Transcript Dias nummer 1 - Centre for Energy, Environment and Health

DMI Modeling Systems
And Plans For CEEH
Activities
A. Gross, A. Baklanov, U. S. Korsholm,
J. H. Sørensen, A. Mahura & A. Rasmussen
Content:
• Off-Line Air Pollution Modeling
• On-Line Air Pollution Modeling
• Emergency Preparednes & Risk
Assessment
• Urban Modeling
Energy
Kick-off.
Environment
Health d. 23-25/1 2007
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Energy
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Air Pollution Modeling At DMI
Aerosol Module
Chemical Solvers
1.
2.
1.
2.
3.
4.
PSC aerosols
Tropospheric
aerosols
Approaches:
Normal distribution,
Bin approach
Gas Phase
Aqueous phase
Chemical equil.
Climate Modeling
Approaches:
RACM, CBIV,
ISORROPIA
Physics:
1. Condensation
2. Evaporation
3. Emission
4. Nucleation
5. Deposition
UTLS Trans. Models
Eulerian trans- Lagrangian
transport, 3-D
port 0..15
regional scale
lat-lon grid,
3-D regional
scale
ECMWF
Met. Models
DMI-HIRLAM
Eulerian transport 0.2-0.05
lat-lon, 25-40
vert. layer,
3-D regional
scale
City-Scale Obstacle
Resolved Modelling
TSU-CORM
Stochastic
Lagrangian
transport,
3-D regional
scale
Tropo. Trans. Models
Off-Line Chemical
Aerosol Trans.
CAC
On-Line Chemical
Aerosol Trans.
ENVIRO-HIRLAM
Emergency Preparednes & Risk Assessment. DERMA
Regional (European) to city
scale air pollution: smog
and ozone.
Regional (European) scale
air pollution: smog and
ozone, pollen.
Nuclear, veterinary and
chemical.
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DMI-HIRLAM
Currently nested versions of HIRLAM:
• T – 15x15 km2, 40 vertical layers.
• S – 5x5 km2, 40 vertical layers.
• Q – 5x5 km2, 40 vertical layers.
• Test version of 1.5x1.5 km2 of DK.
A forecast integration starts out by
assimilation of meteorological
observations whereby a 3-d state of the
atmosphere is produced, which as well
as possible is in accordance with the
observations.
Q
T
S
A numerical weather prediction system consists of preprocessing, climate file generation, data-assimilation and
analysis, initialization, forecast, post-processing and
verification.
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Climate Change Scanarios
Modeling By HIRHAM
Simulation period: Year 2000 to 2100
Modeling Area
• Horizontal resolution 25x25 km2.
• Vertical resolution 19 levels.
Output of meteorological parameter:
From 3-6 hours to once a day depend
on the parameter.
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Off-Line modelling with CAC
T:0.15º×0.15º
Simulation domain
S: 0.05º×0.05º
Horizontal resolution
0.2º×0.2º.
CAC Model Area
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ENSEMBLE JRC project exp. nr. 11
(Off-Line)
Ensemble: DK3, DE1, FR2, CA2
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Ozone
36 hour forecast
48 hour forecast
ppbV
0
15
30
60
90
120
150
“Semi”-operational forecasts 4 times a day of
O3, NO, NO2, CO, SO2, Rn, Pb, “PM2.5”, “PM10”.
