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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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Chinnawat Surussavadee and David H. Staelin

. Rosenkranz’s efficient radiative transfer algorithm TBSCAT ( Rosenkranz 2002 ) that incorporated improved transmittance models ( Liebe et al. 1992 ; Rosenkranz 1998 ), and the complex permittivities for water and ice given by Liebe et al. (1991) and Hufford (1991) , respectively. Sea surface emissivity was computed using FASTEM ( English and Hewison 1998 ), which incorporates geometric optics, Bragg scattering, and foam effects. Sea surface temperatures and 10-m winds were provided by MM5, and land

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Graeme L. Stephens and Christian D. Kummerow

-window measurements The differential absorption and emission of infrared radiation by ice crystals smaller than about 30 μ m in size at two different wavelengths in the atmospheric IR window spectral region was proposed by Prabhakara et al. (1988) as a means for determining cirrus cloud optical thickness and particle size. Since then, the method has been applied extensively to satellite IR radiance data collected at or near wavelengths of 10.8 and 12 μ m. The conceptual idea of the method, referred to as the

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Fuzhong Weng, Tong Zhu, and Banghua Yan

derived from environmental observations. Wang (1995) also used the similar method to generate hurricane initial vortex by specifying wind field first and then invert mass field from the balance equation. Zou and Xiao (2000) developed a bogus data assimilation scheme, in which the sea level pressure of a hurricane vortex is specified according to the observed central pressure and the radius of maximum wind. This sea level pressure field is treated as observation to be included into a 4DVAR cost

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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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Peter M. Norris and Arlindo M. da Silva

techniques. The new scheme is called the Cloud Parameter Estimation System (hereafter CPES). There are at least three different approaches to the assimilation of satellite cloud observations. The direct assimilation of cloudy radiances. This requires a forward radiative transfer model that explicitly accounts for the presence of clouds. Cloud liquid water and ice content may be included as control variables. This approach is the most direct extension of the 3D and 4D variational methods used in

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

are not applicable over land because of the strong and highly variable microwave emission of the land surface. The emission from ocean surfaces is less variable, so cloud LWP can be estimated from satellite-observed microwave radiances. However, LWP retrieval accuracy is affected by the sea surface temperature, surface wind speed, atmospheric precipitable water vapor, and radiometric calibration while uncertainties in the absorption coefficients used in the microwave radiative transfer model also

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Arthur Y. Hou and Sara Q. Zhang

GPCP estimates. To assess the extent to which precipitation assimilation accounts for the agreement between the TRMM reanalysis and GPCP, a GEOS-3 control assimilation without rainfall data was performed for 1 May through 31 August 1998, which corresponds to the intensive observation period (IOP) of the South China Sea Monsoon Experiment (SCSMEX). Figure 5 shows that rainfall assimilation reduces the rms errors and improves temporal correlations of the GEOS-3 precipitation analysis relative to

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