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DMI Modeling Systems And Plans For CEEH
Activities
•Off-Line Air Pollution Modeling•On-Line Air Pollution Modeling•Emergency Preparednes &
Risk Assessment•Urban Modeling
EnergyEnergy EnvironmentEnvironment HealthHealthCC Kick-off.
d. 23-25/1 2007
A. Gross, A. Baklanov, U. S. Korsholm, J. H. Sørensen, A. Mahura & A. Rasmussen
Content:
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Air Pollution Modeling At DMI EnergyEnergy EnvironmentEnvironment HealthHealthCC
1. PSC aerosols2. Tropospheric
aerosols
Approaches:Normal distribution,Bin approach
Physics:1. Condensation2. Evaporation3. Emission4. Nucleation5. Deposition
Aerosol Module1. Gas Phase2. Aqueous phase3. Chemical equil.4. Climate Modeling
Approaches:RACM, CBIV, ISORROPIA
Chemical Solvers
Lagrangiantransport, 3-Dregional scale
UTLS Trans. Models
Eulerian trans-port 0..15lat-lon grid,3-D regional scale
ECMWF
DMI-HIRLAM
Eulerian trans-port 0.2-0.05lat-lon, 25-40 vert. layer, 3-D regional scale
StochasticLagrangian transport,3-D regional scale
On-Line Chemical Aerosol Trans.
ENVIRO-HIRLAM
Off-Line Chemical Aerosol Trans.
CAC
Emergency Pre-parednes & Risk Assess-
ment. DERMA
Nuclear, veterinary and chemical.
Regional (European) to city scale air pollution: smog and ozone.
Regional (European) scale air pollution: smog and ozone, pollen.
Tropo. Trans. Models
Met. Models
City-Scale Obstacle Resolved Modelling
TSU-CORM
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DMI-HIRLAM
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.
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 numerical weather prediction system consists of pre-processing, climate file generation, data-assimilation and analysis, initialization, forecast, post-processing and verification.
TS
Q
EnergyEnergy EnvironmentEnvironment HealthHealthCC
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Climate Change Scanarios Modeling By HIRHAM
EnergyEnergy EnvironmentEnvironment HealthHealthCC
Modeling Area
Simulation period: Year 2000 to 2100
Output of meteorological parameter:
From 3-6 hours to once a day depend
on the parameter.
•Horizontal resolution 25x25 km2.
•Vertical resolution 19 levels.
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Off-Line modelling with CAC
Simulation domain
Horizontal resolution 0.2º×0.2º.
T:0.15º×0.15º
S: 0.05º×0.05º
EnergyEnergy EnvironmentEnvironment HealthHealthCC
CAC Model Area
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ENSEMBLE JRC project exp. nr. 11 EnergyEnergy EnvironmentEnvironment HealthHealthCC
(Off-Line)
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Ensemble: DK3, DE1, FR2, CA2
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Ozone36 hour forecast 48 hour forecast
0 15 30 60 90 120 150
ppbV
EnergyEnergy EnvironmentEnvironment HealthHealthCC
(Off-Line)
“Semi”-operational forecasts 4 times a day of O3, NO, NO2, CO, SO2, Rn, Pb, “PM2.5”, “PM10”.
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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
bewte-en air pollution and meteoro-logy
• All 3D met. variables are ava-ilable at the right time (each time step); No restriction in variability of met. fields
• Does not need meteo- pre/postpro-cessors
Off-line• Possibility of independent
parame-terizations• Low computational cost; • More suitable for ensembles
and oprational activities • Independence of atmospheric
pol-lution model runs on meteorolo-gical model computations
• More flexible grid construction and generation for ACT models
EnergyEnergy EnvironmentEnvironment HealthHealthCC
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Radiation budgets
Temperature profiles
Chemistry/Aerosols
CloudCondensation
Nuclei
Precipitation
Chemistry/Aerosols
Examples of feedbacks
Cloud-radiationinteraction
Temperature profiles
Chemistry/Aerosols
EnergyEnergy EnvironmentEnvironment HealthHealthCC
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Emission
Transport
Dispersion
Deposition
Gas phase chemistry
Aerosol chemistry
Aerosol physics
CloudsPrecipitation
RadiationDMI-HIRLAM
U, V, W, T, q,
U*, L
Concentration/Mixing ratio
On-Line Modeling With ENVIRO-HIRLAM
EnergyEnergy EnvironmentEnvironment HealthHealthCC
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EnergyEnergy EnvironmentEnvironment HealthHealthCC Chernobyl Simulation 0.15°x0.15°, d. 7/5-1986, 18.00 UTC
Dry deposition (kBq/m2)
Total deposition statistics: Corr = 0.59, NMSE 6.3
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Accumulated (reference) dry deposition [μg/m2] +48 h Difference (ref – perturbation) inAccumulated dry deposition [ng/m2]
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Emergency Preparednes & Risk Assessment
