, same panels as for Figure

, III.12 Comparison of the retrieved IWC between V 1 and V 2 :s a m ep a n e l sa sf o r Figures III.10 and

, III.13 Histograms of the relative differences in IWC between V 1 and V 2 (a) and for every modification made in the new version: CALIPSO v4 (b), DARDAR-MASK v2 (c), the new a priori for S (d) and the new Look-up table (e)

, III.14 Comparison of the retrieved effective radius (r e )betweenV 1 and V 2 :s a m e panels as for Figures III.10, III.11 and

. Iv, NAWDEX flying team (From left to right, DLR Falcon, HALO and SAFIRE Falcon) -creadits NAWDEX

, DLR Falcon (the inset shows the transfer flights and two additional fights to the Mediterranean at the end of the campaign), (c) SAFIRE Falcon, and (d) FAAM (the British Facility for Airborne Atmospheric Measurements) BAe 146 (Schäfler et al. 2018), Tracks of research flights (RFs) for (a) HALO, (b)

, Statistics of the parallel backscatter ratio BR // measured by LNG during the entire NAWDEX campaign: histograms of occurences for all the flights (a) and global occurences as a function of distance to the aircraft (b) and altitude (c)

, Temporal evolution of the range dependency of the calibration constant for the 3 LNG channels: (a) 1064nm (IR), (b) 532nm (VIS) and (c) 355nm (UV)

, NAWDEX campaign. The variation of the calibration constants as a function of range is given by the colour gradients, IV.5 LNG channels in the 4 kilometers below the aircraft (1064nm (a), 2016.

, The ratio ? pol,m / d ? pol,m calculated in calibration regions for LNG nadirlooking measurements during NAWDEX

. .. , Uncorrected depolarization ratio measured by LNG throughout the entire NAWDEX campaign in clouds (a) and in clear sky (b), p.96

, Particular depolarization ratios measured by WALES and LNG. Comparison on the common legs. Colocation of both lidars was made using a nearest neighbour method (see IV.3.1 for more information on colocation, p.97

, Temperatures at altitudes between 5 and 12km during NAWDEX, p.98

, NAWDEX campaign (RF12). Red crosses indicate profiles for which the lidar was set to the Aeolus configuration, IV.10 LNG IR (a) and UV (b) in the 4 kilometers below the aircraft, 2016.

, IV.11 Same as Figure IV.10. October, 9 th, p.100, 2016.

, IV.12 Same as Figure IV.10. October, 1 st, p.100, 2016.

, lidar total attenuated backscatter at 532nm (c,d), detail of the available instruments in the cloud (e,f) and HSRL particular and molecular attenuated backscatter at 532nm for WALES (g,i) and at 355nm for LNG (h,j). Regions were saturation was detected on WALES 532nm and LNG 355nm channels are flagged in black, IV.13 Presentation of MIRA, WALES, RASTA and LNG measurements on the common leg of October, 13 th during the NAWDEX campaign: radar reflectivity (a,b), p.102

, lidar attenuated backscatter at 532nm (b) and LNG backscatters at 532 ad 355nm (c), IV.14 Comparison of the measurements statistic from the two platforms (HALO and RALI) on the common leg of October, 13 th :r a d a rr e fl e c t i v i t i e s( a )

, Simulation of the multiple scattering effect on an idealized cloud extinction profile for different lidars: (a) influence of the wavelength and the field of view on the backscatter signal and (b) extinction profile used for the simulation. Results were obtained using the multiscatter code, IV.15, 2008.

. .. , IV.16 Probability density of the backscatter at 355nm as a function of the backscatter at 532nm in clouds. LNG measurements from the 13 th of October, 2016 (measurements are expressed in log 10 (m ?1 .sr ?1 )), p.105

. .. , Comparison of the total attenuated backscatters at 532 and 355nm observed in ice clouds during NAWDEX: measurements, p.106

. Iv, 18 Comparison of the simulated total attenuated backscatters at 532 and 355nm observed in ice clouds during NAWDEX: simulations using a fast multiple scattering model (a) and using the single scattering model (b). Results were obtained with the model developped by, p.107, 2008.

