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Yong Chen, Yong Han, Quanhua Liu, Paul Van Delst, and Fuzhong Weng

are formidable problems in simulating the brightness temperature when considering the cell pressure shift in a line-by-line (LBL) radiative transfer (RT) model with global coverage and over the SSU observation periods. An LBL RT model such as LBLRTM ( Clough et al. 2005 ) is computationally expensive because it requires the averaging of multiple monochromatic calculations within a spectral band. When the spectral response functions (SRFs) change, the entire LBL computation has to be recalculated

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Fred G. Rose, David A. Rutan, Thomas Charlock, G. Louis Smith, and Seiji Kato

clouds and radiative swath (CRS) ( Charlock et al. 1997 , 2006 ), contains modeled irradiances computed by a two-stream radiative transfer model for nearly all CERES footprints. Because estimated global surface irradiance often relies on satellite observations, computations test the accuracy of modeled irradiance by such a radiative transfer model ( Wielicki et al. 1995 ). The CERES project derives TOA irradiances from observed radiances using angular distribution models ( Loeb et al. 2005

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Quanhua Liu, Xingming Liang, Yong Han, Paul van Delst, Yong Chen, Alexander Ignatov, and Fuzhong Weng

1. Introduction The Community Radiative Transfer Model (CRTM) is a sensor-band-based fast radiative transfer model developed at the Joint Center for Satellite Data Assimilation (JCSDA; Han et al. 2006 ). It is a key component in the U.S. data assimilation for weather forecasting at the National Centers for Environmental Prediction (NCEP) of the National Oceanic and Atmospheric Administration (NOAA). It is used in conjunction with the atmospheric and surface data from users of many applications

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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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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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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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Philipp M. Kostka, Martin Weissmann, Robert Buras, Bernhard Mayer, and Olaf Stiller

respective observation types, are vital parts of modern DA systems. For variational DA systems, their linearized and adjoint versions are also required, while for ensemble DA systems the forward operator itself is sufficient. For satellite radiances, the forward operator includes a radiative transfer (RT) model that computes the radiances that would be measured by the satellite instrument for a given atmospheric state. In the presence of clouds, RT computations can become very demanding ( Liou 1992

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B. Petrenko, A. Ignatov, Y. Kihai, and A. Heidinger

possible. Achieving this goal requires closer consideration of cloud effects on the specific products (e.g., Cayula and Cornillon 1996 ; Martins et al. 2002 ; Pellegrini et al. 2006 ), in our case SST and CSR. For this reason the emphasis in ACSM has been made on using simulations with clear-sky TIR radiative transfer model (RTM) and retrieved SST rather than on exploiting radiative properties of clouds. Another difference between CLAVRx and ACSPO is that ACSPO less relies on using reflectance

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