Developing the Resilience to Icelandic Volcanic Eruptions (DRIVE)

Lead Research Organisation: University of Exeter
Department Name: Engineering Computer Science and Maths


When Iceland's Eyjafjallajökull volcano erupted in 2010, the large scale restrictions on European airspace resulted in significant financial loss across the airline industry and the global economy. The Met Office Volcanic Ash Advisory Centre (VAAC) is responsible for providing forecasts of volcanic ash from Icelandic volcanoes and has now established a new network of ten ground-remote sensing sites across the UK providing independent estimates of the mass concentration of volcanic ash. This network uses a combination of sun-photometers and lidars to estimate the volcanic ash concentration. The Met Office is also resourced with the Met Office Civil Contingency Aircraft (MOCCA; equipped with a lidar and in-situ instrumentation for sampling aerosols.
DRIVE will have unique access to data from these facilities and will use in-situ observations to assess the accuracy of the lidar and sun-photometer remote sensing retrievals. The student will use routine forecasts of mineral dust (a proxy for volcanic ash) over the UK from the Met Office and other models (, to assess when a Saharan dust outbreak is imminent (typical lead-time of 48-72 hours). The student and supervisors will direct MOCCA to perform dedicated flights over selected cloud-free lidar/sun-photometer sites impacted by Saharan dust. The student will analyse aircraft in-situ measurements of the aerosol size distribution, the vertical profile of the scattering and aerosol optical depth derived from nephelometers and will use radiative scattering and transfer codes to derive the aerosol specific extinction coefficient for use in the inversion algorithms. The student will assess the surface- and aircraft-based lidar retrievals of mineral dust concentration allowing an objective assessment of the uncertainty associated with each. The student will also use theaircraft data to assess and improve the accuracy of forecasts of mineral dust from the global numerical weather prediction and air quality models.


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