气候变化领域集成服务门户(CCPortal): Deep-learning-based downscaling of precipitation in the middle reaches of the Yellow River using residual-based CNNs

CCPortal

DOI	10.1002/qj.4759
	Deep-learning-based downscaling of precipitation in the middle reaches of the Yellow River using residual-based CNNs
	Fu, He; Guo, Jianing; Deng, Chenguang; Liu, Heng; Wu, Jie; Shi, Zhengguo; Wang, Cailing; Xie, Xiaoning
发表日期	2024
ISSN	0035-9009
EISSN	1477-870X
英文摘要	The middle reaches of the Yellow River (MRYR), located in northern China, are the transition zone between semi-arid and semi-humid climates. As one of the climate-sensitive regions in China, MRYR has a fragile ecological environment and serious soil loss, which leads to geological disasters such as landslides, collapses, and mudslides caused by extreme precipitation. However, scarceness of high-resolution precipitation data over MRYR limits assessment of the environmental impacts caused by climate change, especially for extreme precipitation. In this article, we design a Residual-in-Residual Dense Block based Network (RRDBNet) model for the statistical downscaling of precipitation in MRYR, and compare the proposed RRDBNet with a generalized linear regression model (GLM) and two popular deep-learning-based models. The multi-level residuals and dense connectivity strategies introduced in RRDBNet help it to learn more abstract features and complex nonlinear relationships among climate variables to improve downscaling performance. The results show that the proposed RRDBNet has good performance in precipitation simulations, which can reproduce the spatial-temporal characteristics of high-resolution precipitation well. RRDBNet reduces the root-mean-squared error (RMSE) by 19% and improves the Pearson correlation coefficient (CC) by 6% relative to GLM for climatology mean precipitation. Especially, RRDBNet has substantial improvements in extreme precipitation compared with other models. It reduces RMSE by 58% (79%) and improves CC by 38% (145%) relative to GLM for R95P (R99P), where R95P and R99P represent extreme precipitation and very extreme precipitation, respectively. For the probability density function of daily precipitation, it is further demonstrated that RRDBNet performs better as regards extreme precipitation frequency. Our results suggest that statistical downscaling based on RRDBNet may be an effective tool for historical and future climate simulations from global climate models. A Residual-in-Residual Dense Block based Network (RRDBNet) model is designed for the statistical downscaling of regional precipitation. The evaluation shows that RRDBNet has substantial improvements in precipitation and extreme precipitation compared with other two deep-learning models. For probability density function of daily precipitation, it is further demonstrated that RRDBNet performs better as regards extreme precipitation frequency. Our results suggest that statistical downscaling based on RRDBNet may be an effective tool for historical and future climate simulations from global climate models. image
英文关键词	deep learning; extreme precipitation; Residual-in-Residual Dense Block; statistical downscaling; Yellow River middle reaches
语种	英语
WOS研究方向	Meteorology & Atmospheric Sciences
WOS类目	Meteorology & Atmospheric Sciences
WOS记录号	WOS:001237438900001
来源期刊	QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/292257
作者单位	Xi'an Shiyou University; Chinese Academy of Sciences; Institute of Earth Environment, CAS
推荐引用方式 GB/T 7714	Fu, He,Guo, Jianing,Deng, Chenguang,et al. Deep-learning-based downscaling of precipitation in the middle reaches of the Yellow River using residual-based CNNs[J],2024.
APA	Fu, He.,Guo, Jianing.,Deng, Chenguang.,Liu, Heng.,Wu, Jie.,...&Xie, Xiaoning.(2024).Deep-learning-based downscaling of precipitation in the middle reaches of the Yellow River using residual-based CNNs.QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY.
MLA	Fu, He,et al."Deep-learning-based downscaling of precipitation in the middle reaches of the Yellow River using residual-based CNNs".QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY (2024).