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Peng Lu, Hua Zhang, and Jiangnan Li

1. Introduction The specification of the gaseous transmission, cloud/aerosol optical properties, and radiative transfer are the three main tasks for an atmospheric radiation algorithm. Over the past two decades, there has been a trend in radiative transfer schemes to replace traditional band models for gaseous transmittance with the correlated k -distribution (CKD) method ( Lacis and Oinas 1991 ; Fu and Liou 1992 ; Hollweg 1993 ; Kratz 1995 ; Edwards and Slingo 1996 ; Mlawer et al. 1997

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Gyula Molnar and Wei-Chyung Wang

JOURNAL OF CLIMATEVOLUME S814GYULA MOLNARAtmospheric and Environmental Research, Inc., Cambridge. MassachusettsWEI-CHYUNG WANGAtmospheric Sciences Research Center. Sicie University of New York, Albany, New York(Manuscript received 29 August 1990, in final form 12 August 1991)ABSTRACTCloud optical properties, in particular the optical thickness r, affect the earth-atmosphere radiation budget,and their potential changes associated with climate changes may induce feedback effect. A one

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Yuekui Yang, Alexander Marshak, J. Christine Chiu, Warren J. Wiscombe, Stephen P. Palm, Anthony B. Davis, Douglas A. Spangenberg, Louis Nguyen, James D. Spinhirne, and Patrick Minnis

products ( Palm et al. 2002 ). Since its launch, GLAS has been providing data that contribute significantly to studying cloud and aerosol properties (e.g., Hart et al. 2005 ; Hlavka et al. 2005 ; Spinhirne et al. 2005b ). However, the retrieved optical depths are limited to the relatively thin clouds that can be penetrated by the laser beam (< about 3). Prior to the lidar retrieval process, the reflected solar energy has to be subtracted as noise from the signals received by the photon detectors

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David J. Delene and John A. Ogren

determination of aerosol radiative forcing from satellites requires assumptions about the chemical, microphysical, and optical properties of the aerosols because satellites do not measure several important aerosol properties ( Tanre et al. 1997 ). Therefore, uncertainties in satellite-retrieved parameters can result from variability in aerosol properties that are assumed to be constant in retrieval algorithms. Documenting the magnitude of the spatial and temporal variation in aerosol optical properties is

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Zachary A. Eitzen, Kuan-Man Xu, and Takmeng Wong

different physical properties of marine boundary layer clouds and found that optical depth generally decreased with SST. The changes in cloud and radiative properties with SST are entwined with changes in both dynamic and thermodynamic states of the atmosphere. This has been recognized by many studies and many attempts have been made to link the changes with SST in cloud and radiative properties with those in selected dynamic and thermodynamic measures (e.g., Bony et al. 1997 , 2004 ; Norris and

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Masanori Saito and Ping Yang

are fundamental to the development of many applications involving ice clouds. The bulk optical properties of ice clouds are the counterparts of an ensemble of ice crystals with certain size and shape distributions (e.g., Baum et al. 2005 ). The bulk ice cloud optical properties are used for radiative flux simulations involving ice clouds in general circulation models (GCMs) and remote sensing of ice clouds from satellite measurements. At present, several single-scattering property databases of

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T. H. Lindner and J. Li

approximation), the only cloud optical property needed in the radiative transfer calculation is the absorption coefficient. To avoid the complicated Mie calculation, a parameterization of the absorption coefficient in terms of physical quantities that can be generated in climate models is needed. Chýlek and Ramaswamy (1982) proposed a simple parameterization of the emittance for water clouds in the infrared. A more sophisticated scheme was proposed by Chýlek et al. (1992) . If scattering is considered in

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Chenxi Wang, Ping Yang, Steven Platnick, Andrew K. Heidinger, Bryan A. Baum, Thomas Greenwald, Zhibo Zhang, and Robert E. Holz

of ice clouds is a challenging task because of their widely varying horizontal and vertical distributions, formation–dissipation time scales, and the complicated morphology of nonspherical ice particles ( Heymsfield and Iaquinta 2000 ; Heymsfield et al. 2002 ; Zhang et al. 2009 ). Satellite-based measurements provide an unparalleled opportunity for monitoring the global distribution of ice clouds and their optical and microphysical properties. In comparison with solar

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Takahiro Yabe, Robert Höller, Susumu Tohno, and Mikio Kasahara

( Houghton et al. 2001 ). An evaluation of the aerosol radiative forcing on a global scale is, however, highly uncertain because of uncertainties in the optical properties and the spatial distribution of the aerosol ( Charlson et al. 1992 ; Schwartz 1996 ; Hansen et al. 1997 ). Recently, large field campaigns such as the Tropospheric Aerosol Radiative Forcing Observational Experiment (TARFOX) ( Russell et al. 1999 ), the Indian Ocean Experiment (INDOEX) ( Ramanathan et al. 2001 ), and the Asia Pacific

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Tianle Yuan and Zhanqing Li

the cloud phase is ice. Cloud phase is extremely important as water and ice hydrometeors have completely different optical properties ( Platnick et al. 2003 ). Treating water clouds as ice clouds, or vice versa, will inevitably lead to errors in the retrievals, which need to be avoided for our purpose. b. DCC definition There is no precise definition of a deep convective cloud using just passive remote sensing measurements. In practice, BT has been used to define DCC by setting a threshold of low

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