气候变化领域集成服务门户(CCPortal): Automatic nighttime sea fog detection using GOES-16 imagery

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DOI	10.1016/j.atmosres.2019.104712
	Automatic nighttime sea fog detection using GOES-16 imagery
	Amani M.; Mahdavi S.; Bullock T.; Beale S.
发表日期	2020
ISSN	0169-8095
卷号	238
英文摘要	Accurately detecting sea fog is important for oil and gas operations in the Grand Banks, Newfoundland and Labrador (NL), Canada. Although the Grand Banks is one of the foggiest places in the world, there is no remote sensing technique specifically developed for fog detection in this region. Therefore, an automatic approach was proposed in this study to detect Nighttime Sea Fog (NSF) and distinguish it from clear sky and ice cloud. To this end, Geostationary Operational Environmental Satellite system-16 (GOES-16) imagery along with several ancillary datasets were employed. Selecting the Optimum Threshold Value (OTV) for identifying NSF in satellite images acquired at different times was also extensively discussed. The NSF maps obtained by the proposed method for 25 advection fog events in the study area were compared to surface-based weather observations (i.e., visibility data) and the National Oceanic and Atmospheric Administration's (NOAA) global fog/low cloud products. The Probability of Detection (POD), False Alarm Rate (FAR), Hanssen-Kuiper Skill Score (KSS), and Equitable Threat Score (ETS) were 0.80, 0.08, 0.72, and 0.57, respectively, demonstrating the potential of the proposed NSF detection algorithm. Additionally, the results showed that the proposed method was better at discriminating Grand Banks fog than NOAA's algorithm in terms of both visual and statistical accuracies. © 2019
英文关键词	Brightness temperature; GOES-16; Remote sensing; Sea fog
学科领域	Geostationary satellites; Remote sensing; Satellite imagery; Brightness temperatures; Geostationary operational environmental satellites; GOES-16; National Oceanic and Atmospheric Administration's; Oil and gas operations; Probability of detection; Remote sensing techniques; Sea fog; Fog; algorithm; brightness temperature; fog; GOES; marine atmosphere; NOAA satellite; remote sensing; satellite imagery; Atlantic Ocean; Canada; Grand Banks; Newfoundland and Labrador
语种	英语
scopus关键词	Geostationary satellites; Remote sensing; Satellite imagery; Brightness temperatures; Geostationary operational environmental satellites; GOES-16; National Oceanic and Atmospheric Administration's; Oil and gas operations; Probability of detection; Remote sensing techniques; Sea fog; Fog; algorithm; brightness temperature; fog; GOES; marine atmosphere; NOAA satellite; remote sensing; satellite imagery; Atlantic Ocean; Canada; Grand Banks; Newfoundland and Labrador
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/120439
作者单位	Wood Environment & Infrastructure Solutions, St. John's, NL A1B 1H3, Canada
推荐引用方式 GB/T 7714	Amani M.,Mahdavi S.,Bullock T.,et al. Automatic nighttime sea fog detection using GOES-16 imagery[J],2020,238.
APA	Amani M.,Mahdavi S.,Bullock T.,&Beale S..(2020).Automatic nighttime sea fog detection using GOES-16 imagery.Atmospheric Research,238.
MLA	Amani M.,et al."Automatic nighttime sea fog detection using GOES-16 imagery".Atmospheric Research 238(2020).