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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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Momme C. Hell, Bruce D. Cornelle, Sarah T. Gille, Arthur J. Miller, and Peter D. Bromirski

conventional wave observations. We use these data as a training set to develop a new method to characterize ocean swell observations. Feature comparison in geophysical data is often challenging because the observations are noisy, and the models are too simple. As we outline below, the combination of optimization and Monte Carlo methods enables us to improve our model understanding of the data, while we use the model to identify the relevant data. This is a “machine learning” approach that is constrained by

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Massimo Bonavita, Rossella Arcucci, Alberto Carrassi, Peter Dueben, Alan J. Geer, Bertrand Le Saux, Nicolas Longépé, Pierre-Philippe Mathieu, and Laure Raynaud

First ECMWF–ESA Workshop on Machine Learning for Earth System Observation and Prediction What : ECMWF and ESA convened a workshop to explore the current status, prospects, and opportunities in the application of machine learning/deep learning for Earth system observation and prediction. When : 5–8 October 2020 Where : Online; https://events.ecmwf.int/event/172/ Almost 400 researchers from across the world joined the first ECMWF–ESA Workshop on Machine Learning for Earth System Observation and

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Simon Veldkamp, Kirien Whan, Sjoerd Dirksen, and Maurice Schmeits

adaptive moment estimation (Adam; Kingma and Ba 2014 ), a variant of stochastic gradient descent that is very popular in deep learning. We use early stopping to determine the number of epochs (the number of times the training data are used during training). The neural networks used in this research were programmed using Keras ( Chollet et al. 2015 ), with TensorFlow as backend ( Abadi et al. 2015 ). Adam was employed using default options for all parameters other than the learning rate decay parameter

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Ryan Lagerquist, John T. Allen, and Amy McGovern

: Global relationship between fronts and warm conveyor belts and the impact on extreme precipitation . J. Climate , 28 , 8411 – 8429 , https://doi.org/10.1175/JCLI-D-15-0171.1 . 10.1175/JCLI-D-15-0171.1 Chollet , F. , 2018 : Deep Learning with Python . Manning, 384 pp. Clarke , L. , and R. Renard , 1966 : The U.S. Navy numerical frontal analysis scheme: Further development and a limited evaluation . J. Appl. Meteor. , 5 , 764 – 777 , https://doi.org/10

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Florian Dupuy, Olivier Mestre, Mathieu Serrurier, Valentin Kivachuk Burdá, Michaël Zamo, Naty Citlali Cabrera-Gutiérrez, Mohamed Chafik Bakkay, Jean-Christophe Jouhaud, Maud-Alix Mader, and Guillaume Oller

. The atmospheric research community has already taken advantage of CNN’s ability [see Reichstein et al. (2019) for an overview]. Most of the applications deal with images, for example from satellite observations to create cloud masks or derive rainfalls ( Drönner et al. 2018 ; Moraux et al. 2019 ), or from pictures for weather classification ( Elhoseiny et al. 2015 ). Often, CNNs using NWP data as predictors (predictors are also named features in the deep learning community) are used to produce

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Xiaodong Chen, L. Ruby Leung, Yang Gao, and Ying Liu

: NeuralHydrology – Interpreting LSTMs in hydrology. Explainable AI: Interpreting, Explaining and Visualizing Deep Learning , W. Samek et al., Eds., Springer, 347–362 . 10.1007/978-3-030-28954-6_19 Li , D. , M. L. Wrzesien , M. Durand , J. Adam , and D. P. Lettenmaier , 2017 : How much runoff originates as snow in the western United States, and how will that change in the future? Geophys. Res. Lett. , 44 , 6163 – 6172 , https://doi.org/10.1002/2017GL073551 . 10.1002/2017GL073551 Li , Z

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Kyle A. Hilburn, Imme Ebert-Uphoff, and Steven D. Miller

. Miller , 2020 : Evaluating Geostationary Lightning Mapper flash rates within intense convective storms . J. Geophys. Res. Atmos. , 125 , e2020JD032827, https://doi.org/10.1029/2020JD032827 . 10.1029/2020JD032827 Samsi , S. , C. J. Mattioli , and M. S. Veillette , 2019 : Distributed deep learning for precipitation nowcasting. IEEE High Performance Extreme Computing Conf. , Waltham, MA, IEEE, https://doi.org/10.1109/HPEC.2019.8916416 . 10.1109/HPEC.2019.8916416 Sawada , Y. , K

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Andrew Geiss and Joseph C. Hardin

features), and similar precipitating features occur across many different PPI scans depending on the regional weather, for instance, the presence of a cold front and corresponding heavy precipitation in an extratropical cyclone. By learning common sub-pixel-scale features in the context of large-scale weather in PPI scans, a neural network can outperform interpolation schemes. Though introduced in the late 1980s, deep CNNs have become very popular since about 2010 for various image processing tasks

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Min Wang, Shudao Zhou, Zhong Yang, and Zhanhua Liu

application and low recognition accuracy. Therefore, we need to find a method that can automatically learn different cloud features. The convolutional neural network (CNN) has achieved great success in large-scale image classification tasks. The CNN is a deep, feedforward artificial neural network that has the ability to perform in-depth learning. After in-depth learning, it is possible to express features that are difficult to express in general, fully mine the association between data, extract the

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