气候变化领域集成服务门户(CCPortal): Development of a novel Weighted Average Least Squares-based ensemble multi-satellite precipitation dataset and its comprehensive evaluation over Pakistan

CCPortal

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 K.U.; Shang S.; Shahid M.; Wen Y.; Khan A.J.
发表日期	2020
ISSN	0169-8095
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° 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 DCBA-ESPD. 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. © 2020 Elsevier B.V.
英文关键词	Complex topography; Diverse climate; Dynamic Weighted Average Least Square; Ensemble precipitation dataset; Pakistan; Precipitation estimation; Regional and seasonal evaluation
语种	英语
scopus关键词	Arid regions; Atmospheric thermodynamics; Clock and data recovery circuits (CDR circuits); Errors; Glacial geology; Mean square error; Rain gages; Satellites; Climate prediction centers; Comprehensive evaluation; Correlation coefficient; Precipitation estimation from remotely sensed information; Precipitation products; Root mean square errors; Satellite precipitation; Tropical rainfall measurement missions; Precipitation (meteorology); climate modeling; climate prediction; ensemble forecasting; least squares method; monsoon; precipitation (climatology); regional climate; satellite data; satellite imagery; weather forecasting; Pakistan
来源期刊	Atmospheric Research
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/141785
作者单位	State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, 100084, China; NICE, SCEE, National University of Science & Technology Islamabad, Islamabad, 44000, Pakistan
推荐引用方式 GB/T 7714	Rahman K.U.,Shang S.,Shahid M.,et al. Development of a novel Weighted Average Least Squares-based ensemble multi-satellite precipitation dataset and its comprehensive evaluation over Pakistan[J],2020,246.
APA	Rahman K.U.,Shang S.,Shahid M.,Wen Y.,&Khan A.J..(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 K.U.,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).