The identification and tracking of volcanic ash using the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI)

Naeger, Aaron; Christopher, S. A.

doi:10.5194/amt-7-581-2014

Cited by 15 publications

(12 citation statements)

References 39 publications

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“…is needed to further improve ash mass loading estimates under cloudy conditions (Grainger et al, 2013 of ash clouds between consecutive satellite images may prove fruitful (see for example Naeger and Christopher, 2014). The ultimate goal may be the direct assimilation of satellite-observed radiances in a weather forecast model that also emits and transports ash.…”

Section: Discussionmentioning

confidence: 99%

Impact of meteorological clouds on satellite detection and retrieval of volcanic ash during the Eyjafjallajökull 2010 and Grímsvötn 2011 eruptions: a modelling study

Kylling¹,

Kristiansen²,

Stohl³

et al. 2014

Preprint

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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 effect of ice and liquid water clouds on detection and retrieval of volcanic ash is quantified by simulating synthetic equivalents to satellite infrared images with a 3-D radiative transfer model. The simulations were made both with and without realistic water and ice clouds taken from European Centre for Medium-Range Weather Forecast (ECMWF) analysis data. The volcanic ash cloud fields were taken from simulations by the Lagrangian particle dispersion model FLEXPART. The radiative transfer calculations were made for the geometry and channels of the Spinning Enhanced Visible and Infrared Imager (SEVIRI), for the full duration of the Eyjafjallajökull 2010 and Grímsvötn 2011 eruptions. The synthetic SEVIRI images were then used as input to standard reverse absorption ash detection and retrieval methods. Meteorological 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. The detection efficiency (detected ash pixels relative to Flexpart ash pixels with ash loading > 0.2 g m−2) was on average only 14.6% (22.1%) for the cloudy (cloudless) simulation for the Eyjafjallajökull 2010 eruption, and 3.6% (10.0%) for the Grímsvötn 2011 eruption. If only Flexpart ash pixels with ash loading > 1.0 g m−2 are considered the detection efficiency increase to 54.7% (74.7) for the Eyjafjallajökull 2010 eruption and to 4.8% (15.1%) for the Grímsvötn 2011 eruption. For coincident pixels, i.e., pixels where ash was both present in the Flexpart simulation and detected by the algorithm, the presence of meteorological clouds overall increased the retrieved mean mass loading for the Eyjafjallajökull 2010 eruption by about 13%, while for the Grímsvötn 2011 eruption ash mass loadings the effect was a 4% decrease of the retrieved ash mass loading. However, larger differences were seen between scenes (SD of ±30 and ±20% for Eyjafjallajökull and Grímsvötn respectively) and even larger ones within scenes. If all pixels are included the total mass from all scenes is severely underestimated. For the Eyjafjallajökull 2010 eruption the cloudless (cloudy) mass is underestimateed by 52% (66%) compared to the Flexpart mass, while for the Grímsvötn 2011 eruption the Flexpart mass is underestimated by 82% (91%) for the cloudless (cloudy) simulation. 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, satellite ash measurements should be combined with ash dispersion modelling.

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Section: Discussionmentioning

confidence: 99%

Impact of meteorological clouds on satellite detection and retrieval of volcanic ash during the Eyjafjallajökull 2010 and Grímsvötn 2011 eruptions: a modelling study

Kylling¹,

Kristiansen²,

Stohl³

et al. 2014

Preprint

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“…The split window technique identifies airborne ash by means of a fixed threshold test applied to difference of Brightness Temperatures (BT) measured at the aforementioned wavelengths, i.e., BT 11 − BT 12 [29]. Advanced detection methods, minimizing the impact of atmospheric water vapor on the above-mentioned brightness temperature difference (BTD) or analyzing signals measured in other spectral bands such as MIR (Medium Infrared) and/or VIS (Visible) ones, have shown a higher efficiency in identifying ash clouds (e.g., [30][31][32][33][34][35][36][37][38]). RST ASH [16] is an ash detection method, based on the Robust Satellite Technique (RST) multi-temporal approach [39], running on both polar [14,15,40] and geostationary [17] satellite data.…”

Section: Methodsmentioning

confidence: 99%

Monitoring the Agung (Indonesia) Ash Plume of November 2017 by Means of Infrared Himawari 8 Data

et al. 2018

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Abstract:The Agung volcano (Bali; Indonesia) erupted in later November 2017 after several years of quiescence. Because of ash emissions, hundreds of flights were cancelled, causing an important air traffic disruption in Indonesia. We investigate those ash emissions from space by applying the RST ASH algorithm for the first time to Himawari-8 data and using an ad hoc implementation scheme to reduce the time of the elaboration processes. Himawari-8 is a new generation Japanese geostationary meteorological satellite, whose AHI (Advanced Himawari Imager) sensor offers improved features, in terms of spectral, spatial and temporal resolution, in comparison with the previous imagers of the MTSAT (Multi-Functional Transport Satellite) series. Those features should guarantee further improvements in monitoring rapidly evolving weather/environmental phenomena. Results of this work show that RST ASH was capable of successfully detecting and tracking the Agung ash plume, despite some limitations (e.g., underestimation of ash coverage under certain conditions; generation of residual artefacts). Moreover, estimates of ash cloud-top height indicate that the monitored plume extended up to an altitude of about 9.3 km above sea level during the period 25 November at 21:10 UTC-26 November at 00:50 UTC. The study demonstrates that RST ASH may give a useful contribution for the operational monitoring of ash clouds over East Asia and the Western Pacific region, well exploiting the 10 min temporal resolution and the spectral features of the Himawari-8 data.

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“…Also, detection methods that explore the temporal behaviour of ash clouds between consecutive satellite images may prove fruitful (see for example Naeger and Christopher, 2014). The ultimate goal may be the direct assimilation of satellite-observed radiances in a weather forecast model that also emits and transports ash.…”

Section: Discussionmentioning

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.

show abstract

The identification and tracking of volcanic ash using the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI)

Cited by 15 publications

References 39 publications

Impact of meteorological clouds on satellite detection and retrieval of volcanic ash during the Eyjafjallajökull 2010 and Grímsvötn 2011 eruptions: a modelling study

Impact of meteorological clouds on satellite detection and retrieval of volcanic ash during the Eyjafjallajökull 2010 and Grímsvötn 2011 eruptions: a modelling study

Monitoring the Agung (Indonesia) Ash Plume of November 2017 by Means of Infrared Himawari 8 Data

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

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