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

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.

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Acknowledgements

This research was supported by NSF-IDR CMMI Grant Number 1131074 to E. B. Pitman, and by AFOSR Grant Number FA9550-11-1-0336 to A.K. Patra and discretionary research funds provided by the Australian Bureau of Meteorology. All results and opinions expressed in the foregoing are those of the authors and do not reflect opinions of NSF or AFOSR. We thank E.B. Pitman for useful feedback on the work presented herein and drafts of the manuscript, the Associate Editors of Natural Hazards and Larry Mastin for a very helpful review. The component of the software to calculate MER is available Open Source on GitHub at: https://github.com/marcusbursik/Umbrella_MER.

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Correspondence to Adele Bear-Crozier.

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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. Nat Hazards 101, 1–38 (2020). https://doi.org/10.1007/s11069-019-03847-2

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Keywords

  • Volcanic cloud
  • Mass eruption rate
  • Cloud area
  • Gravity current
  • Automated plume detection
  • Operational tool