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

. Changnon , and S. D. Hilberg , 2009 : Hailstorms across the nation: An atlas about hail and its damages. Illinois State Water Survey Contract Rep. 2009-12, 92 pp., . Cintineo , J. L. , T. M. Smith , V. Lakshmanan , H. E. Brooks , and K. L. Ortega , 2012 : An objective high-resolution hail climatology of the contiguous United States . Wea. Forecasting , 27 , 1235 – 1248 , doi: 10.1175/WAF-D-11-00151.1 . 10.1175/WAF-D-11

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Zeinab Takbiri, Ardeshir Ebtehaj, Efi Foufoula-Georgiou, Pierre-Emmanuel Kirstetter, and F. Joseph Turk

observations from the CloudSat Profiling Radar (CPR), the AMSU-B, and NOAA’s Microwave Humidity Sounder (MHS). More recently, Sims and Liu (2015) used the CloudSat radar and multiple ground-based reanalysis data, including near-surface air temperature, atmospheric moisture, low-level vertical temperature lapse rate, surface skin temperature, surface pressure, and land cover types to diagnose precipitation phase partitioning. This algorithm is deployed in the GPM operational precipitation retrievals

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Veljko Petković, Christian D. Kummerow, David L. Randel, Jeffrey R. Pierce, and John K. Kodros

instruments, limiting rainfall signals to an indirect, nonunique relationship between cloud ice-scattering signatures and surface rainfall. Based on the mean observed ratio between ice aloft and the surface rainfall, these estimates can often be inaccurate, with more pronounced biases observed during extreme events. In addition to the example given in study by Petković and Kummerow (2015) , a difference in mean precipitation rate bias between ground radar measurements and an operational satellite PMW

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Clément Guilloteau and Efi Foufoula-Georgiou

. 1996 , 2001 , 2015 ; Kubota et al. 2007 ; Gopalan et al. 2010 ; Mugnai et al. 2013 ; Ebtehaj et al. 2015 ; Kidd et al. 2016 ; Petković et al. 2018 ). The TRMM ( Kummerow et al. 2000 ) and GPM ( Hou et al. 2014 ; Skofronick-Jackson et al. 2017 ) satellite missions in particular provided the data and the research framework allowing the successful development of research and operational retrieval algorithms. Today, the GPM Microwave Imager (GMI) is integrated in an international constellation

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Kamil Mroz, Mario Montopoli, Alessandro Battaglia, Giulia Panegrossi, Pierre Kirstetter, and Luca Baldini

) and it lacks the polarimetric quality control, which may affect the accuracy of the collocation. Despite these limitations, the inclusion of these data greatly increases the validation dataset size ( CloudSat CPR was fully operational during 2006–11 whereas it has been operated in daylight only after a battery anomaly in March 2011). b. Satellite products The description of the satellite precipitation products is divided into subsections that reflect the instrument used for the retrieval. 1) GPM

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

microwave observations and 3D radar reflectivity profiles that can be used to define parametric empirical or physical relations between precipitation rates and microwave brightness temperatures. Various operational algorithms for the retrieval of surface precipitation from passive microwave such as NASA’s GPROF ( Kummerow et al. 2001 , 2015 ), JAXA’s GSMaP ( Aonashi et al. 2009 ), and the Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF)’s Cloud Dynamics

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Wouter Dorigo, Stephan Dietrich, Filipe Aires, Luca Brocca, Sarah Carter, Jean-François Cretaux, David Dunkerley, Hiroyuki Enomoto, René Forsberg, Andreas Güntner, Michaela I. Hegglin, Rainer Hollmann, Dale F. Hurst, Johnny A. Johannessen, Christian Kummerow, Tong Lee, Kari Luojus, Ulrich Looser, Diego G. Miralles, Victor Pellet, Thomas Recknagel, Claudia Ruz Vargas, Udo Schneider, Philippe Schoeneich, Marc Schröder, Nigel Tapper, Valery Vuglinsky, Wolfgang Wagner, Lisan Yu, Luca Zappa, Michael Zemp, and Valentin Aich

significantly increased ( Rast et al. 2014 ), and programs like ESA’s Climate Change Initiative ( Hollmann et al. 2013 ) have promoted the combination of water cycle observations from multiple satellites into long-term Climate Data Records (CDRs) ( appendix A , Tables A1 , A2). The recent expansion of operational missions (e.g., Copernicus Sentinels, EUMETSAT MetOp, NOAA JPSS) jointly with innovative explorer satellites [e.g., GPM, GRACE(-FO), Aeolus, SMOS, SMAP, SWOT] is improving our observational

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W.-K. Tao, T. Iguchi, and S. Lang

-season retrievals, the six new NASA Unified-Weather Research and Forecasting (NU-WRF) Model 4ICE simulations are used to generate the CSH LUTs (discussed in the flowing sections). CSH tropical/warm-season retrievals are then merged with extratropical/cold-season retrievals based on the height of the freezing level. 1 3. NU-WRF Model and cases The GCE has been used for semi-idealized and longer-term simulations constrained by large-scale forcing derived from sounding networks; the latter ensures its simulated Q

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H. Dong and X. Zou

swath is 885 km wide. Each scan cycle takes 1.875 s. For channels 1–7, the GMI has an operational calibration cycle that repeats every two scans and uses a four-point calibration method. The main channel specifications are provided in Table 1 . Table 1. GMI channel characteristics ( Draper et al. 2015b ). For GMI channels 8–13, all scans have a scan-by-scan calibration cycle and employ the heritage linear sensor radiometric calibration method ( NASA GSFC 2014 ). A mixer/intermediate frequency

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Linda Bogerd, Aart Overeem, Hidde Leijnse, and Remko Uijlenhoet

1. Introduction Precipitation observations are required for environmental applications that are highly embedded in the contemporary society, such as crop yield and flash flood forecasting, water management, and drought monitoring. However, the global coverage of ground-based precipitation measurements is limited, especially over Africa, South America, parts of Asia, and regions that are difficult to access (e.g., oceans, mountainous areas, polar regions; Lorenz and Kunstmann 2012 ; Saltikoff

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