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DMI-ENVIRO-HIRLAM

DMI-ENVIRO-HIRLAM. An On-Line Coupled Multi-Purpose Environment Model. U. Korsholm*, A. Baklanov, A. Mahura, C. Petersen, K. Lindberg, A. Gross, A. Rasmussen, J.H. Sørensen, J. Chenevez. The Danish Meteorological Institute, Copenhagen, Denmark * usn@dmi.dk, phone: +45 39157439.

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DMI-ENVIRO-HIRLAM

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  1. DMI-ENVIRO-HIRLAM An On-Line Coupled Multi-Purpose Environment Model U. Korsholm*, A. Baklanov, A. Mahura, C. Petersen, K. Lindberg, A. Gross, A. Rasmussen, J.H. Sørensen, J. Chenevez The Danish Meteorological Institute, Copenhagen, Denmark * usn@dmi.dk, phone: +45 39157439 ACCENT/GLOREAM Workshop 2006

  2. Purpose • Plans and current status, main model features • Preliminary results: aerosol–meteorology feedbacks • Motivation • Climate: direct, indirect, semi-direct effects, large scale dynamical feedback (Kim & Lee, 2006; Kim et al., 2006) • Local: direct, indirect, semi-direct effects, local scale feedbacks • Importance for short range weather forecasting ? (Perez et al. 2006)

  3. Examples of feedbacks

  4. Definitions off-line models comprise: • Separate CTMs driven by meteorological input data from meteo-preprocessors, measurements or diagnostic models • Separate CTMs driven by analysed or forecasted meteo-data from NWP archives or datasets • Separate CTMs reading output-files from operational NWP models or specific MetMs with limited temporal resolution (e.g. 1, 3, 6 hours) on-line models comprise: • On-line access models, when meteo-data is available at each time-step • On-line integration of a CTM into a MetM; feedbacks are possible: on-line coupled modeling

  5. On-line coupling Only one grid; no interpolation in space No time interpolation Physical parameterizations are the same; no inconsistencies Possibility of feedbacks with meteorology All 3D meteorological variables are available at the right time (each time step); no restriction in variability of met. fields Does not need meteorological- pre/post-processors Off-line Possibility of independent parameterizations Low computational cost; more suitable for ensembles and operational activities Independence of meteorological model computations Advantages of On-line & Off-line modeling

  6. Numerical Weather Prediction Model Future plans Why develop an on-line coupled model ? Climate; NWP; Research; Operational; Emergency 1. Model nesting for high resolutions 2. Improved representation of pbl. and sl. 3. ‘Urbanisation’ of the NWP model 4. Improvement of advection schemes 5. Implementation of chemical mechanisms 6. Implementation of aerosol dynamics 7. Realisation of feedback mechanisms 8. Assimilation of monitoring data

  7. Current status ECMWF Observational database DMI-ENVIRO-HIRLAM Surface analysis Upper air analysis Emission Transport Dispersion Deposition Initialisation Boundaries Output Forecast

  8. Model Description 1

  9. Model Description 2 Transport and dispersion • Bott advection (Bott, 1989) + Easter update for tracers (Easter, 1993); Semi-Lagrangian for meteorology • Risk of mass-wind inconsistency • No horizontal diffusion • Vertical diffusion: CBR-scheme (Cuxart et al., 2000) • Coefficient defined by mixing length formulation in stable/unstable conditions Mass conservation test for ETEX release

  10. ETEX 1, 48 hours after start of release Model Description 3 Semi-Lagrangian Bott scheme Bott-Easter sheme

  11. Model Description 4 Deposition • Particle size dependent parameterizations for dry and wet deposition • Resistance approach for dry deposition (Wesley, 1989; Zanetti, 1990) • Terminal settling velocity in different regimes: • Stokes law • non-stationary turbulence regime • correction for small particles • Dependent on land use classification • Below-cloud scavenging (washout); precipitation rates (Baklanov & Sorensen, 2001) • Scavenging by snow (Maryon et al., 1996) • Different scavenging of particles and gases Next step • Rainout into 3D clouds (based on on-line coupling): • convective precipitation • stratiform precipitation

  12. Preliminary results 1: Deposition Chernobyl accident; point source emissions (Devell et al., 1995, persson et al., 1986) Date: 19860501 18:00 UTC Accumulated dry deposition [kBq/m2] Accumulated wet deposition [kBq/m2]

  13. Preliminary results 2: Feedback For water clouds: r³eff = kr³v r³eff =3L/(4lkN) (Wyser et al. 1999) L : Cloud condensate content N: Number concentration of cloud droplets ΔNcont = 108.06 conc0.48 ΔNmarine = 102.24 conc0.26 (Boucher & Lohmann, 1995) Emission rate: 7.95 gs-1; ETEX Diameter: 1 µm Urban fractions [%; dark green – dark red]

  14. Preliminary results 2: Feedback Accumulated (reference) dry deposition [μg/m2] +48 h Difference (ref - perturbation) in accumulated dry deposition [ng/m2] Accumulated (reference) wet deposition [μg/m2]+48 h Difference (ref - perturbation) in accumulated wet deposition [ng/m2]

  15. Summary • DMI is developing an on-line coupled environment model: DMI-ENVIRO-HIRLAM • emission module, inventories • transport, dispersion, dry and wet deposition • aerosol dynamics • gas-phase and heteorogeneos chemistry • data assimilation • cloud, radiation coupling • To be used for: research, operational, emergency ? • Main advantages of on-line coupled meso-scale NWP model and CTM • no restriction in variability of input fields • possibility of feedbacks • Previously tested for mass conservation • Deposition being tested on Chernobyl accident • looks promising, local hot-spots • Investigation into cloud-aerosol coupling • model sensitivity, large changes in deposition

  16. AcknowledgementsThe HIRLAM development program at DMICopenhagen Global Change Initiative (COGCI) • References • Baklanov A. & Sorensen, H., J., 2001, Physics and Chemistry of the Earth, vol. 26, No. 10, 787-799 • Bott, A., 1989, Mon. Wea. Rev., 117, 1006-1015 • Boucher, O. & Lohmann, U., 1995, Tellus 47, Ser. B, 281-300 • Cuxart, J. et al., 2000, Q.J.R. Meteo. Soc., 126, 1-30 • Devell et al., 1995, CSNI report, OECD/NEA, Paris • Easter, C., Mon. Wea. Rev., vol. 121, 297-304 • Kim, M., K. et al., J. Clim., 2006, in press • Kim, M., K. & Lee, W., S., GRL, vol. 33, L16704, 2006 • Maryon R., H. et al., 1996, Depart. Of Env., UK, Met. Office. DoE Report # DOE/RAS/96.011 • Perez, C. et al., JGR, vol. 111, D16206, 2006 • Persson et al., SMHI/RMK report No. 55, 1986 • Wesley, M.,L., 1989, Atm. Env., vol. 23, No. 6, 1293-1304 • Wyser et al., 1999, Contr. Atmos. Phys., vol. 72, No. 3, 205-218 • Zanetti, P., 1990, Air Pollution Modelling – Theories, Computational Methods and Available Software. Southhampton: Computational Mechanics and New York: Van Nostrand Reinhold

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