Dispersion modeling of volcanic ash clouds: North Pacific eruptions, the past 40 years: 1970–2010

Webley, P. W.; Dean, Kenneth; Peterson, Rorik; Steffke, Andrea; Harrild, M.; Groves, James

doi:10.1007/s11069-011-0053-9

Cited by 7 publications

(3 citation statements)

References 17 publications

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“…They tend to convey the three dimensional structure of a complex plume in a manner that is difficult to do with gridded concentration output. Notably, the puff model which was utilized by the Alaskan Volcano Observatory and the Anchorage VAAC for a number of years mostly employed visualizations of the particle positions [42] and this type of output was generally found to be useful to analysts forecasting the position of discernable ash. On the other hand, concentrations, which are usually the relevant quantity, are not conveyed well by such output.…”

Section: Discussionmentioning

confidence: 99%

The Use of Gaussian Mixture Models with Atmospheric Lagrangian Particle Dispersion Models for Density Estimation and Feature Identification

Crawford

2020

Atmosphere

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Atmospheric Lagrangian particle dispersion models, LPDM, simulate the dispersion of passive tracers in the atmosphere. At the most basic level, model output consists of the position of computational particles and the amount of mass they represent. In order to obtain concentration values, this information is then converted to a mass distribution via density estimation. To date, density estimation is performed with a nonparametric method so that output consists of gridded concentration data. Here we introduce the use of Gaussian mixture models, GMM, for density estimation. We compare to the histogram or bin counting method for a tracer experiment and simulation of a large volcanic ash cloud. We also demonstrate the use of the mixture model for automatic identification of features in a complex plume such as is produced by a large volcanic eruption. We conclude that use of a mixture model for density estimation and feature identification has potential to be very useful.

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Section: Discussionmentioning

confidence: 99%

The Use of Gaussian Mixture Models with Atmospheric Lagrangian Particle Dispersion Models for Density Estimation and Feature Identification

Crawford

2020

Atmosphere

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“…Simulation results have been validated using methods that include comparison to post-event reports, matching the ash cloud trajectory with satellite images, and adjusting the model's input to find the best-fit values (Webley and Mastin, (Fero et al, 2008(Fero et al, , 2009Kratzmann et al, 2010;Webley et al, 2008;Scollo et al, 2011;Webley et al, 2012). Several past studies validated ash dispersal simulations using the PUFF model for smaller-scale eruption of the Sakurajima volcano (Tanaka and Iguchi, 2019;Tanaka et al, 2020).…”

Section: Validationmentioning

confidence: 99%

Long-term ash dispersal dataset of the Sakurajima Taisho eruption for ashfall disaster countermeasure

Rahadianto

Tatano

Iguchi

et al. 2022

Earth Syst. Sci. Data

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Abstract. A large volcanic eruption can generate large amounts of ash which affect the socio-economic activities of surrounding areas, affecting airline transportation, socio-economics activities, and human health. Accumulated ashfall has devastating impacts on areas surrounding the volcano and in other regions, and eruption scale and weather conditions may escalate ashfall hazards to wider areas. It is crucial to discover places with a high probability of exposure to ashfall deposition. Here, as a reference for ashfall disaster countermeasures, we present a dataset containing the estimated distributions of the ashfall deposit and airborne ash concentration, obtained from a simulation of ash dispersal following a large-scale explosive volcanic eruption. We selected the Taisho (1914) eruption of the Sakurajima volcano, as our case study. This was the strongest eruption in Japan in the last century, and our study provides a baseline for a worst-case scenario. We employed one eruption scenario (OES) approach by replicating the actual event under various extended weather conditions to show how it would affect contemporary Japan. We generated an ash dispersal dataset by simulating the ash transport of the Taisho eruption scenario using a volcanic ash dispersal model and meteorological reanalysis data for 64 years (1958–2021). We explain the dataset production and provide the dataset in multiple formats for broader audiences. We examine the validity of the dataset, its limitations, and its uncertainties. Countermeasure strategies can be derived from this dataset to reduce ashfall risk. The dataset is available at the DesignSafe-CI Data Depot: https://www.designsafe-ci.org/data/browser/public/designsafe.storage.published/PRJ-2848v2 or through the following DOI: https://doi.org/10.17603/ds2-vw5f-t920 by selecting Version 2 (Rahadianto and Tatano, 2020).

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“…Globally, more than 1500 volcanoes have been active over the last 10,000 years (Siebert et al 2010). Volcanic eruptions are frequent events, which is exemplified by the regular (every~1.25 months) occurrence of ash clouds over the North Pacific (Webley et al 2012). Many volcanoes emit vast amounts of deposits that traverse the globe to varying extents and directly influence ecosystems particularly close to eruption sites (Nriagu 1980).…”

Section: Introductionmentioning

confidence: 99%

Ecosystem recharge by volcanic dust drives broad‐scale variation in bird abundance

Gunnarsson

Appleton

Méndez

et al. 2015

Ecology and Evolution

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Across the globe, deserts and volcanic eruptions produce large volumes of atmospheric dust, and the amount of dust is predicted to increase with global warming. The effects of long-distance airborne dust inputs on ecosystem productivity are potentially far-reaching but have primarily been measured in soil and plants. Airborne dust could also drive distribution and abundance at higher trophic levels, but opportunities to explore these relationships are rare. Here we use Iceland's steep dust deposition gradients to assess the influence of dust on the distribution and abundance of internationally important ground-nesting bird populations. Surveys of the abundance of breeding birds at 729 locations throughout lowland Iceland were used to explore the influence of dust deposition on bird abundance in agricultural, dry, and wet habitats. Dust deposition had a strong positive effect on bird abundance across Iceland in dry and wet habitats, but not in agricultural land where nutrient levels are managed. The abundance of breeding waders, the dominant group of terrestrial birds in Iceland, tripled on average between the lowest and highest dust deposition classes in both wet and dry habitats. The deposition and redistribution of volcanic materials can have powerful impacts in terrestrial ecosystems and can be a major driver of the abundance of higher trophic-level organisms at broad spatial scales. The impacts of volcanic ash deposition during eruptions and subsequent redistribution of unstable volcanic materials are strong enough to override effects of underlying variation in organic matter and clay content on ecosystem fertility. Global rates of atmospheric dust deposition are likely to increase with increasing desertification and glacier retreat, and this study demonstrates that the effects on ecosystems are likely to be far-reaching, both in terms of spatial scales and ecosystem components.

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Dispersion modeling of volcanic ash clouds: North Pacific eruptions, the past 40 years: 1970–2010

Cited by 7 publications

References 17 publications

The Use of Gaussian Mixture Models with Atmospheric Lagrangian Particle Dispersion Models for Density Estimation and Feature Identification

The Use of Gaussian Mixture Models with Atmospheric Lagrangian Particle Dispersion Models for Density Estimation and Feature Identification

Long-term ash dispersal dataset of the Sakurajima Taisho eruption for ashfall disaster countermeasure

Ecosystem recharge by volcanic dust drives broad‐scale variation in bird abundance

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