Separation of ash and sulfur dioxide during the 2011 Grímsvötn eruption

Moxnes, Eldbjørg Dirdal; Kristiansen, Nina Iren; Stohl, Andreas; Clarisse, Lieven; Durant, Adam J.; Weber, Konradin; Vogel, A.

doi:10.1002/2013jd021129

Cited by 91 publications

(112 citation statements)

References 77 publications

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“…9g retrieved 12 h later, ash is detected to the east of Iceland that had been released before 12 h prior, demonstrating the difficulty of retrieving the opaque ash clouds close to Iceland. For this retrieval, there is also no ash detected over Europe, even though ash was observed over Europe at this time (Pappalardo et al, 2013). The exemplary results in Fig.…”

Section: Forecast Model Results Compared To Satellite Observationsmentioning

confidence: 62%

“…Eckhardt et al (2008) showed that a posteriori calculated with no emission in the a priori emission gave similar results to a posteriori calculated with estimated emissions in the a priori. A posteriori estimates are also calculated with low ash emissions in the a priori estimates in the Moxnes et al (2014) and Kristiansen et al (2015) studies. An a priori emission with ash is considered more conservative due to undetected ash.…”

Section: Discussionmentioning

confidence: 99%

“…Stohl et al (2011) present an inversion method to calculate a source term constrained by satellite observations, using a priori source terms and model simulations. This inversion technique has been successfully applied to calculate ash emissions from the Eyjafjallajökull and Grimsvötn eruptions as well the 2014 Kelut eruption Kristiansen et al, 2012Kristiansen et al, , 2015Moxnes et al, 2014). The method has also been applied to volcanic eruptions with SO 2 emissions (Kristiansen et al 2010;Eckhardt et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

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Uncertainty assessment and applicability of an inversion method for volcanic ash forecasting

et al. 2017

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Abstract. Significant improvements in the way we can observe and model volcanic ash clouds have been obtained since the 2010 Eyjafjallajökull eruption. One major development has been the application of data assimilation techniques, which combine models and satellite observations such that an optimal understanding of ash clouds can be gained. Still, questions remain regarding the degree to which the forecasting capabilities are improved by inclusion of such techniques and how these improvements depend on the data input. This study explores how different satellite data and different uncertainty assumptions of the satellite and a priori emissions affect the calculated volcanic ash emission estimate, which is computed by an inversion method that couples the satellite retrievals and a priori emissions with dispersion model data. Two major ash episodes over 4 days in April and May of the 2010 Eyjafjallajökull eruption are studied. Specifically, inversion calculations are done for four different satellite data sets with different size distribution assumptions in the retrieval. A reference satellite data set is chosen, and the range between the minimum and maximum 4-day average load of hourly retrieved ash is 121 % in April and 148 % in May, compared to the reference. The corresponding a posteriori maximum and minimum emission sum found for these four satellite retrievals is 26 and 47 % of the a posteriori reference estimate for the same two periods, respectively. Varying the assumptions made in the satellite retrieval is seen to affect the a posteriori emissions and modelled ash column loads, and modelled column loads therefore have uncertainties connected to them depending on the uncertainty in the satellite retrieval. By further exploring our uncertainty estimates connected to a priori emissions and the mass load uncertainties in the satellite data, the uncertainty in the a priori estimate is found in this case to have an order-of-magnitudegreater impact on the a posteriori solution than the mass load uncertainties in the satellite. Part of this is explained by a too-high a priori estimate used in this study that is reduced by around half in the a posteriori reference estimate. Setting large uncertainties connected to both a priori and satellite mass load shows that they compensate each other, but the a priori uncertainty is found to be most sensitive. Because of this, an inversion-based emission estimate in a forecasting setting needs well-tested and well-considered assumptions on uncertainties for the a priori emission and satellite data. The quality of using the inversion in a forecasting environment is tested by adding gradually, with time, more observations to improve the estimated height versus time evolution of Eyjafjallajökull ash emissions. We show that the initially too-high a priori emissions are reduced effectively when using just 12 h of satellite observations. More satellite observations (> 12 h), in the Eyjafjallajökull case, place the volcanic injection at higher altitudes. Adding additional satellite...

