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Ali Behrangi, Bin Guan, Paul J. Neiman, Mathias Schreier, and Bjorn Lambrigtsen

(WYs) 1998–2008 ( Dettinger et al. 2011 ). On average, 6–7 ARs per winter account for 40% of the annual snow accumulation in California’s Sierra Nevada during WYs 2004–10 ( Guan et al. 2010 , 2013 ). All seven major flooding events in California’s Russian River basin during WYs 1998–2006 were associated with ARs ( Ralph et al. 2006 ), as were 46 out of 48 annual peak daily flow events in Washington during WYs 1998–2009 ( Neiman et al. 2011 ). Recently, it was found that 33%–74% of droughts in the

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Toshi Matsui, Jiun-Dar Chern, Wei-Kuo Tao, Stephen Lang, Masaki Satoh, Tempei Hashino, and Takuji Kubota

assigned to the shallow warm (SW) category. A category with slightly colder cloud tops (Tb IR > 245 K) and higher echo-top heights (4 < H ET < 7 km) was previously defined as congestus ( Masunaga et al. 2005 ; Matsui et al. 2009 ). The Tb IR threshold of 245 K is based on that in Machado et al. (1998) for separating deep and nondeep clouds. However, this broad range of cloud-top temperatures also encompasses slightly deeper clouds than the traditional definition for congestus (~260 K; Johnson

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Chris Kidd, Toshihisa Matsui, Jiundar Chern, Karen Mohr, Chris Kummerow, and Dave Randel

XT, version 1-2, retrievals ( Kidd 2014a , b , c , d ) for the MHS and CS, version 1-4, retrievals ( Kummerow 2014a , b , c , d , e ) for the Advanced Microwave Scanning Radiometer 2 (AMSR2), GMI, and SSMIS are used in this study. The spatial resolution of the CS GPROF retrievals are set by the size of each sensor’s 3-dB Z sensitivity at 37 GHz; this equates to a retrieval resolution of 12 km × 7 km for AMSR2, 14 km × 8.6 km for GMI, and 44.2 km × 27.5 km for SSMIS. The scan position

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Zhong Liu, Hualan Rui, William Teng, Long Chiu, Gregory Leptoukh, and Steven Kempler

-cycle-related NASA data. For example, TOVAS provides global rainfall data and information ranging from historical to near–real time to users around the world ( Zhang et al. 2005 ; Huffman et al. 2007 ; Liu et al. 2007 ; Yin et al. 2008 ; Meier and Knippertz 2009 ). The Online Precipitation Intercomparison Tool (OPIT; available at http://disc2.nascom.nasa.gov/Giovanni/tovas/rain.ipwg.shtml ) is a main component of the online information system prototype for the validation and intercomparison of global

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Pierre-Emmanuel Kirstetter, Y. Hong, J. J. Gourley, M. Schwaller, W. Petersen, and J. Zhang

. 2008 ) led by the International Precipitation Working Group (IPWG; see http://www.isac.cnr.it/~ipwg/ ). In this study, we focus on the Tropical Rainfall Measurement Mission (TRMM) precipitation radar (PR) quantitative precipitation estimation (QPE) product. The TRMM PR is currently the only active instrument dedicated to the measurement of rainfall from a satellite platform conjointly with a radiometer [TRMM Microwave Imager (TMI)]. PR measurements are considered as the starting point for

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Zed Zulkafli, Wouter Buytaert, Christian Onof, Bastian Manz, Elena Tarnavsky, Waldo Lavado, and Jean-Loup Guyot

Anders 2009 ). The TMPA version 6 algorithm is described in Huffman et al. (2007) , while changes in the version 7 algorithm at various processing levels are described in Huffman et al. (2010) and Huffman and Bolvin (2013) and are summarized here. They include the new Goddard profiling algorithm (GPROF) 2010 algorithm for PMW-based estimation that references TRMM’s available records of storm profiles, PMW brightness temperatures, and precipitation rates, replacing a reference database

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Roongroj Chokngamwong and Long S. Chiu

scales have been performed, for example, by Brown (2006) for the India–Sri Lanka area and by Islam and Uyeda (2006) over Bangladesh. More recently, comparison of operational high-resolution rain products at the daily scale have been carried out by the International Precipitation Working Group (IPWG) for the continental United States, Europe, and Australia ( Ebert 2002 ; Ebert et al. 2007 ; Turk et al. 2006 ). The comparison over Asia and in Thailand, in particular, is lacking. Ten years of

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Li Yan and Gen Li

leading EOF mode (EOF1, with a correlation exceeding 0.9 between the “two definitions”). Unlike the SIOD and the SAOD, it is hard to perfectly separate the SPOD using EOF analysis because the EOF2 mode of the SST in the southwestern Pacific possesses some signals from the ENSO (with a correlation of approximately 0.7 between the two definitions). The definition of the SPOD in Table 1 is somewhat different from the definition provided by Morioka et al. (2013) because the SST variability in the

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Zhong Liu

sensors (HQ) and IR precipitation variables] in Table 2 are used to compute their monthly products. Both TMPA (version 7) and IMERG (version 03D) Final Run data in this study were downloaded from Mirador ( http://mirador.gsfc.nasa.gov ) at the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC; Liu et al. 2012 ). There have been a few processing issues ( Huffman and Bolvin 2014 ) with TMPA before, but all the TMPA data used in this study are current. 3. Results a. Systematic

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Viviana Maggioni, Patrick C. Meyers, and Monique D. Robinson

products with respect to buoy gauge data show an improvement in 3B42-V7 with respect to 3B42-V6 in both RMSE and bias during intense precipitation events over the three tropical oceans at the monthly scale ( Prakash et al. 2013 ). 4. Conclusions This paper contributes to IPWG’s mission of encouraging scientific knowledge on satellite precipitation measurements to stimulate research in this field, to develop better algorithms, and to improve the scientific understanding of remotely sensed precipitation

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