Evaluation of precipitation forecasting methods and an advanced lightweight model

doi:10.1088/1748-9326/ad661f

IOCAS-IR > 海洋环流与波动重点实验室

	Evaluation of precipitation forecasting methods and an advanced lightweight model
	Yang, Nan1,2 ; Wang, Chong1,2 ; Li, Xiaofeng 1,2
	2024-09-01
发表期刊	ENVIRONMENTAL RESEARCH LETTERS
ISSN	1748-9326
卷号	19 期号:9 页码:12
通讯作者	Li, Xiaofeng([email protected])
摘要	Precipitation forecasting is crucial for warning systems and disaster management. This study focuses on deep learning-based methods and categorizes them into three categories: Recurrent Neural Network (RNN-RNN-RNN), Convolutional Neural Network (CNN-CNN-CNN), and CNN-RNN-CNN methods. Then, we conduct a comprehensive evaluation of typical methods in these three categories using the SEVIR precipitation dataset. The results show that RNN-RNN-RNN suffers from instability in long-term forecasts due to error accumulation, CNN-CNN-CNN struggles to capture temporal signals but produces relatively stable forecasts, and CNN-RNN-CNN significantly increases model complexity and inherits the drawbacks of RNN, leading to worse forecasts. Here, we propose an advanced lightweight precipitation forecasting model (ALPF) based on CNN. Experimental results demonstrate that ALPF can effectively forecast spatial-temporal features, maintaining CNN's feature extraction capabilities while avoiding error accumulation in RNN's propagation. ALPF achieves long-term stable precipitation forecasts and can better capture large precipitation amounts.
关键词	precipitation forecasting model analysis deep learning adversarial generative network
DOI	10.1088/1748-9326/ad661f
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809[42306214]; National Natural Science Foundation of China[SDBX2022026]; Shandong Province Postdoctoral Innovative Talents Support Program[2023M733533]; China Postdoctoral Science Foundation; Special Research Assistant Project of the Chinese Academy of Sciences[XDB42000000]; Strategic Priority Research Program of the Chinese Academy of Sciences[2019JZZY010102]; Major Scientific and Technological Innovation Projects in Shandong Province
WOS研究方向	Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
WOS类目	Environmental Sciences ; Meteorology & Atmospheric Sciences
WOS记录号	WOS:001282698700001
出版者	IOP Publishing Ltd
WOS关键词	EXTREME PRECIPITATION ; RADAR DATA ; ASSIMILATION
引用统计	被引频次：1[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/337002/186196
专题	海洋环流与波动重点实验室
通讯作者	Li, Xiaofeng
作者单位	1.Key Lab Ocean Observat & Forecasting, Key Lab Ocean Observat & Forecasting, Key Lab Ocean Circulat & Waves, Qingdao, Peoples R China 2.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Circulat & Waves, Qingdao, Peoples R China
第一作者单位	海洋环流与波动重点实验室
通讯作者单位	海洋环流与波动重点实验室
推荐引用方式 GB/T 7714	Yang, Nan,Wang, Chong,Li, Xiaofeng. Evaluation of precipitation forecasting methods and an advanced lightweight model[J]. ENVIRONMENTAL RESEARCH LETTERS,2024,19(9):12.
APA	Yang, Nan,Wang, Chong,&Li, Xiaofeng.(2024).Evaluation of precipitation forecasting methods and an advanced lightweight model.ENVIRONMENTAL RESEARCH LETTERS,19(9),12.
MLA	Yang, Nan,et al."Evaluation of precipitation forecasting methods and an advanced lightweight model".ENVIRONMENTAL RESEARCH LETTERS 19.9(2024):12.