12th EMS Annual Meeting & 9th ECAC I 10-14 September 2012 I Łódź (Poland)

Determination of atmospheric structures, aerosol optical properties and particle type with the R-MAN510 Raman dual polarization lidar super

ceilometer

P. Royer, A. Bizard, L. Sauvage, M. Boquet, L. Thobois, M. Renaudier, B. Bennai

12th EMS Annual Meeting & 9th ECAC I 10-14 September 2012 I Łódź (Poland)

2

Aerosols and clouds have a strong impact on :

Aerosols can also disturb human activities :

- Air quality- Climate (direct, semi-direct and indirect effects)- Meteorology

- Reduction of visibility - Air traffic disruption (volcanic ashes, desert dust)

Introduction


3

Satellite measurements are limited by clouds and do not supply a vertical profile of the ash cloud

An ambiguity in the models outputs regarding position and density. Need confirmation using vertical monitoring.

MODIS image on April 15th 2010

In complement to satellite and models, ground based sensors are needed to detect plumes, to determine their height, to identify and quantify the aerosols.

Needs appropriate ground based remote sensors

Motivation : needs for ground-based sensors


4Proof of automatic ash detection monitoring over Europe in 2010 with the

ALS aerosol lidar


5Motivation: identification of aerosols

Augustine eruption, [Sassen et al,GRL, 2007]

Pollution aerosol

Ice crystals

Desert dust

dp>30%

10%<dp<25% dp~0%

dp up to 100%


We are lucky, dust and ash are unspherical

6

Characterizing aerosols with depolarization and Raman channels

ASH


• Example of measurements realized by a raman lidar prototype during the Eyjafjallajokull eruption

• Clear separation between ashs, pollution aerosols and dusts

7

RMan510 concept

Detection of the layers Getting the position Classification of the aerosols /clouds Quantification (more challenging) [Chazette et al.

2011, Donovan et al. 2011, Wienhold, 2011]

We learnt from our previous products, redesigned the hardware and software platform and improved the algorithm


8RMan510 Raman lidar Super Ceilometer Networkable operational tool:

• Stand alone with a low maintenance laser (3 years maintenance cycle)

• 24/7 , high data availability

3 channels : 355 //, 355 , 387 N2

• Identify aerosols thanks to → Cross-polarization channel (depolarization ratio)→ 387 nm Raman channel (lidar ratio)

• Improve the retrieval of optical atmospheric parameters

→ Raman channel for the extinction coefficient

Stand alone remote sensor• Self and continuous calibration thanks to the Raman channel• No nephelometer or sun photometer are needed


9

Ranges for the three channels

Low overlap 98% at 150m, full at 300m

30s (Day/Night)

10min (Day/Night)

PR2 // 10 / 16.6 km 13 / 21 kmPR2 7 / 17 km 11.2 / 21 km

PR2 Raman 1.8 / 10 km 3.5 / 18 km

Rayleigh fit 10mn average


An extended range

Range (m)

Ove

rlap

10

Elastic signal

Detection of structures (Aerosol gradients,

aerosol/cloud layers)

N2-Raman signal

Elastic // signal

Analog elastic //

Level 0Raw data

Level 1PR2

Level 1.5Detection of structures and optical properties

Photocounting elastic //

photocomptage

Analog elastic

Analog N2-Raman analogique

Photocounting N2-Raman

Photocounting elastic

photocomptage


Processing chain:structure detection

11

R-Man510 lidar

Gradient + 2D methodNo need for specific

thresholding

ALS lidar1D gradient method

Stable layerConvective layer

Residual layer


Improvment of RMan510 gradient detection

12

RMANALS


Cloud detection

Relative error compared with reference (%)

