干旱区生态环境知识资源中心(Arid): Enhancing Precipitation Estimates Through the Fusion of Weather Radar, Satellite Retrievals, and Surface Parameters

Arid

DOI	10.3390/rs12081342
	Enhancing Precipitation Estimates Through the Fusion of Weather Radar, Satellite Retrievals, and Surface Parameters
	Wehbe, Youssef 1,2; Temimi, Marouane 1; Adler, Robert F.3
通讯作者	Wehbe, Youssef
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2020
卷号	12 期号:8
英文摘要	Accurate and timely monitoring of precipitation remains a challenge, particularly in hyper-arid regions such as the United Arab Emirates (UAE). The aim of this study is to improve the accuracy of the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission's latest product release (IMERG V06B) locally over the UAE. Two distinct approaches, namely, geographically weighted regression (GWR), and artificial neural networks (ANNs) are tested. Daily soil moisture retrievals from the Soil Moisture Active Passive (SMAP) mission (9 km), terrain elevations from the Advanced Spaceborne Thermal Emission and Reflection digital elevation model (ASTER DEM, 30 m) and precipitation estimates (0.5 km) from a weather radar network are incorporated as explanatory variables in the proposed GWR and ANN model frameworks. First, the performances of the daily GPM and weather radar estimates are assessed using a network of 65 rain gauges from 1 January 2015 to 31 December 2018. Next, the GWR and ANN models are developed with 52 gauges used for training and 13 gauges reserved for model testing and seasonal inter-comparisons. GPM estimates record higher Pearson correlation coefficients (PCC) at rain gauges with increasing elevation (z) and higher rainfall amounts (PCC = 0.29 z(0.12)), while weather radar estimates perform better for lower elevations and light rain conditions (PCC = 0.81 z(-0.18)). Taylor diagrams indicate that both the GWR- and the ANN-adjusted precipitation products outperform the original GPM and radar estimates, with the poorest correction obtained by GWR during the summer period. The incorporation of soil moisture resulted in improved corrections by the ANN model compared to the GWR, with relative increases in Nash-Sutcliffe efficiency (NSE) coefficients of 56% (and 25%) for GPM estimates, and 34% (and 53%) for radar estimates during summer (and winter) periods. The ANN-derived precipitation estimates can be used to force hydrological models over ungauged areas across the UAE. The methodology is expandable to other arid and hyper-arid regions requiring improved precipitation monitoring.
英文关键词	precipitation artificial neural networks geographically weighted regression weather radar soil moisture
类型	Article
语种	英语
国家	U Arab Emirates ; USA
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000534628800115
WOS关键词	GEOGRAPHICALLY WEIGHTED REGRESSION ; ARTIFICIAL NEURAL-NETWORKS ; SOIL-MOISTURE ; RAINFALL ESTIMATION ; SPATIAL INTERPOLATION ; FEEDFORWARD NETWORKS ; BIAS CORRECTION ; PERFORMANCE ; PRODUCTS ; MODELS
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/2XILL650/318969
作者单位	1.Khalifa Univ Sci & Technol, Dept Civil Infrastruct & Environm Engn, POB 54224, Abu Dhabi, U Arab Emirates; 2.Natl Ctr Meteorol NCM, POB 4815, Abu Dhabi, U Arab Emirates; 3.Univ Maryland, Earth Syst Sci Interdisciplinary Ctr, College Pk, MD 20740 USA
推荐引用方式 GB/T 7714	Wehbe, Youssef,Temimi, Marouane,Adler, Robert F.. Enhancing Precipitation Estimates Through the Fusion of Weather Radar, Satellite Retrievals, and Surface Parameters[J],2020,12(8).
APA	Wehbe, Youssef,Temimi, Marouane,&Adler, Robert F..(2020).Enhancing Precipitation Estimates Through the Fusion of Weather Radar, Satellite Retrievals, and Surface Parameters.REMOTE SENSING,12(8).
MLA	Wehbe, Youssef,et al."Enhancing Precipitation Estimates Through the Fusion of Weather Radar, Satellite Retrievals, and Surface Parameters".REMOTE SENSING 12.8(2020).