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

, the weak snowfall scattering signal can be masked by the increased atmospheric emission from supercooled cloud droplets ( Kneifel et al. 2010 ; Liu and Seo 2013 ; Wang et al. 2013 ; Panegrossi et al. 2017 ). Second, the changes in the surface emissivity due to snow accumulation (and its subsequent metamorphosis) and/or sea ice variability can be confused with the snow hydrometeor microwave signal ( Noh et al. 2009 ; Turk et al. 2014 ; Munchak et al. 2020 ; Takbiri et al. 2019 ). These issues

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

water within the snowpack strongly enhances the absorption at the expense of the volume scattering ( Rott and Nagler 1995 ; Amlien 2008 ). Hewison and English (1999) developed a model representing the microwave emissivity spectra of sea ice and snow cover obtained by airborne measurements. Different behaviors have been observed for different types of snowpacks, with an evident decrease of the emissivity with increasing frequency (within MW) for dry snow, and a high and stable emissivity for fresh

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Veljko Petković, Christian D. Kummerow, David L. Randel, Jeffrey R. Pierce, and John K. Kodros

instruments, limiting rainfall signals to an indirect, nonunique relationship between cloud ice-scattering signatures and surface rainfall. Based on the mean observed ratio between ice aloft and the surface rainfall, these estimates can often be inaccurate, with more pronounced biases observed during extreme events. In addition to the example given in study by Petković and Kummerow (2015) , a difference in mean precipitation rate bias between ground radar measurements and an operational satellite PMW

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Xinxuan Zhang and Emmanouil N. Anagnostou

precipitation rates by certain retrieval algorithms ( Ebert and Manton 1998 ). These estimates represent an indirect measurement of precipitation, and their accuracy is largely affected by different cloud types, rain systems, and hydroclimatic regimes. The PMW measurements observe the microwave energy emitted by rain droplets or scattered by precipitating ice particles. While the IR/VIS and PMW techniques can only capture horizontal precipitation patterns and intensities, the precipitation radar (PR) can

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Jackson Tan, Walter A. Petersen, and Ali Tokay

observation, we then use IRkalmanFilterWeight to determine which of the three IR-based categories the estimate belongs to: a weight of 0% is morph only, a weight of 100% is IR only, and anything in between is IR + morph. Because of the poor performance of PMW estimates over ice- or snow-covered surfaces, the IMERG estimate may use the direct IR estimate instead. For such instances, the IRkalmanFilterWeight is 100% even though HQprecipSource indicates a PMW observation (D. Bolvin 2016, personal

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