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

demonstrate the impact of GMI data assimilation, the intensity correction component is turned off in all data assimilation experiments presented in this study. However, the vortex relocation is still used for data assimilation experiments to avoid potential phase errors that may put observations in wrong locations and result in harm to the vortex inner core. In addition, the current operational HWRF uses the NCEP GFS analysis to initialize the outer domain (d01) and also uses the GFS forecast to provide

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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

(CTRL) included all conventional and satellite observations operationally assimilated in the GDAS/GFS. The second experiment (DAGMI) added GMI observations on top of the configuration of the CTRL experiment. Both forecast experiments were run at T670/L64 (∼25 km) resolution, and only the GFS forecasts initialized at 0000 UTC between 4 and 12 August were assessed. Figure 3 shows the track forecast for the CTRL experiment ( Fig. 3a ) and DAGMI experiment ( Fig. 3b ) for each GFS forecast. Each point

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

. Traditional global analyses from operational systems are used to initialize the ensemble forecasts at the initial data assimilation cycling period. The large-scale forcing for subsequent forecasts during the cycling period is applied through domain boundaries. To constrain the dynamical forcing in domain interiors, the following data from the operational global observing system are also assimilated: in situ conventional observations and clear-sky radiances from temperature and moisture-sensitive channels

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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

, numerical weather prediction, hydrological model forecasts, and other application and operational uses, ○ combined radar–radiometer swath products will be available within 3 h of observation time, 90% of the time, and ○ radiometer precipitation products will be available within 1 h of observation time 90% of the time. All science requirements have been met and are currently being documented in peer-reviewed literature. Selected publications can be found in the AMS featured collection on GPM ( http

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

the Tropical Rainfall Measuring Mission (TRMM) era . J. Hydrometeor. , 17 , 1101 – 1117 , . 10.1175/JHM-D-15-0190.1 McBride , J. L. , and E. E. Ebert , 2000 : Verification of quantitative precipitation forecasts from operational numerical weather prediction models over Australia . Wea. Forecasting , 15 , 103 – 121 ,<0103:VOQPFF>2.0.CO;2 . 10.1175/1520-0434(2000)015<0103:VOQPFF>2.0.CO;2 Mlawer , E. J

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Wesley Berg, Stephen Bilanow, Ruiyao Chen, Saswati Datta, David Draper, Hamideh Ebrahimi, Spencer Farrar, W. Linwood Jones, Rachael Kroodsma, Darren McKague, Vivienne Payne, James Wang, Thomas Wilheit, and John Xun Yang

1. Introduction The Global Precipitation Measurement (GPM) mission seeks to build upon the success of the Tropical Rainfall Measuring Mission (TRMM) through improved technology, expanded global coverage, and improved temporal sampling from a constellation of research and operational microwave sensors. The GPM Core Observatory provides a number of improvements over TRMM. These include a Dual-Frequency Precipitation Radar (DPR) that adds a Ka-band radar with better sensitivity to light

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M. Petracca, L. P. D’Adderio, F. Porcù, G. Vulpiani, S. Sebastianelli, and S. Puca

( Chandrasekar et al. 2008 ). For this reason, NASA and the Japan Aerospace Exploration Agency (JAXA) carried out an extensive ground validation program in North America (mainly the United States) and Europe ( Schwaller and Morris 2011 ), as well as partnering with various other groups elsewhere in the world. As an example, a scientific collaboration between the EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF) and GPM called “H SAF and GPM

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Robert A. Houze Jr., Lynn A. McMurdie, Walter A. Petersen, Mathew R. Schwaller, William Baccus, Jessica D. Lundquist, Clifford F. Mass, Bart Nijssen, Steven A. Rutledge, David R. Hudak, Simone Tanelli, Gerald G. Mace, Michael R. Poellot, Dennis P. Lettenmaier, Joseph P. Zagrodnik, Angela K. Rowe, Jennifer C. DeHart, Luke E. Madaus, Hannah C. Barnes, and V. Chandrasekar

storms vary strongly in the vertical. In contrast, scanning of operational radars provides broad horizontal coverage from sequences of plan position indicator (PPI) azimuthal scans at a discrete set of elevation angles. OLYMPEX used the operational scans of the Langley Hill and Camano Island, Washington, National Weather Service (NWS) Weather Surveillance Radar-1988 Dopplers (WSR-88Ds) ( Figs. 1 and 2 ) to provide the background horizontal mapping, while the OLYMPEX scanning radars operated in

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Veljko Petković, Christian D. Kummerow, David L. Randel, Jeffrey R. Pierce, and John K. Kodros

instruments, limiting rainfall signals to an indirect, nonunique relationship between cloud ice-scattering signatures and surface rainfall. Based on the mean observed ratio between ice aloft and the surface rainfall, these estimates can often be inaccurate, with more pronounced biases observed during extreme events. In addition to the example given in study by Petković and Kummerow (2015) , a difference in mean precipitation rate bias between ground radar measurements and an operational satellite PMW

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