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Peter Black, Lee Harrison, Mark Beaubien, Robert Bluth, Roy Woods, Andrew Penny, Robert W. Smith, and James D. Doyle

correction (FEC) on two receivers was implemented to improve data reception at ranges > 200 km. Beyond sensor uncertainties, overall data quality is affected by data gaps in the telemetered data. To minimize gaps, the XDD uses FEC to recover data packets having bit errors. In addition, merged data from four redundant receivers minimizes overall data dropouts that appear at different times on the four different receivers, providing a more continuous stream of observations for use in optimal time series

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William A. Komaromi and James D. Doyle

( Braun et al. 2013 ), HS3 made it possible to not only sample the entire depth of the outflow layer, but also to sample the region in which the radial outflow originates over the deepest convection, hereafter referred to as the outflow roots . Several studies have already examined data from GRIP, focusing on both genesis and rapid intensification (e.g., Davis and Ahijevych 2013 ; Zawislak and Zipser 2014 ; Helms and Hart 2015 ; Rogers et al. 2015 ). In this study, we focus on the full quality-controlled

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Patrick Duran and John Molinari

intensive quality control procedure ( Bell et al. 2016 ) removed unrealistic temperature and humidity observations that likely reflected sensor wetting, as well as relative humidity recorded at temperatures below −40°C, where humidity measurements were inaccurate because of slow sensor response time. A more complete description of HDSS’s specifications and error characteristics can be found in Black et al. (2017) and Doyle et al. (2017) , and a comprehensive description of the quality control

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Robert G. Nystrom and Fuqing Zhang

environmental conditions that may potentially allow high intrinsic predictability. Key limiting factors potentially include but are not limited to: data assimilation methodology and the availability and quality of observations (e.g., Zhang et al. 2004 ; Torn and Hakim 2009 ; Weng and Zhang 2012 ; Majumdar et al. 2013 ; Zhang and Pu 2014 ; Poterjoy et al. 2014 ; Aberson et al. 2015 ; Zhang and Weng 2015 ; Poterjoy and Zhang 2016 ), model resolution and physics (e.g., Davis et al. 2008 ; Jin et al

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Eric A. Hendricks, Russell L. Elsberry, Christopher S. Velden, Adam C. Jorgensen, Mary S. Jordan, and Robert L. Creasey

small 6-h variability of the CIMSS VWSs. During 2015, the SHIPS VWS was being calculated based on background 6-h GFS forecasts and included hourly AMVs (thus utilizing ±30-min images), but only at the 6-h synoptic times, which may explain the 6-h variability. Furthermore, the AMVs incorporated in the data assimilation for the GFS had been thinned to be appropriate for the effective GFS horizontal grid resolution. Finally, the quality control criteria between the AMV magnitudes and directions were

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Nannan Qin and Da-Lin Zhang

resolutions herein. It is fortunate that the U.S. Navy’s TCI field campaign ( Doyle et al. 2017 ) and NOAA’s P-3 missions ( Rogers et al. 2017 ) obtained ample high quality observational data, in addition to satellite images, that can be used to verify the simulated structures of Patricia. Figure 5 compares the observed horizontal wind field and radar reflectivity during the WP-3D mission of Patricia’s extreme RI stage to the model simulated that are taken close to the time used for Figs. 4b,e . The

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