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Zhaoxia Pu, Chaulam Yu, Vijay Tallapragada, Jianjun Jin, and Will McCarty

significantly reduce error in NWP, specifically over regions where conventional observations are sparse. For instance, hurricane forecasts, in particular, benefit greatly from the large spatial coverage over oceans and the high temporal resolution of satellite observations. This is because hurricanes form and evolve mostly over the oceans, where conventional observations such as radiosonde and surface observations are less available. Since the last decade, the research community has devoted great effort to

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Liao-Fan Lin, Ardeshir M. Ebtehaj, Alejandro N. Flores, Satish Bastola, and Rafael L. Bras

assimilation include the Goddard Earth Observing System (GEOS) ( Hou et al. 2000a , b , 2001 , 2004 ; Pu et al. 2002 ; Lin et al. 2007 ), the European Centre for Medium-Range Weather Forecasts (ECMWF) operational system ( Lopez and Bauer 2007 ; Geer et al. 2008 ; Lopez 2011 , 2013 ), and the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) ( Lien et al. 2016 ; Shao et al. 2016 ). On a regional scale, studies have assimilated rain rates into models such as the

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Thomas Stanley, Dalia B. Kirschbaum, George J. Huffman, and Robert F. Adler

. Satellite precipitation data are used in many applications such as flood monitoring, crop forecasting, numerical weather prediction, and disease tracking ( Kucera et al. 2013 ; Kirschbaum et al. 2017 ). These user communities have relied upon TMPA data, and several workshops have highlighted the need for long precipitation records ( Ward et al. 2015 ; Ward and Kirschbaum 2014 ). While the GPM mission plans to create a consistent record of precipitation available from 1998 to the present using TRMM

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Yonghe Liu, Jinming Feng, Zongliang Yang, Yonghong Hu, and Jianlin Li

was obtained from the China Meteorological Data Sharing Service ( http://data.cma.cn ). Overall 83 gauging stations with complete records were used here to train the single-station downscaling models, covering the period of 1979–2016. Another 12 stations having a small number of missing records were used to validate the gridded output in “no gauge” areas. ERA-Interim datasets (ERAI) from the European Centre for Medium-Range Weather Forecasts ( Dee et al. 2011 ) were obtained, and only those

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Sara Q. Zhang, T. Matsui, S. Cheung, M. Zupanski, and C. Peters-Lidard

. For instance, the Modern-Era Retrospective Analysis for Research and Applications (MERRA) product used so-called postanalysis adjustment on the surface precipitation field, which adjusts precipitation produced from model forecasts to fit available precipitation retrievals ( Bosilovich et al. 2011 ). Although this approach produces better surface precipitation analysis, it does not directly provide a better representation of the interaction and feedback of the precipitation to model thermodynamics

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Dalia B. Kirschbaum, George J. Huffman, Robert F. Adler, Scott Braun, Kevin Garrett, Erin Jones, Amy McNally, Gail Skofronick-Jackson, Erich Stocker, Huan Wu, and Benjamin F. Zaitchik

planned PMM activities, specifically focusing on the GPM suite of data products relevant for an applications-focused audience. We then provide case studies of how TRMM and GPM data have been applied across four thematic areas: tropical cyclone track forecasting, flood modeling, agricultural monitoring, and disease tracking. Table 1. Examples of applications’ thematic areas and topics where satellite precipitation estimates are being used for situational awareness and decision-making. More information

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Jiaying Zhang, Liao-Fan Lin, and Rafael L. Bras

al. (2016) revealed that the IMERG product has more skill in representing daily precipitation than the post-real-time Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA-3B42) and the ERA-Interim product from the European Centre for Medium-Range Weather Forecasts (ECMWF) in Iran from March 2014 to February 2015. For the midlatitude region of the Ganjiang River basin in southeast China, Tang et al. (2016b) showed that the detection skill of the Day-1 IMERG

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Zeinab Takbiri, Ardeshir Ebtehaj, Efi Foufoula-Georgiou, Pierre-Emmanuel Kirstetter, and F. Joseph Turk

( Doswell et al. 1990 ) for the presented results in Figs. 7 – 9 . We also compare the algorithm outputs with the precipitation phase products of the MRMS on a seasonal basis ( Figs. 10 , 11 ). Finally, some results are presented at a storm scale to demonstrate the detection capabilities of the algorithm for a few precipitation events that are coincidentally captured by the DPR and high-resolution ground-based radars ( Figs. 12 , 13 ) and simulated by the Weather Research and Forecasting (WRF) Model

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W.-K. Tao, T. Iguchi, and S. Lang

-season retrievals, the six new NASA Unified-Weather Research and Forecasting (NU-WRF) Model 4ICE simulations are used to generate the CSH LUTs (discussed in the flowing sections). CSH tropical/warm-season retrievals are then merged with extratropical/cold-season retrievals based on the height of the freezing level. 1 3. NU-WRF Model and cases The GCE has been used for semi-idealized and longer-term simulations constrained by large-scale forcing derived from sounding networks; the latter ensures its simulated Q

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Gail Skofronick-Jackson, Walter A. Petersen, Wesley Berg, Chris Kidd, Erich F. Stocker, Dalia B. Kirschbaum, Ramesh Kakar, Scott A. Braun, George J. Huffman, Toshio Iguchi, Pierre E. Kirstetter, Christian Kummerow, Robert Meneghini, Riko Oki, William S. Olson, Yukari N. Takayabu, Kinji Furukawa, and Thomas Wilheit

availability; 3) improving climate modeling and prediction capabilities; 4) improving weather forecasting and four-dimensional (4D) reanalysis; and 5) improving hydrological modeling and prediction. More details about these scientific objectives can be found in Hou et al. (2014) . GPM CO ’s well-calibrated instruments allow for scientifically advanced observations of precipitation in the midlatitudes, where a majority of Earth’s population lives. The middle panel in Fig. 1 shows the coverage of the GPM

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