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Paloma Borque, Kirstin J. Harnos, Stephen W. Nesbitt, and Greg M. McFarquhar

distribution (PSD) to perform accurate simulations and retrievals. In this work, a new parameterization for the ice-phase PSD based on aircraft measurements collected during the Global Precipitation Measurement (GPM) Cold-Season Precipitation Experiment (GCPEx) is developed. This new parameterization leverages the uncorrelated mass parameter PSD estimation technique developed by Williams et al. (2014) , as described below to eliminate a free parameter in assumed PSD characteristics, which can provide

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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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Xiang Ni, Chuntao Liu, Daniel J. Cecil, and Qinghong Zhang

-00151.1 Dee , D. P. , and Coauthors , 2011 : The ERA-Interim reanalysis: Configuration and performance of the data assimilation system . Quart. J. Roy. Meteor. Soc. , 137 , 553 – 597 , doi: 10.1002/qj.828 . 10.1002/qj.828 Donavon , R. A. , and K. A. Jungbluth , 2007 : Evaluation of a technique for radar identification of large hail across the upper Midwest and central plains of the United States . Wea. Forecasting , 22 , 244 – 254 , doi: 10.1175/WAF1008.1 . 10.1175/WAF1008.1 Dworak , R

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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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Sarah D. Bang and Daniel J. Cecil

( Vivekanandan et al. 1991 ). Cecil (2009) put forth a technique to identify hail cores within convection using the 19-, 37-, and 85-GHz passive microwave channels from the passive microwave radiometer on board the Tropical Rainfall Measuring Mission (TRMM) satellite [the TRMM Microwave Imager (TMI)]. In both the 37- and 19-GHz channels, the likelihood of hail increased drastically with decreasing T b . The relationships between reported hail at the surface and microwave T b allowed Cecil (2009) to

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Stephen E. Lang and Wei-Kuo Tao

radiative characteristics to satellite microwave radiometric observations via a Bayesian technique. This approach later evolved into the “trained radiometer” or TRAIN algorithm ( Grecu and Olson 2006 ; Grecu et al. 2009 ) wherein the passive microwave algorithm is “trained” using space-borne radar profiles; those reflectivity profiles are in turn linked to heating profiles from CRM simulations in a manner similar to the SLH algorithm. The hydrometeor heating (HH) algorithm ( Yang and Smith 1999a , b

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Gail Skofronick-Jackson, Mark Kulie, Lisa Milani, Stephen J. Munchak, Norman B. Wood, and Vincenzo Levizzani

observations. For version 05 of the GPROF algorithm, approximately 1 year of GPM observations are contained in the a priori database. To distinguish liquid precipitation from falling snow, the Sims and Liu (2015) technique is implemented that relies on the 2-m wet-bulb temperature (T2m). Acknowledging DPR’s limitations in estimating light precipitation and discovering that the high-frequency (166–183 GHz) channels of GMI show a response to this lighter precipitation in mid- and high latitudes, GPROF has

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