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RAMS-Chimie

RAMS-Chimie. S.Cautenet, J.Arteta Laboratoire de météorologie physique (LaMP) 24, av. des Landais 63177 Aubière Cedex. RAMS 4.3. Meteorological meso-scale model developed at CSU, Eulerien non-hydrostatic model, Boundaries conditions forced by synoptic fields from large-scale model

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RAMS-Chimie

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  1. RAMS-Chimie S.Cautenet, J.Arteta Laboratoire de météorologie physique (LaMP) 24, av. des Landais 63177 Aubière Cedex

  2. RAMS 4.3 • Meteorological meso-scale model developed at CSU, • Eulerien non-hydrostatic model, • Boundaries conditions forced by synoptic fields from large-scale model • 4DDA assimilation (sounding and surface data) • Nested grids (2-way nesting – up to 8 nested grids) • Soil and hydrological model (R. Walko) • Clouds model with detailed microphysics (7 hydrometeors) • Parallel code with distributed memory

  3. Chemistry schemes • Rams 4.3 is coupled on-line with a preprocessor (SPACK) allowing a fast and easy use of any gas-phase chemistry scheme. Actually : MOCA 2.2 condensed (29 species, 66 reactions, Aumont, 1998) RADM (63 species, 157 reactions, Stockwell et al, 1990) • Dry deposition model (Wesely et al, 1989) • Photolytic constants from TUV (Madronich et al, 1987) • Aerosols and interactions with liquid-phase not considered

  4. Model configuration • 2 nested grids (2-way nesting)- grid 1 : 36x36 meshes at 15 Km resolution- grille 2 : 47x47 meshes at 3 Km resolution • 35 vertical levels with non linear progression(~ 10 in the ABL) • Time resolution : 15 s for the coarse grid and 5 s for the fine one • 1 s for chemistry time resolution

  5. Boundary conditions • Different boundaries conditions considering grids • For coarse grid • ECMWF synoptic fields for coarse grid (every 6 hours, affecting the 6 first lateral meshes • Climatologic values for chemical species (only for initialization) • For fine grid • Coarse grid fields (at each time step, affecting the 3 first lateral meshes)

  6. Run parameters • Meteorology • ECMWF fields (initialization and nudging) • No sounding assimilations • No ESCOMPTE data used • Topography, soil types and vegetation from USGS • SST from the Oceanic Observatory of Marseille and METEOSAT data • Soil and hydrologic models enabled • Clouds microphysics disabled for IOP2 • Chemistry • 2 schemes used : MOCA 2.2 condensed and RADM

  7. Emissions • For ESCOMPTE domain (fine grid) : • Only ESCOMPTE emission files used (not corrections are made) • Point sources treated as area sources (height not used) • Actually, only well identified species are used (i.e. ethane for ethane, ethene for ethene, …, … maybe underestimation for VOCs concentrations • For coarse grid • Anthropic emissions from GENEMIS (ESCOMPTE pre-inventory) / EDGAR • Biogenic emission from GEIA • ESCOMPTE emissions not included

  8. Comparison exercise • Ground surface given by RAMS is approximately the same than observations one, so, no interpolations are made • For exercise outputs, RAMS fields are interpolated : • Horizontally with a quadratic method • Vertically with a linear method

  9. Miscellaneous • Aerosols not treated • Model strengths/weakness • All meteorological field well represented (Taghavi and al, 2003) • Good agreement for chemical fields, but • Some chemical concentrations underestimated in coarse grid

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