IOCAS-IR  > 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, Chong1,2; Li, Xiaofeng1
2023-12-01
Source PublicationJOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY
ISSN0739-0572
Volume40Issue:12Pages:1417-1430
Corresponding AuthorLi, Xiaofeng(lixf@qdio.ac.cn)
AbstractIn 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.
KeywordTropical cyclones Remote sensing Deep learning
DOI10.1175/JTECH-D-23-0026.1
Indexed BySCI
Language英语
Funding ProjectQingdao 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 Research AreaEngineering ; Meteorology & Atmospheric Sciences
WOS SubjectEngineering, Ocean ; Meteorology & Atmospheric Sciences
WOS IDWOS:001124830200001
PublisherAMER METEOROLOGICAL SOC
WOS KeywordADVANCED DVORAK TECHNIQUE ; OBJECTIVE DETECTION ; INTENSITY ; HY-2
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Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.qdio.ac.cn/handle/337002/184180
Collection海洋环流与波动重点实验室
Corresponding AuthorLi, Xiaofeng
Affiliation1.Chinese Acad Sci, Inst Oceanog, CAS Key Lab Ocean Circulat & Waves, Qingdao, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
First Author AffilicationInstitute of Oceanology, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Oceanology, Chinese Academy of Sciences
Recommended Citation
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.
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