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

WAM requires in-depth analysis of this multiscale variability of rainfall. Satellite observations are a powerful tool to cover these scales and to be used for these much needed meteorological investigations over the WAM where the pluviograph network is scarce. The recent generation of combined infrared (IR) and microwave (MW) products ( Hsu et al. 1997 ; Herman et al. 1997 ; Huffman et al. 2001 ; Joyce et al. 2004 ; Ushio et al. 2009 ; Huffman et al. 2007 ; Levizzani et al. 2007 ; Bergès et

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Chinnawat Surussavadee and David H. Staelin

radiometric background provided by oceanic reflections of cosmic radio waves originally near 3 K; fourth, emission from colder nonscattering precipitating hydrometeor layers (e.g., warm rain) can often be seen against the warmer background of microwave-opaque air below. The stochastic link between hydrometeors aloft and those reaching the ground varies with climate and terrain. This relationship can be revealed by faithful cloud-resolving numerical weather prediction models such as the fifth

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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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Song Yang, Fuzhong Weng, Banghua Yan, Ninghai Sun, and Mitch Goldberg

microwave measurements, from July 1987 to the present. These observations are being followed with a similar sensor, the Special Sensor Microwave Imager/Sounder (SSMIS), which will continue to operate for at least the next decade. This long record of consistent measurements from multiple similar sensors is extremely important in generating CDRs for climate change research and analysis. However, the long-term multiple SSM/I measurements are not accurate enough to be directly applied in climate

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

satellite in the A-Train. Aqua carries a suite of passive sensors such as the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit (AIRS/AMSU), Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), Clouds and the Earth’s Radiant Energy System (CERES), and Moderate Resolution Imaging Spectroradiometer (MODIS; http://aqua.nasa.gov ). The Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite, carrying the Cloud-Aerosol Lidar with Orthogonal

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

, 5 – 20 . Jackson , T. J. , A. Y. Hsu , and P. E. O’Neill , 2002 : Surface soil moisture retrieval and mapping using high-frequency microwave satellite observations in the Southern Great Plains. J. Hydrometeor. , 3 , 688 – 699 . Lawrence Livermore National Laboratory , cited . 2009 : CF metadata: NetCDF climate and forecast metadata convention. [Available online at http://cf-pcmdi.llnl.gov ] . National Research Council , 2004 : Climate Data Records from Environmental

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Axel Andersson, Christian Klepp, Karsten Fennig, Stephan Bakan, Hartmut Grassl, and Jörg Schulz

global water cycle datasets from retrievals of relevant ocean and atmospheric parameters such as sea surface temperature, winds, air humidity, and precipitation. Such datasets are provided with a better spatiotemporal sampling in comparison with in situ observations. The microwave part of the electromagnetic spectrum is ideally suited to retrieve precipitation and parameters useful to estimate latent heat flux and evaporation using a parameterization. At low microwave frequencies the emitted

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Feyera A. Hirpa, Mekonnen Gebremichael, and Thomas Hopson

1. Introduction The availability of high-resolution satellite precipitation products has made them very attractive for hydrological applications in regions that have less-dense and less-consistent ground-based measurements. Some of these products are available in (near) real time, making them suitable for flood-forecasting applications. The concept behind these high-resolution satellite precipitation algorithms is to combine information from the more accurate (but infrequent) microwave (MW

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Long S. Chiu and Roongroj Chokngamwong

decades. There are general agreements on the trend of global temperature, but there is less consensus on changes in global precipitation ( Folland et al. 2001 ; Hegerl et al. 2007 ; Karl and Trenberth 2003 ; Allen and Ingram 2002 ; Gu et al. 2007 ). Wentz et al. (2007) showed a 1.4 ± 0.5% increase in global precipitation and a 7% increase in the total amount of water in the atmosphere in response to a 1°C change in surface temperature from satellite observations. Their oceanic precipitation data

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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

1. Introduction The NASA Global Precipitation Measurement (GPM) mission is a multinational, multisatellite mission designed to provide a uniformly calibrated precipitation measurement around the world. GPM consists of two components: a core satellite and a constellation of satellites. The core satellite carries a dual-frequency precipitation radar and a microwave radiometric imager, known as the GPM Microwave Imager (GMI), with high-frequency channels. The constellation of satellites consists

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