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Topic 6 Properties of cloud particles, e.g. optical parameters, morphology, density Bundke, Gayet, Mőhler, Wendisch, Stetzer, Petters Introduction: (Motivation / Importance of knowing the parameters) – Microstructure of ice crystals as function of T, S – Atmospheric optics (some nice pictures) – Terminal velocity – Morphology and radiation – Calculation of scattering – Influences on interpretation of remote sensing http://www.snowcrystals.com/ Introduction Shape Temperature Variations of ice crystal habit with temperature and Supersaturation e.g. Kobayashi (1961) Magoo and Lee (1966), Rottner and Vali (1974) http://www.snowcrystals.com/ Heymsfiled 2000 Morphology and density Morphology (coefficient of drag) and density (gravitational force) govern the sedimentation velocity (final velocity) and thus the collecting/coagulation efficiency for microphysical modeling (growth of hydrometeors by collision, coalescence, riming,.. ) » Aerodynamic shape factor / eff. Diameter » Particle mass / eff. Volume Microstructure influences on » radiative effects » cloud lifetime » precipitation Westbrook, 2008; Heymsfield, 1999 Cirrus particle terminal velocity I Mean Doppler velocity (cm 1/s; top) and equivalent radar reflectivity (in dBZe) Heymsfield 2000 Cirrus particle terminal velocity II Heymsfield ,2000 Cirrus particle terminal velocity III Westbrook, 2008 Introduction atmospheric optics http://www.snowcrystals.com/ Radiative interactions • Understanding of the radiation budget of earth and atmosphere system, and hence its climate, must begin with an understanding of the scattering and the absorption properties of cloud particles, a large number of cloud particles are non-spherical ice crystals. Basic scattering, absorption and polarizing data for these particles are needed. (Liou 2000) Morphology Optically and Radiation thin Cirrus, SOLAR Thin CIRRUS solar = 21° Ice absopion bands s Sphere F s 2. Auswirkungen auf Strahlungsenergiebudget – Kristallform Wendisch et al. [2005] Morphology and thick Optically Thick Radiation CIRRUS = 78° solar Cirrus, Ice absopion bands SOLAR s Sphere F s 2. Auswirkungen auf Strahlungsenergiebudget – Kristallform Wendisch et al. [2005] MorphologyOptically and Thin Radiaion thin Cirrus, THERMAL IR CIRRUS Cloudless thermal F Sphere 2. Auswirkungen auf Strahlungsenergiebudget – Kristallform Wendisch et al. [2007] Interpretation of reflectance R = 22 µm, = 5.2 measuremets R = 24 µm, = 3.3 eff eff 3. Fernerkundung: Probleme – Zirren (Kristallform) Eichler et al. [2009] Calculation of electromagnetic scattering and absorption • Last Century: “soft sphere”/ “fluffy sphere” approach equal mass sphere (blend of ice and air) with dielectric properties derived using Maxwell Garnet (1904) or Bruggemann (1935) mixing rule • Now: – FDTD finite-difference time domain method (Taflove and Hagness 2000) – CDA = coupled dipole approximation also known as DDA=discrete dipole approximation (Purcell and Penypacker, 1973; Dungey and Bohren, 1990;….; Petty and Huang, 2009) Mie Theory scattered wave Incident wave wave inside particle Ansatz: wave is divided into three parts: a) incident light, Diss. Hermann Vortisch b) wave inside the scattering particle c) scattered wave (far field) •All three parts have to fulfill the wave equation •The particle surface marks a discontinuity of the optical parameters (complex refractive index) •To be found: a solution of the coupled differential (Maxwell) equations for each part •Boundary condition: Tangential component of the fields inside and outside the particle match continuously at the particle surface Mie linear scale Phase functions (polar plot) Log scale CDA Model Examples of dipole representations of soft spheres ranging in density from 100% (solid ice) to 2%. Each contains approximately 5000 dipoles. Model snow particle structures. (a) Needle aggregate NA, (b) dendrite aggregate DA1, (c) dendrite aggregate DA2, (d) dendrite aggregate DA3. (Petty et al, 2009) CDA CPU TIME Examples of the dependence of the CPU time requirement on N for different soft sphere