Model sensitivities in the case of high-resolution Eulerian simulations of local tephra transport and deposition

Poulidis, Alexandros Panagiotis; Iguchi, Masato

doi:10.1016/j.atmosres.2020.105136

Cited by 14 publications

(19 citation statements)

References 105 publications

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“…Previous sensitivity studies of dispersion model simulations of volcanic ash clouds have highlighted the importance of constraining the GSD of ash for operational forecasts, as this parameter strongly influences its residence time in the atmosphere (Scollo et al, 2008;Beckett et al, 2015;Durant, 2015;Poret et al, 2017;Osman et al, 2020;Poulidis and Iguchi, 2020). Here we show that the modelled AGSD is also sensitive to the GSD, and the density, of the non-aggregated ash at the source.…”

Section: Dispersion Modellingsupporting

confidence: 56%

Modelling the size distribution of aggregated volcanic ash and implications for operational atmospheric dispersion modelling

Beckett¹,

Rossi

Devenish³

et al. 2021

Preprint

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Abstract. We have developed an aggregation scheme for use with the Lagrangian atmospheric transport and dispersion model NAME, which is used by the London Volcanic Ash Advisory Centre (VAAC) to provide advice and guidance on the location of volcanic ash clouds to the aviation industry. The aggregation scheme uses the fixed pivot technique to solve the Smoluchowski coagulation equations to simulate aggregation processes in an eruption column. This represents the first attempt at modelling explicitly the change in the grain size distribution (GSD) of the ash due to aggregation in a model which is used for operational response. To understand the sensitivity of the output aggregated grain size distribution (AGSD) to the model parameters we conducted a simple parametric study and scaling analysis. We find that the modelled AGSD is sensitive to the density distribution and grain size distribution assigned to the non-aggregated ash at the source. Our ability to accurately forecast the long-range transport of volcanic ash clouds is, therefore, still limited by real-time information on the physical characteristics of the ash. We assess the impact of using the AGSD on model simulations of the Eyjafjallajökull 2010 ash cloud, and consider the implications for operational forecasting. Using the time-evolving AGSD at the top of the eruption column to initialise dispersion model simulations had little impact on the modelled extent and mass loadings in the distal ash cloud. Our aggregation scheme does not account for the density of the aggregates; however, if we assume that the aggregates have the same density of single grains of equivalent size the modelled extent of the Eyjafjallajökull ash cloud with high concentrations of ash, significant for aviation, is reduced by ~3 %. If we assume that the aggregates have a lower density (500 kg m−3) than the single grains of which they are composed and make-up 75 % of the mass in the ash cloud the extent is 1.2 times larger.

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Section: Dispersion Modellingsupporting

confidence: 56%

Modelling the size distribution of aggregated volcanic ash and implications for operational atmospheric dispersion modelling

Beckett¹,

Rossi

Devenish³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Previous sensitivity studies of dispersion model simulations of volcanic ash clouds have highlighted the importance of constraining the GSD of ash for operational forecasts, as this parameter strongly influences its residence time in the atmosphere (Scollo et al, 2008;Beckett et al, 2015;Durant, 2015;Poret et al, 2017;Osman et al, 2020;Poulidis and Iguchi, 2020). Here we show that the modelled aggregated GSD is also sensitive to the GSD, and the density, of the non-Figure 8.…”

Section: Discussionmentioning

confidence: 53%

Modelling the size distribution of aggregated volcanic ash and implications for operational atmospheric dispersion modelling

