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Lisa Milani, Mark S. Kulie, Daniele Casella, Pierre E. Kirstetter, Giulia Panegrossi, Veljko Petkovic, Sarah E. Ringerud, Jean-François Rysman, Paolo Sanò, Nai-Yu Wang, Yalei You, and Gail Skofronick-Jackson

-GMI spatial resolution, lower precipitation rates (also < 0.2 mm h −1 ) could be found. For database purposes, the precipitation phase of GV-MRMS observations is determined by the Sims and Liu (2015) method directly within the GPROF algorithm. Despite the uncertainties, GV-MRMS retrievals are the best available QPE dataset over CONUS and are thus used in the present study to evaluate GPROF performance. For this purpose, we use the GV-MRMS precipitation phase identification based on a RAP surface (wet

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Veljko Petković, Marko Orescanin, Pierre Kirstetter, Christian Kummerow, and Ralph Ferraro

depends on the representativeness, quantity and quality of the training dataset. To establish a baseline model and evaluate the performance of the approach we propose a relatively simple scheme and a widely available satellite dataset. Detailed descriptions of the datasets and DNN model are given below. a. Instruments and data This study employs 2 years, from September 2014 to August 2015 and from January to December 2017, of the GPM core satellite global observations (66°S–66°N) to explore accuracy

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Clément Guilloteau and Efi Foufoula-Georgiou

uncertainty. The use of nonlocal parameters in a neighbor-search algorithm does not require defining explicit relations between TB patterns and precipitation; it is simply assumed that similar TB patterns correspond to similar precipitation systems and geometries. In particular, with this approach, it is not necessary to explicitly correct for the parallax shift as is done in Guilloteau et al. (2018) . The retrieval performance is evaluated over 6 million randomly sampled DPR profiles and collocated GMI

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