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Tomoaki Mega and Shoichi Shige

(see review in Indu and Kumar 2014 ). For an ocean-only footprint which is radiometrically cold and homogeneous, an emission signature from raindrops across the lower-frequency spectrum is essentially used. For a land-only footprint, a scattering signature from ice crystals over the higher-frequency spectrum is used because a radiometrically warm background tends to obscure emissions from raindrops. Coastal regions include radiative contributions from both ocean and land. Because the ratio of

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

different, GPROF 2010 does not make use of the sounding channels over ocean. The products from these two sensors are therefore quite similar. Only ocean trends are shown to avoid sensor differences that can be seen in Fig. 2 over land. As can be seen, different sensors are very consistent across the overlap periods, giving confidence that any trends are physical or physically based rather than sensor artifacts. A larger difference can be seen with the initial record of SSMIS F16 , which is slightly

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

occurrence of rain and that these data can be used as a temporal reference. In the future, both types (conditioned and unconditioned on surface wetness) of temporal reference datasets will be prepared when sufficient DPR data are available. It should be mentioned that at present, only three surface types are defined for the SRT: ocean, land, and coast, where the reference data value is matched to that surface type beneath the raining FOV. The use of a multicategory land classification has been proposed

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

Tropical Rainfall Measuring Mission (TRMM) precipitation products. For surface emissivity at typical MW channels between 10 and 90 GHz, the Tool to Estimate Land Surface Emissivities at Microwave frequencies (TELSEM) passive microwave-based surface classification ( Prigent et al. 2006 ; Aires et al. 2011 ) provides a lookup table method to interpolate the emissivity mean and variance at a specified incidence angle and frequency, using a precalculated 0.25° gridded monthly mean emissivity climatology

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

closer inspection of active- and passive-estimated precipitation distributions confirms that PMW biases exist over the majority of their characteristic precipitation rates. Results over ocean, not shown here, yield the same general conclusions. Fig . 1. Comparison of global over land pixel-level distribution of precipitation rate estimates of GPM’s DPR-combined (gray) and GMI (colored bars) products. (top) Convective and (bottom) stratiform systems are delineated using a 50% threshold for DPR

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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

beamfilling parameterization The application of the day 1 (at launch) version of the combined algorithm to GPM data revealed significant differences between estimates using both Ku- and Ka-band observations and retrievals that did not use the Ka-band radar observations. These differences mainly occurred in retrievals of convective precipitation over land and were associated with large (more than 50%) reductions of estimated surface precipitation when the Ka-band observations were introduced into the

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

was actually used to identify the sidelobe clutter that affects the precipitation/no-precipitation classification method. After the sidelobe events were detected based on the procedures shown in the flowchart, the ratio of the sidelobe events to the total events was calculated at each range gate. Figure 8 shows the results of the ratio in the vertical cross sections. In the analysis for the case of land, an altitude relative to the land surface was adopted as the vertical axis. The pattern of

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Jun Awaka, Minda Le, V. Chandrasekar, Naofumi Yoshida, Tomohiko Higashiuwatoko, Takuji Kubota, and Toshio Iguchi

line) and that by the DF CSF module for NS data (solid line). Counts by the (a) V3 codes and (b) new V4 codes. When the new algorithms are used, the difference between the dotted and solid curves for each rain type becomes very small. Both panels show all the data (over ocean and land) in the entire GPM DPR coverage (66°S–66°N around the globe). At angle bin 25, the radar beam points toward the nadir direction. Figure 6b shows the angle bin dependence of each unified rain type count obtained by

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Atsushi Hamada and Yukari N. Takayabu

that coupled with the sample standard deviation of P r , , since echo power P e is estimated by . Both over land and the oceans, there is still likely to be significant separation between P r and P n below 14.5 dB Z down to ~12 dB Z and ~13 dB Z over the oceans and land, respectively. Although depending on the intended application, one may still use reflectivity in this range as the true precipitation signal, at the risk of increased false positives with lowering threshold reflectivity

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

surface rain rate, and the height above the ellipsoid (mean sea level). Because the NICAM can provide global atmospheric simulation data, this study extends the ideas of Iguchi et al. (2009) . The CLWC database in this study is a function of surface precipitation rate, precipitation type (convective or stratiform), temperature, latitude, and land surface type. This database was constructed using 3.5-km-mesh global simulation data over the nine days noted in section 2b . As noted in section 2a , the

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