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Open AccessArticle

Ensemble-Based Data Assimilation of Volcanic Ash Clouds from Satellite Observations: Application to the 24 December 2018 Mt. Etna Explosive Eruption

1
Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Pisa, 56125 Pisa, Italy
2
Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Nazionale Terremoti, 00143 Rome, Italy
3
Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Bologna, 40100 Bologna, Italy
4
University at Buffalo, Buffalo, NY 14260, USA
*
Author to whom correspondence should be addressed.
Current address: Affiliation 1.
Atmosphere 2020, 11(4), 359; https://doi.org/10.3390/atmos11040359
Received: 12 February 2020 / Revised: 1 April 2020 / Accepted: 2 April 2020 / Published: 7 April 2020
(This article belongs to the Special Issue Forecasting the Transport of Volcanic Ash in the Atmosphere)
Accurate tracking and forecasting of ash dispersal in the atmosphere and quantification of its uncertainty are of fundamental importance for volcanic risk mitigation. Numerical models and satellite sensors offer two complementary ways to monitor ash clouds in real time, but limits and uncertainties affect both techniques. Numerical forecasts of volcanic clouds can be improved by assimilating satellite observations of atmospheric ash mass load. In this paper, we present a data assimilation procedure aimed at improving the monitoring and forecasting of volcanic ash clouds produced by explosive eruptions. In particular, we applied the Local Ensemble Transform Kalman Filter (LETKF) to the results of the Volcanic Ash Transport and Dispersion model HYSPLIT. To properly simulate the release and atmospheric transport of volcanic ash particles, HYSPLIT has been initialized with the results of the eruptive column model PLUME-MoM. The assimilation procedure has been tested against SEVIRI measurements of the volcanic cloud produced during the explosive eruption occurred at Mt. Etna on 24 December 2018. The results show how the assimilation procedure significantly improves the representation of the current ash dispersal and its forecast. In addition, the numerical tests show that the use of the sequential Ensemble Kalman Filter does not require a precise initialization of the numerical model, being able to improve the forecasts as the assimilation cycles are performed. View Full-Text
Keywords: data assimilation; volcanic eruptions; volcanic hazard; numerical modelling; ash dispersal; remote sensing; Mt. Etna data assimilation; volcanic eruptions; volcanic hazard; numerical modelling; ash dispersal; remote sensing; Mt. Etna
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MDPI and ACS Style

Pardini, F.; Corradini, S.; Costa, A.; Esposti Ongaro, T.; Merucci, L.; Neri, A.; Stelitano, D.; de’ Michieli Vitturi, M. Ensemble-Based Data Assimilation of Volcanic Ash Clouds from Satellite Observations: Application to the 24 December 2018 Mt. Etna Explosive Eruption. Atmosphere 2020, 11, 359. https://doi.org/10.3390/atmos11040359

AMA Style

Pardini F, Corradini S, Costa A, Esposti Ongaro T, Merucci L, Neri A, Stelitano D, de’ Michieli Vitturi M. Ensemble-Based Data Assimilation of Volcanic Ash Clouds from Satellite Observations: Application to the 24 December 2018 Mt. Etna Explosive Eruption. Atmosphere. 2020; 11(4):359. https://doi.org/10.3390/atmos11040359

Chicago/Turabian Style

Pardini, Federica; Corradini, Stefano; Costa, Antonio; Esposti Ongaro, Tomaso; Merucci, Luca; Neri, Augusto; Stelitano, Dario; de’ Michieli Vitturi, Mattia. 2020. "Ensemble-Based Data Assimilation of Volcanic Ash Clouds from Satellite Observations: Application to the 24 December 2018 Mt. Etna Explosive Eruption" Atmosphere 11, no. 4: 359. https://doi.org/10.3390/atmos11040359

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