干旱区生态环境知识资源中心(Arid): Global Analysis of Atmospheric Transmissivity Using Cloud Cover, Aridity and Flux Network Datasets

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DOI	10.3390/rs13091716
	Global Analysis of Atmospheric Transmissivity Using Cloud Cover, Aridity and Flux Network Datasets
	Srivastava, Ankur; Rodriguez, Jose F.; Saco, Patricia M.; Kumari, Nikul; Yetemen, Omer
通讯作者	Yetemen, O (corresponding author), Univ Newcastle, Ctr Water Secur & Environm Sustainabil, Callaghan, NSW 2308, Australia. ; Yetemen, O (corresponding author), Univ Newcastle, Sch Engn, Callaghan, NSW 2308, Australia. ; Yetemen, O (corresponding author), Istanbul Tech Univ, Eurasia Inst Earth Sci, TR-34469 Istanbul, Turkey.
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2021
卷号	13 期号:9
英文摘要	Atmospheric transmissivity (tau) is a critical factor in climatology, which affects surface energy balance, measured at a limited number of meteorological stations worldwide. With the limited availability of meteorological datasets in remote areas across different climatic regions, estimation of tau is becoming a challenging task for adequate hydrological, climatic, and crop modeling studies. The availability of solar radiation data is comparatively less accessible on a global scale than the temperature and precipitation datasets, which makes it necessary to develop methods to estimate tau. Most of the previous studies provided region specific datasets of tau, which usually provide local assessments. Hence, there is a necessity to give the empirical models for tau estimation on a global scale that can be easily assessed. This study presents the analysis of the tau relationship with varying geographic features and climatic factors like latitude, aridity index, cloud cover, precipitation, temperature, diurnal temperature range, and elevation. In addition to these factors, the applicability of these relationships was evaluated for different climate types. Thus, empirical models have been proposed for each climate type to estimate tau by using the most effective factors such as cloud cover and aridity index. The cloud cover is an important yet often overlooked factor that can be used to determine the global atmospheric transmissivity. The empirical relationship and statistical indicator provided the best performance in equatorial climates as the coefficient of determination (r(2)) was 0.88 relatively higher than the warm temperate (r(2) = 0.74) and arid regions (r(2) = 0.46). According to the results, it is believed that the analysis presented in this work is applicable for estimating the tau in different ecosystems across the globe.
英文关键词	atmospheric transmissivity solar radiation aridity index cloud cover Fluxnet Ameriflux Ozflux
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:000650734600001
WOS关键词	INCOMING SOLAR-RADIATION ; POLLUTION ; AEROSOLS ; TRENDS ; ENERGY ; CARBON
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/2XILL650/351518
作者单位	[Srivastava, Ankur; Rodriguez, Jose F.; Saco, Patricia M.; Kumari, Nikul; Yetemen, Omer] Univ Newcastle, Ctr Water Secur & Environm Sustainabil, Callaghan, NSW 2308, Australia; [Srivastava, Ankur; Rodriguez, Jose F.; Saco, Patricia M.; Kumari, Nikul; Yetemen, Omer] Univ Newcastle, Sch Engn, Callaghan, NSW 2308, Australia; [Yetemen, Omer] Istanbul Tech Univ, Eurasia Inst Earth Sci, TR-34469 Istanbul, Turkey
推荐引用方式 GB/T 7714	Srivastava, Ankur,Rodriguez, Jose F.,Saco, Patricia M.,et al. Global Analysis of Atmospheric Transmissivity Using Cloud Cover, Aridity and Flux Network Datasets[J],2021,13(9).
APA	Srivastava, Ankur,Rodriguez, Jose F.,Saco, Patricia M.,Kumari, Nikul,&Yetemen, Omer.(2021).Global Analysis of Atmospheric Transmissivity Using Cloud Cover, Aridity and Flux Network Datasets.REMOTE SENSING,13(9).
MLA	Srivastava, Ankur,et al."Global Analysis of Atmospheric Transmissivity Using Cloud Cover, Aridity and Flux Network Datasets".REMOTE SENSING 13.9(2021).