Effects of Variable Eruption Source Parameters on Volcanic Plume Transport: Example of the 23 November 2013 Paroxysm of Etna

Rizza, Umberto; Donnadieu, Franck; Magazù, Salvatore; Passerini, G.; Castorina, Giuseppe; Semprebello, Agostino; Morichetti, Mauro; Virgili, Simone; Mancinelli, Enrico

doi:10.3390/rs13204037

Cited by 8 publications

(7 citation statements)

References 50 publications

(73 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These studies focused on the application of various VATDMs and instruments to analyze volcanic ash and SO 2 dispersal and their impact, compare satellite, ground-based, and UAV/aircraft data with numerical simulations, validate models with field data, improve volcanic ash predictions, increase the understanding of eruption dynamics, and assess the transport of volcanic aerosols over long distances. A number of VATDMs such as Numerical Atmospheric-dispersion Modelling Environment (NAME) [117,126], Modèle Lagrangien de Dispersion de Particules d'ordre zéro (MLDP0) [18], FALL3D [113,114,119,132], CMAQ [115], Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) [124,125,128,135,136], PUFF [129], Massive-Parallel Trajectory Calculations (MAPTRAC) [131], VOLC-CALL PUFF [120], PlumeTraj [133], PlumeMoM [133], chemistry transport model (CHIMERE) [118,139], and Fplume [17] have been used in conjunction with MISR, MODIS, SEVIRI, IASI, OMI, VIIRS, OMPS, AIRS, ABI, and AHI satellite data, ground-based data [116,129,133,136,139], and UAV/aircraft-based remote sensing data [117].…”

Section: E-remote Sensing Data Assimilation Into Numerical Forecastin...mentioning

confidence: 99%

“…Whereas Paez et al [137] investigated volcanic SO 2 and ash emissions with good agreement between HYSPLIT SO 2 concentrations and OMPS Aerosol Index estimations, Gunda et al [138] used HYSPLIT and satellite observations to model Sentinel-2, MODIS, and OMI data. Using ground and satellite remote sensing data, Rizza et al [139] investigated the effects of variable ESP on volcanic plume transport during the 23 November 2013 paroxysm event of Etna. The study of Bruckert et al [17] demonstrated that the online treatment of eruption dynamics enhanced the forecasting of volcanic ash and SO 2 dispersion for the 2019 Raikoke.…”

Section: E-remote Sensing Data Assimilation Into Numerical Forecastin...mentioning

confidence: 99%

See 1 more Smart Citation

Monitoring Volcanic Plumes and Clouds Using Remote Sensing: A Systematic Review

Mota,

Pacheco,

Pimentel

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

Volcanic clouds pose significant threats to air traffic, human health, and economic activity, making early detection and monitoring crucial. Accurate determination of eruptive source parameters is crucial for forecasting and implementing preventive measures. This review article aims to identify the most common remote sensing methods for monitoring volcanic clouds. To achieve this, we conducted a systematic literature review of scientific articles indexed in the Web of Science database published between 2010 and 2022, using multiple query strings across all fields. The articles were reviewed based on research topics, remote sensing methods, practical applications, case studies, and outcomes using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Our study found that satellite-based remote sensing approaches are the most cost-efficient and accessible, allowing for the monitoring of volcanic clouds at various spatial scales. Brightness temperature difference is the most commonly used method for detecting volcanic clouds at a specified temperature threshold. Approaches that apply machine learning techniques help overcome the limitations of traditional methods. Despite the constraints imposed by spatial and temporal resolution and optical limitations of sensors, multiplatform approaches can overcome these limitations and improve accuracy. This study explores various techniques for monitoring volcanic clouds, identifies research gaps, and lays the foundation for future research.

show abstract

Section: E-remote Sensing Data Assimilation Into Numerical Forecastin...mentioning

confidence: 99%

Section: E-remote Sensing Data Assimilation Into Numerical Forecastin...mentioning

confidence: 99%

Monitoring Volcanic Plumes and Clouds Using Remote Sensing: A Systematic Review

Mota,

Pacheco,

Pimentel

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…The Eulerian Weather Research and Forecasting-Chemistry (WRF-Chem) model has been recently utilised to study single paroxysmal events of Mt. Etna (e.g., 23 November 2013 [23], and two sequences of paroxysms of Mt. Etna [24]).…”

Section: Introductionmentioning

confidence: 99%

Modelling Paroxysmal and Mild-Strombolian Eruptive Plumes at Stromboli and Mt. Etna on 28 August 2019

