FALL3D-8.0: a computational model for atmospheric transport and deposition of particles, aerosols and radionuclides – Part 2: Model validation

Prata, Andrew; Mingari, Leonardo; Folch, Arnau; Macedonio, Giovanni; Costa, Antonio

doi:10.5194/gmd-14-409-2021

Cited by 32 publications

(43 citation statements)

References 70 publications

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“…For example, observations show that the Raikoke eruption was actually characterised by a series of explosive eruptions that emitted SO 2 at varying heights (Crafford and Venzke, 2019;Hedelt et al, 2019;Muser et al, 2020;Kloss et al, 2021). Furthermore a study by Prata et al (2021) suggests that activity continued until 10:00 UTC on 22 June rather than 03:00 UTC as reported by the VolRes team. These examples indicate how hard it is to obtain a "correct" time-dependent emission profile for the eruption in near-real time.…”

Section: Discussionmentioning

confidence: 91%

“…Bundke et al, 2020) and satellites (e.g. Muser et al, 2020;Hyman and Pavolonis, 2020;Prata et al, 2021;Kloss et al, 2021;Gorkavyi et al, 2021) in the following months.…”

Section: Introductionmentioning

confidence: 99%

“…While small-magnitude eruptions take place more frequently, largemagnitude eruptions that can emit large amounts of SO 2 into the stratosphere are much more sporadic (e.g. Pyle, 1995;Miles et al, 2004;Carn et al, 2016;Schmidt et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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The 2019 Raikoke volcanic eruption – Part 1: Dispersion model simulations and satellite retrievals of volcanic sulfur dioxide

et al. 2021

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Abstract. Volcanic eruptions can cause significant disruption to society, and numerical models are crucial for forecasting the dispersion of erupted material. Here we assess the skill and limitations of the Met Office's Numerical Atmospheric-dispersion Modelling Environment (NAME) in simulating the dispersion of the sulfur dioxide (SO2) cloud from the 21–22 June 2019 eruption of the Raikoke volcano (48.3∘ N, 153.2∘ E). The eruption emitted around 1.5±0.2 Tg of SO2, which represents the largest volcanic emission of SO2 into the stratosphere since the 2011 Nabro eruption. We simulate the temporal evolution of the volcanic SO2 cloud across the Northern Hemisphere (NH) and compare our model simulations to high-resolution SO2 measurements from the TROPOspheric Monitoring Instrument (TROPOMI) and the Infrared Atmospheric Sounding Interferometer (IASI) satellite SO2 products. We show that NAME accurately simulates the observed location and horizontal extent of the SO2 cloud during the first 2–3 weeks after the eruption but is unable, in its standard configuration, to capture the extent and precise location of the highest magnitude vertical column density (VCD) regions within the observed volcanic cloud. Using the structure–amplitude–location (SAL) score and the fractional skill score (FSS) as metrics for model skill, NAME shows skill in simulating the horizontal extent of the cloud for 12–17 d after the eruption where VCDs of SO2 (in Dobson units, DU) are above 1 DU. For SO2 VCDs above 20 DU, which are predominantly observed as small-scale features within the SO2 cloud, the model shows skill on the order of 2–4 d only. The lower skill for these high-SO2-VCD regions is partly explained by the model-simulated SO2 cloud in NAME being too diffuse compared to TROPOMI retrievals. Reducing the standard horizontal diffusion parameters used in NAME by a factor of 4 results in a slightly increased model skill during the first 5 d of the simulation, but on longer timescales the simulated SO2 cloud remains too diffuse when compared to TROPOMI measurements. The skill of NAME to simulate high SO2 VCDs and the temporal evolution of the NH-mean SO2 mass burden is dominated by the fraction of SO2 mass emitted into the lower stratosphere, which is uncertain for the 2019 Raikoke eruption. When emitting 0.9–1.1 Tg of SO2 into the lower stratosphere (11–18 km) and 0.4–0.7 Tg into the upper troposphere (8–11 km), the NAME simulations show a similar peak in SO2 mass burden to that derived from TROPOMI (1.4–1.6 Tg of SO2) with an average SO2 e-folding time of 14–15 d in the NH. Our work illustrates how the synergy between high-resolution satellite retrievals and dispersion models can identify potential limitations of dispersion models like NAME, which will ultimately help to improve dispersion modelling efforts of volcanic SO2 clouds.

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Section: Discussionmentioning

confidence: 91%

“…Bundke et al, 2020) and satellites (e.g. Muser et al, 2020;Hyman and Pavolonis, 2020;Prata et al, 2021;Kloss et al, 2021;Gorkavyi et al, 2021) in the following months.…”

Section: Introductionmentioning

confidence: 99%

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The 2019 Raikoke volcanic eruption – Part 1: Dispersion model simulations and satellite retrievals of volcanic sulfur dioxide

et al. 2021

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“…Recently, the FALL3D code (Folch et al, 2009) has been redesigned and rewritten in the framework of the EU Center of Excellence for Exascale in Solid Earth, ChEESE. The code version 8.0 (Folch et al, 2020;Prata et al, 2021) is tailored to extreme-scale computing requirements and presents substantial improvements on code scalability, computational efficiency, memory management, and overall capability to handle much larger problems. In addition, the code version 8.1 (Folch et al, 2021) implemented ensemble forecast capabilities and validation metrics.…”

Section: Introductionmentioning

confidence: 99%

“…The code version 8.0 (Folch et al, 2020;Prata et al, 2021) is tailored to extreme-scale computing requirements and presents substantial improvements on code scalability, computational efficiency, memory management, and overall capability to handle much larger problems. In addition, the code version 8.1 (Folch et al, 2021) implemented ensemble forecast capabilities and validation metrics. New developments have led to improved quality of forecasts, enabled to quantify model uncertainties, and laid the foundations for the incorporation of ensemble-based DA techniques into future releases of FALL3D.…”

Section: Introductionmentioning

confidence: 99%

Data Assimilation of Volcanic Aerosols using FALL3D+PDAF

Mingari

Folch

Prata

et al. 2021

Preprint

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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 infrastructures, 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, 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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Tephra fallout probabilistic hazard maps for Cotopaxi and Guagua Pichincha volcanoes (Ecuador) with uncertainty quantification

Tadini

Azzaoui

Roche

et al. 2021

Preprint

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 We develop tephra fallout probabilistic hazard maps for Cotopaxi and Guagua Pichincha volcanoes using the PLUME-MoM/HYSPLIT model  We incorporate the quantification of the uncertainty on the input parameters, the model, and the occurrence of different eruption types  Our study indicates that full uncertainty quantification is essential in volcanic hazard assessment in order to provide robust information

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FALL3D-8.0: a computational model for atmospheric transport and deposition of particles, aerosols and radionuclides – Part 2: Model validation

Cited by 32 publications

References 70 publications

The 2019 Raikoke volcanic eruption – Part 1: Dispersion model simulations and satellite retrievals of volcanic sulfur dioxide

The 2019 Raikoke volcanic eruption – Part 1: Dispersion model simulations and satellite retrievals of volcanic sulfur dioxide

Data Assimilation of Volcanic Aerosols using FALL3D+PDAF

Tephra fallout probabilistic hazard maps for Cotopaxi and Guagua Pichincha volcanoes (Ecuador) with uncertainty quantification

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