Atmospheric Chemistry and Physics www.atmos-chem-phys.net 1680-7316 1680-7324 8 16 2008 10.5194/acp-8-4741-2008 http://www.atmos-chem-phys.net/8/4741/2008/ http://www.atmos-chem-phys.net/8/4741/2008/acp-8-4741-2008.html http://www.atmos-chem-phys.net/8/4741/2008/acp-8-4741-2008.pdf 4741 4757 2008-08-18 Remote sensing of cloud sides of deep convection: towards a three-dimensional retrieval of cloud particle size profiles T. Zinner tobias.zinner@dlr.de A. Marshak S. Lang J. V. Martins B. Mayer NASA &ndash; Goddard Space Flight Center, Climate and Radiation Branch, Greenbelt, MD, USA Deutsches Zentrum für Luft- und Raumfahrt, Inst. für Physik der Atmosphäre, Oberpfaffenhofen, 82230 Wessling, Germany NASA &ndash; Goddard Space Flight Center, Mesoscale Atmospheric Processes Branch, Greenbelt and Science Systems and Applications Inc., Lanham, MD, USA Department of Physics and Joint Center for Earth Systems Technology, University of Maryland Baltimore County, Baltimore, MD, USA The cloud scanner sensor is a central part of a recently proposed satellite remote sensing concept – the three-dimensional (3-D) cloud and aerosol interaction mission (CLAIM-3D) combining measurements of aerosol characteristics in the vicinity of clouds and profiles of cloud microphysical characteristics. Such a set of collocated measurements will allow new insights in the complex field of cloud-aerosol interactions affecting directly the development of clouds and precipitation, especially in convection. The cloud scanner measures radiance reflected or emitted by cloud sides at several wavelengths to derive a profile of cloud particle size and thermodynamic phase. For the retrieval of effective size a Bayesian approach was adopted and introduced in a preceding paper. <br><br> In this paper the potential of the approach, which has to account for the complex three-dimensional nature of cloud geometry and radiative transfer, is tested in realistic cloud observing situations. In a fully simulated environment realistic cloud resolving modelling provides complex 3-D structures of ice, water, and mixed phase clouds, from the early stage of convective development to mature deep convection. A three-dimensional Monte Carlo radiative transfer is used to realistically simulate the aspired observations. <br><br> A large number of cloud data sets and related simulated observations provide the database for an experimental Bayesian retrieval. An independent simulation of an additional cloud field serves as a synthetic test bed for the demonstration of the capabilities of the developed retrieval techniques. For this test case only a minimal overall bias in the order of 1% as well as pixel-based uncertainties in the order of 1 μm for droplets and 8 μm for ice particles were found for measurements at a high spatial resolution of 250 m. Albrecht, B A.: Aerosols, cloud microphysics and fractional cloudiness, Science, 245, 1227–1230, 1989. Anderson, G., Clough, S., Kneizys, F., Chetwynd, J., and Shettle, E.: AFGL atmospheric constituent profiles, Tech. Rep. AFGL-TR-86-0110, AirForce Geophys. Lab., 1986. Andreae, M O., Rosenfeld, D., Artaxo, P., Costa, A A., Frank, G P., Longo, K M., and Silva-Dias, M. A F.: Smoking rain clouds over the Amazon, Science, 303, 1337–1342, 2004. Antyufeev, V S.: Solution of the generalized transport equation with a peak-shaped indicatrix by the Monte Carlo method, Russ. J. Numer. Anal. Math. Model., 11, 113–137, 1996. Baker, M B., Corbin, R G., and Latham, J.: The influence of entrainment on the evolution of cloud droplet spectra: I. A model of inhomogeneous mixing, Q. J. Roy. Meteorol. Soc., 106, 581–598, 1980. Barker, H W., Goldstein, R K., and Stevens, D E.: Monte Carlo simulation of solar reflectances for cloudy atmospheres, J. Atmos. Sci., 60, 1881–1894, 2003. Blyth, A M. and Latham, J.: A climatological parameterization for cumulus cloud, J. Atmos. Sci., 48, 2367–2372, 1991. Brenguier, J.