ice fractions, using DDSCAT (dashed curves) and SCDScat (solid curves). These calculations are for 35.6 GHz and d 5 50 mm on a 1.8-GHz Intel Core Duo workstation. Petty et al 2009 Soft-ice approx. Comparison of computed (top) backscattering cross sections, for soft sphere ice particles with rliq = 1.0 mm. Methods used include CDA with d=64 µm (large dots), Bruggeman (dotted line),Maxwell Garnett with ice as inclusion (solid line) and with air as inclusion (long dashed), and the exponential rule with n = 0.85 (short dashed). Petty et al 2009 Remote Sensing Retrievals • As example: Parameters/ Assumptions for global analysis of Cloud droplet effective radius (CDR) using depolarization (e.g. POLDER instrument) • (Cirrus Cloud optical and microphysical properties determined from AIRS infrared spectra (Yue and Liou 2008) Influence of non-spherical particles on the interpretation of remote sensing devices • Lidar – (Liou and Takano 1994) – (Hu et al. Depolarization signature -> phase discrimination) • Radar (Austin 2009, Heymsfield 2005) – Only ice spheres were assumed for retrieval (e.g. CloudSat) • Satellite retrievals of cloud properties: examples.. • POLDER (Parol 2004, Bréon 1998) • CloudSat (Stephens 2002,2008, Grecu and Olson 2008) • TRMM Tropical Rainfall Measuring Mission (Kummerow 2000) • Advanced Microwave Scanning Microwave Radiometer (Kawanishi 2003) • GPM (Global precipitation Measurement) Sat. to be launched 2013 -> GMI Microwave Imager • … POLDER 130 170 160 120 150 Bréon,GRL, 1998 POLDER (non spheric paticles) Ratio (AOT of large non-spherical particles)/(AOT of large particles) http://www.icare.univ-lille1.fr/parasol/?rubrique=overview_product Sattelite Retrival (POLDER) Cloud droplet radius derived from POLDER measurements during the spring of 1997. The units are microns. White areas correspond to regions where no successful estimate was possible. This image is based upon a compilation of 19,500 estimates.b Breon, Science 2002 TOPIC 6 Measurements: State of the Art Overlaps partly with Topic 16 new sensors Optical Parameters (individual and ensembles of particles) • Extinction Coefficient (e.g. CEP Cloud extinction probe (ICCP 2008, A. Korolev) • Scattering Phase Function (e.g. polar Nephelometer Data (Gayet)) • Scattering Coefficient • Absorption Coefficient ? • Asymmetry parameter of the volume scattering function (e.g.CIN Cloud integrating Nephelometer g-meter) • Sigle Scattering Albedo (Calculations..Yang et al 2010) • Complex Refractive index as mixture of ice and air -> see density, Mixing Rules Optical Parameters (Cloud) what we also need… • • • • Size distribution Ice/ water partitioning Crystal morphology/ composition Effective size of ice particles (def. McFarquhar and Heymsfield 1998, Wyser 1998, Fu 1996) • Cloud solar albedo e.g. as function of IWP Optical parameter: CEP Cloud Extinction Probe Principle Beer-Bouguer law (transmission method) (A. Korolev , Abstracts ICCP 2008) Optical Properties: CEP watercloud Water Cloud Rosemount Icing Detector signal. (A. Korolev,2008) Optical Parameter: CEP ice cloud • CEP in Ice cloud (A. Korolev, 2008) Optical Parameter: CEP Mixed phase cloud Mixed phase cloud In mixed phase cloud regions, the extinction coefficient measured by the Extinction Probe is larger than that calculated from the OAPs mixed phase zones (A. Korolev, 2008) CEP advantages • large sample area (~60cm2) • measurements are practically not contaminated by shattered ice particles • The Cloud Extinction Probe can be used to identify and characterize shattering and plashing efficiency of different cloud particle size spectrometers • threshold sensitivity is estimated at 0.2 1/km. (A. Korolev, 2008) Optical Parameter: Phase function – PN: Polar Nephelometer (Gayet) – SID/SID 2 ( “phase function”) in forward direction , • Ice / Water separation – PHIPS: Particle Habit Imaging and Polar Scattering Probe (Schnaiter, Möhler) Gayet, 2009 Nephelometer I 22° Halo peak Polar Neph. FSSP+Mie Conc : 1.9 cm-3 C100 : 7.3 l-1 IWC : 14 mg m-3 Ext : 0.49 km-1 Deff : 86 mm g : 0.795 a Irregular < 1% 16% b CPI 15% Rosette 5% 7% SidePlane