Examples of multi-sensor determination of eruptive source parameters of explosive events at mount etna

International audienceMulti-sensor strategies are key to the real-time determination of eruptive source parameters (ESPs) of explosive eruptions necessary to forecast accurately both tephra dispersal and deposition. To explore the capacity of these strategies in various eruptive conditions, we analyze data acquiredby two Doppler radars, ground- and satellite-based infrared sensors, one infrasound array, visible video-monitoring cameras as well as data from tephra-fallout deposits associated with a weak and a strong paroxysmal event at Mount Etna (Italy). We find that the different sensors provide complementary observations that should be critically analyzed and combined to provide comprehensive estimates of ESPs. First, all measurements of plume height agree during the strong paroxysmal activity considered, whereas some discrepancies are found for the weak paroxysm due to rapid plume and cloud dilution. Second, the event duration, key to convert the total erupted mass (TEM) in the mass eruption rate (MER) and vice versa, varies depending on the sensor used, providing information on different phases of the paroxysm (i.e., unsteady lava fountaining, lava fountain-fed tephra plume, waning phase associated with plume and cloud expansion in the atmosphere). As a result, TEM and MER derived from different sensors also correspond to the different phases of the paroxysms. Finally, satellite retrievals for grain-size can be combined with radar data to provide a first approximation of total grain-size distribution (TGSD) in near real-time. Such a TGSD shows a promising agreement with the TGSD derived from the combination of satellite data and whole deposit grain-size distribution (WDGSD)

Monitoring Subglacial Volcanic Eruption Using Ground-Based C-Band Radar Imagery

The microphysical and dynamical features of volcanic clouds, due to Plinian and sub-Plinian eruptions, can be quantitatively monitored by using ground-based microwave weather radars. In order to demonstrate the unique potential of this remote sensing technique, a case study of a subglacial volcanic eruption, occurred in Iceland in November 2004, is described and analyzed. Volume data, acquired by a C-band ground-based weather radar, are processed to automatically classify and estimate ash particle concentration. The ash retrieval physical-statistical algorithm is based on a backscattering microphysical model of fine, coarse, and lapilli ash particles, used within a Bayesian classification and optimal regression algorithm. A sensitivity analysis is carried out to evaluate the overall error budget and the possible impact of nonprecipitating liquid and ice cloud droplets when mixed with ash particles. The evolution of the Icelandic eruption is discussed in terms of radar measurements and products, pointing out the unique features, the current limitations, and future improvements of radar remote sensing of volcanic plumes