气候变化领域集成服务门户(CCPortal): A Comparative Study of Four Merging Approaches for Regional Precipitation Estimation

CCPortal

DOI	10.1109/ACCESS.2021.3057057
	A Comparative Study of Four Merging Approaches for Regional Precipitation Estimation
	Fan, Zedong; Li, Weiyue; Jiang, Qin; Sun, Weiwei; Wen, Jiahong; Gao, Jun
通讯作者	Li, WY ; Wen, JH (通讯作者)，Shanghai Normal Univ, Sch Environm & Geog Sci, Shanghai 200234, Peoples R China. ; Li, WY (通讯作者)，Shanghai Normal Univ, Inst Urban Studies, Shanghai 200234, Peoples R China. ; Li, WY (通讯作者)，Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Lanzhou 730000, Peoples R China.
发表日期	2021
ISSN	2169-3536
起始页码	33625
结束页码	33637
卷号	9
英文摘要	To identify suitable merging methods to improve regional precipitation estimates using multiple sources of precipitation data, this study applied four different approaches (multiple linear regression (MLR), feedforward neural network (FNN), random forest (RF) and long short-term memory network (LSTM)) to merge four satellite precipitation products and one reanalysis data in the Jiangsu, Zhejiang and Shanghai of China. The pros and cons of the merging approaches are analyzed comprehensively, using correlation coefficient (CC), root mean square error (RMSE), relative bias (RB), probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) as evaluation indexes. Our results show that: (1) All merging approaches can improve the accuracy of precipitation estimations, but only RF and LSTM can improve the daily precipitation event detection capacity. These approaches can significantly reduce errors in moderate precipitation scenarios, but do not effectively improve accuracy in light and heavy precipitation scenarios. (2) MLR was the least expensive computing cost method in our study and performed better than the other three methods when gauge density was low. However, MLR had the worst daily precipitation event detection capacity (CSI = 0.67). (3) FNN performed moderately in most experiments (CC = 0.87, RMSE = 4.65 mm/day, RB = 1.19 %, POD = 0.94, FAR = 0.29, CSI = 0.70). (4) The merged data generated by RF was the most accurate and had the best daily precipitation event detection capacity (CC = 0.87, RMSE = 4.61 mm/day, RB = - 0.33 %, POD = 0.97, FAR = 0.20, CSI = 0.78). RF performed best in moderate precipitation scenarios. However, it performed worse than other methods when gauge density was low. (5) LSTM was the most robust methods and performed best in light precipitation scenarios. The FAR of the LSTM-generated data was the smallest (0.15) among four fusion methods. However, LSTM had the most expensive computing cost and the worst accuracy of the merged data (CC = 0.86, RMSE = 4.68 mm/day, RB = - 9.36 %).
英文关键词	Rain; Radio frequency; Satellites; Merging; Meteorology; Estimation; Data integration; Accuracy improvement; data merging; gridded precipitation data; precipitation estimation; robustness
语种	英语
WOS研究方向	Computer Science ; Engineering ; Telecommunications
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:000633623800001
来源期刊	IEEE ACCESS
来源机构	中国科学院西北生态环境资源研究院
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/254597
作者单位	[Fan, Zedong; Li, Weiyue; Wen, Jiahong; Gao, Jun] Shanghai Normal Univ, Sch Environm & Geog Sci, Shanghai 200234, Peoples R China; [Li, Weiyue] Shanghai Normal Univ, Inst Urban Studies, Shanghai 200234, Peoples R China; [Li, Weiyue] Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Lanzhou 730000, Peoples R China; [Jiang, Qin] East China Normal Univ, Key Lab Geog Informat Sci, Minist Educ, Shanghai 200241, Peoples R China; [Jiang, Qin] East China Normal Univ, Sch Geog Sci, Shanghai 200241, Peoples R China; [Sun, Weiwei] Ningbo Univ, Dept Geog & Spatial Informat Tech, Ningbo 315211, Peoples R China; [Sun, Weiwei] Ningbo Univ, Ningbo Univ Collaborat Innovat Ctr Land & Marine, Ningbo 315211, Peoples R China; [Sun, Weiwei] Ningbo Univ, Inst East China Sea, Ningbo 315211, Peoples R China
推荐引用方式 GB/T 7714	Fan, Zedong,Li, Weiyue,Jiang, Qin,et al. A Comparative Study of Four Merging Approaches for Regional Precipitation Estimation[J]. 中国科学院西北生态环境资源研究院,2021,9.
APA	Fan, Zedong,Li, Weiyue,Jiang, Qin,Sun, Weiwei,Wen, Jiahong,&Gao, Jun.(2021).A Comparative Study of Four Merging Approaches for Regional Precipitation Estimation.IEEE ACCESS,9.
MLA	Fan, Zedong,et al."A Comparative Study of Four Merging Approaches for Regional Precipitation Estimation".IEEE ACCESS 9(2021).