. ;. Iv, RASTA reflectivity (a) and vertical velocity (b), p.108

, HALO (b) final target classification for the same cloud scene observed during the common leg on October, 9 th ,2 0 1 6, vol.1, p.0

, subset of the first CloudSat-CALIPSO granule of 2008: available information (lidar, radar or both) at each retrieved pixel (a), number of retrieved pixel per profile (b), number of iterations to reach convergence and difference between the two algorithms (d), IV.21 Difference in the convergence process between Varcloud and varpy

, IWC and effective radius using the radar alone (a-d), the lidar alone (e-h) and the two instruments (i-l). The median is shown in red, IV.22 Comparison between Varcloud and varpy retrieved cloud properties, statistics of the first CloudSat-CALIPSO granule of 2008: histograms of the relative differences (Varcloud-varpy)o ft h er e t r i e v e de x t i n c t i o n , N0*, lidar ratio

, List of Tables II.1 RASTA specifications with the 3 antenna configuration, p.53

, LNG specifications: characteristics of the emitter (flashlamp-pumped Nd:YAG Q-switched oscillator)

. Ii, 3 LNG specifications: characteristics of the receiver

, Presentation of the different datasets used in this section, p.73

, CloudSat-CALIPSO observations used in this study

, Minimum values of measured depolarization ratios at 355nm in the calibration regions during all the flights of NAWDEX (absolute minimum is in green)

, Relative standard deviation of the calibration constants, p.101

, Statistics of the relative differences in retrieved cloud properties due to the colocation method (values are in %)

R. Battaglia, A. , J. M. Haynes, T. L'ecuyer, and C. Simmer, Identifying multiplescattering-affected profiles in CloudSat observations over the oceans, Journal of Geophysical Research: Atmospheres, vol.113, issue.D8, 2008.

D. Baumgardner, H. Jonsson, W. Dawson, D. O'connor, and R. Newton, The cloud, aerosol and precipitation spectrometer: a new instrument for cloud investigations, Atmos. Res, pp.119-122, 0251.

D. Baumgardner and C. , Cloud Ice Properties: In Situ Measurement Challenges. Meteorological Monographs, vol.58, issue.1 -9, 2017.

A. Behrendt and T. Nakamura, Calculation of the calibration constant of polarization lidar and its dependency on atmospheric temperature, Opt. Express, vol.10, issue.16, pp.805-817, 2002.

E. Berry and G. Mace, Cloud properties and radiative effects of the asian summer monsoon derived from A-Train data, J. Geophys. Res. -Atmos, vol.119, issue.15, 2014.

B. A. Bodhaine, N. B. Wood, E. G. Dutton, and J. R. Slusser, On Rayleigh Optical Depth Calculations, J. Atmos. Ocean. Tech, vol.16, issue.11, pp.1854-1861, 1999.

S. Bony, H. L. Treut, J. Duvel, and R. S. Kandel, Satellite validation of GCMsimulated annual cycle of the earth radiation budget and cloud forcing, J. Geophys. Res. -Atmos, vol.97, issue.D16, 1992.

S. Bony and C. , How well do we understand and evaluate climate change feedback processes?, J. Climate, vol.19, 2005.
URL : https://hal.archives-ouvertes.fr/hal-00716782

A. Borovoi, A. Konoshonkin, and N. Kustova, Backscatter ratios for arbitrary oriented hexagonal ice crystals of cirrus clouds, Opt. Lett, vol.39, issue.19, 2014.

O. Boucher and C. , The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Climate Change 2013: The Physical Science Basis., IPCC, 2013.

P. R. Brown and P. N. Francis, Improved measurements of the ice water content in cirrus using a total-water probe, J. Atmos Oceanic. Technol, vol.12, 0410.

D. Bruneau, Etude de performance de lidar spatial à Haute Résolution Spectrale, 2015.

D. Bruneau and C. , 355-nm high spectral resolution airborne lidar LNG: system description and first results, Appl. Opt, vol.54, issue.29, 2015.
URL : https://hal.archives-ouvertes.fr/insu-01228951

A. Bucholtz, Rayleigh-scattering calculations for the terrestrial atmosphere, Appl. Opt, vol.34, issue.15, pp.2765-2773, 1995.

M. Ceccaldi, Combinaison de mesures actives et passives pour l ´ étude des nuages dans le cadre de la préparation à la mission EarthCARE, 2014.

M. Ceccaldi, J. Delanoë, R. J. Hogan, N. Pounder, A. Protat et al., From CloudSat-CALIPSO to EarthCare: Evolution of the DARDAR cloud classification and its comparison to airborne radar-lidar observations, J. Geophys. Res. -Atmos, vol.118, pp.7962-7981, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00835305

M. Chakroun, S. Bastin, M. Chiriaco, and H. Chepfer, Characterization of vertical cloud variability over Europe using spatial lidar observations and regional simulation, Clim. Dyn, vol.51, issue.3, p.0, 2016.
URL : https://hal.archives-ouvertes.fr/insu-01282729

T. P. Charlock and V. Ramanathan, The albedo field and cloud radiative forcing produced by a general circulation model with internally generated cloud optics, J. Atmos. Sci, vol.42, 1408.

