dmi modeling systems and plans for ceeh activities off-line air pollution modeling on-line air...

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:

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

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

Climate Change Scanarios Modeling By HIRHAM


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.

Off-Line modelling with CAC

Simulation domain

Horizontal resolution 0.2º×0.2º.

T:0.15º×0.15º

S: 0.05º×0.05º


CAC Model Area

ENSEMBLE JRC project exp. nr. 11 EnergyEnergy EnvironmentEnvironment HealthHealthCC

(Off-Line)

Ensemble: DK3, DE1, FR2, CA2

Ozone36 hour forecast 48 hour forecast

0 15 30 60 90 120 150

ppbV


(Off-Line)

“Semi”-operational forecasts 4 times a day of O3, NO, NO2, CO, SO2, Rn, Pb, “PM2.5”, “PM10”.

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


Radiation budgets

Temperature profiles

Chemistry/Aerosols

CloudCondensation

Nuclei

Precipitation

Chemistry/Aerosols

Examples of feedbacks

Cloud-radiationinteraction

Temperature profiles

Chemistry/Aerosols


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 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

Accumulated (reference) dry deposition [μg/m2] +48 h Difference (ref – perturbation) inAccumulated dry deposition [ng/m2]

Emergency Preparednes & Risk Assessment

Using the 3-D Stochastic Lagragian Regional Scale Model DERMA


1. Probabilistic Risk Assessment.

2. Source Determination by Inverse Modelling.

3. Chemical Emergency Preparednes.

4. Urban Meteorology Effects.

Examples:

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


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, …).


Accidental fire in waste deposit Accidental fire in waste deposit.

Aalborg Portland, 23 October 2005 EnergyEnergy EnvironmentEnvironment HealthHealthCC

Accidental fire in waste deposit

DERMA calculations

Aalborg Portland, 23 October 2005


RoofWall

Street

Momentum Turbu-lence

Heat

DragWake diffu-sion

Radiation

Urban Features EnergyEnergy EnvironmentEnvironment HealthHealthCC

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.


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).


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 DMI-HIRLAM, CAC and /or ENVIRO-HIRLAM.

DMIs Possible Modeling Activities In CEEH


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

DMIs Possible Modeling Activities In CEEH


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

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

dmi modeling systems and plans for ceeh activities off-line air pollution modeling on-line air...

Documents