Tropical cyclone intensity forecasting using model knowledge guided deep learning model

doi:10.1088/1748-9326/ad1bde

IOCAS-IR

	Tropical cyclone intensity forecasting using model knowledge guided deep learning model
	Wang, Chong1,2 ; Li, Xiaofeng 1; Zheng, Gang 3
	2024-02-01
发表期刊	ENVIRONMENTAL RESEARCH LETTERS
ISSN	1748-9326
卷号	19 期号:2 页码:10
通讯作者	Li, Xiaofeng([email protected]) ; Zheng, Gang([email protected])
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.
关键词	tropical cyclone intensity forecast deep learning model knowledge
DOI	10.1088/1748-9326/ad1bde
收录类别	SCI
语种	英语
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研究方向	Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
WOS类目	Environmental Sciences ; Meteorology & Atmospheric Sciences
WOS记录号	WOS:001144262300001
出版者	IOP Publishing Ltd
WOS关键词	PREDICTION SCHEME SHIPS ; ATLANTIC
引用统计	被引频次：4[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.qdio.ac.cn/handle/337002/184399
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.Inst Oceanol, Key Lab Ocean Circulat & Waves, Qingdao, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China 3.Minist Nat Resources, Inst Oceanog 2, State Key Lab Satellite Ocean Environm Dynam, Hangzhou, Peoples R China
第一作者单位	中国科学院海洋研究所
通讯作者单位	中国科学院海洋研究所
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.