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

scales. Thus, observations and sampling strategies for initial condition specification and forecast validation, similar to those employed in past TC field programs, such as, the Coupled Boundary Layer Air–Sea Transfer (CBLAST) experiment during the 2003/04 hurricane season, Tropical Cyclone Structure 2008 (TCS08), and Impact of Typhoons on the Ocean in the Pacific (ITOP) in 2010 ( Black 2012 ; Black et al. 2007 ; D’Asaro et al. 2011 , 2014 ), require continual improvement to match model demands

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Daniel J. Cecil and Sayak K. Biswas

1. Introduction Mapping the surface wind speed in a hurricane is a great challenge that affects the ability to issue accurate forecasts and warnings for the maximum wind speed, wind field structure, and related impacts ( Powell et al. 2009 ; Uhlhorn and Nolan 2012 ; Nolan et al. 2014 ). Buoys can provide useful measurements, but only for the precise parts of a hurricane that happen to track across the buoy. As with any surface stations, buoys are subject to failures in extreme conditions (i

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