Castorina,

Semprebello,

Gattuso

et al. 2023

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Volcanic eruptions pose a major natural hazard influencing the environment, climate and human beings at different temporal and spatial scales. Nevertheless, several volcanoes worldwide are poorly monitored and assessing the impact of their eruptions remains, in some cases, challenging. Nowadays, different numerical dispersion models are largely employed in order to evaluate the potential effects of volcanic plume dispersion due to the transport of ash and gases. On 28 August 2019, both Mt. Etna and Stromboli had eruptive activity; Mt. Etna was characterised by mild-Strombolian activity at summit craters, while at Stromboli volcano, a paroxysmal event occurred, which interrupted the ordinary typical-steady Strombolian activity. Here, we explore the spatial dispersion of volcanic sulphur dioxide (SO2) gas plumes in the atmosphere, at both volcanoes, using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) considering the ground-measured SO2 amounts and the plume-height as time-variable eruptive source parameters. The performance of WRF-Chem was assessed by cross-correlating the simulated SO2 dispersion maps with data retrieved by TROPOMI and OMI sensors. The results show a feasible agreement between the modelled dispersion maps and TROPOMI satellite for both volcanoes, with spatial pattern retrievals and a total mass of dispersed SO2 of the same order of magnitude. Predicted total SO2 mass for Stromboli might be underestimated due to the inhibition from ground to resolve the sin-eruptive SO2 emission due to the extreme ash-rich volcanic plume released during the paroxysm. This study demonstrates the feasibility of a WRF-Chem model with time-variable ESPs in simultaneously reproducing two eruptive plumes with different SO2 emission and their dispersion into the atmosphere. The operational implementation of this method could represent effective support for the assessment of local-to-regional air quality and flight security and, in case of particularly intense events, also on a global scale.

show abstract

“…Numerical modelling of tephra transport, dispersion, and sedimentation has become a key research area in volcanology e.g., [1][2][3][4][5][6]. On the one hand, numerical models are important to better understand and describe the physics of particle sedimentation, e.g., [7,8] or particle aggregation, e.g., [5,9], but also of larger scale phenomena such as ash cloud development [10] or effects of variable source parameters on transport [11]. On the other hand, numerical models are invaluable tools during volcanic crisis for rapid forecasts of volcanic clouds by the different Volcanic Ash Advisory Centers, or VAACs, Refs.…”

Section: Introductionmentioning

confidence: 99%

Particle Sedimentation in Numerical Modelling: A Case Study from the Puyehue-Cordón Caulle 2011 Eruption with the PLUME-MoM/HYSPLIT Models

et al. 2022

Self Cite

View full text Add to dashboard Cite

Numerical modelling of tephra fallout is a fast-developing research area in volcanology. Several models are currently available both to forecast the dispersion of volcanic particles in the atmosphere and to calculate the particles deposited at different locations on the ground. Data from these simulations can then be used both to manage volcanic crises (e.g., protect air traffic) or perform long-term hazard assessment studies (e.g., through hazard maps). Given the importance of these tasks, it is important that each model is thoroughly tested in order to assess advantages and limitations, and to provide useful information for quantifying the model uncertainty. In this study we tested the coupled PLUME-MoM/HYSPLIT models by applying them to the Puyehue–Cordon Caulle 2011 sub-Plinian eruption. More specifically, we tested new features recently introduced in these well-established models (ash aggregation, external water addition, and settling velocity models), we implemented a new inversion procedure, and we performed a parametric analysis. Our main results reaffirm the pivotal role played by mass eruption rate on the final deposit and show that some choices for the input parameters of the model can lead to the large overestimation in total deposited mass (which can be reduced with our inversion procedure). The parametric analysis suggests a most likely value of the mass eruption rate in the range 2.0–6.3 × 106 kg/s. More studies with a similar approach would be advisable in order to provide final users with useful indications about the parameters that should be carefully evaluated before being used as input for this kind of model.

show abstract

Effects of Variable Eruption Source Parameters on Volcanic Plume Transport: Example of the 23 November 2013 Paroxysm of Etna

Cited by 8 publications

References 50 publications

Monitoring Volcanic Plumes and Clouds Using Remote Sensing: A Systematic Review

Monitoring Volcanic Plumes and Clouds Using Remote Sensing: A Systematic Review

Modelling Paroxysmal and Mild-Strombolian Eruptive Plumes at Stromboli and Mt. Etna on 28 August 2019

Particle Sedimentation in Numerical Modelling: A Case Study from the Puyehue-Cordón Caulle 2011 Eruption with the PLUME-MoM/HYSPLIT Models

Contact Info

Product

Resources

About