Characterization of grain-size parameters, determination of mass eruption rate and assimilation of geophysical data to initialize Volcanic Ash Transport and Dispersal Models

  • Early-warning detection schemes (Deliverable 8.1)
  • Near real-time detection of critical eruption source parameters (Deliverable 8.2)
  • Integration of geophysical data in dispersal models (Deliverable 8.3)
  •  Creation of source parameter probability density functions for future eruptions (Deliverable 8.3)

WP progress and accomplishments:

Deliverable 8.1 (completed in January 2019):

An early-warning system based on infrasonic signal and Doppler radar detection of tephra plume has been successfully developed and tested at Etna volcano.

Deliverable 8.2 (due at the end of project):

In the framework of Del. 8.2, various sensors and platforms have been tested to quantify and characterize the Eruptive Source Parameters (ESPs) of explosive eruptions.

Total Grain Size Distribution:

New strategies have been developed to better characterize the Total Grain Size Distribution (TGSD) of volcanic eruptions (e.g. Pioli et al., 2019). As part of the effort to develop new strategies to determine TGSD and fallout rate in real time various optical disdrometers were used to tested. One ASHER has also been installed at Etna volcano for long-term real time monitoring of tephra fallout.

 Mass Eruption Rate:

Ground-Based X-Band and L-Band Microwave Radars: Taking advantage of the capability of the microwave radars to probe the volcanic plume and extending the volcanic ash radar retrieval (VARR) methodology of Marzano et al., (2012), the mass eruption rate (MER) was estimated for the 23 November 2013 paroxysm at Etna using three main techniques: surface-flux approach (SFA), mass continuity based approach (MCA), and top-plume approach (TPA) (Marzano et al., 2020).

Ground-Based L-Band radar (VOLDORAD-2B): A MER proxy is calculated from tephra echo power and maximum Doppler velocities measured right above the eruptive crater by a L-band fixed-beam radar (VOLDORAD-2B; database available at jointly operated by INGV-OE and UCA-OPGC (Donnadieu et al., 2016). The proxy was calibrated using plume ascent models based on observed plume heights (Freret-Lorgeril et al., 2018).

Platforms and Volcanic Ash Transport and Dispersal model:

REFIR: The quantification of MER and their uncertainties has been made using the model REFIR (Dürig et al., 2018) for improving inputs of VATDMs.

HOTVOLC: Satellite observations gathered in the framework of the HOTVOLC platform ( have been combined with the fast-running model PPM to infer on ash cloud characterization and source parameters quantification.

NAME: The Volcanic Ash Transport and Dispersal model NAME has been enhanced with the addition of an ash aggregation scheme.

Deliverable 8.3 (due at the end of project):

Geophysical data have been integrated in VATDMs to improve data assimilation processes and to constrain output parameters. Probability Density Functions (PDFs) of ESPs are also being crated based on past eruptive events. To improve eruption forecasting, a survey was made to determine the best ESPs to use for running VATDMs.


  • An innovative combination of infrasound and Doppler radar signals allowed to build an Early warning system at Mount Etna (Ripepe et al., 2018).
  • Real-time MER estimates based on the VOLDORAD-2B radar (Freret-Lorgeril et al., 2018) will be implemented at INGV-OE in 2020.
  • A new operational forecast of ash emission at Etna has been developed and based on visual observations and fast-running dispersal models (Scollo et al., 2019).
  • A new strategy for the modelling of TGSDs was developed that better describes distribution tails (Pioli et al., 2019).
  • Thanks to satellite-model combination, ash plumes from strong eruptions were shown to be less efficient at carrying very fine ash in the atmosphere (Gouhier et al., 2019).
WP leader: Prof. Costanza Bonadonna. Associate Professor at the University of Geneva and head of the CERGC program.

Cited references:

Donnadieu F., Freville P., Rivet S., Hervier C., Cacault P., 2015. The Volcano Doppler radar data base of Etna (VOLDORAD 2B). Université Clermont Auvergne – CNRS.,
doi: 10.18145/VOLDORAD.ETNA.2009

Dürig, T., Gudmundsson, M. T., Dioguardi, F., Woodhouse, M., Björnsson, H., Barsotti, S., et al., 2018. REFIR- a multi-parameter system for near realtime estimates of plume-height and mass eruption rate during explosive eruptions. J. Volcanol. Geotherm. Res. 360:61–83. doi: 10.1016/j.jvolgeores.2018.07

Freret-Lorgeril V., Donnadieu F., Scollo S., Provost A., Fréville P., Guéhenneux Y., Hervier C., Prestifilippo M.,  Coltelli M., 2018. Mass eruption rates of tephra plumes during the 2011–2015 lava fountain paroxysms at Mt. Etna from Doppler radar retrievals. Front. Earth Sci. 6:73. doi: 10.3389/feart.2018.00073

Freret-Lorgeril V., Donnadieu F., Eychenne J., Soriaux C., Latchimy T., 2019. In situ terminal settling velocity measurements at Stromboli volcano: Input from physical characterization of ash. J. Volcanol. Geotherm. Res. 374, 62–9

Marzano, F.S., E. Picciotti, G. Vulpiani and M. Montopoli (2012), “Synthetic Signatures of Volcanic Ash Cloud Particles from X-band Dual-Polarization Radar”, IEEE Trans. Geosci. Rem. Sens., ISSN: 0196-2892, vol. 50, pp. 193–211

Marzano F.S., Mereu L., Scollo S., Donnadieu F. and Bonadonna C., 2020. Tephra Mass Eruption Rate from Ground-based X-Band and L-Band Microwave Radars during the 23 November 2013 Etna Paroxysm. IEEE Trans. Geosc. Remote Sens. 58, 5:3314–3327. doi: 10.1109/TGRS.2019.2953167

Pioli, L., Bonadonna, C., Pistolesi, M. (2019). Reliability of Total Grain-Size Distribution of Tephra Deposits, Sci. Rep., 9: 10006.

Ripepe, M., Marchetti, E., Delle Donne, D., Genco, R., Innocenti, L., Lacanna, G., Valade, S., 2018. Infrasonic Early Warning System for Explosive Eruptions. J. Geophys. Res. Solid Earth 123(11), 95709585

Scollo, S., Prestifilippo, M., Bonadonna, C., Cioni, R., Corradini, S., Degruyter, W., Rossi, E., Silvestri, M., Biale, E., Carparelli, G., et al., 2019. Near-Real-Time Tephra Fallout Assessment at Mt. Etna, Italy. Remote Sens. 11:2987. doi:10.3390/rs11242987