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Andrea Camplani, Daniele Casella, Paolo Sanò, and Giulia Panegrossi

to the five different 10° latitude bins indicated in the legend. The extremely variable snow-cover extent and snow radiative properties in the MW are one of the main issues in the detection and quantification of snowfall by passive microwave observations, which remain among the most challenging tasks in global precipitation retrieval ( Bennartz and Bauer 2003 ; Skofronick-Jackson et al. 2004 , 2019 ; Noh et al. 2009 ; Levizzani et al. 2011 ; Kongoli and Helfrich 2015 ; Chen et al. 2016

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Daniel Watters, Alessandro Battaglia, Kamil Mroz, and Frédéric Tridon

1. Introduction The Global Precipitation Measurement Core Observatory (GPM CO ) satellite, launched in February 2014, offers unprecedented spaceborne observations of the three-dimensional structure of precipitating systems ( Hou et al. 2014 ). The satellite detects rain rates in the range 0.2–110.0 mm h −1 and travels in a sun-asynchronous orbit, providing coverage between 68°N and 68°S, thus augmenting the 37°N/S coverage of the predecessor Tropical Rainfall Measuring Mission (TRMM

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

1980 ; Hallikainen et al. 1986 , 1987 ). Hence, snow cover has a time-varying effect on snowfall upwelling signal. Physical and empirical approaches have been developed for microwave retrievals of snowfall. Skofronick-Jackson et al. (2004) presented a physical method to retrieve snowfall during a blizzard over the eastern United States using high-frequency observations from the Advanced Microwave Sounding Unit B (AMSU-B) instrument. Kim et al. (2008) simulated atmospheric profiles of a

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

regions, and are fraught with problems like undercatch and wind-blown snow biases ( Fassnacht 2004 ). This measurement gap can be bridged by spaceborne active and passive microwave (PMW) sensors that are tailored to detect and quantify snowfall thanks to their ability to probe within clouds ( Levizzani et al. 2011 ; Skofronick-Jackson et al. 2017 ). Two spaceborne radars paved the way toward ground-breaking vertically resolved observations of falling snow over much of the globe: the CloudSat Cloud

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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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