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Peter M. Norris and Arlindo M. da Silva

polar orbiting satellites by Remote Sensing Systems (version 5) using the algorithm of Wentz (1997) . The data comes in daily files, one for each satellite, each containing CLWP mapped to a regular grid (0.25° × 0.25° resolution) complete with data gaps between orbits. Two maps exist per file, one of ascending orbit segments and the other of descending orbit segments. Data on each of the segment maps are overwritten at both the high latitudes where successive orbits cross and at the “seam” or

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Fuzhong Weng

. A similar process has been also developed for 1DVAR SSM/I retrieval at Meteorological Service of Canada (MSC; Deblonde et al. 2007 ). At National Oceanic and Atmospheric Administration (NOAA), Weng et al. (2007) also developed a Hybrid Variational Scheme (HVAR), which is a 1DVAR retrieval of temperature profiles from the AMSU instrument, and 4DVAR assimilation of retrieved temperature profiles. A similar algorithm has also been tested in the UK Met Office ( English and Une 2006 ). During the

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

Special Sensor Microwave Imager (SSM/I) on the Defense Meteorological Satellites ( Wentz 1997 ), the Advanced Microwave Sounding Unit (AMSU) on the National Oceanic and Atmospheric Administration (NOAA) NOAA-15 , -16 , and -17 platforms ( Grody et al. 2001 ; Ferraro et al. 2005 ), and the Advanced Microwave Scanning Radiometer (AMSR-E; Ashcroft and Wentz 2000 ) on the Aqua satellite. These algorithms utilize the microwave signature emitted by cloud droplets. Microwave retrievals of cloud LWP

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Arthur Y. Hou and Sara Q. Zhang

global hydrological cycle remains a serious problem, even in state-of-the-art NWP analysis systems ( Andersson et al. 2005 ). In seeking ways to use the available precipitation data more effectively, one strategy is to develop assimilation algorithms using the forecast model as a weak constraint to improve the precipitation observation operator within an analysis cycle. For example, model errors of a prescribed temporal form can be estimated along with other increments of state variables within a

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K. Franklin Evans

linear, and adjoint models. Section 3 describes testing of the forward, tangent linear, and adjoint models, and shows examples of the adjoint sensitivities for cloud model fields. 2. The SHDOMPP and SHDOMPPDA algorithms a. SHDOMPP SHDOMPP calculates unpolarized radiative transfer in a plane-parallel medium for either collimated solar and/or thermal emission sources of radiation. The optical properties of the medium input to SHDOMPP are assumed to be uniform in each layer, which is different from

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

absorption coefficients for each layer using the algorithms of Rosenkranz (1998) and Liebe et al. (1991) . For cloud water and ice, spherical monodisperse drop sizes of 10 and 20 μ m are assumed, respectively, and their optical properties were calculated using the Rayleigh approximation (this is valid even up to frequencies of 183 GHz for the sizes assumed). Profiles of rain and snowfall rates were converted to optical properties of extinction, single-scatter albedo, and asymmetry parameter using the

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Graeme L. Stephens and Christian D. Kummerow

likely state taken to be the retrieval state. A version of this approach, popularized by Rodgers (1990 , 2000 ) for problems dealing with sounding retrievals, is generally referred to as the optimal estimation method. Probabilistic methods have been applied in a number of cloud and precipitation retrieval problems, including the Tropical Rainfall Measuring Mission (TRMM) Goddard Profiling Algorithm (GPROF; Kummerow et al. 2001 ) and precipitation examples of Evans et al. (1995) or Mugnai et al

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Fuzhong Weng, Tong Zhu, and Banghua Yan

observations, many attempts were made to improve hurricane analyses for forecasts. Krishnamurti et al. (1991) developed a method to physically initialize the Florida State University global cumulus parameterization spectral model, which mainly depends upon the surface rain rates derived from the Special Sensor Microwave Imager (SSM/I). A comparison study was conducted by Tibbetts and Krishnamurti (2000) to evaluate the performance of four different rain-rate algorithms in hurricane track forecast using

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Chinnawat Surussavadee and David H. Staelin

Microwave Sounder (ATMS; Muth et al. 2004 ). These instruments observe frequencies above 23 GHz with a spatial resolution of approximately 15–50 km. Retrieval accuracies at nadir are also predicted for proposed geosynchronous microwave sounders that could monitor precipitation at intervals as short as approximately 5–15 min ( Solman et al. 1998 ; Bizzarri et al. 2002 ). Development and analysis of AMSU precipitation retrieval algorithms for use at all angles is deferred to future papers. The use of

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

precipitation, rather than cloud, observations because timely quantitative precipitation data from surface networks and satellite inversion algorithms have been more widely available. Even though the improvement of quantitative precipitation forecasts is a strong justification for using precipitation data in analysis systems, it seems unlikely that these additional data alone will be sufficient to reach this goal. Complementary usage of cloud observations may be necessary to dramatically increase the

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