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M.Chiriaco, H.Chepfer, V.Noel, A.Delaval, M.Haeffelin

Lidar/Infrared radiometer coupling for a better determination of particle size in ice cloud. M.Chiriaco, H.Chepfer, V.Noel, A.Delaval, M.Haeffelin Laboratoire de Météorologie Dynamique, IPSL, France P.Yang, Texas University P.Dubuisson, ELICO, France.

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M.Chiriaco, H.Chepfer, V.Noel, A.Delaval, M.Haeffelin

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  1. Lidar/Infrared radiometer coupling for a better determination of particle size in ice cloud M.Chiriaco, H.Chepfer, V.Noel, A.Delaval, M.Haeffelin Laboratoire de Météorologie Dynamique, IPSL, France P.Yang, Texas University P.Dubuisson, ELICO, France Science Team CALIPSO – March 2003

  2. A better determination of particle size in ice cloud Goal : improving split window technique • classical split window technique • improvement from 532nm lidar : scene identification • improvement from lidar depolarisation : shape constrain • improvement from 10.6µm lidar : where is the most absorbing layer within the cloud ? Synthesis of 5 cases studies Science Team CALIPSO – March 2003

  3. Classical split window technique Brightness temperature difference between 2 IR channels : TB(λ1)-TB(λ2)=f(TB(λ1)) Sensitivity to crystal sizes and shapes (3) sph. liq 6µm sph. ice 6µm sph. liq 12µm sph. ice 12µm TB(λ1)-TB(λ2) Optical properties (4) • Asymmetry factor • Single scattering albedo • Extinction cross section Clear sky T(λ1) Opaque cloud Uncertainty on cloud temperature (2) Uncertainty on scene identification (1) Science Team CALIPSO – March 2003

  4. Improvements MEASUREMENTS Temperature differences between 2 channels IR radiometer : brightness temperatures Retrieved several possible values of r, depends on the shape hypothesis Best solution for (r,Q) (1)scene identification (2) cloud temperature Lidar + radiosonde Radiative transfert (P.Dubuisson, ELICO) Absorption & scattering Temperature differences between 2 channels (3) Shape Q deduced from lidar depolarization (V.Noël) (4) Optical properties for non spherical particles (P.Yang, Texas Univ.) SIMULATIONS improvements Science Team CALIPSO – March 2003

  5. Aqua Cloudsat Calipso Parasol Aura Applications TERRA/MODIS λ1 = 8.65µm λ2 = 11.15µm λ3 = 12.05µm Instrumented site of Palaiseau/France : SIRTA ~ IIR SIRTA 532 nm lidar LNA 10.6 µm lidar LVT distance : 200m Science Team CALIPSO – March 2003

  6. SIRTA Cloud identification : improvement from 532nm lidar (a) LNA 220K < Tcloud < 250K MODIS TB,SIRTA> Tcloud semi-transparent cloud TB,SIRTA = 265K Science Team CALIPSO – March 2003

  7. T8.7µm-T12µm T8.7µm-T10.5µm T10.5µm-T12µm T10.5µm T8.7µm T8.7µm Cloud identification : improvement from 532nm lidar (b) • Clear sky temperature fixed owing to lidar • Opaque cloud temperature fixed owing to lidar : cloud top • Each curve corresponds to a cloud defined by a (r, Q) value 17µm<r <19µm for 0.15 < shape ratio Q < 0.5 Science Team CALIPSO – March 2003

  8. SIRTA Shape constrain : improvement from lidar depolarization (a) LNA Tcloud= 220K TB,SIRTA> Tcloud semi-transparent cloud MODIS TB,SIRTA= 260K Science Team CALIPSO – March 2003

  9. ΔP R L Shape ratio Shape constrain : improvement from lidar depolarization (b) Depolarization ratio Shape ratio Q classe I : Q<0.05 classe II : 0.05<Q<0.7 classe III : 0.7<Q<1.05 classe IV : Q>1.05 Noël & al, Applied optics, 2002 Science Team CALIPSO – March 2003

  10. Shape constrain : improvement from lidar depolarization (c) Cloud identification (backscattering) : 31<r<76µm for 0.15<Q<2 Shape constrain (depolarization) : 31<r<46µm for 0.7<Q<2 Lidar depolarization Science Team CALIPSO – March 2003

  11. 10.6 µm lidar SIRTA (Average over 5 minutes) Absorption profile : improvement from 10.6 µm lidar (a) Where is the most absorbing layer in the cloud ? Cloud top temperature? Cloud base temperature? Cloud middle temperature? 532 nm lidar SIRTA Science Team CALIPSO – March 2003

  12. Absorption profile : improvement from 10.6 µm lidar (b) We want an absorption profile in infrared to estimate the most absorbing layer within the cloud position of the cold foot in split window (1) negligible if r>100µm negligible for n<103/m3 if r<100µm k0.5 = k10 (P.Yang) α = n.Q.(π.r²) Qsca,0.5 = 2 for r > 1µm (P.Yang) We finally have Qabs Science Team CALIPSO – March 2003

  13. Absorption profile : improvement from 10.6 µm lidar (c) 532nm maximum : 8300m +/- 15m 10.6µm maximum : 7900m +/- 50m ≠ Qabs maximum : 7300m concentration is not considered : final result of absorption? This difference could change the temperature of opaque cloud in simulations (position of cold foot), and influence the final result of particle size Science Team CALIPSO – March 2003

  14. Synthesis of 5 cases studied cloud type (532nm lidar) 3 wavelength constrain shape constrain 10.6µm lidar results semi transparent T=220K TB=260K 2002/03/05 31<r<76µm 31<r<46µm no measurements 0.7<Q<2 2002/04/02 no solutionno solution no measurements 0.05<Q< ∞ 2002/10/08 17<r<19µm no improvement 0.15<Q<0.5 2002/10/14 23<r<57µm 23<r<28µm 0.15<Q<0.9 0.7<Q<0.9 2002/11/06 21<r<57µm r~25µm 0.15<Q<0.9 Q=0.9 relatively opaque T=230K TB=239K Max 532nm : 7000m Max 10.6µm : 7100m Max Qabs,10 : 7500m semi transparent 220<T<250K TB=265K semi transparent 240<T<250K TB=245K Max 532nm : 6000m Max 10.6µm : 6000m Max Qabs,10 : 5800m Max 532nm : 8300 Max 10.6µm : 7900 Max Qabs,10 : 7000 semi transparent+low one Thigh=240K Tlow=265K TB=252K Science Team CALIPSO – March 2003

  15. Perspectives Further analysis of 10.6µm cases Validation of the method with in situ measurements : data from CRYSTAL-Face field experiment (July 2002) Comparison with method based on more wavelength (Minnis, 1998) Systematic analysis over SIRTA CALIPSO (2005) : application of the method to the first spatial observations Science Team CALIPSO – March 2003

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