W. Chen, C. Chiang, and J. Nee, Lidar ratio and depolarization ratio for cirrus clouds, Appl. Optics, vol.41, p.6, 2002.

H. Chepfer, V. Noel, M. Chiriaco, B. Wielicki, D. Winker et al., The potential of a Multidecade Spaceborne Lidar Record to Constrain Cloud Feedback, 2018.
URL : https://hal.archives-ouvertes.fr/insu-01779101

, J. Geophys. Res. -Atmos, vol.123, issue.10, p.0

R. Collis and P. Russel, Lidar measurements of particles and gases by elastic backscattering and differential absorption, Laser Monitoring of the Atmosphere, vol.7, pp.1-151, 1976.

S. J. Cooper, T. S. Lécuyer, P. Gabriel, A. J. Baran, and G. L. Stephens, Objective assessment of the information content of visible and infrared radiance measurements for cloud microphysical property retrievals over the global oceans. Part 1: Liquid clouds, J. Appl. Meteorol, vol.2, pp.0-4, 2006.

G. Corlay, M. Arnolfo, T. Bret-dibat, A. Lifferman, and J. Pelon, The infrared imaging radiometer for picasso-cena, Tech. rep., Tech. Rep. CNES, 2000.

C. Cornet, H. Isaka, B. Guillemet, and F. Szczap, Neural network retrieval of cloud parameters of inhomogeneous clouds from multispectral and multiscale radiance data: Feasibility study, J. Geophys. Res. -Atmos, vol.109, issue.D12, 2004.
URL : https://hal.archives-ouvertes.fr/hal-01971918

C. Davison, T. Ratvasky, and L. Lilie, Naturally aspirating isokinetic total water content probe: Wind tunnel test results and design modifications. SAE Technical Paper,S A EI, pp.2011-2049, 2011.

J. Delanoë and R. J. Hogan, A variational scheme for retrieving ice cloud properties from combined radar, lidar and infrared radiometer, J. Geophys. Res, vol.113, p.4, 2008.

J. Delanoë and R. J. Hogan, Combined CloudSat-CALIPSO-MODIS retrievals of the properties of ice clouds, J. Geophys. Res, vol.115, p.0, 2010.

J. Delanoë, R. J. Hogan, R. M. Forbes, A. Bodas-salcedo, and T. H. Stein, Evaluation of ice cloud representation in the ECMWF and UK Met Office models using CloudSat and CALIPSO data, Q. J. Roy. Meteor. Soc, vol.137, issue.661, 2011.

J. Delanoë, A. Protat, O. Jourdan, J. Pelon, M. Papazzoni et al., Comparison of airborne in-situ, airborne radar-lidar, and spaceborne radar-lidar retrievals of polar ice cloud properties sampled during the POLARCAT campaign, J. Atmos. Ocean. Tech, vol.30, issue.1, pp.57-73, 2013.

J. Delanoë, A. Protat, J. Testud, D. Bouniol, A. J. Heymsfield et al., Statistical properties of the normalized ice particle size distribution, J. Geophys. Res, vol.110, p.0, 2005.

J. Delanoë and C. , BASTA: A 95-GHz FMCW Doppler Radar for Cloud and Fog Studies, J. Atmos. Ocean. Tech, vol.33, issue.5, 2016.

J. M. Delanoë, A. J. Heymsfield, A. Protat, A. Bansemer, and R. J. Hogan, Normalised Particle Size Distribution for remote sensing application, J. Geophys. Res. -Atmos, 2014.

M. Deng, G. Mace, Z. Wang, and R. P. Lawson, Evaluation of several A-Train ice cloud retrieval products with in situ measurements collected during the SPARTICUS campaign, J. Appl. Meteorol. Clim, vol.52, pp.1014-1030, 2013.

M. Deng, G. G. Mace, Z. Wang, and H. Okamoto, Tropical composition, cloud and climate coupling experiment validation for cirrus cloud profiling retrieval using CloudSat radar and CALIPSO lidar, J. Geophys. Res. -Atmos, vol.115, issue.D10, 2010.

F. Dezitter, A. Grandin, J. Brenguier, F. Hervy, H. Schlager et al., Haic (High altitude ice crystals), Proc. Fifth AIAA Atmospheric and Space Environments Conf, 2013.

D. P. Donovan and C. , Cloud effective particles size and water content profile retrievals using combined radar and lidar observations -2. Comparison with IR radiometer and in-situ measurements of ice clouds, J. Geophys. Res, vol.106, pp.9-27, 2001.

E. Erfani and D. L. Mitchell, Developing and bounding ice particle mass-and area-dimension expressions for use in atmospheric models and remote sensing, Atmos. Chem. Phys, vol.16, p.0, 2016.

