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Philippe Lopez

the model resolution is better than a few hundred meters but is more questionable at coarser resolution, especially in GCMs. For instance, Fowler et al. (1996) developed a large-scale condensation scheme for the Colorado State University (CSU) GCM with a separate prognostic treatment of cloud liquid water, cloud ice, rain, and snow contents. Their scheme also included physically based parameterizations of autoconversion of cloud condensate, depositional growth of snow, multiphase collection of

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Fuzhong Weng

land ( Weng et al. 2001 ). Prior to this model development, constant emissivity values were used for unfrozen land, snow cover, and sea ice in the NOAA global data assimilation system. Modeling the emissivity for such heterogeneous surfaces is a daunting task. In the case of snow it requires an understanding of radiative transfer theory for dense media ( Weng et al. 2001 ). For example, a more physically based emissivity model was developed for snow, which includes the volumetric scattering from

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Christopher W. O’Dell, Peter Bauer, and Ralf Bennartz

layers. Figure 1a shows the distribution of C max , the maximum layer cloud cover fraction of a profile. Both the winter and summer profile sets are similar, with most of the selected profiles having at least one fully cloud-covered layer. Figures 1b,c display the distribution of the rain and snow water path, which are simply the column-integrated mass of rain and snow, respectively. The summer profiles tend to have more rain than the winter cases, while the distributions for snow in each are

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Graeme L. Stephens and Christian D. Kummerow

-term statistics of cloud cover. ISCCP, for example, converts these radiances into a classification scheme based on cloud-top (pressure) height and cloud optical thickness ( Rossow and Schiffer 1999 ). GPCP was established in 1986 and merges infrared and microwave satellite estimates of precipitation with rain gauge data from more than 6000 stations ( Adler et al. 2003 ). These merged data are presented in the form of monthly mean precipitation data on a 2.5° × 2.5° latitude–longitude grid beginning in 1979

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Peter M. Norris and Arlindo M. da Silva

generalization of Slingo (1987) . The core of this scheme is a quadratic variation of cloud fraction, f , with relative humidity, RH, above a critical value, RH 0 , approaching complete cloud cover at 100% RH: The tuned version used in the GEOS-4 sets RH 0 = 87%. For mid–high clouds (<750 hPa) RH 0 is increased with a positive Brunt–Väisälä frequency to account for reduced subgrid-scale variability under stable conditions. For low clouds (≥750 hPa) RH 0 is reduced to 77% over snow-free land to account

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