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

assimilate a higher number of microwave observations than the Early product as all microwave observations are not always available with the 4-h latency. The “uncalibrated” precipitation estimates that do not include gauge adjustment from IMERG-E and IMERG-F products are used in the present study. The January 2018–April 2020 period is selected for the evaluation of the satellite products. The March 2018 and March 2019 months are excluded from the analysis because of a high rate of missing MRMS data (or

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Jackson Tan, Walter A. Petersen, and Ali Tokay

-top temperatures. Much progress has been made in the last two decades with a contingent of low-Earth-orbiting passive microwave satellites and two NASA/JAXA spaceborne radars in the microwave band, the Tropical Rainfall Measuring Mission (TRMM) and the Global Precipitation Measurement (GPM) mission. Unlike infrared radiation, microwave radiation is able to penetrate clouds and interact more directly with precipitation; consequently, microwave retrieval techniques generally provide a superior estimate of

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Xinxuan Zhang and Emmanouil N. Anagnostou

1. Introduction Satellite remote sensing plays an irreplaceable role in precipitation measurement because it is the only mean of gathering data with uninterrupted, quasi-global coverage. Precipitation-related satellite observations are of four main types: longwave infrared (IR), visible spectrum (VIS), passive microwave (PMW), and active microwave retrievals. The satellite IR and VIS sensors measure the cloud-top brightness temperature or reflectivity that researchers use to derive

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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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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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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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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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Pin-Lun Li, Liao-Fan Lin, and Chia-Jeng Chen

.hec.usace.army.mil/software/hec-hms/documentation/HEC-HMS_Technical%20Reference%20Manual_(CPD-74B).pdf . Ushio , T. , and Coauthors , 2009 : A Kalman filter approach to the Global Satellite Mapping of Precipitation (GSMaP) from combined passive microwave and infrared radiometric data. J. Meteor. Soc. Japan , 87A , 137–151, https://doi.org/10.2151/jmsj.87A.137 . 10.2151/jmsj.87A.137 Wang , H. , and B. Yong , 2020 : Quasi-global evaluation of IMERG and GSMaP precipitation products over land using gauge observations. Water , 12 , 243–258, https

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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 , https://doi.org/10.1109/TGRS.2017.2763622 . 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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