M. Esselborn, M. Wirth, A. Fix, M. Tesche, and G. Ehret, Airborne high spectral resolution lidar for measuring aerosol extinction and backscatter coefficients, Appl. Opt, vol.47, issue.3, 2008.

K. F. Evans, J. R. Wang, P. E. Racette, G. Heymsfield, and L. Li, Ice Cloud Retrievals and Analysis with the Compact Scanning Submillimeter Imaging Radiometer and the Cloud Radar System during CRYSTAL FACE, J. Appl. Meteorol, vol.44, pp.839-859, 2005.

F. Ewald, S. Grogroß, M. Hagen, L. Hirsch, J. Delanoë et al., Calibration of a 35-GHz Airborne Cloud Radar: Lessons Learned and Intercomparisons with 94-GHz Cloud Radars, Atmos. Meas. Tech. Discuss, vol.1, issue.3, 2018.
URL : https://hal.archives-ouvertes.fr/insu-02009482

R. J. Ferek, D. A. Hegg, P. V. Hobbs, P. Durkee, and K. Nielsen, Measurements of ship-induced tracks in clouds off the washington coast, J. Geophys. Res, vol.103, pp.31-40, 1998.

P. R. Field, R. J. Hogan, P. R. Brown, A. J. Illingworth, T. W. Chou-larton et al., Parametrization of ice-particle size distributions for mid-latitude stratiform cloud, Quart. J. Roy. Meteor. Soc, vol.131, 1997.

P. R. Field and C. , Secondary Ice Production: Current State of the Science and Recommendations for the, Future. Meteorological Monographs, vol.58, issue.1 -7 . 2 0, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01917785

E. Fontaine, A. Schwarzenboeck, J. Delanoë, W. Wobrock, D. Leroy et al., Constraining mass-diameter relations from hydrometeor images and cloud radar reflectivities in tropical continental and oceanic convective anvils, Atmos. Chem. Phys, vol.14, issue.20, p.0, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00937484

E. Fontaine and C. , Evaluation of radar reflectivity factor simulations of ice crystal populations from in situ observations for the retrieval of condensed water content in tropical mesoscale convective systems, Atmos. Meas. Tech, vol.10, issue.6, pp.3-9, 2017.
URL : https://hal.archives-ouvertes.fr/insu-01386457

J. S. Foot, Some observations of the optical properties of clouds, II: Cirrus. Q. J. Roy. Meteor. Soc, vol.114, issue.479, p.0, 1988.

T. H. Frame, J. Methven, N. M. Roberts, and H. A. Titley, Predictability of Frontal Waves and Cyclones. Weather and Forecasting, vol.30, 1291.

V. Freudenthaler and C. , Depolarization ratio profiling at several wavelengths in pure Saharan dust during SAMUM, Tel lus B, vol.61, issue.1, pp.5-6, 2006.

A. Garnier, J. Pelon, P. Dubuisson, M. Faivre, O. Chomette et al., Retrieval of cloud properties using CALIPSO Imaging Infrared Radiometer. Part I: effective emissivity and optical depth, J. Appl. Meteorol. Clim, vol.51, issue.7, 1407.
URL : https://hal.archives-ouvertes.fr/hal-00680921

A. Garnier, J. Pelon, M. A. Vaughan, D. M. Winker, C. R. Trepte et al., Lidar multiple scattering factors inferred from CALIPSO lidar and IIR retrievals of semi-transparent cirrus cloud optical depths over oceans, Atmos. Meas. Tech, vol.8, pp.2759-2774, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01120207

A. Garnier and C. , Retrieval of cloud properties using CALIPSO Imaging Infrared Radiometer. Part II: effective diameter and ice water path, J. Appl. Meteorol. Clim, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00680921

U. Görsdorf, V. Lehmann, M. Bauer-pfundstein, G. Peters, D. Vavriv et al., A 35-GHz Polarimetric Doppler Radar for Long-Term Observations of Cloud Parameters-Description of System and Data Processing, J. Atmos. Oceanic Technol, vol.32, issue.4, pp.675-690, 2015.

A. Guignard, C. Stubenrauch, A. Baran, and R. Armante, Bulk microphysical properties of semi-transparent cirrus from AIRS: a six year global climatology and statistical analysis in synergy with geometrical profiling data from CloudSat-CALIPSO, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01111221

, Atmos. Chem. Phys, vol.12, p.0, 0503.

I. Gultepe, A. J. Heymsfield, P. R. Field, and D. Axisa, IcePhase Precipitation. Meteorological Monographs, vol.58, p.6, 2017.

J. Hair and C. , Airborne High Spectral Resolution Lidar for profiling aerosol optical properties, Appl. Opt, vol.47, issue.36, p.1, 2008.