-L., Pawlowska, H., Schüller, L., Preusker, R., Fischer, J., and Fouquart, Y.: Radiative properties of boundary layer clouds: Droplet effective radius versus number concentration, J. Atmos. Sci., 57, 803–821, 2000. Cahalan, R F., Oreopoulos, L., Marshak, A., Evans, K F., Davis, A B., Pincus, R., Yetzer, K H., Mayer, B., Davies, R., Ackerman, T P., et al.: The I3RC: Bringing together the most advanced radiative transfer tools for cloudy atmospheres, B. Am. Meteorol. Soc., 86, 1275–1293, 2005. Emde, C. and Mayer, B.: Simulation of solar radiation during a total eclipse: a challenge for radiative transfer, Atmos. Chem. Phys., 7, 2259–2270, 2007. Evans, K F., Walter, S J., Heymsfield, A J., and McFarquhar, G M.: The Submillimeter-wave cloud ice radiometer: Simulations of retrieval algorithm performance, J. Geophys. Res., 107, 4028, doi:10.1029/2001JD000709, 2002. Fletcher, N H.: The Physics of Rain Clouds, Cambridge University Press, Thun, Frankfurt am Main, 1962. French, J., Vali, G., and Kelly, R D.: Observations of microphysics pertaining to the development of drizzle in warm, shallow cumulus clouds, Q. J. Roy. Meteorol. Soc., 126, 415–443, 2000. Freud, E., Rosenfeld, D., Andreae, M. O., Costa, A. A., and Artaxo, P.: Robust relations between CCN and the vertical evolution of cloud drop size distribution in deep convective clouds, Atmos. Chem. Phys., 8, 1661–1675, 2008. Gerber, H.: Entrainment, mixing, and microphysics in RICO cumulus, in: Proceedings of the 12th Conference on Atmospheric Radiation/12th Conference on Cloud Physics, Madison, WI, July 2006. Hallett, J. and Mossop, S C.: Production of secondary ice particles during the riming process, Nature, 249, 26–28, 1974. Hong, S.-Y., Dudhia, J., and Chen, S.-H.: A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation, Mon. Weather Rev., 132, 103–120, 2004. IPCC 2007: Climate Change 2007: The physical science basis, Intergovernmental Panel on Climate Change (IPCC) Secretariat, c/o World Meteorological Organization, Geneva, Switzerland, 2007. Iwabuchi, H.: Efficient Monte Carlo methods for radiative transfer modeling, J. Atmos. Sci., 63, 2324–2339, 2006. Kaufman, Y J. and Koren, I.: Smoke and Pollution Aerosol Effect on Cloud Cover, Science, 313, 655–658, doi:10.1126/science.1126232, 2006. Key, J., Yang, P., Baum, B., and Nasiri, S.: Parameterization of shortwave ice cloud optical properties for various particle habits, J. Geophys. Res., 107, 4181, doi:10.1029/2001JD000742, 2002. Khain, A P., Rosenfeld, D., and Pokrovsky, A.: Simulating convective clouds with sustained supercooled liquid water down to &minus;37.5C using a spectral microphysics model, Geophys. Res. Lett., 28, 3887–3890, 2001. Lang., S., Tao, W.-K., Cifelli, R., Olson, W., Halverson, J., Rutledge, S., and Simpson, J.: Improving simulations of convective systems from TRMM LBA: Easterly and westerly regimes, J. Atmos. Sci., 64, 1141–1164, 2007. Marshak, A. and Davis, A B. (Eds.): 3D Radiative Transfer in Cloudy Atmospheres, Springer, Berlin, 1 edn., 2005. Marshak, A., Martins, J V., Zubko, V., and Kaufman, Y J.: What does reflection from cloud sides tell us about vertical distribution of cloud droplet sizes?, Atmos. Chem. Phys., 6, 5295–5305, 2006. Martin, G M., Johnson, D W., and Spice, A.: The measurement and parameterization of effective radius of droplets in warm stratocumulus clouds, J. Atmos. Sci., 51, 1823–1842, 1994. Martins, J V., Marshak, A., Remer, L A., Rosenfeld, D., Kaufman, Y J., Fernandez-Borda, R., Koren, I., Zubko, V., and Artaxo, P.: Remote sensing the vertical profile of cloud droplet effective radius, thermodynamic phase, and temperature, Atmos. Chem. Phys. Discuss., 7, 4481–4519, 2007. Mayer, B.: I3RC phase 1 results from the MYSTIC Monte Carlo model, in: Intercomparison of three-dimensional radiation codes: Abstracts of the first and second international workshops, University of Arizona Press, ISBN 0-9709609-0-5, 1999. Mayer, B.: I3RC phase 2 results from the