Abstract

<strong class="journal-contentHeaderColor">Abstract.</strong> 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 <span class="inline-formula">∼</span> 2 %, 24 h after the start of the release. If we assume that the aggregates have a lower density (500 kg m<span class="inline-formula"><sup>−3</sup></span>) 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 together, bound by hydro-bonds and electrostatic forces

  • There have been several attempts to provide an empirical description of the aggregated grain size distribution (AGSD) by assigning a specific cluster settling velocity to fine ash (Carey and Sigurdsson, 30 1983) or fitting the distribution used in dispersion models to observations of tephra deposits retrospectively (e.g. Cornell et al, 1983; Bonadonna et al, 2002; Mastin et al, 2013, 2016)

  • The scheme uses an iterative 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 AGSD in discrete bins

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Summary

Introduction

In volcanic plumes ash can aggregate together, bound by hydro-bonds and electrostatic forces. 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 microphysical processes and simulated the interaction of hydrometeors with volcanic ash. We introduce an aggregation scheme coupled to a one-dimensional steady state buoyant plume model which uses a discrete solution of the SCE based on the Fixed Pivot Technique (FPT) (Kumar and Ramkrishna, 1996) As such we are able to model explicitly the evolution of the AGSD with time in the eruption column.

The Aggregation Scheme
Scale Analysis of the Collision Kernel
Discussion
Findings
Limitations
Conclusions

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