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

algorithm. b. Evaluation of the method In this subsection, the developed method was applied to the KuPR L2 algorithm. Here, coefficients of the regression models were calculated using the DPR data for 26–28 March 2014 and were applied to DPR data during 29–31 March 2014. In addition, the coefficients calculated using the data for 2–4, 2014 August were applied to the DPR data during 5–7 August 2014. Figure 13 shows vertical cross sections of the received power of the echo over the ocean without use of

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Veljko Petković, Marko Orescanin, Pierre Kirstetter, Christian Kummerow, and Ralph Ferraro

depends on the representativeness, quantity and quality of the training dataset. To establish a baseline model and evaluate the performance of the approach we propose a relatively simple scheme and a widely available satellite dataset. Detailed descriptions of the datasets and DNN model are given below. a. Instruments and data This study employs 2 years, from September 2014 to August 2015 and from January to December 2017, of the GPM core satellite global observations (66°S–66°N) to explore accuracy

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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

Irrespective of the optimization approach, the minimization of function J requires multiple evaluations. When a gradient-based approach is used, a special technique to evaluate the gradient of J (called adjoint modeling) is necessary to make the solution computationally feasible. However, adjoint modeling can limit the development of new and more advanced physical forward models in the combined algorithm, and so an alternative strategy is employed here. Specifically, instead of sequentially evaluating

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

-induced attenuation. Our strategy ( Fig. 1 ) is to develop a GMF for DPR based upon collocated GMI wind retrievals, and then use this GMF under raining conditions by modifying the GPM Combined Radar–Radiometer Algorithm (CORRA; Olson and Masunaga 2015 ). To have as accurate a wind reference as possible, we evaluate three emissivity models after calculating offsets under clear and calm conditions to achieve consistency with the GMI calibration. Next, we use all available matchups of GMI and DPR under

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

scale with passive microwave satellite observations . J. Geophys. Res. , 111 , D19102 , doi: 10.1029/2005JD006773 . Dee, D. P. , and Coauthors , 2011 : The ERA-Interim reanalysis: Configuration and performance of the data assimilation system . Quart. J. Roy. Meteor. Soc. , 137 , 553 – 597 , doi: 10.1002/qj.828 . Dirmeyer, P. , and Coauthors , 2016 : Confronting weather and climate models with observational data from soil moisture networks over the United States . J. Hydrometeor. , 17

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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

. Evaluation of the hybrid EOF–1DVAR approach using the synthetic data The performance of the optimization approach based on the minimization of Eq. (5) can be evaluated using synthetic data. Specifically, we used the full hydrometeor information in the Q vectors and produced simulated reflectivity and TBs from the WRF simulations described in section 2 . These synthetic data were considered the “truth” and were used as input in Eq. (5) . Only profiles that resemble actual PR observations are

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

-permitting model simulations using a cell-tracking algorithm . Mon. Wea. Rev. , 141 , 557 – 581 , doi: 10.1175/MWR-D-11-00274.1 . Dee, D. P. , and Coauthors , 2011 : The ERA-Interim reanalysis: Configuration and performance of the data assimilation system . Quart. J. Roy. Meteor. Soc. , 137 , 553 – 597 , doi: 10.1002/qj.828 . Elsaesser, G. S. , and Kummerow C. D. , 2008 : Toward a fully parametric retrieval of the non-raining parameters over the global oceans . J. Appl. Meteor. Climatol. , 47

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Olivier Hautecoeur and Régis Borde

the performance of the AMV_2D product is even slightly better than that of the collocated Meteosat-7 and Meteosat-10 AMVs in the Northern and Southern Hemispheres at high and midlevels. Such a result is a good quality check because the Meteosat-7 and Meteosat-10 AMV products have been used and evaluated in NWP for a long time and are known to be reliable products. These results are also in good agreement with the exhaustive comparison of the AMV_2D product against geostationary AMVs from

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

submitted to J. Meteor. Soc. Japan ), probably because of differences in details of cloud microphysics schemes and dynamical cores. Therefore, the model results whose clouds are evaluated in existing observations are ideal for the use of the algorithm development of this study. The current NICAM simulation data were evaluated against satellite data for the cloud microphysics in previous works ( Hashino et al. 2013 , 2016 ; Matsui et al. 2016 ; Roh et al. 2017 ). They were used also in meteorological

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Robert Meneghini, Hyokyung Kim, Liang Liao, Jeffrey A. Jones, and John M. Kwiatkowski

quite close to those from the DPR Ku band. It is worth noting that this good agreement extends to the wider swath (from −18° to 18°) as well (as viewed by an observer on the spacecraft, facing in the direction of motion, negative angles are to the left). The statistics critical to the SRT performance are shown in Fig. 3 , where ρ for ocean (top) and land (bottom) is shown in the left-hand panels. The asymmetry in ρ with respect to nadir, particularly noticeable over ocean, is thought to be

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