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J. Fischer, W. Cordes, A. Schmitz-Peiffer, W. Renger, and P. Mörl

show the potential of multichannelradiance measurements for the detection of physical cloud propertie~I. Introduction There are a few published experimental results dealing with remote sensing of physical cloud properties(Curran and Wu 1982; King et al. 1986, 1987; Foot1988). One of the most important cloud propertieswith respect to global climate changes is cloud-topheight and cloud optical thickness. Ohring and Adler(1978) postulate that an increase of I km in cloudheight would result in a 1

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Yoram J. Kaufman and Robert S. Fraser

of the sample correlationcoefficient for subsets of data are given in Table 2. Thedata are separated by time of day, year, and then thetwo for both years together (indicated by all). Thetimes (t) during the day are divided into three sets:t ~< 1000 EDT, 1000 ~< t ~< 1300 EDT, and bothtogether (all). The visibility (V) is divided into twosets: 5 ~< V ~< 16 km and all visibilities. The apparentregional homogeneity of the optical properties of theaerosol, which is indicated by the

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Albert Ansmann, Jens Bösenberg, Gérard Brogniez, Salem Elouragini, Pierre H. Flamant, Karlheinz Klapheck, Holger Linn, Louis Menenger, Walfried Michaelis, Maren Riebesell, Christoph Senff, Pierre-Yves Thro, Ulla Wandinger, and Claus Weitkamp

, Germany (Manuscript received 14 September 1992, in final form 26 February 1993) Four lidars, located roughly 75 km from each other in the inner German Bight of the North Sea, were usedto measure geometrical and optical properties of cirrus clouds during the International Cirrus Experiment 1989(ICE '89), A complete citrus life cycle was observed simultaneously with three lidars during a case study on 18October 1989. Time series of particle backscatter, depolarization-ratio height profiles

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J. Stum, B. Pinty, and D. Ramond

as long as the appropriate conversion factors are known. A simple model allowing a spectral descriptionof the optical properties of cloud free atmospheres and land surfaces is used to estimate these conversion factors.A sensitivity study of these factors indicates that a knowledge of the optical properties of the surfaces (describedthrough spectrally averaged albedos and spectral band ratios) is decisive for retrieving broadband conversionfactors. A parameterization is proposed which permits

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Guotong Zhang, Lisheng Xu, and Hongbin Chen

, usingversatile cloud drop size distributions ( DSDs). As for single-scattering properties, a new parameterization forcloud optical thickness is proposed by using the separation of the dependence of r on the total number of theDSDs, the cloud thickness, and the liquid water content, combined with equivalent radius. The cloud bulkradiative properties are obtained from the delta-Eddington approximation for our cloud models. It is shownthat the flux reflectivity, transmissivity, and absorptivity are uniquely

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Maria João Costa, Ana Maria Silva, and Vincenzo Levizzani

Introduction The growing consciousness of the strong influence of atmospheric aerosol on atmospheric processes (e.g., Houghton et al. 2001 ), and consequently on climate, prompts local and global studies aimed at quantifying the aerosol load in the atmosphere (aerosol optical thickness: AOT), as well as aerosol optical properties. Aerosol particles play a twofold role in the atmosphere: on one hand they directly scatter and absorb solar radiation, and on the other they enter cloud

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Christopher J. Cox, David D. Turner, Penny M. Rowe, Matthew D. Shupe, and Von P. Walden

1. Introduction Clouds play an important and complex role in the climate system by modulating the surface-energy budget. The net radiative forcing of clouds is caused by the interplay of albedo (negative forcing) and thermal emission (positive forcing) effects ( Ramanathan et al. 1989 ), with the magnitude and sign being dependent on cloud properties. Properties including liquid and ice water paths, optical depth, temperature, particle size, and phase are important components of the radiative

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W. Han, K. Stamnes, and Dan Lubin

cloud optical depth inferred from the near-infrared channel is almost the same as that in the visible. Although use of the 0.85- μ m channel makes retrieval feasible, the snow surface is bright also in this channel. As will be shown below, the low contrast between the cloud and the snow surface leads to large errors in retrieved optical properties for thin clouds because of the sensitivity of the retrieved cloud properties to small errors in the measured or modeled radiance. Key (1999 ) adopted the

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Man-Li C. Wu

throughdifferent physical processes are calculated by using the method of successive orders of scattering.The effective emissivity of thin clouds is decomposed into the effective absorption emissivity, eWe~tivescattering emissivity, and effective reflection emissivity. The effective absorption emissivity depends on theabsorption and emission of the cloud; it is parameterized in terms of optical thickness. The effective scatteringemissivity depends on the scattering properties of the cloud; it is parameterized

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Chenxi Wang, Ping Yang, Bryan A. Baum, Steven Platnick, Andrew K. Heidinger, Yongxiang Hu, and Robert E. Holz

adopted by MODIS. Another benefit is that an IR-based approach can be applied to all data regardless of solar illumination, leading to consistent retrievals for both daytime and nighttime conditions, a distinct advantage for building an ice cloud climatology. Furthermore, the ice crystal optical properties ( Baran 2004 , 2009 ; Yang et al. 2005 , and references cited therein) used to generate the LUTs are fundamental to ice cloud-property retrievals ( Chepfer et al. 1998 ; Wendisch et al. 2005

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