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Md. Abul Ehsan Bhuiyan, Efthymios I. Nikolopoulos, and Emmanouil N. Anagnostou

). Another precipitation data source available at the global scale is from atmospheric reanalyses produced by different national and international organizations, including the National Centers for Environmental Prediction (NCEP; Kalnay et al. 1996 ), the European Centre for Medium-Range Weather Forecasts (ECMWF; Uppala et al. 2005 ; Bosilovich et al. 2008 ), and NASA’s Goddard Space Flight Center (GSFC; Rodell et al. 2004 ). These products are affected by irregularly distributed observation stations

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Xinxuan Zhang and Emmanouil N. Anagnostou

corrections of satellite precipitation products. Future research should investigate the feasibility of using real-time weather forecasts to correct near-real-time high-resolution satellite precipitation products (e.g., CMORPH, GSMaP, PERSIANN-CCS, and IMERG) for heavy precipitation events over complex terrain areas and evaluate hydrologic impacts in terms of flood forecasts. Furthermore, a future study should focus on demonstrating the technique on recently released versions of CMORPH and GSMaP and the

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Jiaying Zhang, Liao-Fan Lin, and Rafael L. Bras

al. (2016) revealed that the IMERG product has more skill in representing daily precipitation than the post-real-time Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA-3B42) and the ERA-Interim product from the European Centre for Medium-Range Weather Forecasts (ECMWF) in Iran from March 2014 to February 2015. For the midlatitude region of the Ganjiang River basin in southeast China, Tang et al. (2016b) showed that the detection skill of the Day-1 IMERG

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Hooman Ayat, Jason P. Evans, Steven Sherwood, and Ali Behrangi

Prediction Center morphing technique (CMORPH), and Tropical Rainfall Measurement Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42RT with NCEP Stage IV radar data in the warm season of 2008 and cold season of 2010, and they found that PERSIANN performed best in capturing the orientation of the objects, 3B42RT depict the location of the storms better than the other products and in terms of the object size, CMORPH is the best. However, the objects were not tracked in this research to capture

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Manikandan Rajagopal, Edward Zipser, George Huffman, James Russell, and Jackson Tan

. 2017 ; Watters et al. 2018 ; Bytheway et al. 2020 ; Tapiador et al. 2020 ; Gowan and Horel 2020 ) involve point-to-point comparisons of GPROF or IMERG precipitation with other observations at different space and time resolutions. As an alternative to the point-to-point comparison, an object-based approach (e.g., Davis et al. 2006 ; Johnson et al. 2013 ) has recently been introduced for evaluating reanalyses, model forecasts, and other global gridded products. This entails identifying objects

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Jackson Tan, Walter A. Petersen, and Ali Tokay

-top temperatures. Much progress has been made in the last two decades with a contingent of low-Earth-orbiting passive microwave satellites and two NASA/JAXA spaceborne radars in the microwave band, the Tropical Rainfall Measuring Mission (TRMM) and the Global Precipitation Measurement (GPM) mission. Unlike infrared radiation, microwave radiation is able to penetrate clouds and interact more directly with precipitation; consequently, microwave retrieval techniques generally provide a superior estimate of

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M. Petracca, L. P. D’Adderio, F. Porcù, G. Vulpiani, S. Sebastianelli, and S. Puca

the Italian operational rain gauge network. To homogenize the two ground datasets, rain gauge data, preprocessed according to range, persistence, step, and spatial consistency ( Shafer et al. 2000 ) to screen out suspect values, have been interpolated over a regular grid (1 km × 1 km) through the Random Generator of Spatial Interpolation from uncertain Observations (GRISO). The GRISO ( Pignone et al. 2010 ; Feidas et al. 2018 ) is an improved kriging-based technique implemented by the

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Zeinab Takbiri, Ardeshir Ebtehaj, Efi Foufoula-Georgiou, Pierre-Emmanuel Kirstetter, and F. Joseph Turk

) developed a statistical approach that partitions high-frequency brightness temperatures (≥89 GHz) into two distinct warm and cold weather regimes by thresholding the brightness temperature at 53 GHz. Another class of empirical approaches relies on Bayesian techniques. These techniques use a database or a lookup table that relates brightness temperatures of snowing clouds to the radar snowfall observations along with the atmospheric temperature profile. As an example, Liu and Seo (2013) used matched

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Clement Guilloteau, Efi Foufoula-Georgiou, Pierre Kirstetter, Jackson Tan, and George J. Huffman

half-hourly gauge–radar QPE from the Ground Validation Multi-Radar Multi-Sensor (GV-MRMS; Petersen et al. 2020 ) suite of products is used in this study as a high-quality reference to evaluate the satellite QPEs. GV-MRMS builds on the MRMS QPE that is derived from 176 WSR-88D radars and more than 18 000 automatic hourly rain gauges over the contiguous United States and Canada ( Zhang et al. 2016 ). Advanced data integration techniques are used to create 3D reflectivity mosaic grids and

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Yagmur Derin, Pierre-Emmanuel Kirstetter, and Jonathan J. Gourley

distance from the coastline. c. GV-MRMS The evaluation of SPPs requires deriving high-quality reference rainfall datasets at the satellite product pixel spatial and temporal resolution. In this study as a reference dataset the high-resolution, ground-based, radar–rain gauge corrected precipitation dataset GV-MRMS ( Kirstetter et al. 2012 , 2018 ) is used. GV-MRMS builds on MRMS that uses advanced data integration techniques to create high-resolution 3D reflectivity mosaic grids and quantitative

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