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

(radars and lidars) providing information on the vertical distribution of clouds and precipitation. The recently launched CloudSat carries a cloud radar that measures vertical profiles of cloud water and ice and vertical cloud boundaries with a 250-m resolution. The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite has a 2-wavelength lidar that measures ice and water extinction profiles, and cloud heights of optically thin clouds, with a vertical resolution of 30

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Fuzhong Weng

advanced microwave and infrared sensors will be deployed in space with higher spatial and spectral resolution so as to increase their sensitivity to aerosols, clouds, precipitation, and surface parameters beyond that of current instruments. To utilize the data from current sensors as well as the next generation of instruments, the forward-modeling capability needs to be enhanced to include the scattering and polarization resulting from these atmospheric and surface features. Only then will the

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Philippe Lopez

observed spatial distribution of clouds and their various effects on the environment, through latent heat release, radiative effects, and precipitation ( Browning 1993 ). Taking advantage of the precious information about moist processes brought by observing systems and laboratory experiments, numerical weather prediction models (NWPMs) and (climate) general circulation models (GCMs) have become able to describe clouds and precipitation with an increasing level of realism and accuracy. However, current

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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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K. Franklin Evans

output radiative quantities are upwelling and downwelling hemispheric fluxes and mean radiances at the layer boundaries and radiances at specified levels and directions. SHDOMPP represents the radiation field using the source function from which the radiance field can be obtained by integrating the radiative transfer equation. The angular aspects of the source function are represented with spherical harmonics series, while the spatial aspects are represented with a discrete vertical grid. Only cosine

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Fuzhong Weng, Tong Zhu, and Banghua Yan

through clouds and precipitation and provide rich information on hurricane structures (e.g., temperature, moisture, rain rate, and surface winds) from the signals received by the sensor. In this study, we are applying the measurements from two microwave sensors for hurricane model initialization. Currently, NCEP’s Global Data Assimilation System (GDAS) uses a 3DVAR approach to assimilate microwave radiances and products, such as AMSU radiances, SSM/I surface wind speed, and precipitation products, in

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Peter M. Norris and Arlindo M. da Silva

assimilation. In section 3 we discuss the need for the assimilation of cloud data within GEOS-4. We also give a brief overview of our current cloud assimilation system and describe the various experiments that led us to arrive at that system. Section 4 then presents the technical details of the system. Section 5 draws some conclusions as they relate to the motivation, design, and implementation of the system. A follow-up paper (Part II) will present detailed results of seasonal assimilation

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

the presence of clouds and precipitation is sensitive to the characterization of these details. Since such details currently are neither analyzed nor modeled well, if at all, radiance observations suspected of being affected by them are often discarded. This includes perhaps half of all current satellite observations that could otherwise be considered for data assimilation. The discarded satellite data presumably contain useful information, not only about the standard dynamical and moisture fields

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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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Graeme L. Stephens and Christian D. Kummerow

precipitation has emerged as a priority in earth observations. Although most past and current observational programs unrealistically treat clouds and precipitation as separate entities, it is the contention of this paper that there is much to be gained scientifically in moving away from these artificial practices toward observing clouds and precipitation properties jointly. Past studies that seek to characterize the distributions of key cloud and precipitation properties and determine the principal factors

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