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Yi Dai, Sharanya J. Majumdar, and David S. Nolan

understanding the TC resistance to strong environmental shear. By introducing the TCSD, we hope that the outflow can be not only a useful diagnostic to infer the upper-level outflow, but also a nice tool that can be used scientifically and operationally for better understanding and forecasting of TC intensity and structure change. This paper is organized as follows: Section 2 describes the idealized modeling framework and definitions of shear. The main results of the idealized simulations are presented in

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David R. Ryglicki, Daniel Hodyss, and Gregory Rainwater

: Improvements in the probabilistic prediction of tropical cyclone rapid intensification with passive microwave observations . Wea. Forecasting , 30 , 1016 – 1038 , . 10.1175/WAF-D-14-00109.1 Ryglicki , D. R. , and R. E. Hart , 2015 : An investigation of center-finding techniques for tropical cyclones in mesoscale models . J. Appl. Meteor. Climatol. , 54 , 825 – 846 , . 10.1175/JAMC-D-14-0106.1 Ryglicki , D. R

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Jonathan Martinez, Michael M. Bell, Robert F. Rogers, and James D. Doyle

few decades, intensity forecasts at long lead times (e.g., 48–120 h) have improved at a statistically significant rate. However, only marginal improvements have been made at shorter lead times (e.g., 24–48 h), suggesting that additional research is required to better understand the underlying mechanisms associated with TC intensification. In the case of eastern North Pacific Hurricane Patricia (2015), extreme rapid intensification was not well predicted by either global or mesoscale models

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Shixuan Zhang, Zhaoxia Pu, and Christopher Velden

improvements in the coverage, density, and quality of AMVs. These “enhanced” AMV datasets can better capture smaller-scale wind flows and provide information on TC-scale flow fields ( Velden et al. 2017 ). Wu et al. (2014 , 2015 ) used an ensemble Kalman filter method to assimilate enhanced AMV data into the mesoscale community Weather Research and Forecasting (WRF) Model. They found that initial analyses of TC vortex location, intensity, and structure are all improved, along with subsequent forecasts

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Russell L. Elsberry, Eric A. Hendricks, Christopher S. Velden, Michael M. Bell, Melinda Peng, Eleanor Casas, and Qingyun Zhao

underestimated the intensity at 1800 UTC 5 October, an alternate explanation is that the HIRAD had detected a mesoscale circulation that was not representative of the Joaquin inner-core circulation. Fig . 11. Forecasts of 10-m wind speed (m s −1 ; color scale below) at 1800 UTC 5 Oct 2015 across domain 3 of the COAMPS-TC model with initial conditions from the (left) Control–GFS and (right) SCDI–GFS analyses after 6 h of assimilating a special AMV dataset at 15-min intervals. 4. Example of SCDI analysis

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Robert G. Nystrom and Fuqing Zhang

1. Introduction Hurricane Patricia (2015) was an extraordinary storm in the eastern North Pacific basin that underwent an unprecedented rapid intensification (RI) process in which it intensified from a tropical storm, with maximum wind speeds of 30 m s −1 , to a category 5 hurricane, with maximum wind speeds of 95 m s −1 , in less than 36 h. While tropical cyclone (TC) track forecasts have been improving substantially over recent decades, and intensity forecast have also improved some in recent

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Shixuan Zhang and Zhaoxia Pu

understand changes in TC intensity and structure, and also to improve our ability to forecast TC intensity, recently, major field campaigns, including the National Oceanic and Atmospheric Administration (NOAA) Hurricane Research Division (HRD) Intensity Forecast Experiments (IFEX; Rogers et al. 2006 , 2013 ), the National Aeronautics and Space Administration (NASA) Genesis and Rapid Intensification Processes (GRIP) field program ( Braun et al. 2013 ), and the National Science Foundation (NSF) Pre

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Xu Lu and Xuguang Wang

speed (Vmax), and minimum sea level pressure (MSLP)] ( Thu and Krishnamurti 1992 ; Kurihara et al. 1995 , 1998 , Liu et al. 2000 , 2006 ; Pu and Braun 2001 ; Tallapragada et al. 2014 ). In the National Oceanic and Atmospheric Administration (NOAA) operational Hurricane Weather Research and Forecasting system (HWRF), vortex initialization (VI) contains two components: vortex relocation (VR) and vortex modification (VM), where VR corrects the storm location and VM modifies the storm intensity

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David R. Ryglicki, Joshua H. Cossuth, Daniel Hodyss, and James D. Doyle

years to construct guidelines for forecasters by relating specific features with current intensity. It has since been refined several times to add objective methods for estimating TC strength ( Dvorak 1984 ; Zehr 1989 ; Guard et al. 1992 ; Velden et al. 1998 ; Olander et al. 2004 ; Olander and Velden 2007 ). A modern version of the Dvorak technique, the advanced Dvorak technique (ADT), has even been used as a reanalysis tool for historical TC studies ( Velden et al. 2017 ). In addition to the

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James D. Doyle, Jonathan R. Moskaitis, Joel W. Feldmeier, Ronald J. Ferek, Mark Beaubien, Michael M. Bell, Daniel L. Cecil, Robert L. Creasey, Patrick Duran, Russell L. Elsberry, William A. Komaromi, John Molinari, David R. Ryglicki, Daniel P. Stern, Christopher S. Velden, Xuguang Wang, Todd Allen, Bradford S. Barrett, Peter G. Black, Jason P. Dunion, Kerry A. Emanuel, Patrick A. Harr, Lee Harrison, Eric A. Hendricks, Derrick Herndon, William Q. Jeffries, Sharanya J. Majumdar, James A. Moore, Zhaoxia Pu, Robert F. Rogers, Elizabeth R. Sanabia, Gregory J. Tripoli, and Da-Lin Zhang

-core data (Base). Studies of the impact of various sources of data on other aspects of the analysis and on track, structure, and intensity forecasts of Patricia are ongoing, including studies using the Navy’s operational tropical cyclone version of the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS-TC; Doyle et al. 2014 ). Fig . 15. Horizontal wind (shaded and vectors) and pressure (black contours) analyses at 1-km height for (a) HRD radar composite, (b) Back, (c) Base, (d) TCI, and (e

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