S. Ham, S. Kato, and F. G. Rose, Examining impacts of mass-diameter (m-D) and area-diameter (A-D) relationships of ice particles on retrievals of effective radius and ice water content from radar and lidar measurements, J. Geophys. Res. -Atmos, vol.122, p.0, 2017.

A. J. Heymsfield, A. Bansemer, P. R. Field, S. L. Durden, J. L. Stith et al., Observations and Parameterizations of Particle Size Distributions in Deep Tropical Cirrus and Stratiform Precipitating Clouds: Results from In Situ Observations in TRMM Field Campaigns, J. Atmos. Sci, vol.59, issue.24, 2002.

A. J. Heymsfield and L. M. Miloshevich, Homogeneous Ice Nucleation and Supercooled Liquid Water in Orographic Wave Clouds, J. Atmos. Sci, vol.50, issue.15, 1993.

A. J. Heymsfield, C. Schmitt, and A. Bansemer, Ice Cloud Particle Size Distributions and Pressure-Dependent Terminal Velocities from In Situ Observations at Temperatures from 0 ? to -86 ? C, J. Atmos. Sci, vol.70, issue.12, 2013.

A. J. Heymsfield, C. Schmitt, A. Bansemer, and C. H. Twohy, Improved Representation of Ice Particle Masses Based on Observations in Natural Clouds, J. Atmos. Sci, vol.67, p.0, 2010.

A. J. Heymsfield, D. Winker, M. Avery, M. Vaughan, G. Diskin et al., Relationships between Ice Water Content and Volume Extinction Coefficient from In Situ Observations for Temperatures from 0 ? to -86 ? C: Implications for Spaceborne Lidar Retrievals, J. Appl. Meteorol. Clim, vol.53, 2014.

A. J. Heymsfield and C. , Cirrus Clouds. Meteorological Monographs, vol.58, 2017.

R. J. Hogan, A Variational Scheme for Retrieving Rainfall Rate and Hail Reflectivity Fraction from Polarization Radar, J. Appl. Meteorol. Clim, vol.46, issue.10, pp.1544-1564, 2007.

R. J. Hogan, Fast Lidar and Radar Multiple-Scattering Models. Part I: SmallAngle Scattering Using the Photon Variance-Covariance Method, J. Atmos. Sci, vol.65, issue.12, 2008.

R. J. Hogan, M. P. Mittermaier, and A. J. Illingworth, The retrieval of ice water content from radar reflectivity factor and temperature and its use in evaluating a mesoscale model, J. Appl. Meteor. Climatol, vol.45, pp.0-1, 2006.

G. Hong, P. Minnis, D. Doelling, J. K. Ayers, and S. Sun-mack, Estimating effective particle size of tropical deep convective clouds with a look-up table method using satellite measurements of brightness temperature differences, J. Geophys. Res, vol.117, issue.D06207, 2012.

Y. Hong and G. Liu, Assessing the Radiative Effects of Global Ice Clouds Based on CloudSat and CALIPSO Measurements, J. Climate, vol.29, issue.D06207, pp.7651-7674, 2016.

Y. Hu and C. , CALIPSO/CALIOP Cloud Phase Discrimination Algorithm, J. Atmos. Oceanic Technol, vol.26, issue.11, 2009.

L. Huang, J. H. Jiang, Z. Wang, H. Su, M. Deng et al., Climatology of cloud water content associated with different cloud types observed by A-Train satellites, 2015.

, J. Geophys. Res. -Atmos, vol.120, issue.9

A. J. Illingworth and C. , The EarthCARE Satellite: The Next Step Forward in Global Measurements of Clouds, Aerosols, Precipitation, and Radiation, Bull. Amer. Meteor. Soc, vol.96, issue.8, pp.1311-1332, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01097699

F. M. Kahnert, Numerical methods in electromagnetic scattering theory, J. Quant. Spectrosc. Radiat. Transfer, vol.7, pp.321-328, 2003.

Z. A. Kanji, L. A. Ladino, H. Wex, Y. Boose, M. Burkert-kohn et al., Overview of Ice Nucleating Particles. Meteorological Monographs, vol.58, pp.1-1, 2017.

S. Kato and N. Loeb, Top-of-atmosphere shortwave broadband observed radiance and estimated irradiance over polar regions from Clouds and the Earth's Radiant Energy System (CERES) instruments on Terra, J. Geophys. Res, issue.D07202, p.110, 2005.

R. G. Knollenberg, The Optical Array: An Aternative to scaterring or extinction for airborne particle size determination, J. Appl. Meteorol, vol.9, pp.6-7, 1970.

A. V. Korolev, G. A. Isaac, and J. Hallett, Ice particle habits in Arctic clouds, Geophys. Res. Lett, vol.26, issue.9, p.0, 2003.

