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Ali Behrangi, Bin Guan, Paul J. Neiman, Mathias Schreier, and Bjorn Lambrigtsen

1. Introduction Atmospheric rivers (ARs) refer to narrow channels of enhanced water vapor transport concentrated in the lower atmosphere ( Zhu and Newell 1994 ; Ralph et al. 2004 ). Occupying less than 10% of the earth’s circumference, ARs account for over 90% of the poleward water vapor transport at midlatitudes ( Zhu and Newell 1998 ). While ARs occur globally, their impacts are most prominent when they make landfall and interact with the topography of the west coast areas of midlatitude

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Chris Kidd, Toshihisa Matsui, Jiundar Chern, Karen Mohr, Chris Kummerow, and Dave Randel

approaches by Surussavadee and Staelin (2008) and Sano et al. (2015) , the Water Vapour Strong Lines at 183 GHz (183-WSL) technique of Laviola et al. (2013) , and a technique using canonical analysis by Casella et al. (2015) . Of particular interest in the context of this paper is the Microwave Integrated Retrieval System (MIRS; Iturbide-Sanchez et al. 2011 ; Boukabara et al. 2011 ) that is used to generate a number of geophysical parameters from both CS and XT passive microwave observations using

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Mark S. Kulie, Lisa Milani, Norman B. Wood, Samantha A. Tushaus, Ralf Bennartz, and Tristan S. L’Ecuyer

other atmospheric state variables (e.g., water vapor content and pressure) are obtained from the ECMWF-AUX product that interpolates ECMWF model data to each CloudSat observation. Table 1. CloudSat products and parameters used in this study from each respective product. The two most important retrieved quantities used in this study are near-surface snowfall rates and cloud classifications obtained from the 2C-SNOW-PROFILE and 2B-CLDCLASS products, respectively. The 2C-SNOW-PROFILE product

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Toshi Matsui, Jiun-Dar Chern, Wei-Kuo Tao, Stephen Lang, Masaki Satoh, Tempei Hashino, and Takuji Kubota

warm category ranges from 0 to 30 K over ocean and from 10 to 40 K over land, which appears to be natural variability (gray shade) in the background microwave emission. Over the Tibetan Plateau, the MSI for the shallow warm category is anomalously large (up to 60 K) most likely because of the presence of surface snow ( Pulliainen and Hallikainen 2001 ). The polarization correction most likely failed to mask the surface signals because of the lack of column water vapor over the Tibetan Plateau. Note

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