Lidar algorithms to retrieve cloud distribution, phase and optical depth Y. Morille, M. Haeffelin, B. Cadet, V. Noel Institut Pierre Simon Laplace SYMPOSIUM.

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Presentation transcript:

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

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

STRAT: Occurrence and distribution * Morille et al, JAOT 2005 (submitted) Pr 2 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)

Palaiseau 10/ /2004 Lidar Cloud and Aerosol Statistics Seasonal variations of cloud occurrence

Cloud and Aerosol Statistics Seasonal variations of aerosol occurrence

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

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

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

Depolarization Temperature profile (RS) Phase IC E LIQUID WATER MIXED PHASE % 0 % 50 % Ice Figures Y. Morille (LMD) Cloud Phase Results

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

CAPRO - Optical thickness Optical thickness Based on lidar backscattered power Pr 2 data Requires normalization in particle-free zone Optimal estimation algorithm

STRAT Classification 2 optical depth retrieval methods PIMI Information outside the cloud Information inside the cloud Requires molecular zones beneath and above the cloud Requires an a priori Lr eff Comparison (Cadet et al. 2004) Particle Integration method:  = k eff ∫ (R(z)-1)  m (z)dz where R(z)=(  m (z)+  c (z))/  m (z) k eff prescribed: 18 sr k eff opt derived from MI method

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

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

Optical thickness - Statistics

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

Statistics - Lidar Ratio