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Thomas Stanley, Dalia B. Kirschbaum, George J. Huffman, and Robert F. Adler

percentile ( Figure 1b ). TMPA-RT tends to show heavier tropical precipitation (blue), and IMERG-L tends to show heavier midlatitude precipitation (red). In particular, the Southern Ocean shows a large and relatively consistent difference between IMERG-L and TMPA-RT. However, many locations do not fit this pattern, including some mountainous regions and inland water bodies. In addition to geographic heterogeneity, the relationship between TMPA-RT and IMERG-L may vary seasonally and interannually

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Randy J. Chase, Stephen W. Nesbitt, and Greg M. McFarquhar

and Modeling Challenges , Meteor. Monogr. , No. 58, Amer. Meteor. Soc. , https://doi.org/10.1175/AMSMONOGRAPHS-D-16-0007.1 . 10.1175/AMSMONOGRAPHS-D-16-0007.1 Milani , L. , and Coauthors , 2018 : CloudSat snowfall estimates over Antarctica and the Southern Ocean: An assessment of independent retrieval methodologies and multi-year snowfall analysis . Atmos. Res. , 213 , 121 – 135 , https://doi.org/10.1016/j.atmosres.2018.05.015 . 10.1016/j.atmosres.2018.05.015 Mishchenko , M. I. , and

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

-A). The performance of the newly developed relationships is then evaluated for three case studies. A summary of the results and conclusions can be found in section 5 . 2. Measurement setup a. Measurement site Hyytiälä The University of Helsinki operates a Forestry Field Station in southern Finland, in Hyytiälä (61.8439°N, 24.2875°E; 150 m above mean sea level), 220 km northwest of Helsinki ( Hari and Kulmala 2005 ). Meteorological instrumentation, including the PIP, is operated in the middle of a

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Stephen E. Lang and Wei-Kuo Tao

domains were expanded from 256 to 512 km, allowing more room for organized systems to grow. In addition, three new cases (two continental and one oceanic) from the Midlatitude Continental Convective Clouds Experiment (MC3E, conducted in late spring/early summer of 2011 over the southern Great Plains), the Green Ocean Amazon Experiment (GOAmazon, conducted in 2014 and 2015 over the Amazon basin), and DYNAMO (conducted from late 2011 to early 2012 over the equatorial Indian Ocean), respectively, were

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Lijing Cheng, Hao Luo, Timothy Boyer, Rebecca Cowley, John Abraham, Viktor Gouretski, Franco Reseghetti, and Jiang Zhu

norm distribution in a Student’s t test does not valid. Nevertheless, CH14 is the best one for removing the zonal mean (metric 4) and the variation (error bar of metric 4) of XBT errors, indicating the importance of including a temperature-dependent term in the correction. CH14 slightly overestimates the errors in the Southern Ocean (70°S–40°S, residuals less than −0.05°C). L09 greatly overestimates the errors in the Southern Ocean (residuals range from −0.2° to −0.1°C) but underestimates the

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

, sampling, snow–rain classification, and algorithm methodologies. While the details of these differences will be provided in section 3 , Figs. 1a–c (DPR NS, DPR HS, DPR MS) indicate more snow over the Gulf of Alaska and Fig. 1f (2CSP) has more snow in the Southern Ocean and Antarctic regions. Other subtle differences between the snow products are found in high mountainous areas (e.g., Greenland, the Andes, and the Himalayas). Fig . 1. GPM and CloudSat mean annual snowfall estimates (mm yr −1

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

Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE), 4) Kwajalein Experiment (KWAJEX), 5) Tropical Warm Pool–International Cloud Experiment (TWP-ICE), 6) Midlatitude Continental Convective Clouds Experiment (MC3E), 7) Dynamics of the MJO (DYNAMO), 8) Green Ocean Amazon Experiment (GoAMAZON), and 9) the U.S. Department of Energy’s Atmospheric Radiation Measurement Southern Great Plains site (1997 and 2002). These field campaigns are used to provide large-scale advective tendencies in

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Catherine M. Naud, James F. Booth, Matthew Lebsock, and Mircea Grecu

intercomparison of cyclone-centered composites of surface precipitation obtained for both the NH and Southern Hemisphere (SH) 30°–60° latitude bands over the oceans. We discuss the impact of observational uncertainties and sampling-related issues as well as explore the sensitivity of precipitation in extratropical cyclones to environmental moisture amount and its implication for seasonal variations according to the three recent datasets: GPM-CMB, IMERG, and CloudSat . 2. Data sources In this study we explore

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Gail Skofronick-Jackson, Walter A. Petersen, Wesley Berg, Chris Kidd, Erich F. Stocker, Dalia B. Kirschbaum, Ramesh Kakar, Scott A. Braun, George J. Huffman, Toshio Iguchi, Pierre E. Kirstetter, Christian Kummerow, Robert Meneghini, Riko Oki, William S. Olson, Yukari N. Takayabu, Kinji Furukawa, and Thomas Wilheit

The GPM mission collects essential rain and snow data for scientific studies and societal benefit. Water is essential to our planet. It literally moves mountains through erosion, transports heat in Earth’s oceans and atmosphere, keeps our planet from freezing as a result of radiative impacts of atmospheric water vapor, and causes catastrophes through droughts, floods, landslides, blizzards, and severe storms, but most importantly water is vital for nourishing all life on Earth. Precipitation as

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Clément Guilloteau and Efi Foufoula-Georgiou

. 2014 ). Fig . 1. Atmospheric profiles with quasi-identical spectral signatures can have very different radar reflectivity profiles and surface rain rates. (a) Two spectral signatures measured by GMI over ocean and (b) corresponding Ku reflectivites measured by the DPR along GMI’s field of view. The profile 1 was observed at latitude −3.29° and longitude 170.89° at 1210 UTC 6 Sep 2016 (orbit 14342 of the GPM Core Observatory ) and has a 15 mm h −1 surface precipitation rate. The profile 2 was

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