The microphysical properties of ice crystals, such as size, shape, concavity and roughness, are important in the context of radiative properties of ice and mixed phase clouds. Limitations of current cloud probes to measure such properties can be circumvented by acquiring light scattering patterns instead of particle images. Recent in situ cloud data from the SID-3 probe is shown which is consistent with ice particles with rough surfaces being dominant

Greenaway, Richard

Hirst, Edwin

Kaye, Paul H.

Ulanowski, Zbigniew

University of Hertfordshire Research Archive

Helsinki 2010 Z Ulanowski et al. Light scattering by ice particles   3 Light scattering by ice particles in the Earth's atmosphere and related laboratory measurements Z. Ulanowski*, P.H. Kaye, E. Hirst, R.S. Greenaway Centre for Atmospheric and Instrumentation Research, University of Hertfordshire, Hatfield AL10 9AB, United Kingdom. The microphysical properties of ice crystals, such as size, shape, concavity and roughness, areimportant in the context of radiative properties of ice and mixed phase clouds. Limitations ofcurrent cloudprobes to measure such properties can be circumventedbyacquiringlight scatteringpatterns insteadof particle images. Recent insituclouddata fromthe SID-3 probe is shown whichis consistent with ice particles with rough surfaces beingdominant.INTRODUCTION Cloud feedbacks remain the largest source of uncertainty in climate models. In particular,uncertainties exist concerning the radiative forcing of clouds containing ice crystals, mostnotably cirrus. Indeed, whether cirrus clouds warm or cool the Earth’s surface depends onice crystal morphology. Reducing this uncertainty requires detailed in situ characterization ofcloud particles, so that the scattering properties of the clouds can be correctlyrepresented inmodels. Also, detailed knowledge of the scattering properties of various cloud particle typesis needed for accurate retrieval of cloud microphysical properties from remote sensing. Oneof the main barriers to achieving these goals is the inabilityof cloud probes to determine thecontribution of small ice crystals (that is crystals smaller than about 50 µm) to the total distri-bution. This is due to crystal breakup on the inlets of these probes [1] and their inability toresolve the geometric structure of small ice crystals because of the conflicting demands ofhigh optical resolution andlarge sample volume [2,3].In addition to size and shape, two other important properties of ice particles, surfaceroughness and concavity, are known to profoundly affect radiative properties of ice clouds,e.g. the asymmetryparameter g[4,5,6]. The measured gof smooth and rough ice analogues istypically 0.8 and 0.63, respectively, at visible wavelengths [7]. This means that, in simpleterms, the rough ice particles in this example might back-reflect nearly twice as much solarradiation as their smooth counterparts. Since the longwave g is not expected to vary by asmuch, the overall radiative forcing of cirrus might shift towards negative values, an issue ofgreat importance in the context of climate change. However, little in situ data on ice rough-ness exists: mainly indirect evidence, e.g. the absence of atmospheric halos [6,8] or the shapeof phase functions [9]. As for the concavity of ice particles, it is thought to occur frequentlybut is seldom quantified, mainly due to cloud probe limitations [10]. Here, we examine therecoveryof size, shape, concavityandroughness of ice particles fromscatteringdata, so as tobypass the optical resolution limitations of cloudprobes.* Correspondingauthor: Joseph Z. Ulanowski (z.ulanowski@ herts.ac.uk)Light scattering by ice particles Z. Ulanowski et al. ELS’XII   4 ICE PARTICLE CHARACTERIZATION USING SCATTERING PATTERNS Particle size, shape and concavity Severallight scattering cloud probes, jointly known as Small Ice Detectors (SID) have beendeveloped over the last decade at the University of Hertfordshire. Successive models obtainscattering patterns with progressively higher angular resolution. The earlier designs rely onmulti-element detectors measuring mainlythe azimuthal scattering. The first one, SID-1, wasintendedto discriminate between water droplets andice crystals bydeterminingtheir spheric-ity, and relies on six photomultipliers arranged symmetrically around the azimuth [11]. Thisinstrument was superseded by SID-2 probes, containing between 24 and 28 detector ele-ments arranged azimuthally in an annulus [12]. Despite their simplicity, azimuthal scatteringpatterns can also provide the size, concavity and aspect ratio of prismatic ice crystals [13]through comparison with the Ray Tracing with Diffraction on Facets (RTDF) scatteringmodel [14]. Some particle information is retained even in the azimuthal frequencyspectrum,as shown by the recovery of the size and aspect ratio of prismatic ice crystals [15] although,not surprisingly, such frequencyanalysis is limitedto cases when some a priori information onparticle shape exists, andeven then some ambiguitycan remain [13,16].High-resolution two-dimensional (2D) scattering patterns offer high potential for de-tailed particle characterization [17]. The latest SID probes, collectively known as SID-3, useintensified CCD cameras to capture 2D patterns. It is possible to recover the shape of smallice particles bycomparing such patterns to the RTDF scattering model [18]. In the next sec-tion we examine the application of 2D patterns to ice particle roughness.Particle roughness An indication that ice roughness can be quantified