Dendrite Plate 57% cirrus sampled near -27°C Column c c Needle d Fig. 1 Gayet, 2009 Nephelometer II No 22° Halo peak Polar Neph. FSSP+Mie Conc : 1.4 cm-3 C100 : 0.3 l-1 IWC : 2 mg m-3 Ext : 0.28 km-1 Deff : 21 mm g : 0.788 5% b a Irregular 5% CPI Rosette SidePlane 48% Dendrite 42% Plate Column cirrus properties near -59°C c c Needle d Fig. 2 Gayet, 2009 cCPI = 50 l-1 IWC = 0.25 g/m3 Deff = 190 mm Z = 20 dBZ R = 1.7 mm/h Diameter (mm) Ext = 10 km-1 g = 0.786 Scattering coefficient (mm-1 sr-1) Concentration (mm-1 l-1) Nephelometer III arctic mixed phase clouds Scattering angle (°) mid-latitude cirrus phase functions (in the visible) are smooth and featureless 0 500 mm From Gayet et al., ACP, 2009 Figure 4 Interpretation of CALIPSO observations (level 2.01) from in situ measurements : CIRCLE-2 Experiment (16 May 2007) (From Mioche et al., 2010, JGR, in press). ● Frontal cirrus over Ocean (West of France) ● Cloud top : -59°C, Visible optical depth ~0.5 CALIOP Extinction coeff. MODIS cloud field Closure Experiment CALIOP vs. PN 11 0.8 12 0.8 Altitude (km) 10 8 σ (km-1) 6 0 45.5 1 46 2 3 46.5 47 Latitude (°N) σCALIOP (km-1) σext / Caliop [km-1] 0.6 0.4 0.6 0.2 0.4 0 16 Mai y = 2.27x ± 0.58 R² = 0.65 0.2 0 00 0.2 0.2 0.4 0.6 0.4 σNP 0.80.6 1 0.8 (km-1) σext / PN [km-1] Figure 3 1 Current state of the art Particle Size and Shape Determination – Single Particle Light Scattering • • • • • • Background Mie Theory FSSP Forward Scattering Spectrometer Probe CDP Cloud Droplet Probe (mostly identical to FSSP) CAS Cloud and Aerosol Spectrometer SID/SID2 Small Ice Detector PMS 2D-C, 2D-P • PDPA Phase Doppler Particle Analyzer or (PDI) Phase Doppler Interferometer Principle of Single particle scattering devices CAS optics • Interpretation using Mie Theory SID-2 hybrid photo-diode (HPD) PHIPS – Particle Habit Imaging and Polar Scattering Probe 8 • 6 • Stereo imaging for reconstruction of 3D particle shape and orientation Simultaneous measurement of the polar scattering function in 1° - 170° angular range log(Intensity) Features 7 Mie PHIPS 100 µm 5 4 3 2 Reconstructed Particle 1 0 Image 1 Image 2 20 40 60 80 100 120 140 160 180 Angle (°) 100 µm Optics Electronics Schnaiter, Möhler 2010, priv. comm Current state of the art Particle Size and Shape Determination – Particle imaging • • • • • VIPS Video Ice Particle Sampler Cloud Scope Imaging microscope CPI Cloud Particle Imager CIP Cloud Imaging Probe NIXE-CAPS (combined device) (New Ice eXpEriment- Cloud and Aerosol Particle Spectrometer) • Holographic methods – digital-holographic particle imaging system (Raupach, Borrmann Vössing, 2006, 2009) (groud based) – HOLODEC (Fugal, Shaw, 2004) – HOLIMO (Amsler, Stezer, 2009) Principles Particle imaging Reflection Refraction Diffraction Principles Particle imaging • Principle optical diagram utilized in the airborne particle imaging probes EUFAR Book Chapter 6 Baumgardner/Brenguier EUFAR Book Chapter 6 Baumgardner/Brenguier Principle Diffraction Image depends only on the dimensionless variable Zd Z R2 Diffraction image equals if Z1 R12 Z1 Z 2 2 Z 2 R2 Strength and limitations Particle imaging technique Positive • • Wide range of measured particle sizes D>2µm Particle sizing is independent to the particle shape and refractive index Negative • Depend on the particle orientation in the sample volume of the probe and it is sensitive to the viewing angle • The image size depends on the distance from the object plane. The error in particle sizing may reach a factor of 1.8. The size retrieval algorithms have to be applied • The depth-of-field and the sample area is a function of particle size • The sizing of particles smaller than 4 pixels in size is subject to large errors related to image digitization. Small images also have larger uncertainty in the depth-of-field definition, which may result in large errors in concentration VIPS Video Ice Particle Sampler M. Krämer, p. c., 2010 NIXE-CAPS Current state of the art Particle Size and Shape Determination • Density (sampling needed) – Tomographic method to obtain particle density (Kersten/Midaner) (ground based, lab only) – Sampling and