Determination of atmospheric structures, aerosol optical properties and particle type with the R-MAN 510 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 September 2012 I Łódź (Poland)

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 12th EMS Annual Meeting & 9th ECAC I September 2012 I Łódź (Poland) 2

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 12th EMS Annual Meeting & 9th ECAC I September 2012 I Łódź (Poland) 3

Proof of automatic ash detection monitoring over Europe in 2010 with the ALS aerosol lidar 12th EMS Annual Meeting & 9th ECAC I September 2012 I Łódź (Poland) 4

Motivation: identification of aerosols Augustine eruption, [Sassen et al,GRL, 2007] Pollution aerosol Ice crystals Desert dust  p >30% 10%<  p <25%  p ~0%  p up to 100% 12th EMS Annual Meeting & 9th ECAC I September 2012 I Łódź (Poland) We are lucky, dust and ash are unspherical 5

Characterizing aerosols with depolarization and Raman channels ASH 12th EMS Annual Meeting & 9th ECAC I September 2012 I Łódź (Poland) Example of measurements realized by a raman lidar prototype during the Eyjafjallajokull eruption Clear separation between ashs, pollution aerosols and dusts 6

RMan 510 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 12th EMS Annual Meeting & 9th ECAC I September 2012 I Łódź (Poland) 7

RMan 510 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 N 2 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 12th EMS Annual Meeting & 9th ECAC I September 2012 I Łódź (Poland) 8

Ranges for the three channels Low overlap 98% at 150m, full at 300m 30s (Day/Night) 10min (Day/Night) PR2 //10 / 16.6 km13 / 21 km PR2  7 / 17 km11.2 / 21 km PR2 Raman1.8 / 10 km3.5 / 18 km Rayleigh fit 10mn average 12th EMS Annual Meeting & 9th ECAC I September 2012 I Łódź (Poland) An extended range Range (m) Overlap 9

Elastic  signal Detection of structures (Aerosol gradients, aerosol/cloud layers ) N 2 -Raman signal Elastic // signal Analog elastic // Level 0 Raw data Level 1 PR2 Level 1.5 Detection of structures and optical properties Photocounting elastic // photocomptage Analog elastic  Analog N 2 -Raman analogique Photocounting N 2 -Raman Photocounting elastic  photocomptage 12th EMS Annual Meeting & 9th ECAC I September 2012 I Łódź (Poland) Processing chain: structure detection 10

R-Man 510 lidar Gradient + 2D method No need for specific thresholding ALS lidar 1D gradient method Stable layer Convective layer Residual layer 12th EMS Annual Meeting & 9th ECAC I September 2012 I Łódź (Poland) Improvment of RMan 510 gradient detection 11

RMAN ALS 12th EMS Annual Meeting & 9th ECAC I September 2012 I Łódź (Poland) Cloud detection Relative error compared with reference (%) Pourcentage of good detection 12

Getting more structure details with dual polarization Detection on parallel channel Detection on BOTH polarization channels 12th EMS Annual Meeting & 9th ECAC I September 2012 I Łódź (Poland) 13

Elastic  signal Volume depolarization ratio Elastic total signal Extinction, backscatter coefficients, AOD Detection of structures (Aerosol gradients, aerosol/cloud layers ) N 2 -Raman signal Elastic // signal Particle depolarization ratio Analog elastic // Level 0 Raw data Level 1 PR2 Level 1.5 Detection of structures and optical properties Photocounting elastic // photocomptage Analog elastic  Analog N 2 -Raman analogique Photocounting N 2 -Raman Photocounting elastic  photocomptage 12th EMS Annual Meeting & 9th ECAC I September 2012 I Łódź (Poland) Processing chain : optical properties 14

Optical properties of aerosols and clouds : extinction coefficient and optical depth NIGHT DAY RMSE = 1.7 % R²= 0.93 Comparison of two RMAN 510 : RMSE ~ 10 % Comparison of AOD retrieved with sunphotometer : - RMAN1 - RMAN2 12th EMS Annual Meeting & 9th ECAC I September 2012 I Łódź (Poland) 15

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  p ) Depolarization ratio  p gives information on particle sphericity (low  p for spherical particle ≠ high  p for non spherical particles) Optical properties of aerosols and clouds : depolarization ratio 20-30% Dust aerosols 50-60% Cirrus clouds 12th EMS Annual Meeting & 9th ECAC I September 2012 I Łódź (Poland) 16

Elastic  signal Volume depolarization ratio Elastic total signal Extinction, backscatter coefficients, AOD Detection of structures (Aerosol gradients, aerosol/cloud layers ) N 2 -Raman signal Elastic // signal Particle depolarization ratio Aerosol/cloud typing Analog elastic // Level 0 Raw data Level 1 PR2 Level 1.5 Detection of structures and optical properties Photocounting elastic // photocomptage Analog elastic  Analog N 2 -Raman analogique Photocounting N 2 -Raman Photocounting elastic  photocomptage Processing chain : classificaction 12th EMS Annual Meeting & 9th ECAC I September 2012 I Łódź (Poland) 17

Typing can be done unambiguously thanks to lidar ratio (raman channel) vs depolarization ratio diagram (Burton et al.) 4 types of aerosols: – Continental pollution – Maritime aerosols – Dust mix – Pure dust / Volcanic ashes Source Burton et al., AMTD, th EMS Annual Meeting & 9th ECAC I September 2012 I Łódź (Poland) Aerosol typing in RMAN

Aerosol/cloud classification Scattering 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 Structures 2 Shape (  p ) 3 Lidar ratio +  p 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 … 12th EMS Annual Meeting & 9th ECAC I September 2012 I Łódź (Poland) 19

All together ! Dust detection Signal 355nm Signal 355nm 12th EMS Annual Meeting & 9th ECAC I September 2012 I Łódź (Poland) Lidar Ratio 20

Depolar+ N 2 Raman for maximum data availability and detection of aerosols RMAN 510 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. 12th EMS Annual Meeting & 9th ECAC I September 2012 I Łódź (Poland) Conclusion and perspective 21

Thank you for your attention 12th EMS Annual Meeting & 9th ECAC I September 2012 I Łódź (Poland) 22

From Lidar measurement to mass concentration of ash particles with accuracy of 70% Lidar data Local observations of chemical properties of erupted ashes* Optical refraction Particle diameter + = Mass concentration of ash layers Accuracy of 70% (Chazette et al 2011) Conversion factor 0.65 to 1g/m2 at 355nm Wienhold, 2010; Chazette et al,2010) Pollution layers Accuracy of 19 to 32% (Royer et al, 2010) Conversion factor 4.5g/m2 at 355nm (Rault et al, 2009) 12th EMS Annual Meeting & 9th ECAC I September 2012 I Łódź (Poland) Quantifying the threat 23

Cloud mask Signal 355nm Cirrus Middle Low cloud 12th EMS Annual Meeting & 9th ECAC I September 2012 I Łódź (Poland) 24

Retrieval of depolarization ratio in cirrus δ cirrus_RMAN1 = 30% ± 6% δ cirrus_RMAN2 = 32% ± 4% AOD cirrus_RMAN1 =0.11 ± 0.04 AOD cirrus_RMAN2 = 0.13 ± 0.05 R-MAN 510 #2 R-MAN 510 #1Depolarization ratio in a cirrus at 6.7 km 12th EMS Annual Meeting & 9th ECAC I September 2012 I Łódź (Poland) 25