et al. 2022

View full text Add to dashboard Cite

Abstract. We have developed an aggregation scheme for use with the Lagrangian atmospheric transport and dispersion model NAME (Numerical Atmospheric Dispersion modelling Environment), which is used by the London Volcanic Ash Advisory Centre (VAAC) to provide advice and guidance on the location of volcanic ash clouds to the aviation industry. The aggregation scheme uses the fixed pivot technique to solve the Smoluchowski coagulation equations to simulate aggregation processes in an eruption column. This represents the first attempt at modelling explicitly the change in the grain size distribution (GSD) of the ash due to aggregation in a model which is used for operational response. To understand the sensitivity of the output aggregated GSD to the model parameters, we conducted a simple parametric study and scaling analysis. We find that the modelled aggregated GSD is sensitive to the density distribution and grain size distribution assigned to the non-aggregated particles at the source. Our ability to accurately forecast the long-range transport of volcanic ash clouds is, therefore, still limited by real-time information on the physical characteristics of the ash. We assess the impact of using the aggregated GSD on model simulations of the 2010 Eyjafjallajökull ash cloud and consider the implications for operational forecasting. Using the time-evolving aggregated GSD at the top of the eruption column to initialize dispersion model simulations had little impact on the modelled extent and mass loadings in the distal ash cloud. Our aggregation scheme does not account for the density of the aggregates; however, if we assume that the aggregates have the same density of single grains of equivalent size, the modelled area of the Eyjafjallajökull ash cloud with high concentrations of ash, significant for aviation, is reduced by ∼ 2 %, 24 h after the start of the release. If we assume that the aggregates have a lower density (500 kg m−3) than the single grains of which they are composed and make up 75 % of the mass in the ash cloud, the extent is 1.1 times larger.

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“…column height, mass eruption rate, particle size distribution, and vertical mass distribution. In fact, uncertainties in ESPs are known to be first-order contributors to model errors (Costa et al, 2016b;Poulidis and Iguchi, 2021). Additionally, in order to properly define the emission source term for complex plume dynamics, models require time-varying ESPs (e.g.…”

Section: Introductionmentioning

confidence: 99%

Data assimilation of volcanic aerosol observations using FALL3D+PDAF

et al. 2022

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Abstract. Modelling atmospheric dispersal of volcanic ash and aerosols is becoming increasingly valuable for assessing the potential impacts of explosive volcanic eruptions on buildings, air quality, and aviation. Management of volcanic risk and reduction of aviation impacts can strongly benefit from quantitative forecasting of volcanic ash. However, an accurate prediction of volcanic aerosol concentrations using numerical modelling relies on proper estimations of multiple model parameters which are prone to errors. Uncertainties in key parameters such as eruption column height and physical properties of particles or meteorological fields represent a major source of error affecting the forecast quality. The availability of near-real-time geostationary satellite observations with high spatial and temporal resolutions provides the opportunity to improve forecasts in an operational context by incorporating observations into numerical models. Specifically, ensemble-based filters aim at converting a prior ensemble of system states into an analysis ensemble by assimilating a set of noisy observations. Previous studies dealing with volcanic ash transport have demonstrated that a significant improvement of forecast skill can be achieved by this approach. In this work, we present a new implementation of an ensemble-based data assimilation (DA) method coupling the FALL3D dispersal model and the Parallel Data Assimilation Framework (PDAF). The FALL3D+PDAF system runs in parallel, supports online-coupled DA, and can be efficiently integrated into operational workflows by exploiting high-performance computing (HPC) resources. Two numerical experiments are considered: (i) a twin experiment using an incomplete dataset of synthetic observations of volcanic ash and (ii) an experiment based on the 2019 Raikoke eruption using real observations of SO2 mass loading. An ensemble-based Kalman filtering technique based on the local ensemble transform Kalman filter (LETKF) is used to assimilate satellite-retrieved data of column mass loading. We show that this procedure may lead to nonphysical solutions and, consequently, conclude that LETKF is not the best approach for the assimilation of volcanic aerosols. However, we find that a truncated state constructed from the LETKF solution approaches the real solution after a few assimilation cycles, yielding a dramatic improvement of forecast quality when compared to simulations without assimilation.

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Model sensitivities in the case of high-resolution Eulerian simulations of local tephra transport and deposition

Cited by 14 publications

References 105 publications

Modelling the size distribution of aggregated volcanic ash and implications for operational atmospheric dispersion modelling

Modelling the size distribution of aggregated volcanic ash and implications for operational atmospheric dispersion modelling

Modelling the size distribution of aggregated volcanic ash and implications for operational atmospheric dispersion modelling

Data assimilation of volcanic aerosol observations using FALL3D+PDAF

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