干旱区生态环境知识资源中心(Arid): Development of a novel Weighted Average Least Squares-based ensemble multi-satellite precipitation dataset and its comprehensive evaluation over Pakistan

Arid

DOI	10.1016/j.atmosres.2020.105133
	Development of a novel Weighted Average Least Squares-based ensemble multi-satellite precipitation dataset and its comprehensive evaluation over Pakistan
	Rahman, Khalil Ur; Shang, Songhao; Shahid, Muhammad; Wen, Yeqiang; Khan, Abdul Jabbar
通讯作者	Shang, SH
来源期刊	ATMOSPHERIC RESEARCH
ISSN	0169-8095
EISSN	1873-2895
出版年	2020
卷号	246
英文摘要	Ensemble multi-satellite precipitation datasets (ESPDs) are alternative to satellite-based precipitation products (SPs), which tend to reduce the errors, combine advantages of individual SPs, and have higher accuracy for hydrological applications. The current study proposes and evaluates a dynamic WALS-ESPD developed using the Weighted Average Least Square (WALS) algorithm, which has 0.25 degrees spatial and daily temporal resolutions across glacial, humid, arid and hyper-arid regions of Pakistan during 2000-2015. WALS-ESPD is developed using three SPs, Tropical Rainfall Measurement Mission (TRMM) Multi-Satellite 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 one re-analysis product, Era-Interim. Mean Bias (MB), Mean Absolute Error (MAE), unbiased Root Mean Square Error (ubRMSE), Correlation Coefficient (R), Kling-Gupta efficiency (KGE score), and Theil's U are used to evaluate the performance of WALS-ESPD both spatially and temporally. Moreover, the skill scores of statistical metrics are used to assess the WALS-ESPD performance against two previously developed ESPDs, DBMA-ESPD and DCBAESPD. TMPA dominated all SPs with average weights of 0.317, 0.341, 0.314, and 0.326 across the glacial, humid, arid and hyper-arid regions. TMPA dominated pre-monsoon (30.26%) and monsoon (35.82%) seasons, while PERSIANN-CDR dominated post-monsoon (27.58%) and winter (29.82%) seasons. WALS-ESPD performed relatively poor across the glacial and humid regions, and during monsoon and pre-monsoon seasons. Skill scores of WALS-ESPD against DBMA-ESPD show better performance of WALS-ESPD in all four regions, especially across the glacial region with the maximum MB, MAE, and ubRMSE scores of 27.36%, 28.34%, and 27.67%, respectively. Meanwhile, WALS-ESPD performed better than DCBA-ESPD in the whole glacial region and most part of other regions, while DCBA-ESPD dominated WALS-ESPD at few stations across humid, arid, and hyper-arid (south-east) regions.
英文关键词	Precipitation estimation Ensemble precipitation dataset Dynamic Weighted Average Least Square Regional and seasonal evaluation Complex topography Diverse climate Pakistan
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000581845700029
WOS关键词	SATELLITE-BASED PRECIPITATION ; SURFACE-TEMPERATURE FORECASTS ; ANALYSIS TMPA ; PRODUCTS ; RAINFALL ; PERFORMANCE ; PREDICTION ; VALIDATION ; TOPOGRAPHY ; ALGORITHM
WOS类目	Meteorology & Atmospheric Sciences
WOS研究方向	Meteorology & Atmospheric Sciences
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/2XILL650/327089
作者单位	[Rahman, Khalil Ur; Shang, Songhao; Wen, Yeqiang] Tsinghua Univ, Dept Hydraul Engn, State Key Lab Hydrosci & Engn, Beijing 100084, Peoples R China; [Shahid, Muhammad; Khan, Abdul Jabbar] Natl Univ Sci & Technol Islamabad, SCEE, NICE, Islamabad 44000, Pakistan
推荐引用方式 GB/T 7714	Rahman, Khalil Ur,Shang, Songhao,Shahid, Muhammad,et al. Development of a novel Weighted Average Least Squares-based ensemble multi-satellite precipitation dataset and its comprehensive evaluation over Pakistan[J]. 清华大学,2020,246.
APA	Rahman, Khalil Ur,Shang, Songhao,Shahid, Muhammad,Wen, Yeqiang,&Khan, Abdul Jabbar.(2020).Development of a novel Weighted Average Least Squares-based ensemble multi-satellite precipitation dataset and its comprehensive evaluation over Pakistan.ATMOSPHERIC RESEARCH,246.
MLA	Rahman, Khalil Ur,et al."Development of a novel Weighted Average Least Squares-based ensemble multi-satellite precipitation dataset and its comprehensive evaluation over Pakistan".ATMOSPHERIC RESEARCH 246(2020).