Future developments in modelling and monitoring of volcanic ash clouds: outcomes from the first IAVCEI-WMO workshop on Ash Dispersal Forecast and Civil Aviation
- First Online:
- Cite this article as:
- Bonadonna, C., Folch, A., Loughlin, S. et al. Bull Volcanol (2012) 74: 1. doi:10.1007/s00445-011-0508-6
- 743 Downloads
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 data-acquisition 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, pre-eruption 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.