Aircraft observations and model simulations of concentration and particle size distribution in the Eyjafjallajökull volcanic ash cloud

Dacre, Helen; Grant, A. L. M.; Johnson, Ben

doi:10.5194/acp-13-1277-2013

Cited by 32 publications

(24 citation statements)

References 30 publications

(32 reference statements)

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“…However, using the 80% aggregated PSD decreases total column mass loadings, due to the higher mass fraction of large aggregates, despite their lower density. Dacre et al [2013] compared NAME simulations of the 2010 Eyjafjallajökull ash cloud to calculated total column mass loadings from measured ash concentration profiles by the FAAM research aircraft and suggest that 2-6% of the total emitted mass was transported by particles with diameter <30 μm. In the London VAAC PSD 96% of the total mass is represented by particles with diameter ≤30 μm.…”

Section: The Sensitivity Of Dispersion Model Forecasts To Particle Chmentioning

confidence: 99%

Sensitivity of dispersion model forecasts of volcanic ash clouds to the physical characteristics of the particles

et al. 2015

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This study examines the sensitivity of atmospheric dispersion model forecasts of volcanic ash clouds to the physical characteristics assigned to the particles. We show that the particle size distribution (PSD) used to initialise a dispersion model has a significant impact on the forecast of the mass loading of the ash particles in the atmosphere. This is because the modeled fall velocity of the particles is sensitive to the particle diameter. Forecasts of the long-range transport of the ash cloud consider particles with diameters between 0.1 μm and 100 μm. The fall velocity of particles with diameter 100 μm is over 5 orders of magnitude greater than a particle with diameter 0.1 μm, and 30 μm particles fall 88% slower and travel up to 5× further than a 100 μm particle. Identifying the PSD of the ash cloud at the source, which is required to initialise a model, is difficult. Further, aggregation processes are currently not explicitly modeled in operational dispersion models due to the high computational costs associated with aggregation schemes. We show that using a modified total grain size distribution (TGSD) that effectively accounts for aggregation processes improves the modeled PSD of the ash cloud and deposits from the eruption of Eyjafjallajökull in 2010. Knowledge of the TGSD of an eruption is therefore critical for reducing uncertainty in quantitative forecasts of ash cloud dispersion. The density and shape assigned to the model particles have a lesser but still significant impact on the calculated fall velocity. Accounting for the density distribution and sphericity of ash from the eruption of Eyjafjallajökull in 2010, modeled particles can travel up to 84% further than particles with default particle characteristics that assume the particles are spherical and have a fixed density.

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Section: The Sensitivity Of Dispersion Model Forecasts To Particle Chmentioning

confidence: 99%

Sensitivity of dispersion model forecasts of volcanic ash clouds to the physical characteristics of the particles

et al. 2015

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“…Ash layers were observed from a large set of measurement instruments, allowing for tracking of the volcanic ash plume over Europe (Gasteiger et al, 2011a;Zakšek et al, 2013;Mona et al, 2012;Dacre et al, 2013;Waquet et al, 2014). Using images from the geostationary instrument SEVIRI (Spinning Enhanced Visible and Infrared Imager) the spatial extent of the ash plumes and their movements could be tracked and compared to the measurement of ground-based instruments (Strohbach, 2015; see Fig.…”

Section: Descriptionmentioning

confidence: 99%

Development and application of a backscatter lidar forward operator for quantitative validation of aerosol dispersion models and future data assimilation

et al. 2017

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Abstract.A new backscatter lidar forward operator was developed which is based on the distinct calculation of the aerosols' backscatter and extinction properties. The forward operator was adapted to the COSMO-ART ash dispersion simulation of the Eyjafjallajökull eruption in 2010. While the particle number concentration was provided as a model output variable, the scattering properties of each individual particle type were determined by dedicated scattering calculations. Sensitivity studies were performed to estimate the uncertainties related to the assumed particle properties. Scattering calculations for several types of non-spherical particles required the usage of T-matrix routines. Due to the distinct calculation of the backscatter and extinction properties of the models' volcanic ash size classes, the sensitivity studies could be made for each size class individually, which is not the case for forward models based on a fixed lidar ratio. Finally, the forward-modeled lidar profiles have been compared to automated ceilometer lidar (ACL) measurements both qualitatively and quantitatively while the attenuated backscatter coefficient was chosen as a suitable physical quantity. As the ACL measurements were not calibrated automatically, their calibration had to be performed using satellite lidar and ground-based Raman lidar measurements. A slight overestimation of the model-predicted volcanic ash number density was observed. Major requirements for future data assimilation of data from ACL have been identified, namely, the availability of calibrated lidar measurement data, a scattering database for atmospheric aerosols, a better representation and coverage of aerosols by the ash dispersion model, and more investigation in backscatter lidar forward operators which calculate the backscatter coefficient directly for each individual aerosol type. The introduced forward operator offers the flexibility to be adapted to a multitude of model systems and measurement setups.