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Section: Forecast Model Results Compared To Satellite Observationsmentioning

confidence: 62%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Uncertainty assessment and applicability of an inversion method for volcanic ash forecasting

et al. 2017

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“…For the Grímsvötn (2011) eruption the 3-D ash clouds estimated by Moxnes et al (2014) using Flexpart combined with optimized emissions based on inversion modelling with IASI measurements, were used as input to the radiative transfer simulations. The ash particles were assumed to be composed of andesite, and the refractive index was taken from Pollack et al (1973).…”

Section: Simulation Of Infrared Seviri Imagesmentioning

confidence: 99%

A model sensitivity study of the impact of clouds on satellite detection and retrieval of volcanic ash

et al. 2015

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Abstract. Volcanic ash is commonly observed by infrared detectors on board Earth-orbiting satellites. In the presence of ice and/or liquid-water clouds, the detected volcanic ash signature may be altered. In this paper the sensitivity of detection and retrieval of volcanic ash to the presence of ice and liquid-water clouds was quantified by simulating synthetic equivalents to satellite infrared images with a 3-D radiative transfer model. The sensitivity study was made for the two recent eruptions of Eyjafjallajökull (2010) and Grímsvötn (2011) using realistic water and ice clouds and volcanic ash clouds. The water and ice clouds were taken from European Centre for Medium-Range Weather Forecast (ECMWF) analysis data and the volcanic ash cloud fields from simulations by the Lagrangian particle dispersion model FLEX-PART. The radiative transfer simulations were made both with and without ice and liquid-water clouds for the geometry and channels of the Spinning Enhanced Visible and Infrared Imager (SEVIRI). The synthetic SEVIRI images were used as input to standard reverse absorption ash detection and retrieval methods. Ice and liquid-water clouds were on average found to reduce the number of detected ash-affected pixels by 6-12 %. However, the effect was highly variable and for individual scenes up to 40 % of pixels with mass loading > 0.2 g m −2 could not be detected due to the presence of water and ice clouds. For coincident pixels, i.e. pixels where ash was both present in the FLEXPART (hereafter referred to as "Flexpart") simulation and detected by the algorithm, the presence of clouds overall increased the retrieved mean mass loading for the Eyjafjallajökull (2010) eruption by about 13 %, while for the Grímsvötn (2011) eruption ashmass loadings the effect was a 4 % decrease of the retrieved ash-mass loading. However, larger differences were seen between scenes (standard deviations of ±30 and ±20 % for Eyjafjallajökull and Grímsvötn, respectively) and even larger ones within scenes. The impact of ice and liquid-water clouds on the detection and retrieval of volcanic ash, implies that to fully appreciate the location and amount of ash, hyperspectral and spectral band measurements by satellite instruments should be combined with ash dispersion modelling.

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“…Kristiansen et al, 2012;Moxnes et al, 2014;Pelley et al, 2015;Stohl et al, 2011). In this way, Kristiansen et al (2012) estimated optimal volcanic ash source terms for the Eyjafjallajökull eruption using an inversion algorithm with satellite-retrieved ash column loadings, a number of 10 emission scenarios and two atmospheric dispersion models.…”

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confidence: 99%

Epistemic uncertainties and natural hazard risk assessment. 1. A review of different natural hazard areas

Beven¹,

Almeida²,

Aspinall³

et al. 2017

Preprint

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This paper discusses how epistemic uncertainties are considered in a number of different natural hazard areas including floods, landslides and debris flows, dam safety, droughts, earthquakes, tsunamis, volcanic ash clouds and pyroclastic flows, and wind storms. In each case it is common practice to treat most uncertainties in the form of aleatory 20 probability distributions but this may lead to an underestimation of the resulting uncertainties in assessing the hazard, consequences and risk. It is suggested that such analyses might be usefully extended by looking at different scenarios of assumptions about sources of epistemic uncertainty, with a view to reducing the element of surprise in future hazard occurrences. Since every analysis is necessarily conditional on the assumptions made about the nature of sources of epistemic uncertainty it is also important to follow the guidelines for good practice suggested in the companion Part 2. 25

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Separation of ash and sulfur dioxide during the 2011 Grímsvötn eruption

Cited by 91 publications

References 77 publications

Uncertainty assessment and applicability of an inversion method for volcanic ash forecasting

Uncertainty assessment and applicability of an inversion method for volcanic ash forecasting

A model sensitivity study of the impact of clouds on satellite detection and retrieval of volcanic ash

Epistemic uncertainties and natural hazard risk assessment. 1. A review of different natural hazard areas

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