A pinboard by
Andrew Prata

Post-Doctoral Research Assistant, University of Reading


I am developing a web-tool that will enable the calculation of along-flight volcanic ash dosages

My research centres around detecting and predicting the dispersion of hazardous volcanic clouds in the atmosphere. The volcanic ash clouds produced by Icelandic volcano Eyjafjallajökull in April/May 2010, for example, resulted in major logistical and financial issues for the aviation industry. During the early phases of the eruption, the London Volcanic Ash Advisory Centre took a ‘zero tolerance’ approach and advised that any suspected ash concentration in the air should be avoided by aircraft. This resulted in the shutdown of airspace over large parts of Europe, leaving millions of air travellers stranded. In response to the crisis, Rolls Royce, in collaboration with the Civil Aviation Authority, produced the ‘safe-to-fly’ chart. As ash concentrations are the primary output of dispersion model forecasts, the chart was designed to illustrate how engine damage progresses as a function of ash concentration. Concentration thresholds were subsequently derived based on previous ash encounters.

My current research focusses on the calculation of volcanic ash dosages; the accumulated concentration over time. Dosages are an improvement to concentrations as they can be used to identify situations where ash concentrations are acceptably low but the exposure time is long enough to cause damage to aircraft engines. Funded by NERC, under the “Environmental Risks to Infrastructure Innovation Program”, we are developing a proof-of-concept volcanic ash dosage calculator in collaboration with airline operators and regulators. This innovative, web-based research tool takes advantage of interactive data visualisation to communicate the uncertainty inherent in dosage calculations. To calculate dosages, we use a dispersion model to simulate an Icelandic eruption scenario, which results in ash dispersal across the North Atlantic, UK and Europe. Ash encounters are simulated based on flight-optimal routes derived from aircraft routing software. Key outputs of the calculator include the along-flight dosage, exposure time and peak concentration. The design of the tool allows users to explore the major areas of uncertainty in the dosage calculation and to visualise how this changes as the planned flight path is varied. We expect that this research will result in better-informed decisions for aircraft routing during volcanic ash events through a deeper understanding of the associated uncertainties in dosage calculations.


Forecasting volcanic ash dispersal and coeval resuspension during the April–May 2015 Calbuco eruption

Abstract: Atmospheric dispersion of volcanic ash from explosive eruptions or from subsequent fallout deposit resuspension causes a range of impacts and disruptions on human activities and ecosystems. The April–May 2015 Calbuco eruption in Chile involved eruption and resuspension activities. We overview the chronology, effects, and products resulting from these events, in order to validate an operational forecast strategy for tephra dispersal. The modelling strategy builds on coupling the meteorological Weather Research and Forecasting (WRF/ARW) model with the FALL3D dispersal model for eruptive and resuspension processes. The eruption modelling considers two distinct particle granulometries, a preliminary first guess distribution used operationally when no field data was available yet, and a refined distribution based on field measurements. Volcanological inputs were inferred from eruption reports and results from an Argentina–Chilean ash sample data network, which performed in-situ sampling during the eruption. In order to validate the modelling strategy, results were compared with satellite retrievals and ground deposit measurements. Results indicate that the WRF-FALL3D modelling system can provide reasonable forecasts in both eruption and resuspension modes, particularly when the adjusted granulometry is considered. The study also highlights the importance of having dedicated datasets of active volcanoes furnishing first-guess model inputs during the early stages of an eruption.

Pub.: 02 May '16, Pinned: 25 Aug '17

Probabilistic detection of volcanic ash using a Bayesian approach.

Abstract: Airborne volcanic ash can pose a hazard to aviation, agriculture, and both human and animal health. It is therefore important that ash clouds are monitored both day and night, even when they travel far from their source. Infrared satellite data provide perhaps the only means of doing this, and since the hugely expensive ash crisis that followed the 2010 Eyjafjalljökull eruption, much research has been carried out into techniques for discriminating ash in such data and for deriving key properties. Such techniques are generally specific to data from particular sensors, and most approaches result in a binary classification of pixels into "ash" and "ash free" classes with no indication of the classification certainty for individual pixels. Furthermore, almost all operational methods rely on expert-set thresholds to determine what constitutes "ash" and can therefore be criticized for being subjective and dependent on expertise that may not remain with an institution. Very few existing methods exploit available contemporaneous atmospheric data to inform the detection, despite the sensitivity of most techniques to atmospheric parameters. The Bayesian method proposed here does exploit such data and gives a probabilistic, physically based classification. We provide an example of the method's implementation for a scene containing both land and sea observations, and a large area of desert dust (often misidentified as ash by other methods). The technique has already been successfully applied to other detection problems in remote sensing, and this work shows that it will be a useful and effective tool for ash detection.Presentation of a probabilistic volcanic ash detection schemeMethod for calculation of probability density function for ash observationsDemonstration of a remote sensing technique for monitoring volcanic ash hazards.

Pub.: 07 Apr '15, Pinned: 25 Aug '17