DMI Modeling Systems And Plans For CEEH Activities Off-Line Air Pollution Modeling On-Line Air Pollution Modeling Emergency Preparednes & Risk Assessment Urban Modeling Energy Energy Environment Environment Health HealthC Kick-off. d / A.Gross, A. Baklanov, U. S. Korsholm, J. H. Sørensen, A. Mahura & A. Rasmussen Content:

Air Pollution Modeling At DMI Energy Energy Environment Environment Health HealthC 1.PSC aerosols 2.Tropospheric aerosols Approaches: Normal distribution, Bin approach Physics: 1.Condensation 2.Evaporation 3.Emission 4.Nucleation 5.Deposition Aerosol Module 1.Gas Phase 2.Aqueous phase 3.Chemical equil. 4.Climate Modeling Approaches: RACM, CBIV, ISORROPIA Chemical Solvers Lagrangian transport, 3-D regional scale UTLS Trans. Models Eulerian trans- port lat-lon grid, 3-D regional scale ECMWF DMI-HIRLAM Eulerian trans- port lat-lon, vert. layer, 3-D regional scale Stochastic Lagrangian 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 km 2, 40 vertical layers. S – 5x5 km 2, 40 vertical layers. Q – 5x5 km 2, 40 vertical layers. Test version of 1.5x1.5 km 2 of DK. A numerical weather prediction system consists of pre- processing, climate file generation, data-assimilation and analysis, initialization, forecast, post-processing and verification. T S Q Energy Energy Environment Environment Health HealthC

Climate Change Scanarios Modeling By HIRHAM Energy Energy Environment Environment Health HealthC 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 km 2. 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 Energy Energy Environment Environment Health HealthC CAC Model Area

ENSEMBLE JRC project exp. nr. 11 Energy Energy Environment Environment Health HealthC (Off-Line)

Ensemble: DK3, DE1, FR2, CA2

Ozone 36 hour forecast48 hour forecast ppbV Energy Energy Environment Environment Health HealthC (Off-Line) “Semi”-operational forecasts 4 times a day of O 3, NO, NO 2, CO, SO 2, 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 Energy Energy Environment Environment Health HealthC

Radiation budgets Temperature profiles Chemistry/ Aerosols Cloud Condensation Nuclei Precipitation Chemistry/ Aerosols Examples of feedbacks Cloud- radiation interaction Temperature profiles Chemistry/ Aerosols Energy Energy Environment Environment Health HealthC

Emission Transport Dispersion Deposition Gas phase chemistry Aerosol chemistry Aerosol physics Clouds Precipitation Radiation DMI-HIRLAM U, V, W, T, q, U*, L Concentration/Mixing ratio On-Line Modeling With ENVIRO-HIRLAM Energy Energy Environment Environment Health HealthC

Energy Energy Environment Environment Health HealthC Chernobyl Simulation 0.15°x0.15°, d. 7/5-1986, UTC Dry deposition (kBq/m 2 ) Total deposition statistics: Corr = 0.59, NMSE 6.3

On-Line Modeling ENVIRO-HIRLAM : Feedbacks For water clouds: r³ eff =3L/(4  l kN) (Wyser et al. 1999) L : Cloud condensate content N: Number concentration of cloud droplets ΔN cont = [CCN] 0.48 ΔN marine = [CCN] 0.26 (Boucher & Lohmann, 1995) Emission rate: 7.95 gs -1 ; ETEX Diameter: 1 µm kN [m -3 ] Marine Cont0.694х10 8 Urban fractions [%; dark green – dark red] Energy Energy Environment Environment Health HealthC

Accumulated (reference) dry deposition [μg/m 2 ] +48 h Difference (ref – perturbation) in Accumulated dry deposition [ng/m 2 ]

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

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, …). Energy Energy Environment Environment Health HealthC

Accidental fire in waste deposit Accidental fire in waste deposit. Aalborg Portland, 23 October 2005 Energy Energy Environment Environment Health HealthC

Accidental fire in waste deposit DERMA calculations Aalborg Portland, 23 October 2005 Energy Energy Environment Environment Health HealthC

Roof Wall Street MomentumTurbu- lence Heat Drag Wake diffu- sion Radiation Urban Features Energy Energy Environment Environment Health HealthC

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. Energy Energy Environment Environment Health HealthC

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). Energy Energy Environment Environment Health HealthC

City-Scale Obstacle-Resolved Modeling (TSU-CORM) Energy Energy Environment Environment Health HealthC Streamlines and air pollution conc 3 d. fluid dynamic air pollution model Resolution: Horizontal: 1x1 m 2 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 Energy Energy Environment Environment Health HealthC Kick-off. d /  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 pollution and population health

DMIs Possible Modeling Activities In CEEH Energy Energy Environment Environment Health HealthC Kick-off. d /  Modify DERMA or CAC for sensitivi- ty, risk/impact minimization and optimization studies.  Sensitivity studies for environmen- tal risk/impact assessments. Optimization modeling of environmental risk/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 NO 2 (  g/m 3 *persons). © Helsingin kaupunki, Kaupunginmittausosasto 576§/1997, ©Aineistot: Espoon, Helsingin, Kauniaisten ja Vantaan mittausosastot Environmental Office Energy Energy Environment Environment Health HealthC FUMAPEX integrated population health impact study