Developing a Data-Driven Transfer Learning Model to Locate Tropical Cyclone Centers on Satellite Infrared Imagery

doi:10.1175/JTECH-D-23-0026.1

CORC > 海洋研究所 > 中国科学院海洋研究所 > 海洋环流与波动重点实验室

	Developing a Data-Driven Transfer Learning Model to Locate Tropical Cyclone Centers on Satellite Infrared Imagery
	Wang, Chong 1,2; Li, Xiaofeng 2
刊名	JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY
	2023-12-01
卷号	40 期号:12 页码:1417-1430
关键词	Tropical cyclones Remote sensing Deep learning
ISSN号	0739-0572
DOI	10.1175/JTECH-D-23-0026.1
通讯作者	Li, Xiaofeng(lixf@qdio.ac.cn)
英文摘要	In this paper, a data-driven transfer learning (TL) model for locating tropical cyclone (TC) centers from satellite infrared images in the northwest Pacific is developed. A total of 2450 satellite infrared TC images derived from 97 TCs between 2015 and 2018 were used for this paper. The TC center location model (ResNet-TCL) with added residual fully connected modules is built for the TC center location. The MAE of the ResNet-TCL model is 34.8 km. Then TL is used to improve the model performance, including obtaining a pretrained model based on the ImageNet dataset, transfer -ring the pretrained model parameters to the ResNet-TCL model, and using TC satellite infrared imagery to fine-train the ResNet-TCL model. The results show that the TL-based model improves the location accuracy by 14.1% (29.3 km) over the no-TL model. The model performance increases logarithmically with the amount of training data. When the training data are large, the benefit of increasing the training samples is smaller than the benefit of using TL. The comparison of model results with the best track data of TCs shows that the MAEs of TCs center is 29.3 km for all samples and less than 20 km for H2-H5 TCs. In addition, the visualization of the TL-based TC center location model shows that the TL model can accurately extract the most important features related to TC center location, including TC eye, TC texture, and con -tour. On the other hand, the no-TL model does not accurately extract these features.
资助项目	Qingdao National Laboratory for Marine Science and Technology ; Special fund of Shandong Province[LSKJ202204302] ; Key Project of the Center for Ocean Mega-Science, Chinese Academy of Sciences[COMS2019R02] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB42000000] ; National Natural Science Foundation of China[U2006211] ; Major scientific and technological innovation projects in Shandong Province[2019JZZY010102]
WOS关键词	ADVANCED DVORAK TECHNIQUE ; OBJECTIVE DETECTION ; INTENSITY ; HY-2
WOS研究方向	Engineering ; Meteorology & Atmospheric Sciences
语种	英语
出版者	AMER METEOROLOGICAL SOC
WOS记录号	WOS:001124830200001
内容类型	期刊论文
源URL	[http://ir.qdio.ac.cn/handle/337002/184179]
专题	海洋研究所_海洋环流与波动重点实验室
通讯作者	Li, Xiaofeng
作者单位	1.Univ Chinese Acad Sci, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Oceanog, CAS Key Lab Ocean Circulat & Waves, Qingdao, Peoples R China
推荐引用方式 GB/T 7714	Wang, Chong,Li, Xiaofeng. Developing a Data-Driven Transfer Learning Model to Locate Tropical Cyclone Centers on Satellite Infrared Imagery[J]. JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY,2023,40(12):1417-1430.
APA	Wang, Chong,&Li, Xiaofeng.(2023).Developing a Data-Driven Transfer Learning Model to Locate Tropical Cyclone Centers on Satellite Infrared Imagery.JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY,40(12),1417-1430.
MLA	Wang, Chong,et al."Developing a Data-Driven Transfer Learning Model to Locate Tropical Cyclone Centers on Satellite Infrared Imagery".JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY 40.12(2023):1417-1430.