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

24-hourly scales at 4-km spatial resolution at Hydrologic Rainfall Analysis Project (HRAP) national grid system. Stage IV radar data are manually quality controlled at NWS River Forecast Centers (RFCs). (More information about stage IV data can be obtained from .) Stage IV precipitation data have been used in different studies (e.g., Ebert et al. 2007 ; Zeweldi and Gebremichael 2009 ; Anagnostou et al. 2010 ; Ashouri et al. 2015 ). In this study

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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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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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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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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

products through hydrologic simulation in a fully distributed hydrologic model . Water Resour. Res. , 47 , W06526 , doi: 10.1029/2010WR009917 . Briggs, W. M. , and Levine R. A. , 1997 : Wavelets and field forecast verification . Mon. Wea. Rev. , 125 , 1329 – 1341 , doi: 10.1175/1520-0493(1997)125<1329:WAFFV>2.0.CO;2 . Casati, B. , Ross G. , and Stephenson D. B. , 2004 : A new intensity-scale approach for the verification of spatial precipitation forecasts . Meteor. Appl. , 11 , 141

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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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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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Toshi Matsui, Jiun-Dar Chern, Wei-Kuo Tao, Stephen Lang, Masaki Satoh, Tempei Hashino, and Takuji Kubota

. 2009 , 2014 ). The MMF uses the Community Land Model (CLM; Bonan et al. 2002 ) to predict the land surface turbulent heat flux and skin temperature in each GEOS4 grid. The MMF was initialized with European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim; Dee et al. 2011 ) and integrated over the entire month of June 2008. NICAM was developed to study the capabilities of fully 3D global CRMs ( Tomita and Satoh 2004 ; Satoh et al. 2008 , 2014 ). The NICAM

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