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

quantify the precipitation rate. With GPM, the design of the PMW precipitation retrieval is a Bayesian-based estimation for all surfaces, whose veracity and accuracy relies upon the capability to physically model the multichannel TB of all scenes, and under all atmospheric conditions ( Kummerow et al. 2011 ). A probabilistic Bayesian type of estimate is appropriate for inversion of profiling radar and/or radiometric observations of precipitation, since the space–time variability of the factors

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

ground sensors since they can provide fine-resolution observations of precipitation at quasi-global scale, uninhibited by mountains or spatial inconsistencies ( Arkin and Ardanuy 1989 ; Kidd et al. 2003 ; Anagnostou et al. 2010 ). The significance of these advantages has been recognized by the hydrologic community, and numerous studies have focused on the use of satellite precipitation retrievals in hydrologic applications in the past two decades (e.g., Guetter et al. 1996 ; Tsintikidis et al

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

trends over recent decades ( Riddle and Cook 2008 ; Williams et al. 2012 ). Satellite precipitation products can thus be a fundamental resource, especially in poorly gauged regions such as East Africa (EA; e.g., Kucera et al. 2013 ). This region experienced in the 2001–11 time frame some of the worst droughts to date, culminating in the high-impact drought in 2010/11 ( Nicholson 2014 ). The frequency and impacts of these extreme events call for a continuous monitoring of precipitation for regional

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

parameterize a statistical technique to create high-quality precipitation maps using gauge stations. A weighted climate–elevation regression function is also used to account for the influence of elevation on climate. Daily and monthly PRISM data are available over the continental United States (CONUS) for several decades. Daily products are used in this study. 2) Satellite products The following satellite-based precipitation products are utilized in the present study: 1) Tropical Rainfall Measuring Mission

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

gauge data. Given the findings of such studies, in order to compare the performance of PERSIANN-CDR with another high-resolution satellite-based precipitation product, TMPA 3B42, version 7 (hereafter TMPA), for the period of 2003–10 is chosen. b. PERSIANN-CDR PERSIANN-CDR ( Ashouri et al. 2015 ) is a newly developed and released satellite-based precipitation product that covers more than three decades (from 1 January 1983 to present) of daily precipitation estimations at 0.25° resolution for the 60

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