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

1. Introduction It has long been recognized that clouds play a dominant role in the earth’s climate and its changes. Clouds strongly affect the energy balance and water cycle, two dominant processes in the climate system. Low-level boundary layer clouds have the most significant influence on cloud radiative forcing because of their areal extent and frequency ( Harrison et al. 1990 ; Hartmann et al. 1992 ). Radiation absorbed by boundary layer clouds also plays an important role in the

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Ronald M. Errico, George Ohring, Fuzhong Weng, Peter Bauer, Brad Ferrier, Jean-François Mahfouf, and Joe Turk

for data assimilation have varying degrees of reliability. Atmospheric dynamics at horizontal scales larger than 100 km or so are typically handled quite well both in terms of analysis and short-term forecast skill. Moreover, operational NWP models are able to predict the location in space and time of clouds associated with large-scale organized systems, but their skill degrades as the strength of synoptic forcing or the degree of larger-scale organization decreases. Large uncertainties remain in

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Philippe Lopez

turbulence. Before discussing the ingredients that are needed to build efficient moist physical parameterizations, it should be recalled that an accurate modeling of the dynamical forcings is, of course, an absolute prerequisite to any realistic simulation of clouds and precipitation. b. Resolved moist processes 1) Existing parameterizations Many prognostic large-scale cloud schemes suitable either for NWPMs or GCMs have been proposed in the literature during the last 30 yr; these can be divided into

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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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K. Franklin Evans

1. Introduction Variational assimilation of visible and infrared radiances by numerical models in cloudy skies requires forward and adjoint radiative transfer models capable of handling scattering. When cloud properties are the target of the assimilation, visible and near-infrared satellite radiances should be considered because reflected solar radiation provides important information about cloud water path and particle size (e.g., Twomey and Cocks 1982 ). Due to heavy computational costs and

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Ronald M. Errico, Peter Bauer, and Jean-François Mahfouf

1. Introduction At any time, approximately 50% of the earth is covered by clouds. Through their effects on both upward and downward transmittance of radiation, they profoundly affect the surface and atmospheric heat budgets. A small percentage of the clouds are precipitating, making up a key component of the earth’s hydrological cycle and, through release of latent heat of evaporation, an internal source of atmospheric heating. The accurate analysis of clouds and precipitation is therefore

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Arthur Y. Hou and Sara Q. Zhang

assimilation by assuming that forecast errors in rain arise primarily from model deficiencies rather than the initial condition and explored the benefits of relaxing the perfect model assumption. In a series of experiments, they showed that rainfall assimilation using the VCA scheme is effective in improving global analyses and forecasts, providing a strong incentive for pursuing the weak constraint approach for assimilating precipitation-related information such as clouds, rain, and latent heating, either

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