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Ali Tokay, Leo Pio D’Adderio, Federico Porcù, David B. Wolff, and Walter A. Petersen

near Earth’s surface result in gaps in the DPR footprint. The vast majority of the previous NUBF studies have focused on the horizontal spatial variability of rainfall using networks of rain gauges. Among those, Ciach and Krajewski (2006) conducted a well-designed experimental study, which allowed examination of the spatial variability at various time scales for different events. The long-term observations as well as the continuity in the record are the key factors in studying the spatial

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

Blankenship (2012) , and Ortega et al. (2016) . TRMM observations have already shed light on where the most intense thunderstorms occur and what their microwave radiometer and Ku-band radar footprints are ( Zipser et al. 2006 ). Because of the high single-scattering albedo of ice particles, passive microwave radiometers feature large brightness temperature depressions corresponding to large amounts of ice ( Cecil 2011 ; Cecil and Blankenship 2012 ). The most extreme storm in the TRMM dataset was

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Md. Abul Ehsan Bhuiyan, Efthymios I. Nikolopoulos, and Emmanouil N. Anagnostou

morphing technique (CMORPH) of the National Oceanic and Atmospheric Administration (NOAA) depends on passive microwave (PMW) satellite precipitation fields propagated by motion vectors calculated from infrared (IR) observations ( Joyce et al. 2004 ). Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) connects IR observations to PMW rainfall estimates through a neutral network ( Sorooshian et al. 2000 ). Tropical Rainfall Measuring Mission (TRMM

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Liao-Fan Lin, Ardeshir M. Ebtehaj, Alejandro N. Flores, Satish Bastola, and Rafael L. Bras

satellite sensors with a temporal resolution of 3 h and a spatial resolution of 0.25° × 0.25° covering 50°S to 50°N latitudes ( Huffman et al. 2007 ). This product uses a series of microwave and infrared estimates of precipitation and removes the bias using rain gauge observations. In addition, we use level-3 SMOS soil moisture retrieval at a spatial resolution of 25 km from the Barcelona Expert Centre, which is based on a level-2 SMOS orbital soil moisture dataset ( Kerr et al. 2010 ). To validate the

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

1. Introduction The Global Precipitation Measurement (GPM) Core Observatory has been collecting data by both the passive GPM Microwave Imager (GMI; Draper et al. 2015 ) and the Dual-Frequency Precipitation Radar (DPR; Furukawa et al. 2015 ) for more than 3 years ( Neeck et al. 2014 ). The DPR consists of a Ku-band (13.6 GHz) precipitation radar, similar to the Precipitation Radar (PR) on board the Tropical Rainfall Measuring Mission (TRMM) satellite ( Kummerow et al. 1998 ), and an

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Paloma Borque, Kirstin J. Harnos, Stephen W. Nesbitt, and Greg M. McFarquhar

multifrequency active and passive microwave sensors to detect and estimate ice-phase precipitation rate. This effort entails numerous GPM-specific and joint-agency field campaigns with state of the art cloud and precipitation observational capabilities (e.g., polarimetric radars, wind profiler radars, rain gauges, disdrometers, and aircraft in situ observations). One such field campaign was GCPEx conducted in cooperation with Environment and Climate Change Canada (ECCC) and NASA in January–February of 2012

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

information of temperature. Section 4 discusses the robustness of the results. Section 5 provides conclusions. 2. Data and methods a. IMERG early- and final-run products This study uses version 5 IMERG early- and final-run products. The IMERG level 3 multisatellite precipitation product combines precipitation estimates from all passive microwave sensors from the GPM constellation, infrared observations from geosynchronous satellites, and monthly gauge measurements ( Huffman et al. 2015 ). IMERG covers

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

scale suitable to their purposes. 2. Data a. IMERG IMERG is a gridded precipitation product that merges measurements from a network of satellites in the GPM constellation ( Huffman et al. 2015 ). IMERG uses the GPM Core Observatory satellite, which has a dual-frequency precipitation radar and a 13-channel passive microwave imager, as a reference standard to intercalibrate and merge precipitation estimates from individual passive microwave (PMW) satellites in the constellation ( Hou et al. 2014

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Hooman Ayat, Jason P. Evans, Steven Sherwood, and Ali Behrangi

. Kalogiros , D. Casella , A. C. Marra , G. Panegrossi , and P. Sano , 2018 : Passive microwave rainfall error analysis using high-resolution X-band dual-polarization radar observations in complex terrain . IEEE Trans. Geosci. Remote Sens. , 56 , 2565 – 2586 , . 10.1109/TGRS.2017.2763622 Derin , Y. , and Coauthors , 2019 : Evaluation of GPM-era global satellite precipitation products over multiple complex terrain regions . Remote Sens. , 11

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