R. P. Lawson, D. O'connor, P. Zmarzly, K. Weaver, B. Baker et al., The 2d-s (stereo) probe: Design and preliminary tests of a new airborne, highspeed, high-resolution particle imaging probe06, J. Atmos. Ocean. Tech, vol.23, pp.1-4, 2006.

L. Li, G. M. Heymsfield, L. Tian, and P. E. Racette, Measurements of Ocean Surface Backscattering Using an Airborne 94-GHz Cloud Radar -Implication for Calibration of Airborne and Spaceborne W-Band Radars, J. Atmos. Oceanic Technol, vol.22, issue.7, pp.1033-1045, 2005.

K. Libbrecht, The physics of snow crystals. Reports on progress in physics, p.68, 2005.

H. J. Liebe, An updated model for millimeter wave propagation in moist air, Radio Science, vol.20, issue.5, p.0, 1069.

K. N. Liou and P. Yang, Light Scattering by Ice Crystals: Fundamentals and Applications, 2016.

C. L. Liu and A. Illingworth, Toward more accurate retrievals of ice water content from radar measurements of clouds, J. Appl. Meteorol, vol.39, p.1, 2000.

N. Loeb, S. Kato, K. Loukachine, and N. Manalo-smith, Angular distribution models for top-of-atmosphere radiative flux estimation from the Clouds and the Earth's Radiant Energy System instrument on the Terra Satellite, Part I: Methodology. J. Atmos. Oceanic Technol, vol.22, pp.3-3, 2005.

N. G. Loeb, B. A. Wielicki, D. R. Doelling, G. L. Smith, D. F. Keyes et al., Toward optimal closure of the earth's top-of-atmosphere radiation budget, Journal of Climate, vol.22, issue.3, pp.748-766, 2009.

K. Marvel, M. Zelinka, S. A. Klein, C. Bonfils, P. Caldwell et al., External influences on modeled and observed cloud trends, J. Climate, vol.28, issue.12, pp.4820-4840, 2015.

G. M. Mcfarquhar and C. , Processing of Ice Cloud In Situ Data Collected by Bulk Water, Scattering, and Imaging Probes: Fundamentals, Uncertainties, and Efforts toward Consistency. Meteorological Monographs, vol.58, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01980968

M. Mech, E. Orlandi, S. Crewell, F. Ament, L. Hirsch et al., HAMP -the microwave package on the High Altitude and LOng range research aircraft (HALO), vol.7, 2014.

G. A. Meehl, J. M. Arblaster, and C. Tebaldi, Understanding future patterns of increased precipitation intensity in climate model simulations, Geophysical Research Letters, issue.18, p.32, 2005.

G. Mie, Beiträge zur Optik trüber Medien, speziell kolloidaler Metallösungen, Annalen der Physik, vol.330, issue.3, p.0, 1908.

G. Mioche, M. O.-jourdan, J. Ceccaldi, and . Delanoë, Variability of mixedphase clouds in the arctic with a focus on the svalbard region: a study based on spaceborne active remote sensing, Atmos. Chem. and Phys, vol.15, issue.5, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01064206

M. I. Mishchenko, G. Videen, V. A. Babenko, N. G. Khlebtsov, and T. Wriedt, T-matrix theory of electromagnetic scattering by particles and its applications: A comprehensive reference database, J. Quant. Spectrosc. Radiat. Transfer, vol.88, pp.357-397, 2004.

D. Mitchell, Use of mass-and area-dimensional power laws for determining precipitation particle terminal velocity, J. Atmos. Sci, vol.53, 1710.

J. Nelson and C. Knight, Snow Crystal Habit Changes Explained by Layer Nucleation, J. Atmos. Sci, vol.55, issue.8, 1998.

H. Okamoto, S. Iwasaki, M. Yasui, H. Horie, H. Kuroiwa et al., An algorithm for retrieval of cloud microphysics using 95-GHz cloud radar and lidar, J. Geophys. Res, vol.108, issue.D7, p.0, 2003.

R. Pachauri and L. Meyer, Contribution of Working Groups I II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, IPCC AR5 Synthesis Report website, vol.151, 2014.

U. Paffrath, C. Lemmerz, O. Reitebuch, B. Witschas, I. Nikolaus et al., The Airborne Demonstrator for the Direct-Detection Doppler Wind Lidar AL-ADIN on ADM-Aeolus. Part II: Simulations and Rayleigh Receiver Radiometric Performance, J. Atmos. Ocean. Tech, vol.26, issue.12, pp.2516-2530, 2009.

C. M. Platt, Remote Sounding of High Clouds: I. Calculation of Visible and Infrared Optical Properties from Lidar and Radiometer Measurements, J. Appl. Meteorol, vol.18, issue.9, pp.1130-1143, 1979.

