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

into the global GCM with interactive radiative impact of aerosols. The product provides continuous spatial distributions of aerosols consistent with the satellite observations, which allows a more realistic investigation on the African easterly jet (AEJ) ( Reale et al. 2011 , 2014 ). This work advances to assimilate precipitation-sensitive radiances into high-resolution mesoscale atmospheric simulations of the WAM. The goal is to create cloud and precipitation distributions that are consistent

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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

accumulation during the winter in the Olympic Mountains were made by airborne lidar aboard the Jet Propulsion Laboratory’s Airborne Snow Observatory (ASO) aircraft ( Painter et al. 2016 ) and by crews carried by helicopter to measure the depth and density of the snowpack. The manual snow surveys were conducted near most of the locations of the snow poles and cameras. The lidar measurements were compared to data obtained on an earlier flight conducted in September 2014 with no snow cover on the mountains to

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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

-point calibration adjustment is done for these sensors. Teams providing results for the sounding channels include TAMU, UCF, Science Systems and Applications, Inc. (SSAI), and the Jet Propulsion Laboratory (JPL) of the California Institute of Technology. For the sounding channels above 100 GHz, the TAMU algorithm uses SST, WS, and the temperature profile provided by GDAS and iteratively adjusts the RH and CLW profiles to match the radiances of the reference radiometer using an algorithm described by

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

provided by the NOAA S4 computer system and Jet supercomputer system, and the University of Utah’s Center for High-Performance Computing (CHPC) are greatly appreciated. Review comments from four reviewers and Dr. Altug Aksoy (editor) were useful for improving the overall manuscript. REFERENCES Aravéquia , J. A. , I. Szunyogh , E. J. Fertig , E. Kalnay , D. Kuhl , and E. J. Kostelich , 2011 : Evaluation of a strategy for the assimilation of satellite radiance observations with the

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Catherine M. Naud, James F. Booth, Matthew Lebsock, and Mircea Grecu

Naud and Booth were funded by NASA PMM Grant NNX16AD82G and by NOAA MAPP Grant NA15OAR4310094, with additional funding to Naud from NASA CloudSat Science Team Recompete Grant NNX13AQ33G. Author Grecu was funded by PMM Grant NNX16AD77G. Author Lebsock’s work was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration through RTOP/WBS 103428/8.A.1.6. REFERENCES Adler , R. F. , and Coauthors , 2003

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

) fellowship. Pierre-Emmanuel Kirstetter acknowledges support from the NASA Precipitation Science Program (NNX16AE39G) and from the GPM mission Ground Validation Program (NNX16AL23G). The contributions from F. Joseph Turk were performed at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with NASA. The GPM data (version 4) are provided courtesy of the NASA Precipitation Processing System at the Goddard Space Flight Center ( https://pmm.nasa.gov/data-access/ ). The MERRA-2

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Gail Skofronick-Jackson, Mark Kulie, Lisa Milani, Stephen J. Munchak, Norman B. Wood, and Vincenzo Levizzani

comparing various satellite-based falling snow products. Acknowledgments Support for part of this work comes from NASA Global Precipitation Measurement (GPM) mission funding. Partial support from NASA Precipitation Measurement Mission Grants NNX16AE21G and NNX16AE87G is also acknowledged. Parts of this research by NBW were performed at the University of Wisconsin–Madison for the Jet Propulsion Laboratory, California Institute of Technology, sponsored by the National Aeronautics and Space Administration

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Kamil Mroz, Alessandro Battaglia, Timothy J. Lang, Simone Tanelli, and Gian Franco Sacco

Atlantic and United Kingdom for the Global Precipitation Mission” funded by the U.K. NERC (NE/L007169/1). Timothy Lang was funded by the GPM Ground Validation program, under the direction of Mathew Schwaller and Ramesh Kakar of the National Aeronautics and Space Administration. The work by Simone Tanelli and Gian Franco Sacco was performed at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration in support to the

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Clément Guilloteau and Efi Foufoula-Georgiou

-1839441. The authors thank Prof. Christian Kummerow, Dr. Dave Randel, and Dr. Wesley Berg from the Precipitation Group at the Colorado State University as well as Dr. Joseph Turk from NASA Jet Propulsion Laboratory for the insightful discussions and shared information which contributed to the present article. APPENDIX A Acronyms AMSR-2 Advanced Microwave Scanning Radiometer 2 CMORPH Climate Prediction Center morphing technique DMSP Defense Meteorological Satellite Program DPR Dual

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