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Stephen E. Lang and Wei-Kuo Tao

radiative characteristics to satellite microwave radiometric observations via a Bayesian technique. This approach later evolved into the “trained radiometer” or TRAIN algorithm ( Grecu and Olson 2006 ; Grecu et al. 2009 ) wherein the passive microwave algorithm is “trained” using space-borne radar profiles; those reflectivity profiles are in turn linked to heating profiles from CRM simulations in a manner similar to the SLH algorithm. The hydrometeor heating (HH) algorithm ( Yang and Smith 1999a , b

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Gail Skofronick-Jackson, Mark Kulie, Lisa Milani, Stephen J. Munchak, Norman B. Wood, and Vincenzo Levizzani

observations. For version 05 of the GPROF algorithm, approximately 1 year of GPM observations are contained in the a priori database. To distinguish liquid precipitation from falling snow, the Sims and Liu (2015) technique is implemented that relies on the 2-m wet-bulb temperature (T2m). Acknowledging DPR’s limitations in estimating light precipitation and discovering that the high-frequency (166–183 GHz) channels of GMI show a response to this lighter precipitation in mid- and high latitudes, GPROF has

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

-changing climate. Despite a long, albeit sparse, record [first known observations date back 2000 BCE ( Wang and Zhang 1988 )], globally complete precipitation measurements did not become available until the modern era of satellite Earth-observing systems that employ infrared and microwave radiometric techniques (e.g., Atlas and Thiele 1981 ). Achieving measurement standards of rainfall in atypical (i.e., extreme) environments on small spatiotemporal scales across the globe, however, has turned out to be more

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Clément Guilloteau and Efi Foufoula-Georgiou

-1839441. The authors thank Prof. Christian Kummerow, Dr. Dave Randel, and Dr. Wesley Berg from the Precipitation Group at the Colorado State University as well as Dr. Joseph Turk from NASA Jet Propulsion Laboratory for the insightful discussions and shared information which contributed to the present article. APPENDIX A Acronyms AMSR-2 Advanced Microwave Scanning Radiometer 2 CMORPH Climate Prediction Center morphing technique DMSP Defense Meteorological Satellite Program DPR Dual

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

in the design for the DF and SF products. First, the clutter-free ranges closest to the ground are identified and it is determined whether precipitation reaches the surface. Second, the surface reference technique (SRT) is used to estimate the path-integrated attenuation (PIA) for each frequency due to the propagation through precipitation using the radar returns from the surface ( Meneghini et al. 2000 ). Different variations of the technique are run and a combination of them provides the final

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Jackson Tan, Walter A. Petersen, Pierre-Emmanuel Kirstetter, and Yudong Tian

), the TRMM Multisatellite Precipitation Analysis (TMPA; Huffman et al. 2007 ), the Climate Prediction Center morphing technique (CMORPH; Joyce et al. 2004 ; Joyce and Xie 2011 ), and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Cloud Classification System (PERSIANN-CCS; Hong et al. 2004 ). These gridded precipitation datasets use a blend of data from various sources with advanced techniques to provide a near-global coverage with high spatial and

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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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Kamil Mroz, Alessandro Battaglia, Timothy J. Lang, Simone Tanelli, and Gian Franco Sacco

surface reference technique (SRT) would be 3.5 dB, marginally higher than 3 dB (i.e., a factor of 2), practically signaling that half of the footprint is fully attenuating and the other half is barely producing any attenuation. Because of 5 dB of attenuation produced by the snow-bearing column, the Ka SRT PIA adds up to 8.1 dB. Therefore, the ratio of the two path-integrated attenuations ( ) is equal to 2.3, which is well below the trigger NUBF detection threshold of 4. Therefore, this profile would

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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

.g., Chang et al. 1990 ; Kelly et al. 2003 ) or from synergistic approaches combining satellite observations with ground data ( Pulliainen 2006 ; Takala et al. 2011 , 2017 ). Standalone passive microwave approaches are somewhat limited in their applicability for hemispheric monitoring, but in combination with in situ data perform similar to reanalysis datasets ( Mortimer et al. 2020 ). Both EO- and model-based approaches can be further improved using appropriate bias correction techniques ( Pulliainen

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Kenneth D. Leppert II and Daniel J. Cecil

, https://doi.org/10.1029/2004RS003129 . 10.1029/2004RS003129 Hou , A. Y. , and Coauthors , 2014 : The Global Precipitation Measurement (GPM) mission . Bull. Amer. Meteor. Soc. , 95 , 701 – 722 , https://doi.org/10.1175/BAMS-D-13-00164.1 . 10.1175/BAMS-D-13-00164.1 Kalina , E. A. , K. Friedrich , B. C. Motta , W. Deierling , G. T. Stano , and N. N. Rydell , 2016 : Colorado plowable hailstorms: Synoptic weather, radar, and lightning characteristics . Wea. Forecasting , 31

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