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Yong Chen, Fuzhong Weng, Yong Han, and Quanhua Liu

1. Introduction The development of fast and accurate thermal infrared (IR) radiative transfer (RT) models for clear atmospheric conditions has enabled the direct assimilation of satellite-based radiance measurements in numerical weather prediction (NWP) models. Most fast RT models are based on fixed transmittance coefficients that relate atmospheric conditions to optical properties. One such fast RT model is the Community Radiative Transfer Model (CRTM; Weng et al. 2005 ; Han et al. 2006

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S. K. Mukkavilli, A. A. Prasad, R. A. Taylor, A. Troccoli, and M. J. Kay

; Kraas et al. 2013 ). However, more recently, studies with direct outputs of DNI, as calculated by the respective radiative transfer schemes, have been made available through Weather Research and Forecasting (WRF) Model developments and evaluations under clear skies (e.g., albeit primarily in North America; Ruiz-Arias et al. 2013b , 2014 ; Jimenez et al. 2016 ). In previous studies where DNI used to be evaluated, it was derived from component separation of the global irradiance ( Lara-Fanego et al

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Quanhua Liu, Changyong Cao, and Fuzhong Weng

al. 2000 ). In this study, we focus on the striping related to the differences in spectral response and geometry among detectors, because such a striping is a real instrument artifact. Both spectral response difference and geometric difference can be considered in radiative transfer calculations. Removing the striping may cause the inconsistency between measurements and radiative transfer calculations, which can be an issue in direct radiance assimilation. The successful launch of the Suomi

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Xianglei Huang and Yuk Ling Yung

did not explicitly clarify that α D is the e -folding half-width. About “ambiguous specifications of the Voigt profile”: if there were no context, we would agree with what was stated in the abstract of S09 , “there is no unique corresponding Voigt profile,” since the word “corresponding” can be interpreted in different ways as S09 elaborated. However, in HY04 , we first presented Fig. 3.2 in Radiative Transfer in the Atmosphere and Ocean by Thomas and Stamnes (1999) as Fig. 1 in HY04

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J. Li and J. S. Dobbie

1. Introduction For climate models and remote sensing applications, approximate methods for solving the radiative transfer equation are required in order to obtain accurate results for reflection, transmission, and absorption for all solar zenith angles and a wide variety of optical thickness values. However, as pointed out by King and Harshvardhan (1986) , to date no two-stream approximation method satisfies this criterion. Specific regions can be identified where one approximation is more

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George P. Kablick III, Robert G. Ellingson, Ezra E. Takara, and Jlujing Gu

were treated by the models ( Ellingson et al. 1991 ). Since that time, the Atmospheric Radiation Measurement (ARM) program has been working on validating model calculations against high-quality spectral observations taken at sites around the world ( Stokes and Schwartz 1994 ; Ackerman and Stokes 2003 ). These data have helped develop accurate, line-by-line (LBL) information about atmospheric constituents, from which a line-by-line radiative transfer model (LBLRTM) has been established for model

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Xiaodong Liu, Shouguo Ding, Lei Bi, and Ping Yang

1. Introduction Ice clouds remain one of the key uncertainty sources in the study of the atmospheric radiation budget and atmospheric remote sensing ( Liou 1986 ; Lynch et al. 2002 ; Wendisch et al. 2007 ; Minnis et al. 1993a , b ; Baum et al. 2000 , 2005 ; Baran 2009 , and references cited therein). These clouds also pose a challenge to atmospheric radiative transfer and remote sensing studies. As the optical properties of ice crystals are fundamental to quantifying the radiative

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Quanhua Liu, Alexander Ignatov, Fuzhong Weng, and XingMing Liang

monitors corresponding Community Radiative Transfer Model (CRTM) model minus observation (M − O) biases and corresponding SST differences over the global ocean ( Liang and Ignatov 2011 ). Data in MICROS suggest that global mean M − O biases in VIIRS M12 [and their corresponding standard deviations (STDs)] are −0.01 K (0.46 K) at night and −1.26 K (1.44 K) during the daytime (example numbers are for 11 February 2013, also representative for other days). As a result, the corresponding VIIRS SST biases

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Mark A. Broomhall, Leon J. Majewski, Vincent O. Villani, Ian F. Grant, and Steven D. Miller

scattering species is much smaller than the wavelength of the radiation being scattered. Visible light is Rayleigh scattered by the gaseous constituents of the atmosphere with the interaction more predominant at the blue end of the spectrum, meaning that uncorrected color imagery from space will have an unwanted “bluish haze.” Quantitative description and correction for the RS component of the satellite-observed signal requires the use of a radiative transfer model. Models such as MODTRAN5 ( Berk et al

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Zhongping Lee and Shaoling Shang

recognizing an object. It is thus necessary to assign a proper attribute—detectability or identifiability—to the visibility values predicted by the Koschmieder model. In this article, after discussions regarding the theoretical derivations and assumptions associated with the CVT, we present a general relationship regarding contrast transmission based on radiative transfer and discuss the impact of the angular dimension of the observed target on contrast reduction. Subsequently, a general model of

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