(Off-Line)
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•
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•
•
•
•
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 bewteen air pollution and meteorology
All 3D met. variables are available at the right time (each
time step); No restriction in
variability of met. fields
Does not need meteo- pre/postprocessors
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•
•
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•
Off-line
Possibility of independent parameterizations
Low computational cost;
More suitable for ensembles and
oprational activities
Independence of atmospheric pollution model runs on meteorological model computations
More flexible grid construction and
generation for ACT models
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Examples of feedbacks
Chemistry/
Aerosols
Cloud-radiation
interaction
Temperature
profiles
Chemistry/
Aerosols
Temperature
profiles
Radiation
budgets
Chemistry/
Aerosols
Cloud
Condensation
Nuclei
Precipitation
Energy
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On-Line Modeling With
ENVIRO-HIRLAM
U, V, W, T, q,
U*, L
Emission
Transport
Dispersion
Gas phase chemistry
Clouds
Precipitation
Radiation
DMI-HIRLAM
Aerosol chemistry
Aerosol physics
Deposition
Concentration/Mixing ratio
Energy
Chernobyl Simulation 0.15°x0.15°, d. 7/5-1986, 18.00 UTC
Environment
Health
Dry deposition (kBq/m2)
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Total deposition statistics: Corr = 0.59, NMSE 6.3
2] +48 h
Difference
(ref dry
– perturbation)
in
Accumulated
(reference)
deposition [μg/m
2
Accumulated dry deposition [ng/m ]
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Emergency Preparednes & Risk
Assessment
Using the 3-D Stochastic Lagragian
Regional Scale Model DERMA
Examples:
1. Probabilistic Risk Assessment.
2. Source Determination by Inverse Modelling.
3. Chemical Emergency Preparednes.
4. Urban Meteorology Effects.
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Probabilistic Risk
Assessment
Risk atlas of potential threats from long-range atmospheric dispersion
and deposition of radionuclides.
Sellafield nuclear fuel reprocessing plant
Yearly deposition
Yearly time-integrated
concentration
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Source Determination by
Inverse Modelling
Hypothetical release of 100 g Anthrax
spores
Inhalation dose calculated by DERMA
based on DMI-HIRLAM.
Monitoring stations
Determination of source location by
adjoint DERMA using monitoring
data. No a priori assumption about
source (point, area, …).
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Aalborg Portland, 23 October 2005
Accidental fire in waste deposit Accidental fire in waste deposit.
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Aalborg Portland, 23 October 2005
Accidental fire in waste
deposit
DERMA calculations
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Drag
Momentum
Urban Features
Wake
diffusion
Turbulence
Radiation
Heat
Roof
Wall
Street
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Urban Effects
The ABL height calculated from different DMIHIRLAM data (left: urbanized, right: operational T).
Main cities and their effect on the ABL height are
shown by arrows.
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Urban Effects
Local-scale RIMPUFF plume corresponding to a hypothetical
release calculated by using DMI-HIRLAM data.
Cs-137 air concentration for different DMI-HIRLAM versions
(left: urbanized 1.4-km resolution, mid: operational 5 km, right:
operational 15 km).
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City-Scale Obstacle-Resolved
Modeling (TSU-CORM)
Streamlines and
air pollution conc
3 d. fluid dynamic air
pollution model
Resolution:
Horizontal: 1x1 m2
Vertical: from 1m
Will be implemented
spring 2007 at DMI
and linked with DMIHIRLAM, CAC and /or
ENVIRO-HIRLAM.
DMIs Possible Modeling
Activities In CEEH
Modeling of the environmental impact
of energy production/consumption
 Long-term simulations:
• ENVIRO-HIRLAM and/or CAC.
 Episodes:
•ENVIRO-HIRLAM.
Climate change impact on air pollution
and population health
 Long-term simulation of ENVIROHIRLAM and/or CAC using HIRHAM
Meteorology.
Energy
Kick-off.
Environment
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DMIs Possible Modeling
Activities In CEEH
Optimization modeling of environmental
risk/impact studies
 Modify DERMA or CAC for sensitivity, risk/impact minimization and
optimization studies.
 Sensitivity studies for environmental risk/impact assessments.
Human exposure modeling
 City scale modeling using TSUCORM.
 Link the air pollution prediction
from ENVIRO-HIRLAM or CAC to
population activity (human exposure modeling).
Energy
Kick-off.
Environment
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Energy
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FUMAPEX integrated population health impact study
© Helsingin kaupunki, Kaupunginmittausosasto 576§/1997, ©Aineistot: Espoon, Helsingin, Kauniaisten ja Vantaan mittausosastot
The predicted exposure of population to NO2 (g/m3 *persons).
Kansanterveyslaitos
Folkhälsoinstitutet
National Public Health Institute
Environmental Office