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Sybille Y. Schoger, Dmitri Moisseev, Annakaisa von Lerber, Susanne Crewell, and Kerstin Ebell

, develop a new relationship particularly suited for a 24-GHz radar. Additionally, we use a new approach to keep the prefactor a zs variable in time by adding information from concurrent surface observations. We make use of the instantaneous PSD from a disdrometer as Rasmussen et al. (2003) have shown that a zs depends strongly on the intercept parameter N 0 of the PSD in the Rayleigh scattering regime. With this approach, the Z e –S relationship takes the local variability of the PSD into

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Randy J. Chase, Stephen W. Nesbitt, and Greg M. McFarquhar

for the evaluation of atmospheric numerical model simulations and their parameterized ice-phase microphysics (e.g., Delanoë et al. 2011 ; Stein et al. 2015 ; Ori et al. 2020 ). Despite many advances in satellite remote sensing techniques and sensors in the past few decades, the uncertainty in the estimate of the atmosphere’s ice water path remains large, and there is poor agreement between observational retrievals and numerical models (e.g., Duncan and Eriksson 2018 ). The best way to retrieve

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Ali Tokay, Leo Pio D’Adderio, Federico Porcù, David B. Wolff, and Walter A. Petersen

. Hydrometeor. , 17 , 2077 – 2104 , doi: 10.1175/JHM-D-15-0214.1 . 10.1175/JHM-D-15-0214.1 Schönhuber , M. , G. Lammer , and W. L. Randeu , 2007 : One decade of imaging precipitation measurement by 2D video disdrometer . Adv. Geosci. , 10 , 85 – 90 , doi: 10.5194/adgeo-10-85-2007 . 10.5194/adgeo-10-85-2007 Schröer , J.-B. , 2011 : Spatial and temporal variability of raindrop size distribution. Diploma thesis, University of Bonn, 133 pp. Seto , S. , T. Iguchi , and T. Oki , 2013 : The basic

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Yonghe Liu, Jinming Feng, Zongliang Yang, Yonghong Hu, and Jianlin Li

for no-gauge sites. Gridded precipitation datasets are usually required in hydrological assessment ( Werner and Cannon 2016 ) and are useful for short-term weather prediction or reconstructing historical precipitation. In this respect, how to exploit SD techniques to obtain gridded products with less computation than that spent by DD is a useful research subject. Gridded SD has been used to improve the small-scale spatial variability of remote sensing products (see Shin and Mohanty 2013 ; Zhang

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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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Dalia B. Kirschbaum, George J. Huffman, Robert F. Adler, Scott Braun, Kevin Garrett, Erin Jones, Amy McNally, Gail Skofronick-Jackson, Erich Stocker, Huan Wu, and Benjamin F. Zaitchik

and specific case studies can be found in Ward and Kirschbaum (2014) and Ward et al. (2015) . NASA’S PMM: BUILDING A LEGACY. TRMM was a research satellite designed to improve our understanding of the distribution and variability of precipitation at daily to yearly time scales over the tropical and subtropical regions of the Earth. It provided much-needed information on rainfall and its associated latent heat release that helps to power global atmospheric circulation and influence both weather

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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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Lijing Cheng, Hao Luo, Timothy Boyer, Rebecca Cowley, John Abraham, Viktor Gouretski, Franco Reseghetti, and Jiang Zhu

of global ocean change, including uncertainty estimation and attribution to natural and anthropogenic drivers. Decades of efforts have been made by the XBT community to identify and understand the cause of errors in XBT measurements and to quantify its magnitude. In brief, the genesis of XBT errors stem from their initial purpose: to make lower-quality measurements primarily for submarine operations. Later, temperature measurements from XBT devices were adopted for oceanographic purposes and the

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Xinxuan Zhang and Emmanouil N. Anagnostou

. 2015 ). Although the past two decades have brought considerable progress in satellite precipitation retrieval techniques and algorithms, producing reliable satellite products over mountainous areas remains a big challenge. Many studies have been devoted to satellite precipitation evaluation over complex terrain. In South America, Dinku et al. (2010) found severe overestimation by PERSIANN and significant underestimation by GSMaP over Colombia. In Africa, Hirpa et al. (2010) showed significant

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

over the past two decades (e.g., Kummerow et al. 1996 , 2011 , 2015 ; Meyers et al. 2015 ). Developed at NASA Goddard Space Flight Center in the mid-1990s from the work of Kummerow and Giglio (1994) , primarily for the purpose of the TRMM ( Simpson et al. 1988 ), GPROF lives to the present day, undergoing a number of versions. At the time of this study, its most recent version, labeled by NASA’s Precipitation Processing System (PPS) as GPROF.GPM.V4 developed for GMI, successfully serves a

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