Automated detection and measurement of volcanic cloud growth: towards a robust estimate of mass flux, mass loading and eruption duration

Adele Bear-Crozier (), Solène Pouget, Marcus Bursik, Emile Jansons, Jarrad Denman, Andrew Tupper and Rose Rustowicz
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Adele Bear-Crozier: Bureau of Meteorology
Solène Pouget: University at Buffalo, SUNY
Marcus Bursik: University at Buffalo, SUNY
Emile Jansons: Bureau of Meteorology
Jarrad Denman: Bureau of Meteorology
Andrew Tupper: University at Buffalo, SUNY
Rose Rustowicz: University at Buffalo, SUNY

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2020, vol. 101, issue 1, No 1, 38 pages

Abstract: Abstract 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.

Keywords: Volcanic cloud; Mass eruption rate; Cloud area; Gravity current; Automated plume detection; Operational tool (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s11069-019-03847-2

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