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F. Joseph Turk, R. Sikhakolli, P. Kirstetter, and S. L. Durden

precalculated monthly mean emissivity climatology derived from the seven-channel (19–85 GHz) Special Sensor Microwave Imager (SSM/I) observations. TELSEM has been successfully implemented into the current (as of March 2015, version 1) GPM radiometer algorithm ( Kummerow et al. 2011 ), using a 15-class emissivity index to stratify the surface. With this algorithm, the Bayesian search is constrained to consider a priori candidate profiles that have the same classification index as the observation. In the

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

precipitation, such as the polarization-corrected temperature ( Spencer 1986 ; Kidd 1998 ) and scattering index ( Ferraro et al. 1998 ), have achieved a degree of success (see Kidd et al. 1998 ). However, while empirically based schemes are generally simpler and computationally faster, physically based schemes generally provide more information on precipitation, such as types of hydrometeor and even atmospheric profiles of precipitation. The Goddard profiling algorithm (GPROF; Kummerow et al. 2001

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

estimates from microwave (MW) and infrared (IR) sensors. One of the most popular multisatellite products, the TRMM Multisatellite Precipitation Analysis–real time (TMPA-RT) algorithm, combines multiple independent precipitation estimates from the TRMM Microwave Imager (TMI; Kummerow et al. 1998 ; McCollum and Ferraro 2003 ), Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E; Shibata et al. 2003 ; Turk and Miller 2005 ; McCollum and Ferraro 2003 ), Special Sensor Microwave

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

satellite-based precipitation products include the PMW-calibrated IR algorithm (PMIR; Kidd et al. 2003 ), the Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) algorithm ( Kuligowski 2002 ), and the Naval Research Laboratory Global Blended-Statistical Precipitation Analysis (NRLgeo; Turk and Miller 2005 ). Despite the efforts of linking multisatellite information to surface precipitation, the accuracy of satellite-based products still remains insufficient ( Boushaki et al. 2009 ). To deal

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Ali Behrangi, Bin Guan, Paul J. Neiman, Mathias Schreier, and Bjorn Lambrigtsen

algorithmic (e.g., poor precipitation detection prior to bias adjustment). Figure 8 also shows that T3B42RT ( Fig. 8b ) captures some feature of orographic precipitation, but not as effective as T3B42. CCS shows skill in capturing the orographic precipitation, but also shows significant false estimate in northeastern Washington. Fig . 8. Maps of mean precipitation rate (mm day −1 ) resulting from an AR that impacted western Washington on 6–8 Jan 2009: (a)–(f) mean precipitation rate from the different

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

retrieves snowfall rates from the CPR reflectivity observations via an optimal estimation approach to derive dynamic radar reflectivity Z e to snowfall rate S relationships ( Wood et al. 2013 ). The 2C-SNOW-PROFILE algorithm leverages the 2C-PRECIP-COLUMN product and automatically generates snowfall rate estimates when 2C-PRECIP-COLUMN indicates near-surface “snow possible” or “snow certain” conditions [e.g., see Haynes et al. (2009) , ( 2013 ) for further details]. The near-surface bin is defined

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Clément Guilloteau, Rémy Roca, and Marielle Gosset

1. Introduction The number of precipitation-relevant observation platforms and algorithmic developments has increased in recent decades, yielding a large corpus of satellite quantitative precipitation estimation (QPE) products over the tropics. The range of applications of the products includes climatology ( Biasutti and Yuter 2013 ; Roca et al. 2014 ), hydrological modeling ( Bitew and Gebremichael 2011 ; Cassé et al. 2015 ), vegetation monitoring ( Pierre et al. 2011 ), and infectious

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

(TRMM) Multisatellite Precipitation Analysis (TMPA) is a combined IR and microwave (MW) product from the National Aeronautics and Space Administration (NASA). The TMPA ( Huffman et al. 2007 ) is available with a near-real-time version adjusted according to a climatological correction algorithm (CCA; 3B42-CCA, hereafter named TR; Huffman et al. 2010 ). In addition to the near-real-time product, the postprocessing gauge-adjusted equivalent product [3B42, version 7 (3B42-V7); hereafter named aTR] is

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

PERSIANN are due to the nature of this being a real-time product with no gauge correction. Table 2 summarizes all the statistics for the three study basins. The lower BIAS in PERSIANN-CDR compared to PERSIANN shows the effectiveness of the bias-removal algorithm in reducing the bias in satellite estimates when compared to ground measurements. Possible reasons for large bias in the stage IV radar data are given in the discussion section. Fig . 4. Simulated and observed streamflow hydrographs and

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

/Sounder (SSMIS); and GOES precipitation index (GPI) estimates based on cloud-top temperature from the geostationary Meteosat satellites ( Xie and Arkin 1996 ). The algorithm is conceived as a two-stage approach: satellite estimates are first combined linearly using predetermined weighting coefficients and then compared with rain gauge data to remove as much bias as possible. Unlike the previous version of RFE, orographic effects are not included in RFE 2.0. The CMORPH global technique propagates SSMIS, SSM

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