gas-pycnometry (e.g. Hänel and Thudium, 1973; Tamari, 2003) – DMA and Mass spectrometer (Zelenyuk, 2005) • Density (Volume) in situ – Holographic method -> 3D-Volume? How to estimate the Mass ??? Density • X-Ray Microtomography (Miedaner Kersten) Murshed , Kersten GRL 2008 Air filled voids inside a natural graupel grain sampled at the Jungfraujoch research station. Long frame edge is just one mm, spatial voxel resolution is 1.4 μm. The total volume of the sample is 0.75 mm3. The porosity of the grain is 5%, while the inner surface yield as much as 6 mm². Midaner ,2007 Density • Gas Pycnometer (Hänel,Thudium 1973) • Principle ; Pressure const vs. volume constant • V can be measure with 0.02% accuracy • Volume definition Problems: – – – – Handling of the probe Separate mass and volume measurement Mass measured by balance No isolated inclusions are accounted • Holographic method • 3D Volume • Time and personal intensive • Mass ? Tamari (2004) Density • Holographic method (Volume) – (Borrmann, Raupach, Vössing 2006) • 10µm – O( mm ) Particle Size • +- 25 µm acc. Particle distance – HOLODEC(Fugal and Shaw, 2009), – HOLIMO (Amsler, Stezer, 2009) In line holography Raupach,2006 In line holography • Interpreting cross-view images • Volume estimation possible • Operator intensive business! Raupach,2009 In line holography: HOLODEC Known Problems – Sampling artifacts (shattering, breakup by aircraft/ sensor influence (Korolev, Field) – Sampling in large convective clouds is not possible – Scattering instruments: Interference of non spherical particles in size distribution measurement (in mixed phase clouds) – Particle imager problems e.g. (2D vs. 3D, depth of focus,..) – Satellite retrievals (non spherical particles issues) optical properties What we need / Future • The single scattering properties of at least the predominant particle habits must be determined, these optical properties should serve as the basis for parameterizations of the radiative properties of clouds / contrails/ contrail cirrus for application to climate models and the retrieval of cloud / contrail / contrail cirrus properties from satellite observations • Laboratory measurements of optical properties of ice crystals are limited in terms of spectral coverage (scattering phase function) • Lack of (simultaneous) measurements of complete sets of single scattering properties (phase function, extinction cross section, single scattering albedo optical properties What we need / Future • Solution? Theoretical determination (3D modeling, Monte Carlo, CDA, etc.. ) of these properties for a wide variety of ice crystal habits and statistical based database for the different cloud types (here many flight hours have to be spent) Known from medicine • Most climate model parameterizations refer to natural cirrus - what about contrails / contrail cirrus (reduction of uncertainty) What we need / future In situ density measurements – Is there a way inverting “special mixing rules” using phase function measurements (hypothetical) – 3D Particle Imager / HOLODEC – Piezoelectric measurement of the momentum (Andy’s idea) – quantitative Computer tomography (QCT) ? Used in medicine – Dual-Energy X-ray Absorptiometry, DXA/DEXA ? – Sonographic techniques ? • ICE particle sampler (Martina Krämer has set up one!!) for lab analysis • • Please add your suggestions END Let’s have a good discussion! Appendix • References • Additional-slides References •Amsler, P., et al. (2009), Ice crystal habits from cloud chamber studies obtained by inline holographic microscopy related to depolarization measurements, Appl Optics, 48(30), 5811-5822. •Austin, R. T., et al. (2009), Retrieval of ice cloud microphysical parameters using the CloudSat millimeter-wave radar and temperature, J Geophys Res-Atmos, 114, -. •Battaglia, A., et al. (2010), Multiple-scattering in radar systems: A review, Journal of Quantitative Spectroscopy & Radiative Transfer, 111(6), 917-947. •Breon, F.