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Chanh Q. Kieu and Da-Lin Zhang

approaches and realistic hurricane-like vortices. The objectives of this study are to a) extend the PV inversion algorithm of Wang and Zhang (2003 , hereafter WZ03) from one PV piece to multiple PV piece applications and then b) examine the dynamical effects of the above-mentioned axisymmetric PVAs on the intensity and structures of hurricane vortices using the piecewise PV inversion algorithm. This will be done by treating PV rings in the outer eyewall and the inner eyewall as well as the upper and

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Arief Sudradjat, Nai-Yu Wang, Kaushik Gopalan, and Ralph R. Ferraro

( Ferraro et al. 1986 , 1998 ; Grody 1991 ) has been the most applied methodology for microwave land precipitation since its introduction. The methodology is built in within the Goddard profiling algorithm (GPROF; Kummerow et al. 2001 ), a widely used passive microwave precipitation retrieval algorithm. Various versions of GPROF have been applied in the SSM/I, TMI, and AMSR-E missions ( McCollum and Ferraro 2003 ; Wang et al. 2009 ; Gopalan et al. 2010 ). An improved GPROF algorithm will also be

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Zhaoyan Liu, Mark Vaughan, David Winker, Chieko Kittaka, Brian Getzewich, Ralph Kuehn, Ali Omar, Kathleen Powell, Charles Trepte, and Chris Hostetler

of attenuated backscatter coefficients ( Hostetler et al. 2006 ; Powell et al. 2009 ) are reported in the CALIOP level 1B data products. These level 1 profiles are further analyzed in level 2 processing to derive the optical and physical properties of clouds and aerosols ( Vaughan et al. 2004 ). The level 2 processing algorithms include three primary modules: a layer detection algorithm known as the selective, iterated boundary locator (SIBYL), the scene classification algorithms (SCA), and the

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Yingjie Liu, Ge Chen, Miao Sun, Shuai Liu, and Fenglin Tian

significance on analyzing and understanding ocean energy and mass transport, as well as the spatiotemporal variability of global eddies. A large number of automatic eddy identification algorithms have been developed to study eddy activity. These algorithms can be divided into three categories: 1) the physical-parameter-based method, including the Okubo–Weiss parameter method ( Chelton et al. 2007 ; Henson and Thomas 2008 ), the winding angle method ( Chaigneau et al. 2008 ; Sadarjoen and Post 2000 ), and

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Haonan Chen, V. Chandrasekar, and Renzo Bechini

DPR and passive radiometer on board GPM extend the observation range attained by TRMM from tropics to most of the globe and provide accurate measurement of rainfall, snowfall, and other precipitation types. Through improved measurements of precipitation, the GPM mission is helping to advance our understanding of Earth’s water and energy cycle, as well as climate changes. As an indispensable part of the GPM mission, ground validation helps to develop the radar and radiometer retrieval algorithms by

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Jun Awaka, Minda Le, V. Chandrasekar, Naofumi Yoshida, Tomohiko Higashiuwatoko, Takuji Kubota, and Toshio Iguchi

1. Introduction The GPM Core Observatory carries the Dual-Frequency Precipitation Radar (DPR) operating at Ku band and Ka band ( Kobayashi and Iguchi 2003 ; Kubota et al. 2014 ). Rain type classification is very important for accurate measurement of precipitation rate by the DPR because the reflectivity factor Z and the attenuation due to precipitation depend on rain types (e.g., Battan 1973 ; Meneghini and Kozu 1990 ). In the GPM DPR algorithms, rain type classification is made in three

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Sungwook Hong, Hwa-Jeong Seo, and Young-Joo Kwon

the Aqua satellite. Currently, active and passive microwave remote sensing have become established as critical operational tools for TC analysis. In particular, passive microwave imagery (36–37 and 85–91 GHz), using an Advanced Microwave Sounding Unit (AMSU), provides direct diagnosis of the inner structure of TCs ( Brueske and Velden 2003 ; Herndon et al. 2004 ; Demuth et al. 2004 , 2006 ). In this study, we present a physical algorithm for estimating surface wind speed using passive

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Stephen E. Lang and Wei-Kuo Tao

great importance, LH is hard to measure directly. However, the launch of the Tropical Rainfall Measuring Mission satellite (TRMM; Simpson et al. 1996 ; Kummerow et al. 2000 ) in November of 1997 made it possible to obtain quantitative precipitation measurements over the global tropics and, as a consequence of their close connection, estimates of tropical LH as well. In support of this effort, five different LH algorithms were developed to retrieve profiles of LH using TRMM rainfall products

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Kiel L. Ortega, John M. Krause, and Alexander V. Ryzhkov

1. Introduction a. Hail identification and sizing by radar The identification of hail is important for the U.S. National Weather Service (NWS) severe weather warnings, especially detection of hail exceeding the severe diameter threshold of 25 mm (1 in.; NWS 2014 ). Currently many hail detection and sizing algorithms and techniques for NWS operations are based upon single-polarization radar, such as vertically integrated liquid (VIL; Greene and Clark 1972 ) and VIL density ( Amburn and Wolf

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Darrel M. Kingfield and Joseph C. Picca

) and quantitative precipitation estimation (e.g., Giangrande and Ryzhkov 2008 ) algorithms. For a detailed overview and discussion regarding commonly used polarimetric radar variables, including radar reflectivity factor at horizontal polarization Z H , copolar cross-correlation coefficient ρ hv , specific differential phase K DP , and differential reflectivity Z DR , the reader is referred to Doviak and Zrnić (1993) , Bringi and Chandrasekar (2001) , and Kumjian (2013a , b , c) . Perhaps

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