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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. Lidar Data processing:. 2 algorithms: - STRAT : STRucture of the Atmosphere - PowerPoint PPT Presentation
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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)
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)
Palaiseau 10/2002-09/2004 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 > 0.2Temp < -42°CIce
Mixed-phaseclouds
Cloud Phase retrieval algorithm
Depolarization - 2004-09-02Temperatureprofile (RS)
Phase
ICE
LIQUIDWATER
MIXEDPHASE
0.6
0.0
0.2
0.4
100 %
0 %
50 % Ice
FiguresY. Morille(LMD)
Cloud Phase Results
Cloud Phase Statistics Cloud Phase Statistics
Vertical distribution of Cloud thermodynamic phase (seasonal variations)
Mixed phaseLiquid water
Ice water
CAPRO - Optical thickness
Optical thickness• Based on lidar backscattered power Pr2 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 Lreff
Comparison
(Cadet et al. 2004)
Particle Integration method:
= keff ∫ (R(z)-1)m(z)dz
where R(z)=(m(z)+c(z))/m(z)
keff prescribed: 18 sr
keffopt derived from MI method
Optimal estimation technique using:• PR2 profile• Optical depth• Uncertainties
For the retrieval of:• Extinction profile• Lidar ratio
Extinction Profile
Extinction ProfileOptimal 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