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Daniel Watters, Alessandro Battaglia, Kamil Mroz, and Frédéric Tridon

2016 ), though other studies have been conducted over Japan; Iguchi et al. (2016) compared Ku-band annual rainfall estimates from the GPM DPR to Automated Meteorological Data Acquisition System (AMeDAS) rain gauge data at 0.5° resolution, finding a DPR bias of −4.5%. However, this bias was found to be regionally variable with negative biases over land and positive biases over coastal areas. Recently, a few studies have been conducted over Europe. Speirs et al. (2017) compared the GPM DPR level

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Sarah D. Bang and Daniel J. Cecil

) data. Punge et al. (2017) developed a hail retrieval using infrared (IR) data from Meteosat Second Generation (MSG) satellites, applying an overshooting cloud-top detection algorithm developed by Bedka (2011) and Griffin et al. (2016) and used this retrieval to estimate a climatology of hail over Europe. All of the aforementioned approaches use ground reports of hail to train their hail retrievals. Leppert and Cecil (2015) and Mroz et al. (2017) use instead ground radar dual

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Xiang Ni, Chuntao Liu, Daniel J. Cecil, and Qinghong Zhang

different set of years used in each, and the less robust sample size in Fig. 4b . The largest concentration of hailstorms is indicated in central Africa, with other hot spots in the central and southeastern United States and in Mexico, Argentina, Bangladesh, and Pakistan. In midlatitude regions ( Fig. 4b ), there are fewer storms satisfying these criteria than in tropical and subtropical regions but there are considerable concentrations in Europe, eastern Eurasia, and central North America. There are

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M. Petracca, L. P. D’Adderio, F. Porcù, G. Vulpiani, S. Sebastianelli, and S. Puca

( Chandrasekar et al. 2008 ). For this reason, NASA and the Japan Aerospace Exploration Agency (JAXA) carried out an extensive ground validation program in North America (mainly the United States) and Europe ( Schwaller and Morris 2011 ), as well as partnering with various other groups elsewhere in the world. As an example, a scientific collaboration between the EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF) and GPM called “H SAF and GPM

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Yonghe Liu, Jinming Feng, Zongliang Yang, Yonghong Hu, and Jianlin Li

was obtained from the China Meteorological Data Sharing Service ( ). Overall 83 gauging stations with complete records were used here to train the single-station downscaling models, covering the period of 1979–2016. Another 12 stations having a small number of missing records were used to validate the gridded output in “no gauge” areas. ERA-Interim datasets (ERAI) from the European Centre for Medium-Range Weather Forecasts ( Dee et al. 2011 ) were obtained, and only those

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Xinxuan Zhang and Emmanouil N. Anagnostou

underestimation of precipitation by PERSIANN over the Ethiopian Plateau, and Milewski et al. (2015) reported underestimation of precipitation by the TMPA product over high-elevation areas of Morocco. In Europe, Stampoulis and Anagnostou (2012) found that CMORPH and TMPA underestimated rainfall over the Italian Alps region. In Asia, Chen et al. (2013) showed significant underestimation of the 2009 extreme Typhoon Morakot by the CMORPH, PERSIANN, and TMPA precipitation products, while Tong et al. (2014

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Catherine M. Naud, James F. Booth, Matthew Lebsock, and Mircea Grecu

are the AMSR-E precipitation product ( Kummerow et al. 2011 ), the GPCP One-Degree Daily precipitation product (GPCP-1DD; Huffman et al. 2001 ), and precipitation from two reanalyses: the European Centre for Medium-Range Weather Forecasts interim reanalysis (ERA-Interim; Dee et al. 2011 ) and the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2; Gelaro et al. 2017 ). The aim of this study is not to search for the best product but instead to diagnose specific

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Clément Guilloteau, Efi Foufoula-Georgiou, Christian D. Kummerow, and Veljko Petković

convective systems. In Guilloteau et al. (2017) , the best retrieval of the finescale patterns of surface precipitation over land from GMI with the GPROF algorithm (not accounting for the parallax) was seen over Europe and Siberia, where convection is less deep and the bright band is less elevated compared to lower latitudes. The 89-GHz channels of GMI have a smaller footprint at the surface than any of the lower-frequency channels (the 10.6-GHz footprint is 19 km × 32 km, while the 89-GHz footprint is

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Jiaying Zhang, Liao-Fan Lin, and Rafael L. Bras

al. (2016) revealed that the IMERG product has more skill in representing daily precipitation than the post-real-time Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA-3B42) and the ERA-Interim product from the European Centre for Medium-Range Weather Forecasts (ECMWF) in Iran from March 2014 to February 2015. For the midlatitude region of the Ganjiang River basin in southeast China, Tang et al. (2016b) showed that the detection skill of the Day-1 IMERG

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Dalia B. Kirschbaum, George J. Huffman, Robert F. Adler, Scott Braun, Kevin Garrett, Erin Jones, Amy McNally, Gail Skofronick-Jackson, Erich Stocker, Huan Wu, and Benjamin F. Zaitchik

constellation of satellites and ground systems from partner agencies located in the United States, Japan, Europe, and India ( Hou et al. 2014 ; Fig. 1 ). Fig . 1. The GPM constellation uses data from several microwave imagers and sounders, contributed by partner agencies and countries. As of May 2016, these include the U.S. Defense Meteorological Satellite Program (DMSP) series, JAXA Global Change Observation Mission–Water 1 (GCOM-W1), the NASA-JAXA GPM Core Observatory , the Centre National d

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