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

( Førland et al. 2011 ). Additionally, the measurement of snow particles and the identification of the true amount of snow at the ground is a challenging task due to the complex and strongly variable microphysical properties of snow and ice crystals. For classical precipitation gauges, the liquid equivalent amount of snow is a direct measure; however, it is prone to large uncertainties especially in windy conditions ( Rasmussen et al. 2012 ) and still only a point information of precipitation. Radar

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Toshio Iguchi, Nozomi Kawamoto, and Riko Oki

also found with CloudSat observations ( Liu 2008 ; Kulie et al. 2016 ). Since only very intense ice precipitation is flagged in the current algorithm, the frequency of detecting ice precipitation is much lower with DPR than with CloudSat , but the patterns of frequency distribution have some similarity. However, relatively frequent detection of ice precipitation over the North Atlantic Ocean to the south of Greenland and over the Sea of Okhotsk and the Bering Sea is not seen in the snowfall

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Xiang Ni, Chuntao Liu, and Edward Zipser

central Africa, which is consistent with the larger hail sizes with stronger ice scattering signal at 37-GHz microwave radiance in the subtropics ( Cecil and Blankenship 2012 ; Ni et al. 2017 ). Though only a few DCC cases are found north of the Mediterranean Sea, they have high DFR values. It is interesting to see high DFR values at 12 km over northeast China, which is close to the hot spot of convection overshooting the tropopause over the region ( Liu and Liu 2018 ). To further test if DFR values

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Sarah D. Bang and Daniel J. Cecil

including any other radar, surface, or environmental data), snowy or icy surfaces may falsely register as “hail” to a retrieval. The much wider range of latitudes surveyed by the GPM satellite (69°S–69°N) presents the challenge wherein sea ice and other ice- and snow-covered surfaces must be accounted for in precipitation retrievals. As in Ferraro et al. (2015) , our preliminary methods retrieved high concentrations of hail over Greenland, and also over the Antarctic Peninsula. There are also regions

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Clément Guilloteau and Efi Foufoula-Georgiou

rain drops, are consistent with the radar observations. Fig . 4. Exploration of the information in the spatial structure of the GMI TBs for an oceanic convective system (South China Sea, at 0445 UTC 9 Oct 2016). (top) Observed TBs at 89, 37, and 10.6 GHz (vertical polarization). (bottom) Near-surface precipitation rates derived from the DPR and from GMI using the GPROF algorithm; the white line corresponds to the cross section shown in Fig. 5 . The ice scattering signature at 89 GHz is shifted to

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Daniel J. Cecil and Themis Chronis

(GPROF surface types 3–5, corresponding to “maximum vegetation,” “high vegetation,” and “moderate vegetation”) or ocean (GPROF surface type 1). The “ocean” classification can include large water bodies, for example, the Great Lakes. Sea ice, arid regions, surface snow cover, rivers, coasts, and precipitation scenes are excluded. Each orbit is divided into 5° latitude bins. Statistics are derived separately for each of these bins that has at least 10 land and 10 water pixels without precipitation

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

instruments, limiting rainfall signals to an indirect, nonunique relationship between cloud ice-scattering signatures and surface rainfall. Based on the mean observed ratio between ice aloft and the surface rainfall, these estimates can often be inaccurate, with more pronounced biases observed during extreme events. In addition to the example given in study by Petković and Kummerow (2015) , a difference in mean precipitation rate bias between ground radar measurements and an operational satellite PMW

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W.-K. Tao, T. Iguchi, and S. Lang

COARE 2D GCE simulations and six high-latitude oceanic cases over the Sea of Japan. On the other hand, the CSH algorithm CRM database has several multiweek tropical cases for both ocean and land (10 cases with 355 total days of 2D GCE model simulations; see Table 3 ) plus six new high-latitude synoptic weather simulations (presented in this paper). For tropical and warm-season retrievals, the CSH LUTs are based on the 10 2D GCE 4ICE simulations; whereas for higher-latitude and cold

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Zhaoxia Pu, Chaulam Yu, Vijay Tallapragada, Jianjun Jin, and Will McCarty

associated with surface emissivity (following Garrett et al. 2010 ). First, over the sea surface, a column CLW is computed using measurements from GMI channels 1–7, 12, and 13: where and are prescribed regression coefficients, and is the GMI brightness temperature at channel . Another column cloud ice [e.g., graupel water path (GWP)] parameter is computed in a similar fashion: Table 2 summarizes the values of , , , and . Constant thresholds of 0.05 are used for both and to toss pixels

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