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“…Ash layers were observed from a large set of measurement instruments, allowing for tracking the volcanic ash cloud movement over Europe (Gasteiger et al, 2011a;Zakšek et al, 2013;Mona et al, 2012;Dacre et al, 2013;Waquet et al, 2014). Using images from the geostationary instrument SEVIRI (Spinning Enhanced Visible and Infrared Imager) the spatial extend of the ash cloud and its movement could be tracked and compared to the measurement of ground-based instruments (see Fig.…”

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A Backscatter Lidar Forward Operator for Particle-Representing Atmospheric Chemistry Models

Geisinger

Behrendt

Wulfmeyer

et al. 2016

Preprint

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Abstract.State-of-the-art atmospheric chemistry models are capable of simulating the transport and evolution of particles and trace gases but there is a lack of reliable methods for model validation and data assimilation. Networks of automated ceilometer lidar systems (ACLs) could be used to fill this gap. These are already used for the detection of clouds and aerosols, providing a 3D dataset of atmospheric backscatter profiles. However, as the aerosol number concentration cannot be obtained from the ACL 5 data alone, one needs a backscatter-lidar forward model to simulate lidar profiles from the model variables. Such an operator allows then for a qualitative and quantitative model validation based on ACL data. In this work, we present a new backscatterlidar operator which contains most of the microphysical properties of aerosol particles, discuss sensitivity studies and compare simulated with measured ACL profiles. A major challenge is the high sensitivity of the optical cross sections to the particle size and shape: A slightly different particle radius may lead to quite a large change of the scattering properties. As most particle 10 size distributions are continuous in reality, the optical cross sections are averaged over certain size-intervals which also reduces the problematic and unrealistic sensitivity significantly. To calculate the attenuated backscatter coefficient, the size-dependent particle number concentration and the scattering properties of each particle type and size have to be simulated. While the particle number concentration is a model output variable, the scattering properties have to be determined by extensive scattering calculations. As these scattering calculations require assumptions about the particle refractive indices and shapes, sensitivity 15 studies were performed to estimate the uncertainties related to the particle properties as represented by the model system.The strong sensitivity of the scattering characteristics to the particle radius was largely reduced by size-averaging algorithms.We focus on a case study of the eruption of the Islandic volcano Eyjafjallajökull from 20 March 2010 to 24 May 2010. The Consortium for Small-scale Modeling -Aerosols and Reactive Trace gases (COSMO-ART) model of DWD (Deutscher Wetterdienst) and KIT (Karlsruhe Institute of Technology) was used during this event for ash-dispersion simulation over Europe. 20For the forward model, the attenuated backscatter coefficient is used as lidar-independent variable, as it only relies on the laser wavelength. Finally, the forward modeled lidar profiles were compared to ACL measurements. Significant differences between ACL profiles and the output of the forward operator applied to the COSMO-ART data were found but also several identical features have been observed. Comparing the data quantitatively revealed that the model-predicted ash number concentration is slightly too high. We identified the following key issues which are mandatory for performing quantitative comparisons be-

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Aircraft observations and model simulations of concentration and particle size distribution in the Eyjafjallajökull volcanic ash cloud

Cited by 32 publications

References 30 publications

Sensitivity of dispersion model forecasts of volcanic ash clouds to the physical characteristics of the particles

Sensitivity of dispersion model forecasts of volcanic ash clouds to the physical characteristics of the particles

Development and application of a backscatter lidar forward operator for quantitative validation of aerosol dispersion models and future data assimilation

A Backscatter Lidar Forward Operator for Particle-Representing Atmospheric Chemistry Models

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