Pour

cent

age

of g

ood

dete

ction

13

Getting more structure details with dual polarization

Detection on parallel channel Detection on BOTH polarization channels


14

Elastic signal

Volume depolarization

ratio

Elastic total signal

Extinction, backscatter

coefficients, AOD



N2-Raman signal

Elastic // signal

Particle depolarization

ratio

Analog elastic //

Level 0Raw data

Level 1PR2



photocomptage

Analog elastic




photocomptage


Processing chain : optical properties

15Optical properties of aerosols and clouds : extinction coefficient and optical

depth

NIGHT NIGHTDAY

RMSE = 1.7 %R²= 0.93

Comparison of two RMAN510 :

RMSE ~ 10 %

Comparison of AOD retrieved with sunphotometer :

- RMAN1- RMAN2


16

Specific design to minimize cross-talk (10-6 max cross-talk following Freudenthaler, 2010).

Absolute on-site calibration method realized for each lidar (Alvarez et al. 2006, less than 10% relative error on dp)

Depolarization ratio dp gives information on particle sphericity (low dp for spherical particle ≠ high dp for non spherical particles)

Optical properties of aerosols and clouds : depolarization ratio

20-30%Dust aerosols

50-60%Cirrus clouds


17

Elastic signal

Volume depolarization

ratio

Elastic total signal

Extinction, backscatter

coefficients, AOD



N2-Raman signal

Elastic // signal

Particle depolarization

ratio

Aerosol/cloud typing

Analog elastic //

Level 0Raw data

Level 1PR2



photocomptage

Analog elastic




photocomptage

Processing chain : classificaction


18

Typing can be done unambiguously thanks to lidar ratio (raman channel) vs depolarization ratio diagram (Burton et al.)

4 types of aerosols:o Continental pollutiono Maritime aerosolso Dust mixo Pure dust / Volcanic ashes

Source Burton et al., AMTD, 2011


Aerosol typing in RMAN510

19Aerosol/cloud classificationScattering ratio Clouds Aerosols

Ice cloud/Water cloud Spherical/Unspherical

High Clouds Middle clouds Low clouds

In the PBL In the free troposphere

Continental pollution Maritime aerosols Dust mix Pure dust / Volcanic ashes

1

Height of Structures2

Shape (dp)3

Lidar ratio + dp values

4

Clouds :CALIOP Algorithm Theoretical Basis Document, Part 3: Scene Classification Algorithms (Liu et al, 2005)

Aerosols : Burton et al 2011, Communication G.Pappalardo 2012, T. Petzold, DLR, Royer 2011, David et al 2012 …


20All together ! Dust detectionSignal co-polarized @ 355nm Signal cross-polarized @ 355nm


Lidar Ratio

21

Depolar+ N2 Raman for maximum data availability and detection of aerosols

RMAN510 an industrial and operationnal tool for network monitoring (IAA, evaluation by Meteofrance)

To be used in the lidar/ceilometer network (affordable and operational system)

Under validation by Meteofrance and by EARLINET SCC

Response to Climate Research, Air quality and emergency needs.


Conclusion and perspective

22

Thank you for your attention


23

From Lidar measurement to mass concentration of ash particles with accuracy of 70%

Lidar dataLocal observationsof chemical properties of erupted ashes*•Optical refraction•Particle diameter

+ =

Mass concentration of ash layersAccuracy of 70% (Chazette et al 2011)Conversion factor 0.65 to 1g/m2 at 355nmWienhold, 2010; Chazette et al ,2010)

Pollution layersAccuracy of 19 to 32% (Royer et al , 2010)Conversion factor 4.5g/m2 at 355nm (Rault et al, 2009)


Quantifying the threat

24Cloud maskSignal co-polarized @ 355nm

Cirrus

Middle

Low cloud


25Retrieval of depolarization ratio in cirrus

δcirrus_RMAN1 = 30% ± 6%δcirrus_RMAN2 = 32% ± 4%AODcirrus_RMAN1 =0.11 ± 0.04AODcirrus_RMAN2= 0.13 ± 0.05

R-MAN510 #2

R-MAN510 #1 Depolarization ratio in a cirrus at 6.7 km


Documents

12th EMS Annual Meeting & 9th ECAC I 10-14 September 2012 I Łódź (Poland)