from 2D scattering is provided by expe-rimental patterns from ice analogue crystals with smooth and rough surfaces, which showdistinct differences: while the former have sharp, well-definedbright arcs andspots, the latterhave much more random, “speckly” appearance, but with greater azimuthal symmetry [7].The first in situ cloud data using the SID-3 probe was obtained during the UK Met OfficeCONSTRAIN campaign in Scotland in February 2010. Fig. 1 shows a random selection ofparticles observed in cirrus and mixed phase clouds. Comparison with laboratory ice analo-gue data, also shown in Fig. 1, demonstrates qualitativelythat the image texture of the majori-tyof the insitupatterns is consistent with the presence of significant roughness.Image texture can be quantified using statistical measures, such as the gray-level co-occurrence matrix (GLCM), which deals with the spatial relationships of pairs of grayvaluesof images pixels. In the past, GLCM was applied to retrieving surface roughness from laserspeckle images of surfaces [19]. Here, four features of GLCM were chosen: contrast, correla-tion, energy and homogeneity, in addition to image entropy. They were calculated for 2Dpatterns from cirrus and mixed phase cloud particles as well as smooth and rough ice analo-gues, and correlated with a subjective measure of pattern speckle. Energy had the strongestcorrelation with the subjective pattern speckle: -0.7. A combined contrast-energy-homogeneity feature had correlation similar in magnitude with opposite sign, but showedslightly larger difference for test patterns of smooth and rough analogues. Interestingly, theHelsinki 2010 Z Ulanowski et al. Light scattering by ice particles   5 GLCMenergy shows good correlation with surface roughness and is most robust with re-spect to variation of "the setup configuration, the position, and the orientation of the surface to be meas-ured" in the context of laser speckle [19]. Distributions of the statistical features for a randomsample of 500 patterns from CONSTRAIN are shown in Fig. 2. Like for the qualitativecomparison in Fig. 1, the properties of most of the patterns were consistent with the pres-ence of significant particle roughness, at levels exceeding that found in the moderatelyroughanalogue crystal usedas areference. The spreadof roughness was slightlywider in cirrus.Figure 1. Six randomlyselectedSID-3 patterns fromice particles seen duringCONSTRAINcirrus (top) andmixedphase (bottom) flights, comparedto patterns fromice-analogue ro-settes with smooth (top right) andmoderatelyrough surfaces (bottomright).Figure 2. Frequencydistributions of statistical features of 2D patterns fromcirrus (top row)andmixedphase (lower row) flights. Arrows showvalues for the smooth (S) andmoderatelyrough (R) ice analogues fromFig. 1. The “combined” feature is (energy+ homogeneity–contrast). Note that all but entropyandcontrast are anticorrelatedwith roughness.REFERENCES [1] P.R. Field, A.J. Heymsfield and A. Bansemer. Shattering and particle interarrivaltimes measuredbyoptical arrayprobes in ice clouds. JTECH 23, 1357-1371 (2006).[2] Z. Ulanowski, P. Connolly, M. Flynn et al. Using ice crystal analogues to validatecloud ice parameter retrievals from the CPI ice spectrometer. In Proc. 14 Int. Conf.Clouds Precip., 1175 http://strc.herts.ac.uk/ls/ICCP14_poster_P2.8.29.pdf (2004).Light scattering by ice particles Z. Ulanowski et al. ELS’XII   6 [3] P.J.Connolly, M.J. Flynn, Z. Ulanowski et al. Calibration of 2-D imaging probes us-ingcalibration beads andice crystal analogues. JTECH 24, 1860–1879 (2007).[4] Q. Fu. A newparameterization of an asymmetry parameter of cirrus clouds for cli-mate models. J. Atmos. Sci. 64, 4140-4150 (2007).[5] P. Yang, Z. Zhang, G.W. Kattawar et al. Effect of cavities on the optical propertiesof bullet rosettes. Appl. Meteor. Climate 47, 2311-2330 (2008).[6] P. Yang, G.W. Kattawar, G. Hong, P. Minnis and Y. Hu. Uncertainties associatedwith the surface texture of ice particles in satellite-based retrieval of cirrus. IEEETrans. Geosci. Remote Sens. 46, 1940-1957 (2008).[7] Z. Ulanowski, E. Hesse, P.H. Kaye and A.J. Baran Light scattering by complex ice-analogue crystals. JQSRT 100, 382-392 (2006).[8] Z. Ulanowski. Ice analoghalos. Appl. 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A 3D implementation of raytracingcombinedwith diffraction on facets. JQSRT 100, 103-114 (2006).[15] Z. Ulanowski, C. Stopford, E. Hesse, P.H. Kaye, E. Hirst and M. Schnaiter. Charac-terization of small ice crystals using frequency analysis of azimuthal scattering pat-terns. In Proc. 10 Int. Conf. onEM& Light Scatt., 225-228 (2007).[16] C. Stopford, Z. Ulanowski, E. Hesse, P.H. Kaye, E. Hirst, M. Schnaiter and D.McCall. Initial investigation into using Fourier spectra as a means of classifying icecrystal shapes. In Proc. 11 Int. Conf. EM& Light Scatt., 247-251 (2008).[17] P.H. Kaye, K. Aptowicz, R.K. Chang, V. Foot and G. Videen. Angularly resolvedelastic scattering from airborne particles. In Optics of Biological Particles, ed. A. Hoek-stra et al., Springer, pp.31-61 (2007).[18] P.H. Kaye, E. Hirst, R.S. Greenaway et al. Classifying atmospheric ice crystals byspatial light scattering. Opt. Lett. 33, 1545-1547 (2008).[19] R.S. Lu, G.Y. Tian, D. Gledhill and S. Ward. Grinding surface roughness measure-ment based on the co-occurrence matrix of speckle pattern texture. Appl. Opt. 45,8839-8847 (2006).

Light scattering by ice particles in the Earth's atmosphere and related laboratory measurements

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