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Stephanie M. Wingo, Walter A. Petersen, Patrick N. Gatlin, Charanjit S. Pabla, David A. Marks, and David B. Wolff

1. Introduction NASA’s Global Precipitation Measurement (GPM) mission aims to advance understanding of Earth’s water and energy cycles and has a broader goal of improving prediction capability for high-impact weather and climate events in order to benefit society ( Hou et al. 2014 ; Skofronick-Jackson et al. 2017 ). Recent decades have seen tremendous precipitation science advancement, and there is now an unprecedented suite of space- and ground-based precipitation sensors in use around the

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

1. Introduction To understand and manage water systems under a changing climate and meet increasing demand for water, a quantitative understanding of the precipitation variability at a regional to global scale is important. Over complex terrain, in particular, extreme precipitation is the main trigger of natural hazards like floods, landslides, and avalanches and its variability would affect freshwater security, energy, and tourism activities. Nevertheless, the measurement of precipitation over

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Mircea Grecu, Lin Tian, Gerald M. Heymsfield, Ali Tokay, William S. Olson, Andrew J. Heymsfield, and Aaron Bansemer

mathematical terms, the PSD is the derivative of the concentration of ice particles smaller than a size with respect to that size ( Sekhon and Srivastava 1970 ). Although the gamma function was found to represent well the variability of observed PSDs ( Heymsfield et al. 2002 , 2018 ), in this study we do not use analytical functions to relate PSDs to observed reflectivities. Instead, we use directly observed PSDs to calculate the associated reflectivities at the Ku, Ka, and W bands using accurate

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Robert A. Houze Jr., Lynn A. McMurdie, Walter A. Petersen, Mathew R. Schwaller, William Baccus, Jessica D. Lundquist, Clifford F. Mass, Bart Nijssen, Steven A. Rutledge, David R. Hudak, Simone Tanelli, Gerald G. Mace, Michael R. Poellot, Dennis P. Lettenmaier, Joseph P. Zagrodnik, Angela K. Rowe, Jennifer C. DeHart, Luke E. Madaus, Hannah C. Barnes, and V. Chandrasekar

. With its onboard Dual-Frequency Precipitation Radar (DPR) and 13-channel GPM Microwave Imager (GMI), the GPM satellite extends into future decades the global surveillance of precipitation provided until 2014 by the Tropical Rainfall Measuring Mission (TRMM) satellite and broadens coverage to higher latitudes, where many of Earth’s snow-covered mountain ranges are located. GPM also serves as a reference for other satellites carrying a variety of microwave imaging or sounding radiometers [see Hou et

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

and the developing world. Satellite-based precipitation products offer an alternative and have been the subject of accelerated development in recent decades, motivated in part by the launch of the U.S.–Japan Tropical Rainfall Monitoring Mission (TRMM) in 1997 ( Kummerow et al. 1998 ), and its successor, the Global Precipitation Measurement (GPM) mission, in 2014 ( Hou et al. 2014 ). Over the years, numerous studies have been performed to evaluate satellite-based precipitation products through

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Andrew Heymsfield, Aaron Bansemer, Norman B. Wood, Guosheng Liu, Simone Tanelli, Ousmane O. Sy, Michael Poellot, and Chuntao Liu

. The deployment of satellite-borne active remote sensors over the past two decades has resulted in multiple sources of cloud and precipitation datasets covering large portions of Earth. However, the quantification and understanding of uncertainties associated with remotely sensed satellite data remain a challenging research topic ( AghaKouchak et al. 2012 ). The uncertainties of satellite precipitation data arise from different factors, including the sensor itself, retrieval error, and spatial and

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