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

of CloudSat to light rainfall. Described herein is an algorithm and methodology for quantifying profiles of rain rate from measurements of radar backscatter. The technique is designed to augment the existing suite of level-2 environmental data records produced by CloudSat . In light of the multiple challenges (both algorithmic and sensor hardware) associated with harnessing the potential of this new sensor dataset, the results presented here are regarded as preliminary. 2. CloudSat’s 94-GHz

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Zhong Liu, Hualan Rui, William Teng, Long Chiu, Gregory Leptoukh, and Steven Kempler

? This paper describes ongoing work at GES DISC to develop an online information system prototype for the validation and intercomparison of global satellite precipitation algorithms. This prototype provides users with customized information on the expected bias and accuracy of the products and gives algorithm developers a better understanding of the strengths and weaknesses of different algorithmic approaches and data sources. Section 2 of this paper describes the system and data. Section 3

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

function of space, time, and rainfall intensity over a study region where there is excellent radar coverage and a dense gauge network. We provide an analysis of the spatial patterns, temporal variability, and intensities of rainfall from satellites, radars, rain gauges, and combinations. The secondary objective of this study is to help guide users of the individual algorithms, so that the rainfall products can be appropriately utilized for various applications in other regions outside of Oklahoma where

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Daniel Vila, Ralph Ferraro, and Hilawe Semunegus

and sense the emitted and scattered radiation by raindrops and precipitation-sized ice particles, respectively; and (iii) the conical-scan viewing geometry allows for maintaining a fixed viewing angle and a constant footprint size along the scan for each frequency ( Poe et al. 2001 ). The primary algorithm used in this particular study is an 85-GHz scattering-based algorithm over land, while a combined 85-GHz scattering and 19/37-GHz emission is used over ocean [see Ferraro (1997) appendix A1

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

Evaluate High Resolution Precipitation Product (PEHRPP; Turk et al. 2008 ), along the lines of previous validation efforts such as the Precipitation Intercomparison Projects and the Algorithm Intercomparison Projects of the Global Precipitation Climatology Project (GPCP; Arkin and Xie 1994 ; Ebert et al. 1996 ). The aim of PEHRPP is to characterize errors in various HRPPs on different spatial and temporal scales in different climate regimes to help developers improve their accuracy and help users be

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Chinnawat Surussavadee and David H. Staelin

clues to their physical origin. The retrieval algorithm used in this study ( Surussavadee and Staelin 2008c ) is summarized here in section 3 after presentation in section 2 of a new correction for radio-frequency interference on the National Oceanic and Atmospheric Administration NOAA-15 ( N15 ) and NOAA-16 ( N16 ) satellites that contaminates the two 183-GHz water vapor channels most sensitive to lower-tropospheric water vapor and midtropospheric hydrometeors. This interference arises from

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Tufa Dinku, Pietro Ceccato, Keith Cressman, and Stephen J. Connor

. Satellite data The evaluated satellite rainfall products are the African rainfall estimation algorithm (RFE; Herman et al. 1997 ; Xie et al. 2002 ), African rainfall climatology (ARC; Love et al. 2004 ), CMORPH ( Joyce et al. 2004 ), the National Aeronautics and Space Administration (NASA) Tropical Rainfall Measuring Mission (TRMM) 3B42 and its real-time version 3B42RT ( Huffman et al. 2007 ), and the Global Satellite Mapping of Precipitation moving vector with Kalman filter (GSMaP-MVK, hereinafter

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

precipitation estimation algorithms have been introduced and made operational during the past few years. Although some of these products depend on GEO single infrared channel to track cloud motions or fill the gap of PMW rain estimate [the Climate Prediction Center morphing method (CMORPH; Joyce et al. 2004 ) and the Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis (TMPA; Huffman et al. 2007 )], others use infrared data as a main rain estimator after being adjusted by PMW estimate

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Long S. Chiu and Roongroj Chokngamwong

are from the unified microwave ocean retrieval algorithm (UMORA) applied to Special Sensor Microwave Imager (SSM/I) data on board the Defense Meteorological Satellite Program (DMSP) satellites ( Hilburn and Wentz 2008 ). Based on global rain maps produced by the Global Precipitation Climatology Project (GPCP), Adler et al. (2008) showed a 2.3% increase in global precipitation per 1°C increase in surface temperature, although their results are sensitive to the region and period of analysis

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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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