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Implementation and validation of a prognostic large-scale cloud and

Implementation and validation of a prognostic large-scale cloud and precipitation scheme in ARPEGE (F.Bouyssel,Y.Bouteloup, P.Marquet). Tartu,Estonia, 24-26/01/2005. HIRLAM workshop on convection and clouds. Outlook. Description of the scheme Validation Conclusions and perspectives.

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Implementation and validation of a prognostic large-scale cloud and

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  1. Implementation and validation of a prognostic large-scale cloud and precipitation scheme in ARPEGE (F.Bouyssel,Y.Bouteloup, P.Marquet) Tartu,Estonia, 24-26/01/2005 HIRLAM workshop on convection and clouds

  2. Outlook • Description of the scheme • Validation • Conclusions and perspectives

  3. Description of the large-scale cloud and precipitation scheme

  4. Generalities • Developed by P. Lopez (QJRMS, 2002) • Designed for variational assimilation of cloud and RR obs • Prognostic var : Qc (cloud condensates) & Qp (precip water) • Semi-lagrangian treatment of the fall of precipitation (Lopez,2002)

  5. Specification of hydrometeor distributions: • Concentration : N(D) = N0exp(-lD) (Marshall-Palmer) • Mass: M(D)=aDb • Fall velocity: V(D)=gDd • Condensation/evaporation of cloud water: Smith (1990) Triangular PDF  Cloud water amount (Qc) and Cloud fraction (Neb) • Autoconversion: Kessler-type (1969) • Collection of cloud water by rain and snow: Integration of the collection eq. over N(D) and V(D) • Precipitation evaporation: Integration over N(D) of the equation: • Precipitation sedimentation: Originality: conservative semi-lagrangien treatment based on constant fall speeds for snow and rain (0.9 and 5 m/s)

  6. Partition of Qc into Ql,Qi

  7. Tuning of Rhcrit

  8. Sensitivity to fall speeds (Lopez,2002)

  9. Sensitivity to main tunable parameters (Lopez,2002)

  10. Validation

  11. Diffusion on conservative variables (qt, l)

  12. Validation on 4 months integration (T149C1.0) Qliq Qice Qneb Oper New

  13. Validation on 4 months integration (T149C1.0) Oper MEAN=8.53 W.m-2 RMS=25.4 W.m-2 New MEAN=5.42 W.m-2 RMS=23.0 W.m-2

  14. Validation on 4 months integration (T149C1.0) Oper MEAN=2.13 W.m-2 RMS=12.5 W.m-2 New MEAN=0.72 W.m-2 RMS=11.8 W.m-2

  15. Validation on 4 months integration (T149C1.0)

  16. Preliminary results in T358C2.4 (forecasts only)

  17. Case study : 09/12/2004 Oper New

  18. Case study : 09/12/2004 Oper New

  19. Conclusions Perspectives

  20. Conclusions • Importance of diffusion on conservative variable • Stability and acceptable sensitivity to time step length • Promising results to improve mountain precipitations (intensity and localisation) • Improvment of LWP in the Tropics • Overestimation of cloud condensate and cloudiness near the ground

  21. Perspectives • Modify snow melting • Evaluation in assimilation experiments • Take into account turbulence to diagnose Qc and Neb • Introduce further sophistications

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