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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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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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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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Zongsheng Zheng, Chenyu Hu, Zhaorong Liu, Jianbo Hao, Qian Hou, and Xiaoyi Jiang

Abstract

Tropical cyclone, also known as typhoon, is one of the most destructive weather phenomena. Its intense cyclonic eddy circulations often cause serious damages to coastal areas. Accurate classification or prediction for typhoon intensity is crucial to the disaster warning and mitigation management. But typhoon intensity-related feature extraction is a challenging task as it requires significant pre-processing and human intervention for analysis, and its recognition rate is poor due to various physical factors such as tropical disturbance. In this study, we built a Typhoon-CNNs framework, an automatic classifier for typhoon intensity based on convolutional neural network (CNN). Typhoon-CNNs framework utilized a cyclical convolution strategy supplemented with dropout zero-set, which extracted sensitive features of existing spiral cloud band (SCB) more effectively and reduces over-fitting phenomenon. To further optimize the performance of Typhoon-CNNs, we also proposed the improved activation function (T-ReLU) and the loss function (CE-FMCE). The improved Typhoon-CNNs was trained and validated using more than 10,000 multiple sensor satellite cloud images of National Institute of Informatics. The classification accuracy reached to 88.74%. Compared with other deep learning methods, the accuracy of our improved Typhoon-CNNs was 7.43% higher than ResNet50, 10.27% higher than InceptionV3 and 14.71% higher than VGG16. Finally, by visualizing hierarchic feature maps derived from Typhoon-CNNs, we can easily identify the sensitive characteristics such as typhoon eyes, dense-shadowing cloud areas and SCBs, which facilitates classify and forecast typhoon intensity.

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David R. Ryglicki, Daniel Hodyss, and Gregory Rainwater

.1063/1.868929 Courtney , J. B. , and Coauthors , 2019 : Operational perspectives on tropical cyclone intensity change. Part II: Forecasts by operational agencies . Trop. Cyclone Res. Rev. , 8 , 226 – 239 , https://doi.org/10.1016/j.tcrr.2020.01.003 . 10.1016/j.tcrr.2020.01.003 Darrigol , O. , and U. Frisch , 2008 : From Newton’s mechanics to Euler’s equations . Physica D , 237 , 1855 – 1869 , https://doi.org/10.1016/j.physd.2007.08.003 . 10.1016/j.physd.2007.08.003 DeMaria , M. , M. Mainelli

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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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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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