A New Radar‐Based Statistical Model to Quantify Mass Eruption Rate of Volcanic Plumes

Abstract

Accurate forecasting of volcanic particle (tephra) dispersal and fallout requires a reliable estimation of key Eruption Source Parameters (ESPs) such as the Mass Eruption Rate (QM). QM is usually estimated from the Top Plume Height (HTP) using empirical and analytical models. For the first time, we combine estimates of HTP and QM derived from the same sensor (radar) with mean wind velocity values (vW) for lava‐fountain fed tephra plumes associated with 32 paroxysms of Mt. Etna (Italy) to develop a new statistical model based on a Markov Chain Monte Carlo approach for model parameter estimation. This model is especially designed for application to radar data to quickly infer QM from observed HTP and vW, and estimate the associated uncertainty. It can be easily applied and adjusted to data retrieved by radars worldwide, improving our capacity to quickly estimate QM and related uncertainties required for the tephra dispersal hazard.

New Insights Into the Relationship Between Mass Eruption Rate and Volcanic Column Height Based On the IVESPA Data Set

“…Last, the use of time‐averaged eruption source parameters might prevent the detection of atmospheric influence on plume dynamics in a database with such a variety of eruptions. Advances in near real‐time measurements of MER (Bear‐Crozier et al., 2020; Caudron et al., 2015; Freret‐Lorgeril et al., 2018, 2021; Mereu et al., 2022, 2023) are critical to build time‐dependencies in global databases like IVESPA. These database development should aid understanding of the dynamics of volcanic plumes, and in particular detect and model the influence of atmospheric conditions (Section 5.1).…”

“…Although column height can often be directly observed, MER is more challenging to constrain (Dürig et al., 2018; Pioli & Harris, 2019). Satellite, radar, cameras, or infrasound sensors have been used to directly estimate MER in near real‐time (e.g., Bear‐Crozier et al., 2020; Freret‐Lorgeril et al., 2021; Mereu et al., 2022, 2023), but these pioneering applications are either not operational or limited to a few of the world's best‐monitored volcanoes (e.g., Etna volcano, Italy). Therefore, computationally inexpensive empirical scaling relationships and one‐dimensional (1D) eruptive column models remain the most common tools to estimate MER based on observed height.…”

“…real-time (e.g., Bear-Crozier et al, 2020;Freret-Lorgeril et al, 2021;Mereu et al, 2022Mereu et al, , 2023, but these pioneering applications are either not operational or limited to a few of the world's best-monitored volcanoes (e.g., Etna volcano, Italy). Therefore, computationally inexpensive empirical scaling relationships and one-dimensional (1D) eruptive column models remain the most common tools to estimate MER based on observed height.…”

Assessment of eruption source parameters using infrasound: a case study from the 2021 eruption of Mt.Etna, Italy

“…This estimate equals the central point of the range bin volume. The distance between the radar and the summit craters and the half-power beam width of the radar are used to reconstruct the radar beam cone; an uncertainty on HTP of ± 300 m is evaluated as the half-aperture (radius) with respect to the axis of the truncated cone in the proximal area above the crater 55 .…”

Toward a Real-Time Analysis of Column Height by Visible Cameras: An Example from Mt. Etna, in Italy

“…The VONA messages are usually sent by fax or email by the observatory to the pertinent Area Control Centre, Meteorological Watch Office, and Volcanic Ash Advisory Centre [10]. Plume height estimation is also essential to evaluate the mass eruption rate of an explosive event [11][12][13][14], and represents a critical factor in forecasting volcanic ash dispersion [15][16][17] through numerical modeling [18][19][20][21][22]. Moreover, the level reached by the volcanic plume is essential in some gas plumes and aerosol retrievals by satellite [4].…”