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Qing Yue, K. N. Liou, S. C. Ou, B. H. Kahn, P. Yang, and G. G. Mace

1. Introduction Satellite data assimilation in numerical weather prediction models requires an efficient and accurate radiative transfer model for the computation of radiances and Jacobians. Present thermal infrared radiative transfer models for satellite data assimilation have been developed primarily for clear conditions (i.e., pure absorbing atmospheres). However, many studies have found that a great majority of satellite observations is “contaminated” by clouds. For example, Saunders (2000

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Peter M. Norris and Arlindo M. da Silva

source of uncertainty in GCM studies of future climate. Part of the historical problem has been that, in the face of these complexity and scale mismatch problems, simple empirical cloud parameterizations have been devised and then just tuned to give reasonable top-of-atmosphere radiative forcing in a globally or zonally averaged sense. Sufficient attention has not generally been given to the validation of the predicted cloud properties. In the NWP community even less attention has historically been

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Fuzhong Weng, Tong Zhu, and Banghua Yan

. Community radiative transfer model (CRTM) For a plane-parallel atmosphere, the radiance vector can be derived from and where 𝗠 is the phase matrix; I = [ I , Q , U , V ] T ; B ( T ) is the Planck function at a temperature T ; F 0 is the solar spectral constant; μ 0 and ϕ 0 are the cosine of zenith angle and the azimuthal angle of sun; μ and ϕ are the cosine of zenith angle and the azimuthal angle at scattering direction; ϖ is the single-scattering albedo; and τ is the optical

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Arthur Y. Hou and Sara Q. Zhang

/or SSM/I rain retrievals using the VCA scheme improves not only precipitation analysis but also related climate parameters such as the upper-tropospheric moisture and top-of-the-atmosphere radiative fluxes. Hou et al. (2004) provided examples of improved hurricane track forecasts and precipitation forecast threat scores using the VCA scheme. However, these early studies were based on limited months of assimilated data. In the following section, we will use four years of the GEOS-3 “TRMM reanalysis

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Ruiyue Chen, Fu-Lung Chang, Zhanqing Li, Ralph Ferraro, and Fuzhong Weng

cloud base is weak for such thick clouds. In these cases, the retrieved r e 2 probably represents the middle portion of a thick cloud and does not represent for the cloud base. Over all, the uncertainties in r e 2 can be larger by 2–3 factors than the uncertainties in r e 1 . Figure 3 shows the frequency distributions of the retrieved r e 1 and r e 2 for the data shown in Fig. 2 . While the mean and standard deviation of the r e 1 distribution are similar to those of the r e 3

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