DOI: 10.1007/s11069-019-03847-2
论文题名: Automated detection and measurement of volcanic cloud growth: towards a robust estimate of mass flux, mass loading and eruption duration
作者: Bear-Crozier A. ; Pouget S. ; Bursik M. ; Jansons E. ; Denman J. ; Tupper A. ; Rustowicz R.
刊名: Natural Hazards
ISSN: 0921030X
出版年: 2020
卷: 101, 期: 1 语种: 英语
中文关键词: Automated plume detection
; Cloud area
; Gravity current
; Mass eruption rate
; Operational tool
; Volcanic cloud
英文关键词: algorithm
; detection method
; gravity field
; loading
; mantle plume
; remote sensing
; satellite imagery
; source parameters
; spatiotemporal analysis
; volcanic ash
; volcanic cloud
; volcanic eruption
英文摘要: Identifying the spatial extent of volcanic ash clouds in the atmosphere and forecasting their direction and speed of movement has important implications for the safety of the aviation industry, community preparedness and disaster response at ground level. Nine regional Volcanic Ash Advisory Centres were established worldwide to detect, track and forecast the movement of volcanic ash clouds and provide advice to en route aircraft and other aviation assets potentially exposed to the hazards of volcanic ash. In the absence of timely ground observations, an ability to promptly detect the presence and distribution of volcanic ash generated by an eruption and predict the spatial and temporal dispersion of the resulting volcanic cloud is critical. This process relies greatly on the heavily manual task of monitoring remotely sensed satellite imagery and estimating the eruption source parameters (e.g. mass loading and plume height) needed to run dispersion models. An approach for automating the quick and efficient processing of next generation satellite imagery (big data) as it is generated, for the presence of volcanic clouds, without any constraint on the meteorological conditions, (i.e. obscuration by meteorological cloud) would be an asset to efforts in this space. An automated statistics and physics-based algorithm, the Automated Probabilistic Eruption Surveillance algorithm is presented here for auto-detecting volcanic clouds in satellite imagery and distinguishing them from meteorological cloud in near real time. Coupled with a gravity current model of early cloud growth, which uses the area of the volcanic cloud as the basis for mass measurements, the mass flux of particles into the volcanic cloud is estimated as a function of time, thus quantitatively characterising the evolution of the eruption, and allowing for rapid estimation of source parameters used in volcanic ash transport and dispersion models. © 2020, Springer Nature B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/168398
Appears in Collections: 气候变化与战略
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作者单位: Volcanic Ash Advisory Centre (Darwin), Bureau of Meteorology, Melbourne, Australia; Department of Geology, University at Buffalo, SUNY, Buffalo, NY 14260, United States
Recommended Citation:
Bear-Crozier A.,Pouget S.,Bursik M.,et al. Automated detection and measurement of volcanic cloud growth: towards a robust estimate of mass flux, mass loading and eruption duration[J]. Natural Hazards,2020-01-01,101(1)