-M., et al. (2002a), Aerosol Effect on Cloud Droplet Size Monitored from Satellite, Science, 295(5556), 834-838. •Breon, F. M. (1998), Comment on Rayleigh-scattering calculations for the terrestrial atmosphere, Appl Optics, 37(3), 428-429. •Breon, F. M., and P. Goloub (1998), Cloud droplet effective radius from spaceborne polarization measurements, Geophysical Research Letters, 25(11), 1879-1882. •Breon, F. M., et al. (2002b), Scientific results from the POLarization and Directionality of the Earth's Reflectances (POLDER), Earth's Atmosphere, Ocean and Surface Studies, 30(11), 2383-2386. •Deschamps, P. Y., et al. (1994), The Polder Mission - Instrument Characteristics and Scientific Objectives, Ieee Transactions on Geoscience and Remote Sensing, 32(3), 598615. References •Eichler, H., et al. (2009), Influence of ice crystal shape on retrieval of cirrus optical thickness and effective radius: A case study, J Geophys Res-Atmos, 114, -. •Fugal, J. P., et al. (2004), Airborne Digital Holographic System for Cloud Particle Measurements, Appl. Opt., 43(32), 5987-5995. •Gayet, J. F., et al. (2009), Microphysical and optical properties of Arctic mixed-phase clouds. The 9 April 2007 case study., Atmos Chem Phys, 9(17), 6581-6595. •Grecu, M., and W. S. Olson (2008), Precipitating snow retrievals from combined airborne cloud radar and millimeter-wave radiometer observations, J Appl Meteorol Clim, 47(6), 1634-1650. •Hänel, G., and J. Thudium (1977), Mean bulk densities of samples of dry atmospheric aerosol particles: A summary of measured data, Pure and Applied Geophysics, 115(4), 799-803. •Heymsfield, A. J., and J. Iaquinta (2000), Cirrus crystal terminal velocities, J Atmos Sci, 57(7), 916-938. •Heymsfield, A. J., et al. (2005), Improved radar ice water content retrieval algorithms using coincident microphysical and radar measurements, J Appl Meteorol, 44(9), 13911412. References •Kawanishi, T., et al. (2003), The Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), NASDA's contribution to the EOS for global energy and water cycle studies, Ieee Transactions on Geoscience and Remote Sensing, 41(2), 184-194. •Kobayashi, T. (1961), The Growth of Snow Crystals at Low Supersaturations, Philosophical Magazine, 6(71), 1363-&. •Korolev, A. (2008), NEW AIRBORNE CLOUD EXTINCTION PROBE, in International Conference on Cloud and Precipitation (ICCP), edited, Cancun. •Liou, K. N., and Y. Takano (1994), Light scattering by nonspherical particles: Remote sensing and climatic implications, Atmos Res, 31(4), 271-298. •Mishchenko, M. I., et al. (2000), Light scattering by nonspherical particles : theory, measurements and applications, xxx, 690 p., [610] p. of plates pp., Academic, San Diego ; London. •Murshed, M. M., et al. (2008), Natural gas hydrate investigations by synchrotron radiation X-ray cryotomographic microscopy (SRXCTM), Geophysical Research Letters, 35(23), -. •Parol, F., et al. (2004), Review of capabilities of multi-angle and polarization cloud measurements from POLDER, Climate Change Processes in the Stratosphere, Earth-Atmosphere-Ocean Systems, and Oceanographic Processes from Satellite Data, 33(7), 1080-1088. •Petty, G. W., and W. Huang (2010), Microwave Backscatter and Extinction by Soft Ice Spheres and Complex Snow Aggregates, J Atmos Sci, 67(3), 769-787. •Raupach, S. M. F., et al. (2006), Digital crossed-beam holography for in situ imaging of atmospheric ice particles, Journal of Optics a-Pure and Applied Optics, 8(9), 796-806. •Raupach, S. M. F. (2009a), Observation of Interference Patterns in Reconstructed Digital Holograms of Atmospheric Ice Crystals, J Atmos Ocean Tech, 26(12), 2691-2693. References •Raupach, S. M. F. (2009b), Cascaded adaptive-mask algorithm for twin-image removal and its application to digital holograms of ice crystals, Appl Optics, 48(2), 287-301. •Rottner, D., and G. Vali (1974), Snow Crystal Habit at Small Excesses of Vapor Density over Ice Saturation, J Atmos Sci, 31(2), 560-569. •Stephens, G. L., et al. (2008), CloudSat mission: Performance and early science after the first year of operation, J Geophys Res-Atmos, 113(D23), -. •Tamari, S., and A. Aguilar-Chávez (2004), Optimum design of the variable-volume gas pycnometer for determining the volume of solid particles, Measurement Science and