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Yumeng Tao, Xiaogang Gao, Kuolin Hsu, Soroosh Sorooshian, and Alexander Ihler

1. Introduction Weather forecasts, climate variability, hydrology, and water resources management require sufficient information about precipitation, one of the most important variables in the natural water cycle. Precipitation observation, monitoring, and analysis tools provide fundamental information needed in order for society to cope with increasing extreme hydrometeorological events in recent decades. Satellite-based precipitation products mainly estimate precipitation indirectly based on

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Yiwen Mei, Efthymios I. Nikolopoulos, Emmanouil N. Anagnostou, and Marco Borga

nearest neighbor interpolation technique. b. Hydrologic model The Integrated Catchment Hydrological Model (ICHYMOD) is used in this study. This is an offline version of the modeling scheme run operationally by the Hydrologic Office of the Autonomous Province of Bolzano as part of the Adige River Flood Forecasting System. ICHYMOD involves a semidistributed conceptual rainfall–runoff model that consists of a snow routine, a soil moisture routine, and a flow routine. This model has been successfully

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Chris Kidd, Toshihisa Matsui, Jiundar Chern, Karen Mohr, Chris Kummerow, and Dave Randel

evolved to extract information on precipitation from the satellite observations. Although the Vis–IR observations are relatively indirect, their frequent temporal availability from GEO sensors permits the timely production of near-real-time products for applications such as flood forecasting. The more direct observations made by PM sensors have led to a range of precipitation estimates using empirical and/or physically based schemes. Empirical techniques built on basic radiometric properties of

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Hamed Ashouri, Phu Nguyen, Andrea Thorstensen, Kuo-lin Hsu, Soroosh Sorooshian, and Dan Braithwaite

key hydrometeorological variables in generating floods—has gained significant attention in the recent past. Numerous efforts have been made to produce satellite-based precipitation estimates at high spatiotemporal resolution in global scale. Examples are the CPC morphing technique (CMORPH; Joyce et al. 2004 ), Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN; Hsu et al. 1997 , 1999 ; Sorooshian et al. 2000 ), PERSIANN–Climate Data Record

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E. Cattani, A. Merino, and V. Levizzani

1. Introduction Multidisciplinary studies and operational applications to water cycle and water management stimulate the exploitation of satellite precipitation estimates (SPEs) thanks to the growth of long-term (10 years or longer), space-based datasets. Satellite precipitation real-time and rapid update products also enter the assimilation schemes of numerical weather prediction (NWP) models, contributing to improve short-range precipitation forecasts of extreme rainfall ( Michaelides et al

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Clément Guilloteau, Rémy Roca, and Marielle Gosset

. (2009) and Sohn et al. (2010) aggregated satellite rain fields at various spatiotemporal resolutions to compare them with ground data. In this paper, the rain/no rain discrimination ability of a suite of high-resolution products derived from spaceborne passive sensors is evaluated in West Africa. Products considered are the Tropical Amount of Precipitation with an Estimate of Errors (TAPEER) intermediate data rain mask, Climate Prediction Center morphing technique (CMORPH), Global Satellite

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Mark S. Kulie, Lisa Milani, Norman B. Wood, Samantha A. Tushaus, Ralf Bennartz, and Tristan S. L’Ecuyer

“radar reflectivity” or “reflectivity” for brevity) profiles with 240-m grid spacing in the CloudSat data products. The following level 2 products (release R04) are used in this study: 2B-Geometric Profile (2B-GEOPROF), 2C-Snow Water Content and Snowfall Rate (2C-SNOW-PROFILE), 2B-Cloud Scenario Classification (2B-CLDCLASS), 2C-Precipitation Column (2C-PRECIP-COLUMN), and European Centre for Medium-Range Weather Forecasts–Auxiliary (ECMWF-AUX). The aforementioned products are orbital swath products

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Abebe Sine Gebregiorgis, Pierre-Emmanuel Kirstetter, Yang E. Hong, Nicholas J. Carr, Jonathan J. Gourley, Walt Petersen, and Yaoyao Zheng

. Consequently, an improved understanding of the error structure of satellite precipitation estimates at quasi-global scale is particularly pertinent from a scientific perspective and would be valuable for numerous hydrometeorological applications such as quantitative precipitation forecasting and numerical weather prediction models ( Turk et al. 1999 ), flood forecasting and water resources monitoring ( Hong et al. 2007a ; Gebregiorgis and Hossain 2011 , 2013 ), land data assimilation ( Gottschalck et al

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