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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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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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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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Clément Guilloteau and Efi Foufoula-Georgiou

-1839441. The authors thank Prof. Christian Kummerow, Dr. Dave Randel, and Dr. Wesley Berg from the Precipitation Group at the Colorado State University as well as Dr. Joseph Turk from NASA Jet Propulsion Laboratory for the insightful discussions and shared information which contributed to the present article. APPENDIX A Acronyms AMSR-2 Advanced Microwave Scanning Radiometer 2 CMORPH Climate Prediction Center morphing technique DMSP Defense Meteorological Satellite Program DPR Dual

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Kamil Mroz, Mario Montopoli, Alessandro Battaglia, Giulia Panegrossi, Pierre Kirstetter, and Luca Baldini

in the design for the DF and SF products. First, the clutter-free ranges closest to the ground are identified and it is determined whether precipitation reaches the surface. Second, the surface reference technique (SRT) is used to estimate the path-integrated attenuation (PIA) for each frequency due to the propagation through precipitation using the radar returns from the surface ( Meneghini et al. 2000 ). Different variations of the technique are run and a combination of them provides the final

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