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C. Energy Environment Health. DMI Modeling Systems And Plans For CEEH Activities. Gross, A. Baklanov, U. S. Korsholm, J. H. Sørensen, A. Mahura & A. Rasmussen. Content:. Off-Line Air Pollution Modeling On-Line Air Pollution Modeling

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dmi modeling systems and plans for ceeh activities

C

Energy

Environment

Health

DMI Modeling Systems And Plans For CEEH Activities

  • 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

Kick-off.

d. 23-25/1 2007

air pollution modeling at dmi

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Energy

Environment

Health

Chemical Solvers

Aerosol Module

UTLS Trans. Models

Air Pollution Modeling At DMI

  • PSC aerosols
  • Tropospheric
  • aerosols
  • Approaches:
  • Normal distribution,
  • Bin approach
  • Physics:
  • Condensation
  • Evaporation
  • Emission
  • Nucleation
  • Deposition
  • Gas Phase
  • Aqueous phase
  • Chemical equil.
  • Climate Modeling
  • Approaches:
  • RACM, CBIV,
  • ISORROPIA

Lagrangian

transport, 3-D

regional scale

Eulerian trans-

port 0..15

lat-lon grid,

3-D regional

scale

ECMWF

DMI-HIRLAM

Met. Models

Eulerian trans-

port 0.2-0.05

lat-lon, 25-40

vert. layer,

3-D regional

scale

Stochastic

Lagrangian

transport,

3-D regional

scale

Tropo. Trans. Models

City-Scale Obstacle

Resolved Modelling

TSU-CORM

Emergency Pre-

parednes & Risk Assess-

ment. DERMA

Off-Line Chemical

Aerosol Trans.

CAC

On-Line Chemical

Aerosol Trans.

ENVIRO-HIRLAM

Regional (European) scale air pollution: smog and ozone, pollen.

Nuclear, veterinary and chemical.

Regional (European) to city scale air pollution: smog and ozone.

dmi hirlam

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Energy

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Health

  • 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.

DMI-HIRLAM

Q

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.

S

T

A numerical weather prediction system consists of pre-processing, climate file generation, data-assimilation and analysis, initialization, forecast, post-processing and verification.

climate change scanarios modeling by hirham

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Health

Modeling Area

Simulation period: Year 2000 to 2100

Climate Change Scanarios Modeling By HIRHAM

  • Horizontal resolution 25x25 km2.
  • Vertical resolution 19 levels.

Output of meteorological parameter:

From 3-6 hours to once a day depend

on the parameter.

off line modelling with cac

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Energy

Environment

Health

Off-Line modelling with CAC

T:0.15º×0.15º

Simulation domain

Horizontal resolution 0.2º×0.2º.

S: 0.05º×0.05º

CAC Model Area

slide6

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ENSEMBLE JRC project exp. nr. 11

(Off-Line)

slide8

Ozone

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Health

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)

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

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Advantages of On-line & Off-line modeling
slide10

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Examples of feedbacks

slide11

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On-Line Modeling With ENVIRO-HIRLAM

U, V, W, T, q,

U*, L

Emission

Transport

Dispersion

Clouds

Precipitation

Radiation

DMI-HIRLAM

Gas phase chemistry

Aerosol chemistry

Aerosol physics

Deposition

Concentration/Mixing ratio

slide12

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Health

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

slide13

Difference (ref – perturbation) in

Accumulated dry deposition [ng/m2]

Accumulated (reference) dry deposition [μg/m2] +48 h

emergency preparednes risk assessment using the 3 d stochastic lagragian regional scale model derma

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Emergency Preparednes & Risk Assessment Using the 3-D Stochastic Lagragian Regional Scale Model DERMA

Examples:

  • Probabilistic Risk Assessment.
  • Source Determination by Inverse Modelling.
  • Chemical Emergency Preparednes.
  • Urban Meteorology Effects.
slide15

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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

slide16

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Source Determination by

Inverse Modelling

Hypothetical release of 100 g Anthrax spores

Determination of source location by adjoint DERMA using monitoring data. No a priori assumption about source (point, area, …).

Inhalation dose calculated by DERMA based on DMI-HIRLAM.

Monitoring stations

slide17

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Health

Aalborg Portland, 23 October 2005

Accidental fire in waste deposit Accidental fire in waste deposit.

slide18

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Health

Aalborg Portland, 23 October 2005

Accidental fire in waste depositDERMA calculations

slide19

Wake diffu-sion

Radiation

Drag

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Health

Wall

Roof

Momentum

Turbu-lence

Heat

Street

Urban Features

slide20

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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.

slide21

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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).

city scale obstacle resolved modeling tsu corm

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Health

Streamlines and

air pollution conc

City-Scale Obstacle-Resolved Modeling (TSU-CORM)

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

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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 ENVIRO-

HIRLAM and/or CAC using HIRHAM

Meteorology.

Kick-off.

d. 23-25/1 2007

dmis possible modeling activities in ceeh24

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DMIs Possible Modeling Activities In CEEH

Optimization modeling of environmental

risk/impact studies

  • Modify DERMA or CAC for sensitivi-

ty, risk/impact minimization and

optimization studies.

  • Sensitivity studies for environmen-

tal risk/impact assessments.

Human exposure modeling

  • City scale modeling using TSU-

CORM.

  • Link the air pollution prediction

from ENVIRO-HIRLAM or CAC to

population activity (human expo-

sure modeling).

Kick-off.

d. 23-25/1 2007

slide25

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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).

Environmental Office

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