Technology, 15(6), 1146. •Vortisch, H. (2002), Beobachtung von Phasenübergängen in einzeln levitierten Schwefelsäuretröpfchen mittels Raman-Spectroskopie und elastischer Lichtstreuung, book thesis, 260 pp, Freie Universität Berlin, Berlin. •Wendisch, M., et al. (2005), Impact of cirrus crystal shape on solar spectral irradiance: A case study for subtropical cirrus, J Geophys Res-Atmos, 110(D3), -. •Wendisch, M., et al. (2007), Effects of ice crystal habit on thermal infrared radiative properties and forcing of cirrus, J Geophys Res-Atmos, 112(D8), -. •Westbrook, C. D. (2008), The fall speeds of sub-100 mu m ice crystals, Q J Roy Meteor Soc, 134(634), 1243-1251. •Yue, Q., et al. (2007), Interpretation of AIRS data in thin cirrus atmospheres based on a fast radiative transfer model, J Atmos Sci, 64(11), 3827-3842. •Zelenyuk, A., et al. (2005), High Precision Density Measurements of Single Particles: The Density of Metastable Phases, Aerosol Science and Technology, 39(10), 972-986. The Afternoon or "A-Train" satellite constellation The Afternoon or "A-Train" satellite constellation presently consists of five satellites flying in formation around the globe (NASA's Aqua and Aura satellites and CNES' PARASOL satellite). The CALIPSO and CloudSat satellite missions were inserted in orbit behind Aqua in April 2006. Two additional satellites, OCO and Glory, will join the constellation in the next few years. Each satellite within the A-Train has unique measurement capabilities that greatly complement each other. A-Train: instrumentation Satelite Mission Instruments Aqua +0 sec Synergistic instrument package studies global climate with an emphasis on water in the Earth/atmosphere system, including its solid, liquid and gaseous forms AIRS/AMSU-A/HSB AMSR-R CERES MODIS CloudSat +0.5 – 2 min Cloud Profiling Radar (CPR) will allow for most detailed study of clouds to date and should better characterize the role clouds play in regulating the Earth's climate. CPR CALIPSO +2 min 15 sec Observations from spaceborne lidar, combined with passive imagery, will lead to improved understanding of the role aerosols and clouds play in regulating the Earth's climate, in particular, how the two interact with one another. CALIOP IIR WFC http://www-calipso.larc.nasa.gov/about/atrain.php A-Train: instrumentation Satelite Mission Instru ments PARASOL +3 min 15 sec Polarized light measurements will allow better characterization of clouds and aerosols in the Earth's atmosphere,in particular, distinguishing natural and manmade aerosols. POLDER Aura + 15 min Synergistic payload will study atmospheric chemistry, focusing on the horizontal and vertical distribution of key atmospheric pollutants and greenhouse gases and how these distributions evolve and change with time. HIRDLS MLS OMI TES FUTURE: •OCO •GLORY CO2 column 3 spectrometer Aerosol prop.; BC; solar irradiance APS, TIM, Cloud Camera http://www-calipso.larc.nasa.gov/about/atrain.php Current state of the art Particle Size and Shape Determination • Remote sensing (Liou et al. 2000) – Bidirectional reflectance Solar radiances reflected from clouds can be used to determine their composition and structure (cloud optical depth), – Linear polarization of reflected sunlight interpretation of the polarization of scattered sunlight using scattering data of nonspherical ice crystals – Lidar backscattering depolarization used to differentiate between ice and water clouds. Nonspherical particles will depolarize incident polarized light in backscattering direction (spherical particles will do not) (circular (Hu et al) / linear pol.) – Information content of 1.38 µm and thermal infrared spectra Water vapor exhibits a number of absorption bands in the solar spectrum. Bidirectional reflectance at the top of the atmosphere in these bands will contain information of highlevel clouds. Specially at the 1.38µm band for cirrus clouds – Solar albedo Reflection (broad-band solar albedo) of solar radiation by clouds determines the amount of solar energy absorbed within the atmosphere and by the surface. Information on ice water path (IWP) • LIDAR • RADAR