Future developments in modelling and monitoring of volcanic ash clouds: outcomes from the first IAVCEI-WMO workshop on Ash Dispersal Forecast and Civil Aviation

Abstract

As a result of the serious consequences of the 2010 Eyjafjallajökull eruption (Iceland) on civil aviation, 52 volcanologists, meteorologists, atmospheric dispersion modellers and space and ground-based monitoring specialists from 12 different countries (including representatives from 6 Volcanic Ash Advisory Centres and related institutions) gathered to discuss the needs of the ash dispersal modelling community, investigate new dataacquisition strategies (i.e. quantitative measurements and observations) and discuss how to improve communication between the research community and institutions with an operational mandate. Based on a dedicated benchmark exercise and on 3 days of in-depth discussion, recommendations have been made for future model improvements, new strategies of ash cloud forecasting, multidisciplinary data acquisition and more efficient communication between different communities. Issues addressed in the workshop include ash dispersal modelling, uncertainty, ensemble forecasting, combining dispersal models and observations, sensitivity analysis, model variability, data acquisition, preeruption forecasting, first simulation and data assimilation, research priorities and new communication strategies to improve information flow and operational routines. As a main conclusion, model developers, meteorologists, volcanologists and stakeholders need to work closely together to develop new and improved strategies for ash dispersal forecasting and, in particular, to: (1) improve the definition of the source term, (2) design models and forecasting strategies that can better characterize uncertainties, (3) explore and identify the best ensemble strategies that can be adapted to ash dispersal forecasting, (4) identify optimized strategies for the combination of models and observations and (5) implement new critical operational strategies.

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).…”

Inverting for volcanic SO<sub>2</sub> flux at high temporal resolution using spaceborne plume imagery and chemistry-transport modelling: the 2010 Eyjafjallajökull eruption case study

“…gas mass flux, emission altitude/profile) is essential to simulate with reliability the atmospheric fate of the volcanic gas cloud Bonadonna et al, 2012). During the eruption of a volcano monitored by an observatory, ground geophysical and geochemical measurements may help to estimate these parameters Petersen et al, 2012).…”

Volcanic ash modeling with the NMMB-MONARCH-ASH model: quantification of off-line modeling errors

“…uncertainties in column height and related mass eruption rate), known to be one of the main reasons (first order) for VATDM output variability (e.g. Bonadonna et al, 2010). 5…”

“…Bonadonna et al, 2012Bonadonna et al, , 2015 including, among others, characterization of the source term and related uncertainties in model inputs (e.g. Costa et al, 2016) model parameterization of relevant physical phenomena (e.g.…”