气候变化领域集成服务门户(CCPortal): Tropical Cyclone Intensity Estimation Using Himawari-8 Satellite Cloud Products and Deep Learning

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DOI	10.3390/rs14040812
	Tropical Cyclone Intensity Estimation Using Himawari-8 Satellite Cloud Products and Deep Learning
	Tan, Jinkai; Yang, Qidong; Hu, Junjun; Huang, Qiqiao; Chen, Sheng
通讯作者	Chen, S (通讯作者)，Sun Yat Sen Univ, Minist Educ, Sch Atmospher Sci, Zhuhai 519000, Peoples R China.
发表日期	2022
EISSN	2072-4292
卷号	14 期号:4
英文摘要	This study develops an objective deep-learning-based model for tropical cyclone (TC) intensity estimation. The model's basic structure is a convolutional neural network (CNN), which is a widely used technology in computer vision tasks. In order to optimize the model's structure and to improve the feature extraction ability, both residual learning and attention mechanisms are embedded into the model. Five cloud products, including cloud optical thickness, cloud top temperature, cloud top height, cloud effective radius, and cloud type, which are level-2 products from the geostationary satellite Himawari-8, are used as the model training inputs. We sampled the cloud products under the 13 rotational angles of each TC to augment the training dataset. For the independent test data, the model shows improvement, with a relatively low RMSE of 4.06 m/s and a mean absolute error (MAE) of 3.23 m/s, which are comparable to the results seen in previous studies. Various cloud organization patterns, storm whirling patterns, and TC structures from the feature maps are presented to interpret the model training process. An analysis of the overestimated bias and underestimated bias shows that the model's performance is highly affected by the initial cloud products. Moreover, several controlled experiments using other deep learning architectures demonstrate that our designed model is conducive to estimating TC intensity, thus providing insight into the forecasting of other TC metrics.
关键词	ADVANCED DVORAK TECHNIQUE TRACK WATER
英文关键词	tropical cyclone; intensity; Himawari-8 satellite; estimation; deep learning
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000763142200001
来源期刊	REMOTE SENSING
来源机构	中国科学院西北生态环境资源研究院
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/254659
作者单位	[Tan, Jinkai; Chen, Sheng] Sun Yat Sen Univ, Minist Educ, Sch Atmospher Sci, Zhuhai 519000, Peoples R China; [Yang, Qidong] Columbia Univ, Dept Appl Phys & Appl Math, New York, NY 10027 USA; [Hu, Junjun] Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Key Lab Land Surface Proc & Climate Change Cold &, Lanzhou 730000, Peoples R China; [Hu, Junjun] Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Nagqu Stn Plateau Climate & Environm, Nagqu 852000, Peoples R China; [Huang, Qiqiao] Nanning Normal Univ, Sch Math & Stat, Nanning 530001, Peoples R China
推荐引用方式 GB/T 7714	Tan, Jinkai,Yang, Qidong,Hu, Junjun,et al. Tropical Cyclone Intensity Estimation Using Himawari-8 Satellite Cloud Products and Deep Learning[J]. 中国科学院西北生态环境资源研究院,2022,14(4).
APA	Tan, Jinkai,Yang, Qidong,Hu, Junjun,Huang, Qiqiao,&Chen, Sheng.(2022).Tropical Cyclone Intensity Estimation Using Himawari-8 Satellite Cloud Products and Deep Learning.REMOTE SENSING,14(4).
MLA	Tan, Jinkai,et al."Tropical Cyclone Intensity Estimation Using Himawari-8 Satellite Cloud Products and Deep Learning".REMOTE SENSING 14.4(2022).