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Ali Behrangi, Koulin Hsu, Bisher Imam, and Soroosh Sorooshian

1. Introduction With continuous improvement over the past three decades, satellite precipitation estimation techniques now offer the means to map both occurrence and distribution of global rain rate. With the deployment of the first Special Sensor Microwave Imager (SSM/I; Hollinger et al. 1987 ), passive microwave (PMW) remote sensing of precipitation was recognized as the most reliable source of instantaneous precipitation estimates ( Adler et al. 2001 ; Ebert et al. 1996 ). To date, all

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Meike Kühnlein, Boris Thies, Thomas Nauß, and Jörg Bendix

resolution. The variety of existing satellite-based rainfall retrieval techniques can be categorized by their complexity. Because the identified demand for area-wide precipitation detection in a high spatiotemporal resolution necessary for a quasi-continuous rainfall monitoring in near–real time can only be fulfilled by geostationary satellite systems, the following overview is restricted to optical sensors available on geostationary satellite systems. A comprehensive overview of existing satellite

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M. Tugrul Yilmaz, Paul Houser, Roshan Shrestha, and Valentine G. Anantharaj

and atmospheric cycles. There are three main methods employed to acquire the precipitation information: ground observations (gauges and radars), numerical model simulations, and satellite-based techniques. Gauges are regarded as the most reliable direct precipitation estimation method. However, they are unable to sample large-area spatial means because of sparse or nonexisting spatial coverage, are often subject to wind-induced undercatch, and have significant cold-season precipitation observation

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Alan J. Geer, Peter Bauer, and Christopher W. O’Dell

satellite observations (e.g., Kummerow 1998 ). Here, even when two fields of view contain the same mass of rain or cloud, variations in fractional cloudiness can cause large differences in measured radiances. Rain- and cloud-affected microwave radiances are assimilated at the European Centre for Medium-Range Weather Forecasts (ECMWF; Bauer et al. 2006a , b ), improving forecasts of tropical moisture and wind ( Kelly et al. 2008 ). However, large biases between simulated and observed brightness

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Hilawe Semunegus, Wesley Berg, John J. Bates, Kenneth R. Knapp, and Christian Kummerow

and Atmospheric Administration (NOAA) National Climatic Data Center (NCDC) through the Comprehensive Large-Array Stewardship System (CLASS). This paper addresses the techniques that have been applied to improve, extend, and reprocess the available SSM/I data from NCDC’s archive. SSM/I data measurements have been used extensively to generate climate datasets in support of both national and international programs. Ferraro et al. (1996) developed a set of hydrological products at 1° and 2.5° grid

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J. J. Shi, W-K. Tao, T. Matsui, R. Cifelli, A. Hou, S. Lang, A. Tokay, N-Y. Wang, C. Peters-Lidard, G. Skofronick-Jackson, S. Rutledge, and W. Petersen

estimation algorithms ( Petersen et al. 2007 ). In this study, the Weather Research and Forecasting model (WRF) with the Goddard microphysics scheme was utilized. WRF has also been coupled with multisensor, multifrequency satellite simulators in the Goddard Satellite Data Simulation Unit (SDSU) for model evaluation and GPM algorithm support. The goal is to combine radar, satellite, and in situ measurements in addition to model data to improve precipitation measurement. The Goddard cloud microphysics

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Frank S. Marzano, Domenico Cimini, Tommaso Rossi, Daniele Mortari, Sabatino Di Michele, and Peter Bauer

profiles ( Marzano et al. 2009 ). These simplified inversion algorithms were developed for an algorithm intercomparison analysis over a specific target area using a case study. A systematic feasibility study of the FLORAD potential capabilities should generalize and verify these results through more sophisticated retrieval techniques, such as the variational ones, over different regions at the global scale and with a bigger data sample ( Deblonde and English 2003 ; Bauer and Di Michele 2007 ). In view

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Cristian Mitrescu, Tristan L’Ecuyer, John Haynes, Steven Miller, and Joseph Turk

numerical model runs (produced operationally at the European Centre for Medium-Range Weather Forecasts) are also used to flag the precipitation phase. To keep consistency between the present profiling algorithm and that developed by Haynes et al. (2009) , we adopt all the tests and thresholds defined within. More details about these two precipitation techniques can be downloaded from the Data Processing Center at Colorado State University ( ). Given the

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Rémy Roca, Philippe Chambon, Isabelle Jobard, Pierre-Emmanuel Kirstetter, Marielle Gosset, and Jean Claude Bergès

. 2008a ). Its future evolution is also of concern in the context of the global climate change (e.g., Giannini et al. 2008b ). The need for a deeper understanding and forecasting capability of the WAM prompted the community to devote a vast observational program over the region, the African Monsoon Multidisciplinary Analysis (AMMA; Redelsperger et al. 2006 ); the data from the AMMA campaign are used in this study. The main feature of the seasonal march of the monsoon is the rapid onset occurring in

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Jonathan J. Gourley, Yang Hong, Zachary L. Flamig, Li Li, and Jiahu Wang

applied. Because the gauges are automated, most of the instruments are either tipping bucket or weighing gauges. Erroneous rain gauge data are removed according to a manually edited, infrequently updated “bad gauge list.” Rain gauge measurements are objectively analyzed on a grid having 4.76-km spacing using the optimal estimation technique described in Seo (1998) . The method accounts for the fractional coverage of rainfall due to sparse gauge networks and rainfall fields with high spatial

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