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Samantha H. Hartke, Daniel B. Wright, Dalia B. Kirschbaum, Thomas A. Stanley, and Zhe Li

phenomena in locations and at scales not previously possible. SMPPs use algorithms that merge passive microwave and infrared sensing data from multiple satellites (e.g., Kidd and Levizzani 2011 ; Kidd and Huffman 2011 ; Tapiador et al. 2012 ; Wright 2018 ). Commonly used SMPPs include the TRMM Multisatellite Precipitation Analysis (TMPA; Huffman et al. 2007 ), the Climate Prediction Center (CPC) morphing technique (CMORPH; Joyce et al. 2004 ), and the Precipitation Estimation from Remotely Sensed

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Chandra Rupa Rajulapati, Simon Michael Papalexiou, Martyn P. Clark, Saman Razavi, Guoqiang Tang, and John W. Pomeroy

products [e.g., Climate Prediction Center (CPC) unified precipitation estimates, Global Precipitation Climatology Center (GPCC) precipitation dataset, NCEP–NCAR reanalysis data] have become available at various spatial and temporal resolutions based on different data sources (e.g., ground observations, satellites, radar, reanalysis) and data merging techniques. While such datasets are useful to investigate the spatial and temporal behavior in global precipitation ( Fischer and Knutti 2014 ; Ghosh 2012

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Yagmur Derin, Pierre-Emmanuel Kirstetter, and Jonathan J. Gourley

distance from the coastline. c. GV-MRMS The evaluation of SPPs requires deriving high-quality reference rainfall datasets at the satellite product pixel spatial and temporal resolution. In this study as a reference dataset the high-resolution, ground-based, radar–rain gauge corrected precipitation dataset GV-MRMS ( Kirstetter et al. 2012 , 2018 ) is used. GV-MRMS builds on MRMS that uses advanced data integration techniques to create high-resolution 3D reflectivity mosaic grids and quantitative

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Clement Guilloteau, Efi Foufoula-Georgiou, Pierre Kirstetter, Jackson Tan, and George J. Huffman

half-hourly gauge–radar QPE from the Ground Validation Multi-Radar Multi-Sensor (GV-MRMS; Petersen et al. 2020 ) suite of products is used in this study as a high-quality reference to evaluate the satellite QPEs. GV-MRMS builds on the MRMS QPE that is derived from 176 WSR-88D radars and more than 18 000 automatic hourly rain gauges over the contiguous United States and Canada ( Zhang et al. 2016 ). Advanced data integration techniques are used to create 3D reflectivity mosaic grids and

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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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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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Shruti A. Upadhyaya, Pierre-Emmanuel Kirstetter, Jonathan J. Gourley, and Robert J. Kuligowski

resolutions are critical for near-real-time applications such as rapid monitoring and forecasting of high-impact societal events like flash floods, debris flows, and shallow landslides. Such resolution can be obtained primarily from satellite sensors on board geostationary Earth orbit (GEO) platforms. NOAA’s Advanced Baseline Imager (ABI) sensor on board the latest generation of Geostationary Operational Environmental Satellites (GOES-R Series) provides 3 times more spectral channels, 4 times the

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