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

) reported that optically thin cirrus clouds with visible optical depths less than 1.4 were found in 20% of the HIRS data from 1979 to 2001. The effect of cirrus clouds on the energy balance of the earth–atmosphere system is a topic of critical importance because on the one hand, they affect solar radiation, referred to as the albedo effect, and on the other hand, they trap a significant amount of thermal infrared radiation emitted from the atmosphere below and the surface, referred to as the greenhouse

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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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Fuzhong Weng

and Weng 2006 ). The dielectric constant of water is computed from a microwave model ( Ulaby et al. 1986 ) and an infrared and visible model ( Irvine and Pollack 1968 . To quantify the effect of aerosols in the data assimilation system, a radiative transfer model including aerosol is requested. In the current NOAA air quality forecast and satellite data assimilation system, aerosol distributions and types are taken from Goddard Chemistry Aerosol Radiation and Transport (GOCART) model that

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Graeme L. Stephens and Christian D. Kummerow

’s storm systems and, in turn, to the precipitation produced by these systems. Clouds further exert a profound influence on the solar and infrared radiation that enters and leaves the atmosphere. This influence is complex and not entirely understood, yet it has the potential to exert profound effects on climate and on forces that affect climate change ( Stephens 2005 ). It is for these reasons, among others, that the need to observe the distribution and variability of the properties of clouds and

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

satellites provide the bulk of observations, are now almost as accurate as those for the Northern Hemisphere. However, the progress in forecasting weather elements that are of particular public interest, such as clouds, quantitative precipitation, and precipitation type, has been less dramatic. To date, the assimilation of satellite measurements has focused on the clear atmosphere. But satellite observations in the visible, infrared, and microwave provide a great deal of information on clouds and

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

in a −25 W m −2 change in the net cloud forcing at a solar zenith angle of 75°. Satellites provide the only means of acquiring global and long-term LWP estimates. The LWP is estimated from satellite measurements of either microwave radiation emitted by the cloud or visible/near infrared (NIR) solar reflectance from the cloud. Beginning in the 1980s, several efforts have been made to determine the global distribution of cloud LWP from satellite microwave measurements, such as those made by the

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

. Int. J. Infrared Millimeter Waves , 12 , 659 – 675 . Liou , K-N. , 2002 : An Introduction to Atmospheric Radiation . Academic Press, 583 pp . Mace , G. G. , and S. Benson-Troth , 2002 : Cloud-layer overlap characteristics derived from long-term cloud radar data. J. Climate , 15 , 2505 – 2515 . Morcrette , J. J. , and C. Jakob , 2000 : The response of the ECMWF model to changes in the cloud overlap assumption. Mon. Wea. Rev. , 128 , 1707 – 1732 . O’Dell , C. W. , A. K

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

critical for characterizing climate. Since they directly or indirectly affect many human activities, their accurate prediction on several time scales is also strongly desired. Remote sensing now provides critical observations for analyzing the atmosphere. The propagation of infrared or microwave radiation is strongly affected by details of clouds or precipitation, including the shape and size distributions of hydrometeors. Thus retrieving temperature and moisture fields from radiance observations in

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Peter M. Norris and Arlindo M. da Silva

preliminary experiments to investigate the assimilation of surface and top-of-atmosphere radiation observations using a one-dimensional variational data assimilation (1DVAR) approach in which linearized cloud diagnostic parameterizations and radiative forward models are used to find increments to the control variables (temperature and humidity profiles and surface pressure) that minimize radiative flux differences between the forward model and cloudy radiance observations. Using both simulated

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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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