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

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