MYSTIC Monte Carlo model, in: Intercomparison of three-dimensional radiation codes: Abstracts of the first and second international workshops, University of Arizona Press, ISBN 0-9709609-0-5, 2000. Mayer, B. and Kylling, A.: Technical Note: The libRadtran software package for radiative transfer calculations: Description and examples of use, Atmos. Chem. Phys., 5, 1855–1877, 2005. McFarlane, S A., Evans, K F., and Ackerman, A S.: A Bayesian algorithm for the retrieval of liquid water properties from microwave radiometer and millimiter radar data, J. Geophys. Res., 107, 4317, doi:10.1029/2001JD001011, 2002. McFarquhar, G M., Iacobellis, S., and Somerville, R. C J.: SCM simulations of tropical ice clouds using observationally based parameterizations of microphysics, J. Climate, 16, 1643–1664, 2003. Meyers, M P., DeMott, P J., and Cotton, W R.: New primary ice-nucleation parameterization in an explicit cloud model, J. Appl. Meteorol., 31, 708–721, 1992. Nakajima, T. and King, M D.: Determination of the optical thickness and effective particle radius of clouds from reflected solar radiation measurements. Part I: Theory, J. Atmos. Sci., 47, 1878–1893, 1990. Platnick, S., King, M D., Ackerman, S A., Menzel, W P., Baum, B A., Riedi, J C., and Frey, R.: The MODIS cloud products: Algorithms and examples from TERRA, IEEE T. Geosci. Rem., 41, 459–473, 2003. Rosenfeld, D.: ATMOSPHERE: Aerosols, Clouds, and Climate, Science, 312, 1323–1324, \doi10.1126/science.1128972, 2006. Rosenfeld, D. and Woodley, W L.: Satellite-based insights into the precipitation formation processes in continental and maritime convective clouds, B. Am. Meteorol. Soc., 79, 2457–2476, 1998. Rosenfeld, D. and Woodley, W L.: Deep convective clouds with sustained supercooled liquid water down to &minus;37.5&deg;C, Nature, 405, 440–442, 2000. Rutledge, S A. and Hobbs, P V.: The mesoscale and microscale structure and organization of clouds and precipitation in midlatitude cyclones. Part XII: A diagnostic modeling study of precipitation development in narrow cold-frontal rainbands, J. Atmos. Sci., 41, 2949–2972, 1984. Stamnes, K., Tsay, S C., Wiscombe, W., and Jayaweera, K.: A numerically stable algorithm for discrete-ordinate-method radiative transfer in multiple scattering and emitting layered media, Appl. Optics, 27, 2502–2509, 1988. Stith, J L., Dye, J E., Bansemer, A., Heymsfield, A J., Grainger, C A., Petersen, W A., and Cifelli, R.: Microphysical observations of tropical clouds, J. Appl. Meteorol., 41, 97–117, 2002. Tao, W.-K. and Simpson, J.: The Goddard Cumulus Ensemble Model. Part I: Model description, Terr. Atmos. Oceanic Sci., p. 1954, 1993. Tao, W.-K., Simpson, J., Baker, D., Braun, S., Chou, M.-D., Ferrier, B., Johnson, D., Khain, A., Lang, S., Lynn, B., et al.: Microphysics, radiation and surface processes in the Goddard Cumulus Ensemble (GCE) model, Meteorol. Atmos. Phys., 82, 97–137, 2003. Twomey, S.: The influence of pollution on the shortwave albedo of clouds, J. Atmos. Sci., 34, 1149–1152, 1977. Williams, E., Rosenfeld, D., Madden, M., Gerlach, J., Gears, N., Atkinson, L., Dunnemann, N., Frostrom, G., Antonio, M., Biazon, B., et al.: Contrasting convective regimes over the Amazon: Implications for cloud electrification, J. Geophys. Res., 107, 8082, doi:10.1029/2001JD000380, 2002. Wiscombe, W J.: Mie scattering calculations: advances in technique and fast, vector-speed computer codes, Tech. rep., National Center for Atmospheric Research, Boulder, CO, USA, 1979, revised 1996. Wyser, K.: The effective radius in ice clouds, J. Climate, 11, 1793–1802, 1998. Zinner, T. and Mayer, B.: Remote sensing of stratocumulus clouds: Uncertainties and biases due to inhomogeneity, J. Geophys. Res., 111, D14 209, doi:10.1029/2005JD006955, 2006. Zinner, T., Mayer, B., and Schröder, M.: Determination of three-dimensional cloud structures from high-resolution radiance data, J. Geophys. Res., 111, D08204, doi:10.1029/2005JD006062, 2006.