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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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Qian Cao, Thomas H. Painter, William Ryan Currier, Jessica D. Lundquist, and Dennis P. Lettenmaier

comparisons with radar rainfall estimates (e.g., Stampoulis et al. 2013 ; Gebregiorgis et al. 2017 ), gauge observations (e.g., Mei et al. 2014 ; Prat and Nelson 2015 ; Miao et al. 2015 ), and merged radar and gauge rainfall estimates such as the National Centers for Environmental Prediction (NCEP) Stage IV ( Lin and Mitchell 2005 ) products (e.g., Gourley et al. 2010 ; Mehran and AghaKouchak 2014 ). Radar precipitation estimates are subject to errors from, for example, radar calibration, beam

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Yagmur Derin, Emmanouil Anagnostou, Marios Anagnostou, and John Kalogiros

measurements. The difficulty of representing spatial rainfall variability from ground-based observations highlights the need to use multisatellite precipitation datasets—that is, datasets that combine infrared (IR) radiances and passive microwave (PMW) precipitation retrievals—which can represent the space–time variability of rainfall with quasi-global coverage ( Huffman et al. 2007 , 2010 ; Joyce et al. 2004 ; Kubota et al. 2007 ; Ushio et al. 2009 ). However, the effective use of satellite

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