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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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T. Ghosh and T. N. Krishnamurti

1. Introduction Consensus forecasts for meteorological events were operationally used in the pioneering studies of Toth and Kalnay (1993 1997 ), Molteni et al. (1996) , Houtekamer et al. (1996) , and Goerss (2000) . Krishnamurti et al. (1999) introduced the notion of a multimodel superensemble (MMSE) to combine multimodel forecast datasets using a linear multiple regression approach that utilized the mean-square error reduction principle. Studies reported on the efficiency of this

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Jie Feng and Xuguang Wang

. (2013) investigated the impact of assimilating the G-IV dropsonde observations on the track forecast from the perspective of the interaction of the outflow with the large-scale environment. Wu et al. (2015) found that the interior and upper-level (100–350 hPa) AMVs played an important role in improving the forecasts of TC intensity and wind structures. The assimilation of the new AMV data was also found to positively influence the TC track and intensity predictions ( Lim et al. 2019 ). Compared

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

to an operational forecast. For demonstration, we choose a high-impact atypical RI TC that was not part of the original six from Part I but nevertheless underwent RI in moderate vertical wind shear: 2016 northern Atlantic (NATL) Matthew ( Stewart 2017 ). For this analysis, we use SHIPS and CIMSS shear analyses, GOES-13 WV observations, CIMSS AMVs, and 0.5° GFS analyses. We use the GFS analyses here in an attempt to simulate operational conditions more closely. The decomposition of the GFS

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Robert G. Nystrom, Fuqing Zhang, Erin B. Munsell, Scott A. Braun, Jason A. Sippel, Yonghui Weng, and Kerry Emanuel

1. Introduction Tropical cyclone (TC) track forecasts have improved substantially over the past few decades. The 48-h track errors in the North Atlantic today have been reduced by 50% over the last 15 years ( Cangialosi and Franklin 2016 ). While these improvements in the forecast tracks generally hold, Hurricane Joaquin (2015) presents an unusual case in which current numerical weather prediction models struggled with the track forecast. The initial poor track forecast of Joaquin resulted in

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

1. Introduction Accurate forecasts of tropical cyclone (TC) intensity changes remain one of the most difficult weather predictions, even for short lead times. This is in part due to multiscale interactions, which require operational forecast models to precisely capture the evolution of the atmosphere over a vast range of scales in the vicinity of a TC. DeMaria et al. (2014) demonstrated that although intensity forecast errors have not improved as much as track forecast errors over the past

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Nannan Qin and Da-Lin Zhang

questions in this study by conducting a series of cloud-permitting simulations with the Weather Research and Forecasting (WRF) Model. It should be pointed out that the abovementioned poor predictability of the RI and extraordinary intensity of Patricia has been of typical concern for many tropical cyclones (TCs) ( Tallapragada and Kieu 2014 ), despite significant improvements in hurricane models and rapid progress in predicting hurricane tracks during the recent decades ( Rappaport et al. 2009 ). In

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