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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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Randy J. Chase, Stephen W. Nesbitt, and Greg M. McFarquhar

the APR, flying straight overtop the Citation ( Fig. 6a ). The sounding from the start of the mission (2218 UTC 30 January 2012) shows temperatures from the surface to 750 hPa are between −5° and −10°C, with the dendritic growth zone located between 700 and 600 hPa ( Fig. 6b ). The King City ground-based C-band radar plan position indicator (PPI) scan shows widespread snow, with localized bands of higher Z e ( Fig. 6a ). Fig . 6. (a) Map with of GCPEX domain with a PPI from the Environment and

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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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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

://pmm.nasa.gov/data-access/downloads/gpm ). JAXA’s GPM products in general can also be obtained online ( www.gportal.jaxa.jp/gp/top.html ), as can the GSMaP multisatellite merged dataset ( http://sharaku.eorc.jaxa.jp/ ). GPM data (level 0–3) are periodically reprocessed as retrieval algorithms are improved. The at-launch [version 03 (V03)] IMERG products were biased high for heavy rain events, while the upgrades to V04 (March 2017) and to V05 (planned for mid-2017) progressively reduced this bias. GPM retrieval algorithms use the dual

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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

al. (2014) for details]. It is therefore important to determine how accurately GPM instruments can determine rain and snowfall in storms passing over mountain ranges. To assess and improve the ability of the GPM satellite, the Olympic Mountains Experiment (OLYMPEX) was planned. OLYMPEX was an international, multiorganization field campaign 1 designed to collect detailed measurements by aircraft and ground sites to correspond with GPM satellite measurements over an area including a midlatitude

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Paloma Borque, Kirstin J. Harnos, Stephen W. Nesbitt, and Greg M. McFarquhar

observations were also an integral part of GCPEx; such observations are of fundamental importance to link the in situ measured particle properties to remotely sensed bulk characteristics. As part of GCPEx, ECCC’s dual-polarization scanning C-band radar located in King City, Ontario, Canada, performed (in addition to their operational scanning) range–height indicator sector scans and plan position indicator scans ( Hudak 2013 ). For some IOPs, the UND Citation flew in the coverage region of the King City

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Minda Le and V. Chandrasekar

radar hydrometeor identification. a. Validation with NEXRAD KDVN—Flatland case Figure 3 illustrates a snowstorm observed by both GPM DPR and NEXRAD KDVN on 21 November 2015 near Davenport, Iowa. DPR flew over the KDVN location at 0830:59 UTC with the orbit number of 9828. Figure 3a shows KDVN radar S-band reflectivity at plan position indicator (PPI) scan of 0.46°. The time of the scan starts at 0832:59 UTC. The time difference within 10 min is considered coincidence in this study, while the time

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

. REFERENCES AghaKouchak , A. , and A. Mehran , 2013 : Extended contingency table: Performance metrics for satellite observations and climate model simulations . Water Resour. Res. , 49 , 7144 – 7149 , https://doi.org/10.1002/wrcr.20498 . 10.1002/wrcr.20498 Barros , A. , and Coauthors , 2014 : NASA GPM-Ground Validation Integrated Precipitation and Hydrology Experiment 2014 Science Plan. Duke University Tech. Rep., 64 pp., https://doi.org/10.7924/G8CC0XMR . 10.7924/G8CC0XMR Bech , J. , B

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Jackson Tan, Walter A. Petersen, and Ali Tokay

: Implications for its successor Global Precipitation Measurement Mission . Bull. Amer. Meteor. Soc. , 96 , 283 – 296 , doi: 10.1175/BAMS-D-14-00017.1 . Zhang, J. , and Coauthors , 2011 : National Mosaic and Multi-Sensor QPE (NMQ) System: Description, results, and future plans . Bull. Amer. Meteor. Soc. , 92 , 1321 – 1338 , doi: 10.1175/2011BAMS-D-11-00047.1 . Zhang, J. , Qi Y. , Howard K. , Langston C. , and Kaney B. , 2012 : Radar quality index (RQI)—A combined measure of beam blockage

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E. F. Stocker, F. Alquaied, S. Bilanow, Y. Ji, and L. Jones

products are renamed using GPM conventions. In February 2014, the GPM ( Skofronick-Jackson et al. 2017 ) Core Observatory was launched by the Japan Aerospace Exploration Agency (JAXA) from the Tanegashima Space Center, Japan. From its very conception as a mission, GPM always planned to create a consistent dataset that extended back to the beginning of the TRMM mission and to apply the latest GPM algorithms to TRMM-era data. The 1-yr overlap of the TRMM and GPM Core satellites was key to

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