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

observational/sampling errors. In the first scenario, we have evaluated the performance change resulting from using different land surface parameters as truth. The second scenario assessed the benefit of adding more precipitation products. In the third scenario, the effect of time-window width was explored. The last scenario investigated the impact of using the merging method with noncalibrated models. 1) Optimization variable effect In the first experiment, the optimization variable effect was explored

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

. Remote Sens. , 46 , 99 – 108 . Surussavadee , C. , and D. H. Staelin , 2008b : Global millimeter-wave precipitation retrievals trained with a cloud-resolving numerical weather prediction model, Part II: Performance evaluation. Trans. Geosci. Remote Sens. , 46 , 109 – 118 . Tapiador , F. J. , C. Kidd , V. Levizzani , and F. S. Marzano , 2004 : A neural networks-based PMW-IR fusion technique to derive half-hourly rainfall estimates at 0.1° resolution. J. Appl. Meteor. , 43

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

simple odd–even yearday criterion with odd days chosen for training and model development and even days being retained for evaluation and comparison. 3. Method As described in section 1 , PERSIANN-CCS algorithm includes four key steps, which are cloud segmentation, cloud-patch feature extraction, cloud-patch classification, and rain-rate estimation. PERSIANN-MSA, which is a multispectral gridbox-based approach, furnishes an alternative method for rain-rate estimation through multidimensional

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

scaled using the TRMM Combined Instrument. Applying a bias correction without the need for monthly gauge accumulations will have significant benefits for real-time users of rainfall products, especially in ungauged basins. PERSIANN-CCS has been evaluated in the continental United States for its general performance ( Hong et al. 2004 ) and in the complex terrain region in western Mexico for its ability to capture the climatological structure of precipitation with respect to the diurnal cycle and

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

28 April 2006, is to provide, from space, the first global survey of cloud vertical structure, layering, and content. Placed in a sun-synchronous polar orbit (1330 local time ascending node), CloudSat is capable of capturing the seasonal and geographical distributions necessary to evaluate, understand, and ultimately improve the way clouds and cloud feedbacks are handled within global weather and climate forecast models ( Stephens et al. 2002 ). With its nominal 2-yr mission lifetime having

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Feyera A. Hirpa, Mekonnen Gebremichael, and Thomas Hopson

living along the river. For example, according to the NOAA State of the Climate Global Hazards Monthly Report for August 2005 (available online at http://www.ncdc.noaa.gov/sotc/?report=hazards&year=2005&month=8&submitted=Get+Report ; verified in September 2009), 7500 people were forced out of their homes as the Awash River rose above the flood stage and at least 5000 ha of agricultural land were submerged just in August of 2005. The need for evaluating and improving the accuracy of satellite

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

1. Introduction The small-scale variability of cloud and precipitation must be carefully modeled in order to get accurate simulations of atmospheric radiative transfer. For example, the amount of overlap between different cloud layers can strongly affect quantities such as heating rates and the earth’s albedo (e.g., Morcrette and Fouquart 1986 ; Morcrette and Jakob 2000 ). At microwave frequencies, the nonlinear dependence of radiance on hydrometeor amount causes a “beamfilling effect” in

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

Global Precipitation Climatological Center is used for a monthly rescaling ( Huffman et al. 1997 ). 2) The Error Model The evaluation of the satellite products sampling errors when averaged at some scales from instantaneous estimates is a difficult task. Several approaches have been explored, all involving the modeling of the spatial covariance functions of the rain field or its autocorrelation function. Direct estimation using the integrated estimates of the satellite rain retrievals and the “true

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B. J. Sohn, Hyo-Jin Han, and Eun-Kyoung Seo

, Japan, as a visiting professor. This work was supported by the Korea Meteorological Administration Research and Development Program under Grant CATER 2006-2103. REFERENCES Arkin , P. A. , and P. Xie , 1994 : The Global Precipitation Climatology Project: First algorithm intercomparison project. Bull. Amer. Meteor. Soc. , 75 , 401 – 419 . Betts , A. K. , and C. Jakob , 2002 : Evaluation of the diurnal cycle of precipitation, surface thermodynamics, and surface fluxes in the ECMWF model

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