Using the 3-D Stochastic Lagragian Regional Scale Model DERMA
EnergyEnergy EnvironmentEnvironment HealthHealthCC
1. Probabilistic Risk Assessment.
2. Source Determination by Inverse Modelling.
3. Chemical Emergency Preparednes.
4. Urban Meteorology Effects.
Examples:
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Probabilistic Risk Assessment
Yearly time-integrated concentration
Yearly deposition
Risk atlas of potential threats from long-range atmospheric dispersion and deposition of radionuclides.
Sellafield nuclear fuel reprocessing plant
EnergyEnergy EnvironmentEnvironment HealthHealthCC
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Hypothetical release of 100 g Anthrax spores
Monitoring stations
Source Determination by Inverse Modelling
Inhalation dose calculated by DERMA based on DMI-HIRLAM.
Determination of source location by adjoint DERMA using monitoring data. No a priori assumption about source (point, area, …).
EnergyEnergy EnvironmentEnvironment HealthHealthCC
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Accidental fire in waste deposit Accidental fire in waste deposit.
Aalborg Portland, 23 October 2005 EnergyEnergy EnvironmentEnvironment HealthHealthCC
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Accidental fire in waste deposit
DERMA calculations
Aalborg Portland, 23 October 2005
EnergyEnergy EnvironmentEnvironment HealthHealthCC
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RoofWall
Street
Momentum Turbu-lence
Heat
DragWake diffu-sion
Radiation
Urban Features EnergyEnergy EnvironmentEnvironment HealthHealthCC
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Urban Effects
The ABL height calculated from different DMI-HIRLAM data (left: urbanized, right: operational T). Main cities and their effect on the ABL height are shown by arrows.
EnergyEnergy EnvironmentEnvironment HealthHealthCC
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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).
EnergyEnergy EnvironmentEnvironment HealthHealthCC
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City-Scale Obstacle-Resolved Modeling (TSU-CORM)
EnergyEnergy EnvironmentEnvironment HealthHealthCC
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 DMI-HIRLAM, CAC and /or ENVIRO-HIRLAM.
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DMIs Possible Modeling Activities In CEEH
EnergyEnergy EnvironmentEnvironment HealthHealthCC Kick-off.
d. 23-25/1 2007
Long-term simulations:• ENVIRO-HIRLAM and/or CAC.
Episodes:•ENVIRO-HIRLAM.
Modeling of the environmental impact of energy production/consumption
Long-term simulation of ENVIRO-
HIRLAM and/or CAC using HIRHAM
Meteorology.
Climate change impact on air pollutionand population health
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DMIs Possible Modeling Activities In CEEH
EnergyEnergy EnvironmentEnvironment HealthHealthCC Kick-off.
d. 23-25/1 2007
Modify DERMA or CAC for sensitivi-
ty, risk/impact minimization and
optimization studies.Sensitivity studies for
environmen-tal risk/impact assessments.
Optimization modeling of environmentalrisk/impact studies
City scale modeling using TSU-
CORM.Link the air pollution
prediction from ENVIRO-HIRLAM or CAC to population activity (human
expo-sure modeling).
Human exposure modeling
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The predicted exposure of population to NO2 (g/m3 *persons).
© Helsingin kaupunki, Kaupunginmittausosasto 576§/1997, ©Aineistot: Espoon, Helsingin, Kauniaisten ja Vantaan mittausosastot
Environmental OfficeKansanterveyslaitosFolkhälsoinstitutetNational Public Health Institute
EnergyEnergy EnvironmentEnvironment HealthHealthCC FUMAPEX integrated population health impact study