C. M. Platt, Remote Sounding of High Clouds: III. Monte Carlo Calculations of Multiple-Scattered Lidar Returns, J. Atmos. Sci, vol.38, issue.1, pp.6-7, 1981.

C. M. Platt, S. Scott, and A. Dilley, Remote Sounding of High Clouds: IV. Optical properties of midlatitude and tropical cirrus, J. Atmos. Sci, vol.44, pp.9-16, 1987.

C. M. Platt, S. A. Young, R. T. Austin, G. R. Patterson, D. L. Mitchell et al., LIRAD Observations of tropical cirrus clouds in MCTEX. Part I: Optical properties and detection of small particles in cold cirrus, J. Atmos. Sci, vol.59, pp.3145-3162, 2002.

K. A. Powell and C. , CALIPSO Lidar Calibration Algorithms. Part I: Nighttime 532-nm Parallel Channel and 532-nm Perpendicular Channel, J. Atmos. Ocean. Tech, vol.26, issue.10, pp.2015-2033, 2009.

H. R. Pruppacher and J. D. Klett, Microphysics of Clouds and Precipitation, 2010.

J. Reichardt, S. Reichardt, A. Behrendt, and T. J. Mcgee, Correlations among the optical properties of cirrus-cloud particles: Implications for spaceborne remote sensing, Geophysical Research Letters, vol.29, issue.14, 2002.

C. D. Rodgers, Inverse methods for atmospheric sounding: Theory and practice, vol.238, p.pp, 2000.

P. B. Russell, T. J. Swissler, and M. P. Mccormick, Methodology for error analysis and simulation of lidar aerosol measurements, Appl. Opt, vol.18, issue.22, 1979.

M. Saito, H. Iwabuchi, P. Yang, G. Tang, M. D. King et al., Ice particle morphology and microphysical properties of cirrus clouds inferred from combined CALIOP-IIR measurement, J. Geophys. Res. -Atmos, vol.122, 2017.

A. Schäfler and C. , The North Atlantic Waveguide and Downstream Impact Experiment, Bull. Amer. Meteor. Soc, vol.99, issue.8, 2018.

C. She, Spectral structure of laser light scattering revisited: bandwidths of nonresonant scattering lidars, vol.40, 2001.

O. Sourdeval, L. C. Labonnote, A. J. Baran, and G. Brogniez, A methodology for simultaneous retrieval of ice and liquid water cloud properties. part 1: Information content and case study, Q. J. Roy. Meteor. Soc, vol.141, 0870.
URL : https://hal.archives-ouvertes.fr/hal-01955832

O. Sourdeval, L. C. Labonnote, A. J. Baran, J. Mülmenstädt, and G. Brogniez, 2016: A methodology for simultaneous retrieval of ice and liquid water cloud properties. part 2: Near-global retrievals and evaluation against a-train products, Q. J. Roy. Meteor. Soc, vol.142, issue.701, p.1

T. M. Stein, J. Delanoë, and R. J. Hogan, A comparison among four different retrieval methods for ice-cloud properties using data from cloudsat, calipso, and modis, J. Appl. Meteorol. Climatology, vol.50, pp.1952-1969, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00590875

G. L. Stephens, Cloud feedbacks in the climate system: A critical review, J. Clim, vol.18, issue.2, pp.237-273, 2005.

G. L. Stephens and C. , The Cloudsat Mission and the A-Train, Bull. Amer. Meteor. Soc, vol.83, issue.12, pp.1771-1790, 2002.

T. Stocker and C. , Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, IPCC 2013: The Summary for Policymakers, 2013.

J. Strandgren, L. Bugliaro, F. Sehnke, and L. Schröder, Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks, Atmos. Meas. Tech, vol.10, issue.9, pp.3547-3573, 2017.

C. J. Stubenrauch, S. Cros, A. Guignard, and N. Lamquin, A 6-year global cloud climatology from the Atmospheric InfraRed Sounder AIRS and a statistical analysis in synergy with CALIPSO and CloudSat, Atmos. Chem. and Phys, vol.10, issue.15, pp.7197-7214, 2010.
URL : https://hal.archives-ouvertes.fr/hal-01135800

C. J. Stubenrauch, R. Holz, A. Chédin, D. L. Mitchell, and A. J. Baran, Retrieval of cirrus ice crystal sizes from 8.3 and 11.1µ memissivitiesdeterminedb ytheimpro v ed initialization inversion of TIROS-N Operational Vertical Sounder observations, J. Geophys. Res, vol.104, pp.3-8, 1999.

