Abstract
Abstract. We have developed an aggregation scheme for use with the Lagrangian atmospheric transport and dispersion model NAME (Numerical Atmospheric Dispersion modelling Environment), which is used by the London Volcanic Ash Advisory Centre (VAAC) to provide advice and guidance on the location of volcanic ash clouds to the aviation industry. The aggregation scheme uses the fixed pivot technique to solve the Smoluchowski coagulation equations to simulate aggregation processes in an eruption column. This represents the first attempt at modelling explicitly the change in the grain size distribution (GSD) of the ash due to aggregation in a model which is used for operational response. To understand the sensitivity of the output aggregated GSD to the model parameters, we conducted a simple parametric study and scaling analysis. We find that the modelled aggregated GSD is sensitive to the density distribution and grain size distribution assigned to the non-aggregated particles at the source. Our ability to accurately forecast the long-range transport of volcanic ash clouds is, therefore, still limited by real-time information on the physical characteristics of the ash. We assess the impact of using the aggregated GSD on model simulations of the 2010 Eyjafjallajökull ash cloud and consider the implications for operational forecasting. Using the time-evolving aggregated GSD at the top of the eruption column to initialize dispersion model simulations had little impact on the modelled extent and mass loadings in the distal ash cloud. Our aggregation scheme does not account for the density of the aggregates; however, if we assume that the aggregates have the same density of single grains of equivalent size, the modelled area of the Eyjafjallajökull ash cloud with high concentrations of ash, significant for aviation, is reduced by ∼ 2 %, 24 h after the start of the release. If we assume that the aggregates have a lower density (500 kg m−3) than the single grains of which they are composed and make up 75 % of the mass in the ash cloud, the extent is 1.1 times larger.
Highlights
In volcanic plumes ash can aggregate, bound by hydro-bonds and electrostatic forces
In our case study of the Eyjafjallajökull 2010 eruption, we found that, mass was lost from bins representing smaller grain sizes, the mode of the aggregated grain size distribution (GSD) did not differ from the source GSD of the erupted non-aggregated particles; for example, the output aggregated GSD at 19:00 UTC on 4 May 2010 has lost mass from ash ≤ 16 μm but the mode remains at 64–125 μm (Fig. 7)
The scheme uses a buoyant plume model to simulate the eruption column dynamics, and the Smoluchowski coagulation equations are solved with a sectional technique which allows us to simulate the aggregated GSD in discrete bins
Summary
In volcanic plumes ash can aggregate, bound by hydro-bonds and electrostatic forces. Aggregates typically have diameters > 63 μm (Brown et al, 2012), and their fall velocity differs from that of the single grains of which they are composed (Lane et al, 1993; James et al, 2003; Taddeucci et al, 2011; Bagheri et al, 2016). Textor et al (2006a, b) introduced a more sophisticated aggregation scheme to the Active Tracer High-resolution Atmospheric Model (ATHAM), designed to model eruption columns, which included a more robust representation of the microphysical processes and simulated the interaction of hydrometeors with volcanic ash. Microphysicalbased aggregation schemes which represent multiple collision mechanisms have been introduced to atmospheric dispersion models FALL3D (Costa et al, 2010; Folch et al, 2010), WRF-Chem (Egan et al, 2020), and an eruption column model, FPLUME (Folch et al, 2016) They all use an approximate solution of the Smoluchowski coagulation equations, which assumes that aggregates can be described by a fractal geometry and particles aggregating onto a single effective aggregate class defined by a prescribed diameter.
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