Tropical cyclone intensity forecasting using model knowledge guided deep learning model

doi:10.1088/1748-9326/ad1bde

CORC > 海洋研究所 > 中国科学院海洋研究所

	Tropical cyclone intensity forecasting using model knowledge guided deep learning model
	Wang, Chong 1,2; Li, Xiaofeng 2; Zheng, Gang 3
刊名	ENVIRONMENTAL RESEARCH LETTERS
	2024-02-01
卷号	19 期号:2 页码:10
关键词	tropical cyclone intensity forecast deep learning model knowledge
ISSN号	1748-9326
DOI	10.1088/1748-9326/ad1bde
通讯作者	Li, Xiaofeng(lixf@qdio.ac.cn) ; Zheng, Gang(zhenggang@sio.org.cn)
英文摘要	This paper developed a deep learning (DL) model for forecasting tropical cyclone (TC) intensity in the Northwest Pacific. A dataset containing 20 533 synchronized and collocated samples was assembled, which included ERA5 reanalysis data as well as satellite infrared (IR) imagery, covering the period from 1979 to 2021. The u-, v- and w-components of wind, sea surface temperature, IR satellite imagery, and historical TC information were selected as the model inputs. Then, a TC-intensity-forecast-fusion (TCIF-fusion) model was developed, in which two special branches were designed to learn multi-factor information to forecast 24 h TC intensity. Finally, heatmaps capturing the model's insights are generated and applied to the original input data, creating an enhanced input set that results in more accurate forecasting. Employing this refined input, the heatmaps (model knowledge) were used to guide TCIF-fusion model modeling, and the model-knowledge-guided TCIF-fusion model achieved a 24 h forecast error of 3.56 m s-1 for Northwest Pacific TCs spanning 2020-2021. The results show that the performance of our method is significantly better than the official subjective prediction and advanced DL methods in forecasting TC intensity by 4% to 22%. Additionally, compared to operational approaches, model-guided knowledge methods can better forecast the intensity of landfalling TCs.
资助项目	Zhejiang Provincial Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB42000000] ; Major Scientific and Technological Innovation Projects in Shandong Province[2019JZZY010102] ; [LR21D060002]
WOS关键词	PREDICTION SCHEME SHIPS ; ATLANTIC
WOS研究方向	Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
语种	英语
出版者	IOP Publishing Ltd
WOS记录号	WOS:001144262300001
内容类型	期刊论文
源URL	[http://ir.qdio.ac.cn/handle/337002/184398]
专题	中国科学院海洋研究所
通讯作者	Li, Xiaofeng; Zheng, Gang
作者单位	1.Univ Chinese Acad Sci, Beijing, Peoples R China 2.Inst Oceanol, Key Lab Ocean Circulat & Waves, Qingdao, Peoples R China 3.Minist Nat Resources, Inst Oceanog 2, State Key Lab Satellite Ocean Environm Dynam, Hangzhou, Peoples R China
推荐引用方式 GB/T 7714	Wang, Chong,Li, Xiaofeng,Zheng, Gang. Tropical cyclone intensity forecasting using model knowledge guided deep learning model[J]. ENVIRONMENTAL RESEARCH LETTERS,2024,19(2):10.
APA	Wang, Chong,Li, Xiaofeng,&Zheng, Gang.(2024).Tropical cyclone intensity forecasting using model knowledge guided deep learning model.ENVIRONMENTAL RESEARCH LETTERS,19(2),10.
MLA	Wang, Chong,et al."Tropical cyclone intensity forecasting using model knowledge guided deep learning model".ENVIRONMENTAL RESEARCH LETTERS 19.2(2024):10.