C. J. Stubenrauch, W. B. Rossow, F. Chéruy, A. Chédin, and N. A. Scott, Clouds as Seen by Satellite Sounders (3I) and Imagers (ISCCP). Part I: Evaluation of Cloud Parameters, J. Climate, vol.12, issue.8, p.0, 1999.

C. J. Stubenrauch and C. , Assessment of Global Cloud Datasets from Satellites: Project and Database Initiated by the GEWEX Radiation Panel, Bull. Amer. Meteor. Soc, vol.94, issue.7, pp.1031-1049, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01091218

S. Sun-mack and C. , Integrated cloud-aerosol-radiation product using CERES, MODIS, CALIPSO, and CloudSat data, Proc. SPIE, p.6745, 2007.

F. Szczap, C. Cornet, A. Alqassem, Y. Gour, L. C. -labonnote et al., AIP Conference Proceedings, vol.1531, issue.A3, pp.139-142, 2013.

D. G. Tan and C. , The ADM-Aeolus wind retrieval algorithms. Tel lus A: Dynamic Meteorology and Oceanography, vol.60, pp.191-205, 2008.
URL : https://hal.archives-ouvertes.fr/meteo-00293975

J. Testud, S. Oury, R. A. Black, P. Amayec, and X. K. Dou, The concept of "normalized" distribution to describe raindrop spectra: a tool for cloud physics and cloud remote sensing, J. Appl. Meteor, vol.40, p.1, 2001.

C. H. Twohy, A. J. Schanot, and W. A. Cooper, Measurement of condensed water content in liquid and ice clouds using an airborne counterflow virtual impactor, J. Atmos. Oceanic Technol, vol.14, issue.2, 1997.

S. Twomey, The influence of pollution on the shortwave albedo of clouds, J. Atmos. Sci, vol.34, 1149.

J. Um, G. M. Mcfarquhar, Y. P. Hong, S. Lee, C. H. Jung et al., Dimensions and aspect ratios of natural ice crystals, vol.15, p.0, 2015.

G. Vaughan and C. , Cloud Banding and Winds in Intense European Cyclones: Results from the DIAMET Project, Bull. Amer. Meteor. Soc, vol.96, issue.2, pp.9-265, 2015.

M. Vaughan, S. Young, D. Winker, K. Powell, A. Omar et al., Fully automated analysis of space-based lidar data: an overview of the CALIPSO retrieval algorithms and data products, Proc. SPIE, vol.5575, pp.6-9, 2004.

K. L. Verlinden, D. W. Thompson, and G. L. Stephens, The Three-Dimensional Distribution of Clouds over the Southern Hemisphere High Latitudes, J. Climate, vol.24, issue.22, pp.5799-5811, 2011.

Z. Wang and K. Sassen, Cirrus cloud microphysical property retrieval using lidar and radar measurements -1. Algorithm description and comparison with in situ data, J. Appl. Meteorol, vol.41, 0218.

D. M. Winker, C. A. Hostetler, M. A. Vaughan, and A. H. Omar, Caliop algorithm theoretical basis document part 1 : Caliop instrument, and algorithms overview, 2006.

M. Wirth, A. Fix, P. Mahnke, H. Schwarzer, F. Schrandt et al., The airborne multi-wavelength water vapor differential absorption lidar WALES: system design and performance, Applied Physics B, vol.96, issue.1, 2009.

J. E. Yorks, D. L. Hlavka, W. D. Hart, and M. J. Mcgill, Statistics of cloud optical properties from airborne lidar measurements, J. Atmos. Ocean. Tech, vol.28, pp.869-883, 2011.

M. D. Zelinka, S. A. Klein, and D. L. Hartmann, Computing and Partitioning Cloud Feedbacks Using Cloud Property Histograms. Part I: Cloud Radiative Kernels, J. Climate, vol.25, p.3, 2012.

M. D. Zelinka, S. A. Klein, and D. L. Hartmann, Computing and Partitioning Cloud Feedbacks Using Cloud Property Histograms. Part II: Attribution to Changes in Cloud Amount, Altitude, and Optical Depth, J. Climate, p.25, 2012.

M. D. Zelinka, D. A. Randall, M. J. Webb, and S. A. Klein, Clearing clouds of uncertainty, Nature Climate Change, vol.7, p.0, 2010.

Y. Zhang, A. Macke, and F. Albers, Effect of crystal size spectrum and crystal shape on stratiform cirrus radiative forcing, Atmos. Res, vol.52, pp.9-16, 1999.

Y. P. Zhou, K. Xu, Y. C. Sud, and A. K. Betts, Recent trends of the tropical hydrological cycle inferred from global precipitation climatology project and international satellite cloud climatology project data, Journal of Geophysical Research, p.116, 2011.