Airborne in-situ investigations of the Eyjafjallajökull volcanic ash plume on Iceland and over north-western Germany with light aircrafts and optical particle counters

Weber, Karin; Eliasson, Jonas; Vogel, A.; Fischer, Christian; Pohl, Tobias; Haren, Guenther van; Meier, Mario; Grobéty, Bernard; Dahmann, D.

doi:10.1016/j.atmosenv.2011.10.030

Cited by 65 publications

(64 citation statements)

References 13 publications

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“…The measurement route and values are demonstrated in Fig. 1b, c and the details are described in Weber et al (2012) and Fu et al (2016). The observational network at time t k is defined by the operator H k which maps the state vector x to the observational vector y by…”

Section: Methodology Of the Volcanic Ash Da Systemmentioning

confidence: 99%

Accelerating volcanic ash data assimilation using a mask-state algorithm based on an ensemble Kalman filter: a case study with the LOTOS-EUROS model (version 1.10)

Lin

Heemink

et al. 2017

Geosci. Model Dev.

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Abstract. In this study, we investigate a strategy to accelerate the data assimilation (DA) algorithm. Based on evaluations of the computational time, the analysis step of the assimilation turns out to be the most expensive part. After a study of the characteristics of the ensemble ash state, we propose a mask-state algorithm which records the sparsity information of the full ensemble state matrix and transforms the full matrix into a relatively small one. This will reduce the computational cost in the analysis step. Experimental results show the mask-state algorithm significantly speeds up the analysis step. Subsequently, the total amount of computing time for volcanic ash DA is reduced to an acceptable level. The mask-state algorithm is generic and thus can be embedded in any ensemble-based DA framework. Moreover, ensemble-based DA with the mask-state algorithm is promising and flexible, because it implements exactly the standard DA without any approximation and it realizes the satisfying performance without any change in the full model.

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Section: Methodology Of the Volcanic Ash Da Systemmentioning

confidence: 99%

Accelerating volcanic ash data assimilation using a mask-state algorithm based on an ensemble Kalman filter: a case study with the LOTOS-EUROS model (version 1.10)

Lin

Heemink

et al. 2017

Geosci. Model Dev.

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“…Note that some satellites can provide very detailed vertical information on plumes (e.g., Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) lidar measurements) but are spatially sparse (Winker et al, 2012). Fortunately, the in situ volcanic ash state variables can be directly and accurately measured nowadays by means of airborne observations of volcanic ash (Weber et al, 2012). These aircraft-based measurements can be obtained in the boundaries of volcanic ash plume, which are probably the most direct volcanic ash observations possible.…”

Section: G Fu Et Al: Assimilating Aircraft In Situ Measurementsmentioning

confidence: 99%

“…First, aircraft data are usually obtained from optical particle counters. Thus real-time particle concentration observation can be directly measured (Weber et al, 2012). Second, the in situ aircraft measurements can be compared directly to a 3-D model state variable (e.g., concentration).…”

Section: G Fu Et Al: Assimilating Aircraft In Situ Measurementsmentioning

confidence: 99%

“…Third, an aircraft is flexible in flight route to follow the ash clouds. Although an aircraft measurement plan is usually designed beforehand for a region and/or altitude of interest, the aircraft operator can adjust the detailed route according to the real conditions (e.g., ash concentration level, wind direction) of the plume transport in order to always obtain useful volcanic ash concentrations (Weber et al, 2012).…”

Section: G Fu Et Al: Assimilating Aircraft In Situ Measurementsmentioning

confidence: 99%

“…During the period of the Eyjafjallajökull eruption in AprilMay 2010, the outskirts of the eruption plume were entered directly by research flights, delivering most direct measurements within the eruption plume during this eruptive event (Weber et al, 2012). The measurement aircraft (Fig.…”

Section: Real-life Aircraft In Situ Measurementsmentioning

confidence: 99%

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Model-based aviation advice on distal volcanic ash clouds by assimilating aircraft in situ measurements

Heemink

Sha

et al. 2016

Atmos. Chem. Phys.

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Abstract. The forecast accuracy of distal volcanic ash clouds is important for providing valid aviation advice during volcanic ash eruption. However, because the distal part of volcanic ash plume is far from the volcano, the influence of eruption information on this part becomes rather indirect and uncertain, resulting in inaccurate volcanic ash forecasts in these distal areas. In our approach, we use real-life aircraft in situ observations, measured in the northwestern part of Germany during the 2010 Eyjafjallajökull eruption, in an ensemble-based data assimilation system combined with a volcanic ash transport model to investigate the potential improvement on the forecast accuracy with regard to the distal volcanic ash plume. We show that the error of the analyzed volcanic ash state can be significantly reduced through assimilating real-life in situ measurements. After a continuous assimilation, it is shown that the aviation advice for Germany, the Netherlands and Luxembourg can be significantly improved. We suggest that with suitable aircrafts measuring once per day across the distal volcanic ash plume, the description and prediction of volcanic ash clouds in these areas can be greatly improved.

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Separation of ash and sulfur dioxide during the 2011 Grímsvötn eruption

Moxnes

Kristiansen

Stohl

et al. 2014

J. Geophys. Res. Atmos.

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Modeling the transport of volcanic ash and gases released during volcanic eruptions is crucially dependent on knowledge of the source term of the eruption, that is, the source strength as a function of altitude and time. For the first time, an inversion method is used to estimate the source terms of both volcanic sulfur dioxide (SO 2 ) and ash. It was applied to the explosive volcanic eruption of Grímsvötn, Iceland, in May 2011. The method uses input from the particle dispersion model, FLEXPART (flexible particle dispersion model), a priori source estimates, and satellite observations of SO 2 or ash total columns from Infrared Atmospheric Sounding Interferometer to separately obtain the source terms for volcanic SO 2 and fine ash. The estimated source terms show that SO 2 was emitted mostly to high altitudes (5 to 13 km) during about 18 h (22 May, 00-18 UTC) while fine ash was emitted mostly to low altitudes (below 4 km) during roughly 24 h (22 May 06 UTC to 23 May 06 UTC). FLEXPART simulations using the estimated source terms show a clear separation of SO 2 (transported mostly northwestward) and the fine ash (transported mostly southeastward). This corresponds well with independent satellite observations and measured aerosol mass concentrations and lidar measurements at surface stations in Scandinavia. Aircraft measurements above Iceland and Germany confirmed that the ash was located in the lower atmosphere. This demonstrates that the inversion method, in this case, is able to distinguish between emission heights of SO 2 and ash and can capture resulting differences in transport patterns.

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Airborne in-situ investigations of the Eyjafjallajökull volcanic ash plume on Iceland and over north-western Germany with light aircrafts and optical particle counters

Cited by 65 publications

References 13 publications

Accelerating volcanic ash data assimilation using a mask-state algorithm based on an ensemble Kalman filter: a case study with the LOTOS-EUROS model (version 1.10)

Accelerating volcanic ash data assimilation using a mask-state algorithm based on an ensemble Kalman filter: a case study with the LOTOS-EUROS model (version 1.10)

Model-based aviation advice on distal volcanic ash clouds by assimilating aircraft in situ measurements

Separation of ash and sulfur dioxide during the 2011 Grímsvötn eruption

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