干旱区生态环境知识资源中心(Arid): Evaluation of Precipitation Estimates from Remote Sensing and Artificial Neural Network Based Products (PERSIANN) Family in an Arid Region

Arid

DOI	10.3390/rs15041078
	Evaluation of Precipitation Estimates from Remote Sensing and Artificial Neural Network Based Products (PERSIANN) Family in an Arid Region
	Baig, Faisal; Abrar, Muhammad; Chen, Haonan; Sherif, Mohsen
通讯作者	Sherif, M
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2023
卷号	15 期号:4
英文摘要	Accurate and continuous rainfall monitoring is essential for effective water resources management, especially in arid and semi-arid regions such as the United Arab Emirates (UAE). Significant spatio-temporal precipitation variation in the UAE necessitates the use of the latest techniques to measure rainfall intensity accurately. This study investigates the consistency and applicability of four satellite precipitation products, namely PERSIANN, PERSIANN-CCS, PERSIANN-CDR, and PDIR-Now, over the UAE. Daily time series data from 2011 to 2020 were analyzed using various statistical measures and climate indices to develop the belief in the products and for their inter-comparison. The analysis revealed that the average probability of detection (POD) for PDIR and CDR was the highest, with values ranging from 0.7-0.9 and 0.6-0.9, respectively. Similarly, CDR has a better Heidke Skill Score (HSS) with an average value of 0.26. CDR outperformed its counterparts with an average correlation coefficient value of 0.70 vs. 0.65, 0.40, and 0.34 for PDIR, CCS, and PERSIANN, respectively. Precipitation indices analysis revealed that all the products overestimated the number of consecutive wet days by 15-20%, while underestimating consecutive dry days by 5-10%. The quantitative estimations indicate that all the products were matching with the gauge values during the wet months (January-April), while they showed significant overestimation during the dry months. CDR and PDIR were in close agreement with the gauge data in terms of maximum daily rainfall with an error of less than 10% for both products. As compared to others, PERSIANN-CDR provided better estimates, particularly in terms of capturing extreme rainfall events and spatial distribution of rainfall. This study provides the first comprehensive evaluation of four PERSIANN family products based on recent daily rainfall data of UAE. The findings can provide future insights into the applicability and improvement of PERSIANN products in arid and semi-arid regions.
英文关键词	PERSIANN precipitation products UAE rainfall evaluation extreme precipitation indices IDF curves
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000942265800001
WOS关键词	SATELLITE PRECIPITATION ; RAIN-GAUGE ; CMORPH ; TMPA ; VARIABILITY ; CHIRPS ; RADAR
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/398236
推荐引用方式 GB/T 7714	Baig, Faisal,Abrar, Muhammad,Chen, Haonan,et al. Evaluation of Precipitation Estimates from Remote Sensing and Artificial Neural Network Based Products (PERSIANN) Family in an Arid Region[J],2023,15(4).
APA	Baig, Faisal,Abrar, Muhammad,Chen, Haonan,&Sherif, Mohsen.(2023).Evaluation of Precipitation Estimates from Remote Sensing and Artificial Neural Network Based Products (PERSIANN) Family in an Arid Region.REMOTE SENSING,15(4).
MLA	Baig, Faisal,et al."Evaluation of Precipitation Estimates from Remote Sensing and Artificial Neural Network Based Products (PERSIANN) Family in an Arid Region".REMOTE SENSING 15.4(2023).