干旱区生态环境知识资源中心(Arid): Application of a Dynamic Clustered Bayesian Model Averaging (DCBA) Algorithm for Merging Multisatellite Precipitation Products over Pakistan

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DOI	10.1175/JHM-D-19-0087.1
	Application of a Dynamic Clustered Bayesian Model Averaging (DCBA) Algorithm for Merging Multisatellite Precipitation Products over Pakistan
	Rahman, Khalil Ur 1; Shang, Songhao 1; Shahid, Muhammad 1; Wen, Yeqiang 1; Khan, Zeeshan 2
通讯作者	Shang, Songhao
来源期刊	JOURNAL OF HYDROMETEOROLOGY
ISSN	1525-755X
EISSN	1525-7541
出版年	2020
卷号	21 期号:1 页码:17-37
英文摘要	Merged multisatellite precipitation datasets (MMPDs) combine the advantages of individual satellite precipitation products (SPPs), have a tendency to reduce uncertainties, and provide higher potentials to hydrological applications. This study applied a dynamic clustered Bayesian model averaging (DCBA) algorithm to merge four SPPs across Pakistan. The DCBA algorithm produced dynamic weights to different SPPs varying both spatially and temporally to accommodate the spatiotemporal differences of SPP performances. The MMPD is developed at daily temporal scale from 2000 to 2015 with spatial resolution of 0.25 degrees using extensively evaluated SPPs and a global atmospheric reanalysis-precipitation dataset: Tropical Rainfall Measurement Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42V7, Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), Climate Prediction Center morphing technique (CMORPH), and ERA-Interim. The DCBA algorithm is evaluated across four distinct climate regions of Pakistan over 102 ground precipitation gauges (GPGs). DCBA forecasting outperformed all four SPPs with average Theil's U of 0.49, 0.38, 0.37, and 0.36 in glacial, humid, arid, and hyperarid regions, respectively. The average mean bias error (MBE), mean error (MAE), root-mean-square error (RMSE), correlation coefficient (CC), and standard deviation (SD) of DCBA over all of Pakistan are 0.54, 1.40, 4.94, 0.77, and 5.17 mm day(-1), respectively. Seasonal evaluation revealed a dependency of DCBA performance on precipitation magnitude/intensity and elevation. Relatively poor DCBA performance is observed in premonsoon/monsoon seasons and at high/mild elevated regions. Average improvements of DCBA in comparison with TMPA are 59.56% (MBE), 49.37% (MAE), 45.89% (RMSE), 19.48% (CC), 46.7% (SD), and 18.66% (Theil's U). Furthermore, DCBA efficiently captured extreme precipitation trends (premonsoon/monsoon seasons).
英文关键词	Precipitation Remote sensing Bayesian methods Ensembles
类型	Article
语种	英语
国家	Peoples R China
开放获取类型	Bronze
收录类别	SCI-E
WOS记录号	WOS:000506354600001
WOS关键词	SATELLITE-BASED PRECIPITATION ; SPATIAL PREDICTION ; ANALYSIS TMPA ; PERFORMANCE ; TOPOGRAPHY ; ACCURACY ; TERRAIN ; REGION ; SCHEME
WOS类目	Meteorology & Atmospheric Sciences
WOS研究方向	Meteorology & Atmospheric Sciences
EI主题词	2020-01-01
来源机构	清华大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/312006
作者单位	1.Tsinghua Univ, Dept Hydraul Engn, State Key Lab Hydrosci & Engn, Beijing, Peoples R China; 2.Tsinghua Univ, Sch Econ & Management, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Rahman, Khalil Ur,Shang, Songhao,Shahid, Muhammad,et al. Application of a Dynamic Clustered Bayesian Model Averaging (DCBA) Algorithm for Merging Multisatellite Precipitation Products over Pakistan[J]. 清华大学,2020,21(1):17-37.
APA	Rahman, Khalil Ur,Shang, Songhao,Shahid, Muhammad,Wen, Yeqiang,&Khan, Zeeshan.(2020).Application of a Dynamic Clustered Bayesian Model Averaging (DCBA) Algorithm for Merging Multisatellite Precipitation Products over Pakistan.JOURNAL OF HYDROMETEOROLOGY,21(1),17-37.
MLA	Rahman, Khalil Ur,et al."Application of a Dynamic Clustered Bayesian Model Averaging (DCBA) Algorithm for Merging Multisatellite Precipitation Products over Pakistan".JOURNAL OF HYDROMETEOROLOGY 21.1(2020):17-37.