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.
Monitoring Subglacial Volcanic Eruption Using Ground-Based C-Band Radar Imagery / Marzano, FRANK SILVIO; S., Barbieri; E., Picciotti; S., Karlsdottir. - In: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING. - ISSN 0196-2892. - STAMPA. - 48:1(2010), pp. 403-414. [10.1109/tgrs.2009.2024933]
Monitoring Subglacial Volcanic Eruption Using Ground-Based C-Band Radar Imagery
MARZANO, FRANK SILVIO;
2010
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.