Lidar algorithms to retrieve cloud distribution, phase and optical depth

1. SYMPOSIUM Lidar algorithms to retrieve cloud distribution, phase and optical depth Y. Morille, M. Haeffelin, B. Cadet, V. Noel Institut Pierre Simon Laplace This work is supported by the French Space Agency

2. Lidar Data processing: 2 algorithms: -STRAT : STRucture of the Atmosphere -CAPRO : Cloud Aerosol Properties L2 Thermo Phase Opt Depth Extinction L2 classification flag L1 Pr2 CAPRO STRAT

3. STRAT: Occurrence and distribution Pr2 paral. polarization STRAT flag STRAT detects: - cloud layers (wavelet method) - aerosol layers (wavelet method) - boundary layer (wavelet method) - molecular layers (slope method) - noise (SNR threshold) * Morille et al, JAOT 2005 (submitted)

4. Cloud and Aerosol Statistics Seasonal variations of cloud occurrence Palaiseau 10/2002-09/2004 Lidar

5. Cloud and Aerosol Statistics Seasonal variations of aerosol occurrence

6. CAPRO - Cloud thermodynamic phase • Cloud thermodynamic phase • Based on lidar depolarization ratio + temperature • Requires normalization in particle-free zone (2.74%)

7. Cloud Phase retrieval algorithm Cloud Distribution : Lidar Depolarization vs Temperature 3 years dataset - SIRTA # data points

8. Cloud Phase retrieval algorithm Cloud Distribution : Lidar Depolarization vs Temperature 3 years dataset - SIRTA # data points Depol > 0.2 Temp < -42°C Ice Mixed-phase clouds Temp > 0 C, Depol < 0.2 : Liquid water

9. 0.6 0.4 0.2 0.0 Cloud Phase Results Temperatureprofile (RS) Depolarization - 2004-09-02 100 % Ice 50 % ICE MIXEDPHASE LIQUIDWATER FiguresY. Morille(LMD) Phase 0 %

10. Ice water Mixed phase Liquid water Cloud Phase Statistics Vertical distribution of Cloud thermodynamic phase (seasonal variations)

11. CAPRO - Optical thickness • Optical thickness • Based on lidar backscattered power Pr2 data • Requires normalization in particle-free zone • Optimal estimation algorithm

12. 2 optical depth retrieval methods MI PI STRAT Classification Information outside the cloud Information inside the cloud Requires molecular zones beneath and above the cloud Requires an a priori Lreff Comparison Particle Integration method: t = keff ∫ (R(z)-1)bm(z)dz where R(z)=(bm(z)+bc(z))/bm(z) keff prescribed: 18 sr keffopt derived from MI method (Cadet et al. 2004)

14. Extinction Profile • Optimal estimation technique using: • PR2 profile • Optical depth • Uncertainties • For the retrieval of: • Extinction profile • Lidar ratio

15. Extinction Profile • Optimal estimation technique using: • PR2 profile • Optical depth • Uncertainties • For the retrieval of: • Extinction profile • Lidar ratio

16. Optical thickness - Statistics

17. Conclusions • Develop algorithms to interpret lidar profiles in terms of cloud and aerosol macrophysical and microphysical properties • Objective: • Process long-term data sets • Derive regional statistics of cloud properties • Conduct process studies • STRAT applied to multiple lidar systems. • Has been distributed to several research groups • Available on demand • Phase and optical depth retrievals validation under way • Interested in collaborations with other lidar groups

18. Statistics - Lidar Ratio