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LIDAR OBSERVATIONS CONSTRAINT FOR CIRRUS MODELISATION IN Large Eddy Simulations

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). Introduction.

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LIDAR OBSERVATIONS CONSTRAINT FOR CIRRUS MODELISATION IN Large Eddy Simulations

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  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. Introduction 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.

  3. Introduction 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?

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

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

  6. The model • 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

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

  8. Resolution domain 2D simulations Limit Conditions: Cyclic Sponge zone 50 m 100 m

  9. Cirrus cloud generation 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 Duration : 5 hours Radiation turned off

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

  11. Base run results after 4 hours: Direct output

  12. Base run results after 4 hours: Deducted output

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

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

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

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

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

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

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