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Mircea Grecu, William S. Olson, Stephen Joseph Munchak, Sarah Ringerud, Liang Liao, Ziad Haddad, Bartie L. Kelley, and Steven F. McLaughlin

uniformly calibrated rain algorithms for all radiometers in the GPM constellation ( Kummerow et al. 2011 ). The constellation of radiometers provides the temporal sampling necessary to achieve the mission objective. During the Tropical Rainfall Measuring Mission (TRMM) era, several algorithms for estimating precipitation from a combination of radar and microwave radiometer observations were developed. The TRMM observatory included a single-frequency (Ku band) cross-track scanning radar and a

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Christian D. Kummerow, David L. Randel, Mark Kulie, Nai-Yu Wang, Ralph Ferraro, S. Joseph Munchak, and Veljko Petkovic

1. Introduction The Goddard profiling (GPROF) algorithm was first developed in the early 1990s to retrieve surface rainfall and its vertical structure from spaceborne passive microwave observations ( Kummerow and Giglio 1994 ). The impetus for that work came from the Tropical Rainfall Measuring Mission (TRMM) ( Simpson et al. 1988 ) that was seeking to quantify not only the surface rainfall but also the three-dimensional structure of latent heat release in the tropics. While the primary

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Takuji Kubota, Shinta Seto, Masaki Satoh, Tomoe Nasuno, Toshio Iguchi, Takeshi Masaki, John M. Kwiatkowski, and Riko Oki

. 2012 ; Iguchi 2020 ). In generating precipitation datasets, it is necessary to develop computationally efficient, fast-processing DPR level-2 (L2) algorithms that can provide estimated precipitation rates, radar reflectivity factors, and precipitation information, such as the DSD and precipitation type ( Kubota et al. 2014 ; Iguchi et al. 2018 ; Iguchi 2020 ). In the L2 algorithms, an assumption related to clouds is one of uncertain factors; the algorithm assumes cloud liquid water content (CLWC

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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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F. Joseph Turk, Z. S. Haddad, and Y. You

derived from Special Sensor Microwave Imager (SSM/I) observations. TELSEM has been successfully implemented into the current GPROF GPM radiometer algorithm ( Kummerow et al. 2015 ) using a 15-class emissivity index to catalog the surface in the a priori database. This ancillary model and surface class information is attached to all a priori profiles, both precipitating and nonprecipitating. With these ancillary data for the environment and the surface, the current GPROF GPM Bayesian retrieval is

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Minda Le, V. Chandrasekar, and Sounak Biswas

conclusions are in section 5 . 2. Brief review of dual-frequency classification module This section briefly summarizes the dual-frequency classification module. The dual-frequency classification module is a new module in the GPM DPR level 2 algorithm. The module is developed using observations from both Ku and Ka bands; thus, it is applied only to the GPM DPR inner swath. The outputs of the module follow the legacy format used for TRMM PR. They include two parts, namely, precipitation type classification

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Tomoaki Mega and Shoichi Shige

systems because of the weak link between cloud properties and precipitation ( Adler et al. 1993 ). Because microwave emission and scattering are more directly related to precipitation than cloud-top temperature, microwave radiometer (MWR) algorithms generally provide more accurate instantaneous estimates of rainfall than IR algorithms ( Arkin and Ardanuy 1989 ; Ebert et al. 1996 ; Smith et al. 1998 ). The superior spatial and temporal sampling of geostationary IR instruments relative to MWRs on

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Olivier Hautecoeur and Régis Borde

coverage areas of geostationary and polar winds based on three successive orbits. In 2012 EUMETSAT launched the MetOp-B satellite (engineering name MetOp-1 or M01 ), which took over the primary operations on April 2013. The tandem configuration with MetOp-A and MetOp-B satellites in the same orbital plane provided an interesting opportunity to create AVHRR AMVs using the two MetOp satellites. Three different AVHRR wind products are derived by the same algorithm, using a pair or triplet of

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Takuji Kubota, Toshio Iguchi, Masahiro Kojima, Liang Liao, Takeshi Masaki, Hiroshi Hanado, Robert Meneghini, and Riko Oki

launched at 1837 UTC 28 February 2014. The Dual-Frequency Precipitation Radar (DPR) was developed by the Japan Aerospace Exploration Agency (JAXA) and the National Institute of Information and Communications Technology (NICT), and was installed on the GPM Core Observatory ( Kojima et al. 2012 ). The DPR consists of Ku-band precipitation radar (KuPR) and Ka-band precipitation radar (KaPR). The level 2 (L2) product of the DPR, which was developed by the NASA–JAXA Joint Algorithm Team, provides the

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S. Joseph Munchak, Robert Meneghini, Mircea Grecu, and William S. Olson

1. Introduction Algorithms for estimating precipitation from spaceborne radars at attenuating frequencies [e.g., TRMM PR ( Iguchi et al. 2000 , 2009 ), CloudSat ( Mitrescu et al. 2010 ), Global Precipitation Measurement (GPM) dual-frequency precipitation radar (DPR; Grecu et al. 2011 )] have long realized the benefit of an estimate of the path-integrated attenuation (PIA) that is independent of the reflectivity profile for the purposes of constraining the integrated and surface

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