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

number: 10 986), which was slightly higher than that using all the events. A correlation coefficient using events with an echoCount equal to or greater than 252 was 0.54 (sample number: 71). Because a squared correlation coefficient shows a proportion of variances explained by a linear regression model, this suggests an effectiveness of the regression model with an echoCount value equal to or greater than 252 is lower than that with an echoCount of less than 252. For the analysis of the results shown

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

conditions, the information content within the TB observations themselves is studied as a means to characterize the emissivity state and to track the associated environmental conditions. In this study, we focus on conically scanning MW radiometers with low-frequency (89 GHz and below) capabilities and near-constant Earth incidence angle. Over one full year of combined GMI–DPR data, together with the NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA) ( Reichle et al. 2011 ) model

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

supplement to PMW observations. It is therefore important to assess if convective/stratiform information can be inferred from the passive microwave information itself. Yet, despite sustained, decades-long effort to identify a robust link between PMW observations and convective fraction, only a few regression methods with modest skill are available. These methods largely utilize the spatial variability of the brightness temperature (Tb) of the high-frequency channels (e.g., 30 GHz and above). Thus, the

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

been part of the operational processing of the TRMM and now GPM data and is used in the analysis of CloudSat data ( L’Ecuyer and Stevens 2002 ; Mitrescu et al. 2010 ). The method has also been used in the analysis of airborne weather radar data (e.g., Liao and Meneghini 2005 ; Liao et al. 2008 ). 2. Surface reference technique a. Single-frequency method (SRT) Calculation of the NRCS (σ 0 ) of the surface follows the formulation given by Kozu (1995) that relates σ 0 to parameters of the

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Tomoaki Mega and Shoichi Shige

and geometric data of the footprint, to make the coastal area of the land–ocean–coast flag smaller. 2. Data The analysis presented herein is based on observations from the TMI because the location and time of its observation are similar to the PR. The TMI is one of five sensors aboard the TRMM satellite, which was launched into low-Earth orbit in November 1997 to provide data on the characteristics of precipitation in the tropics and subtropics (35°S–35°N). The PR is a single-frequency (13.8 GHz

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

combined convective and stratiform rain, RR = RR conv (CSP) + RR strat (1 − CSP). The algorithm was thus a hybrid scheme with a Bayesian formulation over oceans coupled with a regression-based approach over land and coastal regions. The general algorithm flow is depicted in Fig. 1 . Fig . 1. Algorithm flow for GPROF 2010. The only aspect of GPROF 2010 not covered above is the extension of the a priori databases over oceans to colder backgrounds than those observed directly by TRMM. While the code

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

) , who found that differences between the two depended on wind speed and water vapor (a consequence of the aforementioned cross talk between parameters). The authors also attempted to combine the two sets of measurements via multiple regression. They found that adding QuikSCAT to WindSat did not improve wind retrievals outside of rain, but they did note a slight improvement under raining conditions. More recently, the Aquarius satellite, which offers active and passive measurements at L band for

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