气候变化领域集成服务门户(CCPortal): A Deep Learning-Based Downscaling Method Considering the Impact on Typhoons to Future Precipitation in Taiwan

CCPortal

DOI	10.3390/atmos15030371
	A Deep Learning-Based Downscaling Method Considering the Impact on Typhoons to Future Precipitation in Taiwan
	Lin, Shiu-Shin; Zhu, Kai-Yang; Wang, Chen-Yu
发表日期	2024
EISSN	2073-4433
起始页码	15
结束页码	3
卷号	15 期号:3
英文摘要	This study proposes a deep neural network (DNN)-based downscaling model incorporating kernel principal component analysis (KPCA) to investigate the precipitation uncertainty influenced by typhoons in Taiwan, which has a complex island topography. The best tracking data of tropical cyclones from the Joint Typhoon Warning Center (JTWC) are utilized to calculate typhoon and non-typhoon precipitation. KPCA is applied to extract nonlinear features of the BCC-CSM1-1 (Beijing Climate Center Climate System Model version 1.1) and CanESM2 (second-generation Canadian Earth System Model) GCM models. The length of the data used in the two GCM models span from January 1950 to December 2005 (historical data) and from January 2006 to December 2099 (scenario out data). The rainfall data are collected from the weather stations in Taichung and Hualien (cities of Taiwan) operated by the Central Weather Administration (CWA), Taiwan. The period of rainfall data in Taichung and in Hualien spans from January 1950 to December 2005. The proposed model is constructed with features extracted from the GCMs and historical monthly precipitation from Taichung and Hualien. The model we have built is used to estimate monthly precipitation and uncertainty in both Taichung and Hualien for future scenarios (rcp 4.5 and 8.5) of the GCMs. The results suggest that, in Taichung and Hualien, the summer precipitation is mostly within the normal range. The rainfall in the long term (January 2071 to December 2080) for both Taichung and Hualien typically fall between 100 mm and 200 mm. In the long term, the dry season (January to April, November, and December) precipitation for Taichung and that in the wet season (May to October) for Hualien are less and more affected by typhoons, respectively. The dry season precipitation is more affected by typhoons in Taichung than Hualien. In both Taichung and Hualien, the long-term probability of rainfall exceeding the historical average in the dry season is higher than that in the wet season.
英文关键词	IPCC Fifth Assessment Report; climate change; deep neural network; typhoon; uncertainty; kernel principal component analysis
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
WOS类目	Environmental Sciences ; Meteorology & Atmospheric Sciences
WOS记录号	WOS:001191590100001
来源期刊	ATMOSPHERE
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/291119
作者单位	Chung Yuan Christian University
推荐引用方式 GB/T 7714	Lin, Shiu-Shin,Zhu, Kai-Yang,Wang, Chen-Yu. A Deep Learning-Based Downscaling Method Considering the Impact on Typhoons to Future Precipitation in Taiwan[J],2024,15(3).
APA	Lin, Shiu-Shin,Zhu, Kai-Yang,&Wang, Chen-Yu.(2024).A Deep Learning-Based Downscaling Method Considering the Impact on Typhoons to Future Precipitation in Taiwan.ATMOSPHERE,15(3).
MLA	Lin, Shiu-Shin,et al."A Deep Learning-Based Downscaling Method Considering the Impact on Typhoons to Future Precipitation in Taiwan".ATMOSPHERE 15.3(2024).