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Supervised by Dr. Antonio Costa

 

 

Volcanic ash:

FiEld, expeRimenTal and numerIcal investiGations of PrOcesses during its lifecycle.

In 2010, a famous Icelandic volcano, Eyjafjallajökull, threatened the northern part of the world by erupting and injecting ash into the atmosphere. Such a volcanic crisis entirely disrupted the air traffic due to the release of large quantities of volcanic ash and the long-lasting dispersion at different flight levels.

 

For better understanding  such volcanic ash emission context, Europe developed a project which aims at broadly improving knowledge of "Ash". This project is created and funded under the FP 7 framework "FP7-PEOPLE-2013-ITN", grant n°607905.

This project is called

Vertigo

My Ph.D. project

Improvements for characterizing eruption processes are made commonly through field data analysis, remote-sensing instruments, lab experiments and numerical models. From a computational point of view, the inter-dependency of the main volcanological parameters makes challenging the assessment of tephra dispersion and sedimentation, from which mass eruption rate, total erupted mass, and Total Grain-Size Distribution (TGSD) are typically estimated. My Ph.D. project aims at better constraining the Eruption Source Parameters (ESP) and in particular the TGSD, usually derived from field sample analysis only. The estimation of very fine ash (i.e. < 30 μm) fraction, within the TGSD, commonly suffers from the lack of distal field data, especially for basaltic eruptions, which contain a relatively small fraction of fine ash. Besides, particle-particle aggregation affects ash dispersal and deposition. Although numerical simulations can account for ash aggregation, they need an accurate TSGD as input.

During my Ph.D., I used the FALL3D model together with airborne and ground-based data in order to quantify i) the very fine ash (e.g. PM20 and PM10) and the effect on the simulation results and ii) the occurrence of ash aggregation during ash transport. I focused on integrating field, ground-based and satellite data for better estimating the TGSD and the very fine ash fraction especially. Such method was applied to both the 23 February and 23 November 2013 Etna paroxysms. These two cases benefited from north-easterly winds dispersing tephra towards the Puglia region (southern Italy; ~410 km from the source), and allowing sampling at very distal areas. Then, I also studied ash aggregation processes characterizing the explosive eruption of La Soufrière St. Vincent on 26 April 1979. During this event, a significant aggregate fraction was observed contributing to premature tephra fallout from the vent to Bequia Island (36 km southwards). This eruption was selected for numerically investigating the effect of various TGSD together with different aggregation schemes on the resulting tephra loading and ash dispersal.

Objectives

One of the aim of this project was to explicitly model the tephra dispersal including fine and very fine ash which may be influenced by ash aggregation. Numerical simulations were performed through the FALL3D model, which requires meteorological fields together with a set ESP. I proposed a multi-disciplinary approach that integrates all available data for capturing the main eruptive features (e.g. tephra loading and airborne ash dispersal). I used field measurements, ground-based (e.g. visible and thermal images, X-band and L-band radars, sun-photometers), and satellite-based (e.g. SEVIRI) retrievals. Among the crucial ESP used as inputs within FALL3D, the TGSD was carefully investigated, being responsible of air traffic and public health issues.

Achievments

  1. Estimating the TGSD from field measurements

  2. Estimating the TGSD from X-band weather radar retrievals

  3. Assessing the very fine ash distribution 

  4. Validating simulations against field measurements (e.g. tephra loadings, grain-size distributions)

  5. Inverting the very fine ash distribution for capturing satellite retrievals

  6. Investigating such distribution up to ultra distal area (AERONET network)

  7. Validating the simulations with different meteorological databases (WRF and NMMB).

Applications

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