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.
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)
语种	英语
来源机构	Atmospheric Research
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/132334
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]. Atmospheric Research,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.,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".246(2020).