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Jie Feng and Xuguang Wang

observations were preprocessed using the superobbing approach ( Alpert and Kumar 2007 ). Specifically, observations within a defined spatial grid box Δ x × Δ y × Δ z and a defined time interval Δ t are averaged to construct a single observation. Δ x and Δ y are 0.04° (~4 km), Δ z nearly spans two model layers, and Δ t is 15 min. The selection of these parameters achieves nearly optimal performance in the analysis and prediction of TC intensity ( Feng and Wang 2019 ). The three-dimensional (3D

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

.g., Rios-Berrios and Torn 2017 ). On the other hand, idealized modeling studies offer the ability to create a controlled environment that strictly isolates the effects of shear. A drawback is that their conclusions may be sensitive to the model configuration. For example, previous idealized simulations have added strong shear onto a weak initial vortex, or shocked the vortex by instantaneously adding the shear. Furthermore, the added environmental shear might not maintain its strength or direction

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Xu Lu and Xuguang Wang

between VM and DA on TC analyses and forecasts The performances of VM and DA are first compared together with NoDA in this subsection to investigate their impacts on TC analyses and forecasts in the HWRF Model. The horizontal wind structures at different levels produced by the VM and DA analyses are first verified against the observations and the radar wind composite. Figure 3 shows the model-derived wind and the corresponding verifications at the surface and 3-km height valid at 1800 UTC 22 October

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Shixuan Zhang, Zhaoxia Pu, and Christopher Velden

separate vortex initialization (VI) procedure. It is our goal to use a research version of the HWRF Model and the NCEP Gridpoint Statistical Interpolation analysis system (GSI)-based ensemble–variation hybrid DA system to further examine the potential impacts of assimilating enhanced AMVs on the HWRF analyses and forecasts, especially mature hurricanes. Specifically, we will evaluate two different DA strategies—a method that is similar to the current operational approach, in which VI is performed

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Shixuan Zhang and Zhaoxia Pu

enhance the simulation of TC intensity changes? 2) In what way can the potential benefit from these observations be maximized in the model initialization? By exploring the answers to these questions, this study evaluates the potential impact of assimilating these new, innovative HDSS dropsonde observations from the TCI field campaign on improving the numerical simulations of TC intensity changes with a research version of the NCEP Hurricane Weather Research and Forecasting (HWRF) Model. Specifically

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Jie Feng and Xuguang Wang

(DA) efficiency and performance ( Alpert and Kumar 2007 ; LW19b). Observations within a defined spatial grid box Δ x × Δ y × Δ z and for a certain time interval Δ t are averaged to construct a single observation, the so-called super-obbing. Tests performed by the authors and LW19b show that the superobbing prism with approximately two times the model grid spacing produces the best TC intensity forecast. Specifically, Δ x and Δ y are 0.04° (~4 km), Δ z nearly spans two model layers (at an

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Quanjia Zhong, Jianping Li, Lifeng Zhang, Ruiqiang Ding, and Baosheng Li

been proposed to evaluate atmospheric and oceanic predictability. The predictability limit of the TC MCP obtained from the IBTrACS dataset is ~102 h, slightly lower than the predictability limit of TC MSW, which is ~108 h. Similar results were reported by Kieu and Moon (2016) , who found that the predictability limit of TC intensity forecasts was 108–120 h in a low-order hurricane-scale dynamical model ( Kieu and Moon 2016 ), thereby exceeding the performance of most numerical and statistical

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

intensification process; therefore, a thorough diagnostic evaluation is undertaken. a. Intensity Figure 3 shows the minimum pressure, maximum mean tangential wind (MMTW), and RMW, all at the lowest scalar model level (50 m), for the four simulations. The maximum mean tangential wind and the RMW are taken from the wind maximum center-finding method. It should be noted that the evolutions of the sheared simulations are closer to dry vortices at the earliest model times ( Jones 1995 , 2000 ; Smith et al. 2000

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

-0190.1 . 10.1175/WAF-D-17-0190.1 Herrera , M. A. , and Coauthors , 2018 : Regionally enhanced global (REG) 4D-VAR: Methodology and global model performance. Mon. Wea. Rev ., 146 , 4015 – 4038 , https://doi.org/10.1175/MWR-D-17-0228.1 . 10.1175/MWR-D-17-0228.1 Hogan , T. F. , and Coauthors , 2014 : Navy global environmental model . Oceanography , 27 , 116 – 125 , https://doi.org/10.5670/oceanog.2014.73 . 10.5670/oceanog.2014.73 Kim , M. , H. M. Kim , J. W. Kim , S.-M. Kim , C. Velden

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

1. Introduction Widely used tropical cyclone (TC) models include regional air–sea coupled dynamical models such as COAMPS-TC ( Jin et al. 2014 ), HWRF ( Tallapragada et al. 2014 ; Kim et al. 2014 ) and GFDL ( Bender et al. 2007 ; Gall et al. 2011 ); global dynamical models such as GFS and ECMWF; and statistical–dynamical intensity-prediction models such as SHIPS, the Statistical Typhoon Intensity Prediction Scheme (STIPS), the Logistic Growth Equation Model (LGEM), and the rapid intensity

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