perez, C. et al., JGR, vol. 111, D16206, 2006• Persson et al., SMHI/RMK report No. 55, 1986• Wesley, M.,L., 1989, Atm. Env., vol. 23, No. 6, 1293-1304• Wyser et al., 1999, Contr. Atmos. Phys., vol. 72, No. 3, 205-218• Zanetti, P., 1990, Air Pollution Modelling – Theories, Computational
Methods and Available Software. Southhampton: Computational Mechanics and New York: Van Nostrand Reinhold
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