1. LIDAR OBSERVATIONS CONSTRAINT FOR CIRRUS MODELISATION IN Large Eddy Simulations O. Thouron, V. Giraud (LOA - Lille) H. Chepfer, V. Noël(LMD - Palaiseau) / J. Pelon (SA - Paris) / J-L Redelsperger (CNRM - Toulouse)

2. The reason that cirrus clouds are not well understood is that many atmospheric processes affect their development, structure and evolution: on the locale scale : radiation, aerosol properties, gravity waves, shear instability, latent heating, microphysical properties, …. On a larger scale : interaction with jet streams, interaction with planetary-scale waves, passing pressure systems, large scale lifting or descent... Successful parameterization of cirrus clouds needs to be based on an understanding of all the processes and their interactions. Introduction

3. How active remote sensing bring the signatures of processes and their interactions at local scales? How active remote sensing may be convenient to constrain physical parameterizations in Cloud Resolving Models? Introduction

4. - strategy - model used to make LES simulations - cirrus cloud generation - sensitivity study: microphysical processes - conclusion - perspective Plan

5. Aircraft + ECMWF +radio sonde data Synthetic Observations fields 2/3D Active Observations Passive Observations Radiatif transfer calculation Microphysical Scheme MESO-NH Observations Modelisation Comparison Idealized case Sensibility Study of the lidar to the microphysical processes Strategy

6. Use the French atmospheric simulated system meso-NH Run in 1, 2 or 3 dimensions Non hydrostatic meso scale model Bulk microphysical scheme Designed to study convective cloud or precipitating cloud It was necessary:- to adapt the microphysical scheme to simulate cirrus - to prognostic ice number concentration to be abble to calculate synthetic observations The model

7. - spherical particles - size distribution : gamma modified yFirst type SP : ySecond type NSP : - non spherical particles (columns or plates) - size distribution : gamma modified Microphysical scheme Sedimentation Aggregation Transformation Water Vapor SP NSP Sublimation Sedimentation Nucleation Deposition

8. 50 m 100 m Sponge zone Limit Conditions: Cyclic2D simulations Resolution domain

9. Cirrus clouds are generated in a similar way to the GEWEX Cloud Systems Study (GCSS) cirrus cloud intercomparison (Starr et al. 2000) Cloud Forcing: - cooling equivalent to ascent at 3 cm/s - between 7 and 10 km Turbulent structure: - initialized by artificial heat perturbation (+/- 0.01K) between 8 and 9 km Cirrus cloud generation Duration : 5 hours Radiation turned off

10. Base run: Nu=1000 l -3 Ri * =20mg.m -3 Adjustment on 100% Velocity: Starr (1985) nucleation: Deposition Sedimentation Meyer: - Supersaturation ratio with respect to ice - Ice nuclei number: Nu Transformation Depend on the primary ice water content threshold Depend on the sursaturation in the cirrus Depend on velocity-mass relation parameters c and d: The sensitivity study

11. Base run results after 4 hours: Direct output

12. Base run results after 4 hours: Deducted output

13. Sedimentation Aggregation Transformat Water Vapor SP NSP Sublimation Sedimentation Nucleation Deposition Sensitivity study: Ice nuclei number Ice primary backscattering(km-1) Ice cristal backscattering(km -1 ) Total backscattering(km -1 ) Depolarisation 1 h 2 h 4 h 500 l -1 1000 l -1 1500 l -1

14. Sedimentation Aggregation Transformat Water Vapor SP NSP Sublimation Sedimentation Nucleation Deposition Sensitivity study: the primary ice water content threshold Mean backscatterring Depolarisation 1h1h 2h2h 4h4h 10 mg.m -3 20 mg.m -3 30mg.m -3

15. Water Vapor Sedimentation Aggregation Transformat SP NSP Sublimation Sedimentation Nucleation Deposition Sensitivity study: Fall speed velocity Depolarisation Mean backscatterring 1h 2h 4h

16. Sensitivity study: Deposition Water Vapor Sedimentation Aggregation Transformat SP NSP Sublimation Sedimentation Nucleation Deposition Mean backscatterring Depolarisation 1h1h 2h2h 4h4h 50% 100% 80%

17. Depolarisation Lidar Backscaterring Lidar Backscaterring Depolarisation Nucleation Transformation Fall speed velocity Déposition Sublimation v v v v v v v Conclusion v

18. Perspectives Structure analysis: FFT Radar data Used of real case in order to constrain the model