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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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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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Fuzhong Weng

radiances and those computed based on the NWP output of the atmospheric state. In the absence of cloud absorption and atmospheric scattering from precipitation an accurately parameterized radiative transfer model was used to assimilate satellite measurements into global NWP models for clear atmospheric conditions (e.g., Eyre 1989 ; Garand et al. 2001 ). However, in order to utilize the full capabilities of AMSU and other advanced instruments, which includes all weather conditions, an accurate

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

1. Introduction There is currently a need for fast yet accurate radiative transfer (RT) models for scattering atmospheres. Numerical weather prediction (NWP) models rely increasingly on assimilation of radiance data directly, rather than derived products ( English et al. 2000 ). Operational centers are beginning to assimilate microwave and infrared radiances under all weather conditions, instead of under clear skies only, as is currently done ( Greenwald et al. 2002 ). For example, recently the

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Fuzhong Weng, Tong Zhu, and Banghua Yan

NOAA-18 satellites and the Advanced Microwave Scanning Radiometer for Earth Observing System (EOS; AMSR-E) on board the National Aeronautics and Space Administration (NASA) EOS Aqua satellite. To extend radiance assimilation to all-sky conditions, we have upgraded the assimilation system with fast radiative transfer models that include scattering and emission from clouds and precipitation. 2. Satellite microwave observations under cloudy conditions The passive microwave radiation can penetrate

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

Aeronautics and Space Administration (NASA), and Department of Defense (DoD), sponsored an international workshop in May 2005. Participants included experts in the multiple scientific disciplines involved—satellite observations of clouds and precipitation, radiative transfer (RT), modeling clouds and precipitation in NWP, and data assimilation. 2. Issues concerning observations Starting with the Tropical Rainfall Measuring Mission (TRMM), passive instruments are now complemented with active sensors

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Chinnawat Surussavadee and David H. Staelin

1. Introduction To assimilate passive microwave precipitation observations or retrievals into numerical weather prediction (NWP) models, the modeled radiances must be consistent with those observed. This paper tests the sensitivity of that consistency to assumptions in a particular radiative transfer model (RTM), and in a cloud-resolving NWP model that predicts hydrometeor habits and profiles. The precipitation and water path retrieval accuracies are shown to be less sensitive to the physical

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Graeme L. Stephens and Christian D. Kummerow

parameters (as in Table 1 ) whose measurement over some prescribed time and space scale is the objective of the observing system. Two basic components define the observing system transfer function. The first component has to do with the fundamental relationship between the input parameters and the measurement. This relationship is most typically established by the physical principles of radiative transfer and related processes defined by nature’s forward model F ( x , b ) and the parameters b that

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Peter M. Norris and Arlindo M. da Silva

1. Introduction It is widely recognized that clouds play an essential role in moderating climate and are therefore an important feature to accurately model in GCMs. This is not a simple task, owing to the mismatch in scales between the typical GCM grid box (∼100 km) and the smaller scales at which clouds form and evolve and due to the complexity of cloud microphysical processes and their interaction with cloud dynamics and radiative transfer. As a result, clouds continue to represent a major

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

few regional model simulation experiments. The limitations of representativeness and potential errors embedded in the retrieval process can still therefore create undesirable correlations between errors in retrieved values. Most operationally available cloud and rain retrieval algorithms are based on offline simulations of combined models of cloud properties and radiative transfer that are fed into a training database. Similar or refined schemes are used for the observation operators in the

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