Sensitivity of dispersion model forecasts of volcanic ash clouds to the physical characteristics of the particles

Abstract

This study examines the sensitivity of atmospheric dispersion model forecasts of volcanic ash clouds to the physical characteristics assigned to the particles. We show that the particle size distribution (PSD) used to initialise a dispersion model has a significant impact on the forecast of the mass loading of the ash particles in the atmosphere. This is because the modeled fall velocity of the particles is sensitive to the particle diameter. Forecasts of the long-range transport of the ash cloud consider particles with diameters between 0.1 μm and 100 μm. The fall velocity of particles with diameter 100 μm is over 5 orders of magnitude greater than a particle with diameter 0.1 μm, and 30 μm particles fall 88% slower and travel up to 5× further than a 100 μm particle. Identifying the PSD of the ash cloud at the source, which is required to initialise a model, is difficult. Further, aggregation processes are currently not explicitly modeled in operational dispersion models due to the high computational costs associated with aggregation schemes. We show that using a modified total grain size distribution (TGSD) that effectively accounts for aggregation processes improves the modeled PSD of the ash cloud and deposits from the eruption of Eyjafjallajökull in 2010. Knowledge of the TGSD of an eruption is therefore critical for reducing uncertainty in quantitative forecasts of ash cloud dispersion. The density and shape assigned to the model particles have a lesser but still significant impact on the calculated fall velocity. Accounting for the density distribution and sphericity of ash from the eruption of Eyjafjallajökull in 2010, modeled particles can travel up to 84% further than particles with default particle characteristics that assume the particles are spherical and have a fixed density.

Assessing tephra total grain-size distribution: Insights from field data analysis

“…In particular, the TGSD has already been shown to represent one of the most critical ESPs, significantly effecting tephra dispersal model outputs (e.g. Scollo et al, 2008;Beckett et al, 2015). Numerical models used for the real-time forecasting of volcanic clouds typically model only the fine-ash fraction of the TGSD (i.e.…”

“…Nonetheless, an accurate characterization of the whole size range of erupted particles is necessary to assign the associated mass and describe the mass distribution in the eruptive plume (e.g. Beckett et al, 2015).…”

Statistical analysis of dispersal and deposition patterns of volcanic emissions from Mt. Sakurajima, Japan

“…6b). Volcanic ash deposition heavily depends on the size: 'coarse' ash (>1 mm in diameter) is deposited close to the vent, while lighter ash has increasingly longer flight times and can thus be subject to complex interactions with the local air flow (Carey and Sparks, 1986;Bonadonna et al, 1998;Beckett et al, 2015;Watt et al, 2015). Proximal to the vent, ash fallout is also heavily affected by volcanological effects such as changes in the eruption dynamics and sedimentation regime (Bonadonna and Costa, 2013), ash aggregation (i.e.…”

A New One‐Equation Model of Fluid Drag for Irregularly Shaped Particles Valid Over a Wide Range of Reynolds Number

“…In volcanology and Earth sciences in general, the shape-dependent aerodynamic drag is crucial in controlling the transport and deposition of nonspherical solid particles in dust storms (e.g., Doronzo et al, 2015;Kok et al, 2012), rivers and lakes (e.g., DIOGUARDI ET AL. AERODYNAMIC DRAG OF IRREGULAR PARTICLES 144 Zhu et al, 2017), pyroclastic flows (e.g., Dellino et al, 2008;, eruptive columns (e.g., Cerminara et al, 2016;Folch et al, 2016), and distal ash clouds (e.g., Beckett et al, 2015;Bonadonna et al, 2012;Bonasia et al, 2010;Costa et al, 2012Costa et al, , 2006). Therefore, a major effort has been posed to find reliable shapedependent drag laws that work on the widest possible range of fluid dynamic regimes quantified by Re (Alfano et al, 2011;Bagheri & Bonadonna, 2016;Chhabra et al, 1999;Chien, 1994;Dellino et al, 2005;Dioguardi et al, 2017;Dioguardi & Mele, 2015;Ganser, 1993;Haider & Levenspiel, 1989;Hölzer & Sommerfeld, 2008;Loth, 2008;Pfeiffer et al, 2005;Swamee & Ojha, 1991;Tran-Cong et al, 2004;Wilson & Huang, 1979).…”