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

scatterometer winds in rainy conditions . IEEE Trans. Geosci. Remote Sens. , 48 , 3114 – 3122 , doi: 10.1109/TGRS.2010.2049362 . Tran, N. , Chapron B. , and Vandemark D. , 2007 : Effect of long waves on Ku-band ocean radar backscatter at low incidence angles using TRMM and altimeter data . IEEE Trans. Geosci. Remote Sens. , 4 , 542 – 546 , doi: 10.1109/LGRS.2007.896329 . Tretyakov, M. , Parshin V. , Koshelev M. , Shanin V. , Myasnikova S. , and Krupnov A. , 2003 : Studies of 183

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

NRCS of the surface and the sidelobe clutter. Fig . 4. Vertical cross section of (a) r (km) and (b) θ S (°). Horizontal axes are beam scan angles (°). Vertical axes are altitudes (km). 3. Features of sidelobe clutter in the KuPR In this section, features of the sidelobe clutter in the KuPR are described using the KuPR observations. Figure 5 shows vertical cross sections of averaged received power of the KuPR over the ocean from 26–28 March 2014 ( Fig. 5a ) and 2–4 Aug 2014 ( Fig. 5b ) when

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Eun-Kyoung Seo, Sung-Dae Yang, Mircea Grecu, Geun-Hyeok Ryu, Guosheng Liu, Svetla Hristova-Veleva, Yoo-Jeong Noh, Ziad Haddad, and Jinho Shin

improve through the selective use [i.e., as a function of dynamical–thermodynamical–hydrological (DTH) geographical–seasonal (GS) factors] of the underlying cloud-radiation database. In our approach, we mitigate the impact of GS variability on the quality of the derived cloud-radiation database by focusing exclusively on a specific region and season, that is, the seas and oceans east of Asia during the summer season. Given the use of vertical reflectivity profiles in derivation of the cloud

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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

(midlatitude cyclones, tropical convection, etc.). Cloud ice is currently not represented in the combined algorithm, and therefore it is neither included in nor prescribed otherwise. Surface emissivity is modeled as a function of surface wind and temperature over oceans ( Meissner and Wentz 2012 ; Munchak et al. 2016 ) and is prescribed based on the Tool to Estimate Land Surface Emissivities at Microwave Frequencies (TELSEM) database ( Aires et al. 2011 ) over land. The sea surface wind is therefore

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Shinta Seto and Toshio Iguchi

unknown parameters, N w (m −3 mm −1 ) and D m (mm): where D (mm) is drop size, N (m −3 mm −1 ) is the number density of rain drops, and n is defined below: where μ is the third DSD parameter but is fixed to 3 in this study, and Γ is the complete gamma function. When the DSD is given by Eq. (9) , Z e is calculated by the following: where f (s −1 ) is frequency and I b is defined by where c (mm s −1 ) is the speed of an electromagnetic wave, σ b (mm 2 ) is the backscattering

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

constrained to weight a priori candidate profiles with the same classification index, and similar surface temperature ( T sfc ) and total column water vapor (TWV) as the observation ( Kummerow et al. 2015 ), although more recent studies have suggested the use of the 2-m air temperature ( T 2m ) ( Sims and Liu 2015 ). Differences between forecast models can arise between the formulation (gridpoint spacing, or wave resolution in spectral models) and its temporal resolution. Furthermore, the surface

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