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

observations are available alongside the GMI TB observations within approximately the middle third of the GMI swath (the GMI, DPR Ku-band, and DPR Ka-band swaths are 885, 245, and 120 km, respectively). One proposed strategy involves utilizing the DPR precipitation profile retrievals ( Munchak et al. 2016 ; Grecu et al. 2011 ) within this narrow swath, to forward simulate the TB observations using MW radiative transfer models and satellite sensor simulators ( Matsui et al. 2013 ). Ideally, these

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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

vapor and cloud water profiles. The extinction due to water vapor is determined using the model of Rosenkranz (1998) . The extinction coefficient, single-scattering albedo, and asymmetry parameter profiles at the GMI frequencies are also derived from the Ku-band observations, and these parameters are employed to simulate the DPR-resolution brightness temperatures that are required to update the initial ensemble of Ku band–derived profiles using the GMI observations. The radiative transfer model

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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

raindrop size distribution), often increasing the Marshall–Palmer (equivalent to decreasing the mean drop size diameter) in agreement with other studies. Next, in step 4, we use microwave radiometer simulators (radiative transfer models) to compute the passive microwave signatures (brightness temperatures) of the radar-optimized hydrometeor profiles, still at the higher spatial resolution of the radar observations. Step 5 convolves the high-resolution simulated brightness temperatures to the scale of

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

probability of observing a particular rainfall structure x , when a set of brightness temperatures Tb, denoted by the vector y is observed. Term P ( y | x ) is the probability of making observation y when x is present, while P ( x ) and P ( y ) are the a priori probabilities of x and y , respectively. The latter may come from global statistics of precipitating cloud states and observations, respectively. The determination of P ( y | x ) requires a radiative transfer model that translates

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

matching. Table 1. Bias (before applying offsets) and rmse (after applying offsets; K) of clear sky, nearly calm wind (<3.5 m s −1 )-simulated Tb forced with buoy observations of SST, and 10-m wind and MERRA atmospheric parameters. No offsets were applied to the 183-GHz channels. The forward model is derived from the Community Radiative Transfer Model (CRTM) Emission (nonscattering atmosphere) Model, modified to include the downwelling pathlength correction for roughened water surfaces as described by

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

documented in work of Kummerow et al. (2015) . GPROF utilizes a Bayesian approach that employs a priori information on the relationship between hydrometeor profiles and corresponding radiances. Using the DPR-combined algorithm as a primary source of precipitation profiles, coupled with radiative transfer models, GPROF computes Tbs for any sensor that forms part of the GPM constellation ( Kummerow et al. 2011 ). The algorithm first groups the entire a priori database by using ancillary information (TPW

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