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Nobuyuki Utsumi, F. Joseph Turk, Ziad S. Haddad, Pierre-Emmanuel Kirstetter, and Hyungjun Kim

1. Introduction Global precipitation products capitalize upon the long period of record of satellite-based passive microwave (MW) radiometer observations ( Aonashi and Ferraro 2020 ). The passive MW brightness temperature (TB) represents the net top-of-atmosphere upwelling radiation, after taking into consideration the emission and scattering properties of hydrometeors within the top-to-bottom profile, including the contribution from the surface emissivity. The surface precipitation represents

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F. Joseph Turk, Sarah E. Ringerud, Yalei You, Andrea Camplani, Daniele Casella, Giulia Panegrossi, Paolo Sanò, Ardeshir Ebtehaj, Clement Guilloteau, Nobuyuki Utsumi, Catherine Prigent, and Christa Peters-Lidard

of global precipitation products ( Skofronick-Jackson et al. 2018 ). The GPM Microwave Imager (GMI) observations are taken near coincidentally with the DPR on the core satellite, but the other constellation members have passive MW-only capabilities. There are two radar-based precipitation products produced from the GPM core spacecraft, the Combined Radar–Radiometer Algorithm (CORRA) ( Grecu et al. 2016 ), and the DPR radar-only algorithm ( Seto et al. 2013 ), both of which have a single

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

surprisingly better detection rate (21%) than its calibrator MWCOMB (10%). Similar observations are made with the Snow type, with overall better detection with SCaMPR (23%) than MWCOMB (9%). A possible explanation is that surface emissivity affects microwave observations in the range [10–37] GHz more significantly than infrared observations. Surface emissivity variability associated with surface snow is particularly challenging for microwave observations (e.g., Takbiri et al. 2019 ; Gebregiorgis et al

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Alberto Ortolani, Francesca Caparrini, Samantha Melani, Luca Baldini, and Filippo Giannetti

from multiple instruments are presented in Haese et al. (2017) . They use a stochastic approach called random mixing to generate precipitation fields from a set of rain gauge observations and path-averaged rain rates estimated using commercial microwave links. They apply their method to both synthetic (generated via the COSMO model) and real data in a study area in Germany, adopting an hourly time step. Bianchi et al. (2013) also present a technique to combine measurements from rain gauges

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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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Zhe Li, Daniel B. Wright, Sara Q. Zhang, Dalia B. Kirschbaum, and Samantha H. Hartke

such as the GPM “constellation” ( Skofronick-Jackson et al. 2017 ). These multisensor observations must therefore be converted into precipitation rates and interpolated onto a consistent spatial and temporal grid. The “workhorse” satellite instruments for precipitation estimates are passive microwave (PMW) radiometers, which observe along a satellite’s “swath,” the relatively narrow band over Earth sampled by the onboard sensor as a satellite moves along its orbit. Infrared (IR) observations from

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Phu Nguyen, Mohammed Ombadi, Vesta Afzali Gorooh, Eric J. Shearer, Mojtaba Sadeghi, Soroosh Sorooshian, Kuolin Hsu, David Bolvin, and Martin F. Ralph

small-scale convective storm rainfall variability . J. Hydrol. , 173 , 283 – 308 , https://doi.org/10.1016/0022-1694(95)02703-R . 10.1016/0022-1694(95)02703-R Grecu , M. , W. S. Olson , and E. N. Anagnostou , 2004 : Retrieval of precipitation profiles from multiresolution, multifrequency active and passive microwave observations . J. Appl. Meteor. , 43 , 562 – 575 , https://doi.org/10.1175/1520-0450(2004)043<0562:ROPPFM>2.0.CO;2 . 10.1175/1520-0450(2004)043<0562:ROPPFM>2.0.CO;2

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

of ARI and in the conditional CSGDs of ARI ( Fig. 3d ) are unsurprisingly much lower than that for daily precipitation. It has been previously shown that IMERG error depends on the amount and source of passive microwave and infrared data used ( Tan et al. 2016 ). Since this data availability varies over relatively short time scales (generally subhourly), it is not feasible to consider it when modeling multiday ARI. Since both Stage IV and IMERG observations of ARI are available over the entire

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

a myriad of observations into numerical weather prediction models. Ground measurements using precipitation gauges are the main source of information for point precipitation. However, observational records have limitations of sparse station network and/or gaps in records ( Bell et al. 2015 ; Kidd et al. 2017 ). Satellite data, using infrared and microwave instruments, cover most parts of the globe overcoming the limitation of sparse network. Despite the limitations of the short record length

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