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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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Veljko Petković, Marko Orescanin, Pierre Kirstetter, Christian Kummerow, and Ralph Ferraro

especially pronounced in satellite observations. Since the first spaceborne passive microwave instruments were launched in early 1970s, satellite precipitation retrievals have exploited the link between upwelling radiation and state of atmospheric column. Leveraging decades of ever-improving algorithms, coverage, and data latency, the Global Precipitation Measurement (GPM) mission ( Skofronick-Jackson et al. 2018 ; Hou et al. 2014 ) represents the most advance satellite precipitation project to date

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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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Clément Guilloteau and Efi Foufoula-Georgiou

of orbiting imagers providing frequent observations of clouds and precipitation all over the globe ( Skofronick-Jackson et al. 2018 ). The passive microwave retrieval of precipitation relies on the measurement of radiances at the top of the atmosphere, which are the product of the surface emission, emission and absorption by liquid rain drops and water vapor and scattering by ice particles. Vertically and horizontally polarized radiances are measured at various frequencies between 5 and 200 GHz

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Efi Foufoula-Georgiou, Clement Guilloteau, Phu Nguyen, Amir Aghakouchak, Kuo-Lin Hsu, Antonio Busalacchi, F. Joseph Turk, Christa Peters-Lidard, Taikan Oki, Qingyun Duan, Witold Krajewski, Remko Uijlenhoet, Ana Barros, Pierre Kirstetter, William Logan, Terri Hogue, Hoshin Gupta, and Vincenzo Levizzani

from satellites through soil moisture gravimetry (e.g., the GRACE satellite) ( Behrangi et al. 2018 ) and microwave scatterometers ( Brocca et al. 2014 ) are promising areas of research and development. In the context of the unprecedented wealth of observations with diverse information content, the use of data analytics and ML concepts to learn complex relationships from large precipitation datasets was discussed ( Sadeghi et al. 2019 ), along with the need for physically based dimensionality

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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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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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