干旱区生态环境知识资源中心(Arid): Superiority of Dynamic Weights against Fixed Weights in Merging Multi-Satellite Precipitation Datasets over Pakistan

Arid

DOI	10.3390/w16040597
	Superiority of Dynamic Weights against Fixed Weights in Merging Multi-Satellite Precipitation Datasets over Pakistan
	Ejaz, Nuaman; Khan, Aftab Haider; Shahid, Muhammad; Zaman, Kifayat; Balkhair, Khaled S.; Alghamdi, Khalid Mohammed; Rahman, Khalil Ur; Shang, Songhao
通讯作者	Shang, SH
来源期刊	WATER
EISSN	2073-4441
出版年	2024
卷号	16 期号:4
英文摘要	Satellite precipitation products (SPPs) are undeniably subject to uncertainty due to retrieval algorithms and sampling issues. Many research efforts have concentrated on merging SPPs to create high-quality merged precipitation datasets (MPDs) in order to reduce these uncertainties. This study investigates the efficacy of dynamically weighted MPDs in contrast to those using static weights. The analysis focuses on comparing MPDs generated using the dynamic clustered Bayesian averaging (DCBA) approach with those utilizing the regional principal component analysis (RPCA) under fixed-weight conditions. These MPDs were merged from SPPs and reanalysis precipitation data, including TRMM (Tropical Rainfall Measurement Mission) Multi-satellite Precipitation Analysis (TMPA) 3B42V7, PERSIANN-CDR, CMORPH, and the ERA-Interim reanalysis precipitation data. The performance of these datasets was evaluated in Pakistan's diverse climatic zones-glacial, humid, arid, and hyper-arid-employing data from 102 rain gauge stations. The effectiveness of the DCBA model was quantified using Theil's U statistic, demonstrating its superiority over the RPCA model and other individual merging methods in the study area The comparative performances of DCBA and RPCA in these regions, as measured by Theil's U, are 0.49 to 0.53, 0.38 to 0.45, 0.37 to 0.42, and 0.36 to 0.43 in glacial, humid, arid, and hyper-arid zones, respectively. The evaluation of DCBA and RPCA compared with SPPs at different elevations showed poorer performance at high altitudes (>4000 m). The comparison of MPDs with the best performance of SPP (i.e., TMPA) showed significant improvement of DCBA even at altitudes above 4000 m. The improvements are reported as 49.83% for mean absolute error (MAE), 42.31% for root-mean-square error (RMSE), 27.94% for correlation coefficient (CC), 40.15% for standard deviation (SD), and 13.21% for Theil's U. Relatively smaller improvements are observed for RPCA at 13.04%, 1.56%, 10.91%, 1.67%, and 5.66% in the above indices, respectively. Overall, this study demonstrated the superiority of DCBA over RPCA with static weight. Therefore, it is strongly recommended to use dynamic variation of weights in the development of MPDs.
英文关键词	precipitation estimation merged precipitation datasets dynamic clustered Bayesian averaging (DCBA) regional principal component analysis (RPCA) regional- and elevation-based evaluation
类型	Article
语种	英语
开放获取类型	gold, Green Published
收录类别	SCI-E
WOS记录号	WOS:001175205400001
WOS关键词	PERSIANN-CDR ; PRODUCTS ; PERFORMANCE ; ALGORITHM
WOS类目	Environmental Sciences ; Water Resources
WOS研究方向	Environmental Sciences & Ecology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/405855
推荐引用方式 GB/T 7714	Ejaz, Nuaman,Khan, Aftab Haider,Shahid, Muhammad,et al. Superiority of Dynamic Weights against Fixed Weights in Merging Multi-Satellite Precipitation Datasets over Pakistan[J],2024,16(4).
APA	Ejaz, Nuaman.,Khan, Aftab Haider.,Shahid, Muhammad.,Zaman, Kifayat.,Balkhair, Khaled S..,...&Shang, Songhao.(2024).Superiority of Dynamic Weights against Fixed Weights in Merging Multi-Satellite Precipitation Datasets over Pakistan.WATER,16(4).
MLA	Ejaz, Nuaman,et al."Superiority of Dynamic Weights against Fixed Weights in Merging Multi-Satellite Precipitation Datasets over Pakistan".WATER 16.4(2024).