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Jie Feng and Xuguang Wang

1. Introduction Over the past few decades, great efforts have been made to improve the accuracy of tropical cyclone (TC) forecasts. The major endeavors include the development of high-resolution cloud-resolving numerical weather prediction (NWP) models, advanced data assimilation (DA) systems, and novel observing systems for TCs. So far, the accuracy of TC analysis and prediction has been steadily and significantly improved. For example, the yearly averaged track forecast at the 5-day lead time

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Shixuan Zhang, Zhaoxia Pu, and Christopher Velden

1. Introduction In contrast to the significant improvements in tropical cyclone (TC) track forecasts, only limited progress has been made in TC intensity forecasting in the last two decades ( Rogers et al. 2006 , 2013 ; Rappaport et al. 2009 ; Gall et al. 2013 ). Part of the difficulty in forecasting the intensity of TCs originates from deficiencies in the representation of the initial vortices in numerical weather prediction (NWP) models due to the general lack of high

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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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David R. Ryglicki, James D. Doyle, Daniel Hodyss, Joshua H. Cossuth, Yi Jin, Kevin C. Viner, and Jerome M. Schmidt

.1175/1520-0469(1987)044<0542:AAITFT>2.0.CO;2 . 10.1175/1520-0469(1987)044<0542:AAITFT>2.0.CO;2 Rozoff , C. M. , C. S. Velden , J. Kaplan , J. P. Kossin , and A. J. Wimmers , 2015 : 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

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

model, a “verification” in a strict sense—such as a direct comparison of a model forecast with observational data (e.g., Smith et al. 2017 )—cannot be readily performed. With that in mind, this section describes basic features of the simulations including the intensity, the general similarities between synthetic satellite imagery of the CM1 and satellite observations, and perhaps most importantly, the tilt of the vortex. Understanding the behavior of the tilt of the vortex is critical to the entire

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William A. Komaromi and James D. Doyle

13 km out to a radius of 100 km from high-resolution Weather Research and Forecasting (WRF) Model simulations, and demonstrate a reduction of I in the TC core and moat region along with an increase in I outside of the primary eyewall during secondary eyewall formation. However, they do not include the outflow region in their analyses. As is the case with outflow, the TC warm core has traditionally been infrequently documented by in situ observations because of its combination of high altitude

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