Volcanic emission estimates from the inversion of ACTRIS lidar observations and their use for quantitative dispersion modeling

Abstract

Modeling the dispersion of volcanic particles following explosive eruptions is critical for aviation safety. To constrain the dispersion of volcanic plumes and assess hazards, calculations rely on the accurate characterization of the eruption's source term, e.g., variation in emission rate and column height with time and the prevailing wind fields. This study introduces an inverse modeling framework that integrates a Lagrangian dispersion model with lidar observations to estimate emission rates of volcanic particles released during an Etna eruption. The methodology consists of using the FLEXPART model to generate source-receptor relationships (SRRs) between the volcano and the lidar system that observed the volcanic plume. These SRRs are then used to derive the emission rates based on observational data, including volcanic ash plume heights from the INGV-EO observatory and PollyXT lidar retrievals. We leverage data from the ACTRIS PollyXT lidar that operates at the PANhellenic GEophysical observatory of Antikythera of the National Observatory of Athens (PANGEA-NOA). The inversion algorithm utilizes lidar observations and an empirical a priori emission profile to estimate the volcanic particle source strength, accounting for altitude and time of the plume's evolution. Additionally, to study the impact that the wind fields have on volcanic ash forecasting, the experiment is repeated using fields that assimilate Aeolus wind lidar data. Our approach applied to the 12 March 2021 Etna eruption and accurately captures a dense aerosol layer between 8 and 12 km above the PANGEA-NOA station. Results show a minimal difference of the order of 2 % between the observed and the simulated ash concentrations. Furthermore, the structure of the a posteriori ash plume closely resembles the ash cloud image captured by the SEVIRI satellite above Antikythera island, highlighting the novelty of the inversion results. The presented inversion algorithm, coupled with Aeolus data, optimizes both the vertical emission distribution and Etna emission rates, advancing our understanding and preparedness for volcanic events.