Eyjafjallajökull volcanic ash concentrations determined using Spin Enhanced Visible and Infrared Imager measurements

Prata, A. J.; Prata, Andrew

doi:10.1029/2011jd016800

Cited by 148 publications

(187 citation statements)

References 55 publications

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“…An object is a group of adjacent grid cells that have an ash cloud loading value above a given threshold. Here, the threshold is defined as the typical ash detection limit 5 for most satellite retrievals (~ 0.2 g m -2 - Prata and Prata, 2012). Modeled ACL values below this threshold are omitted from all evaluation metrics.…”

Section: Evaluation Methods 15mentioning

confidence: 99%

Volcanic ash modeling with the NMMB-MONARCH-ASH model: quantification of off-line modeling errors

Marti¹,

Folch²

2017

Preprint

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Abstract.Volcanic ash modeling systems are used to simulate the atmospheric dispersion of volcanic ash and to generate forecasts that quantify the impacts from volcanic eruptions on infrastructures, air quality, 10 aviation, and climate. The efficiency of response and mitigation actions is directly associated to the accuracy of the volcanic ash cloud detection and modeling systems. Operational forecasts build on offline coupled modeling systems where meteorological variables are updated at the specified coupling intervals. Despite the concerns from other communities regarding the accuracy of this strategy, the quantification of the systematic errors and shortcomings associated to the off-line modeling systems has 15 received no attention. This paper employs the NMMB-MONARCH-ASH model to quantify these errors by employing different quantitative and categorical evaluation scores. The skills of the off-line coupling strategy are compared against those from an on-line forecast considered to be the best estimate of the true outcome. Case studies are considered for a synthetic eruption with constant eruption source parameters and for two historical events, which suitably illustrate the severe aviation disruptive effects 20 of European (2010 Eyjafjallajökull) and South-American (2011 Cordón Caulle) volcanic eruptions. Evaluation scores indicate that systematic errors credited by off-line modeling are of the same order of magnitude as those associated to the source term uncertainties. In particular, traditional off-line forecasts employed in operational model setups can result in significant uncertainties, failing to reproduce, in the worst cases, up to 45-70% of the ash cloud of an on-line forecast. These 25 inconsistencies are anticipated to be even more relevant in scenarios where the meteorological conditions change rapidly in time. The outcome of this paper encourages operational groups responsible for real-time advisories for aviation to consider employing computationally efficient on-line dispersal models. 30Atmos. Chem. Phys. Discuss.,

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Section: Evaluation Methods 15mentioning

confidence: 99%

Volcanic ash modeling with the NMMB-MONARCH-ASH model: quantification of off-line modeling errors

Marti¹,

Folch²

2017

Preprint

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“…Regulators therefore quickly decided to impose ash concentration limits which could be used to assess the ash hazard and allow, or prevent, implies that concentrations can be measured and forecast. Prata and Grant [64] and Prata and Prata [83] have shown that ash mass loadings can be determined from thermal infrared satellite data with a lower detection limit of about 0.2 g m −2 and a standard error of ±0.15 g m −2 and hence can meet the goal of determining concentration zones. To forecast concentrations however, requires accurate dispersion models and most importantly, knowledge of the eruptive behavior of the volcano, commonly referred to as the eruption "source" term [84].…”

Section: E Remote Sensing Of Volcanic Ashmentioning

confidence: 99%

Remote Sensing of Volcanic Hazards and Their Precursors

2012

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Ash, tephra and gas ejected during explosive eruptions provides a major far-reaching threat to population, health and air traffic. Lava flows, lahars and floods from ice capped volcanoes can also have a major influence, as well as landslides that have a potential for tsunami generation if they reach into sea or lakes. Remote sensing contributes to the mitigation of these hazards through the use of synthetic aperture radar interferometry (InSAR) and spectroradiometry. In the case of InSAR, displacements of a volcano's surface can be interpreted in terms of magma movement beneath the ground. Thus the technique can be used to identify precursors to eruptions and to track the evolution of eruptions. Recent advances in algorithm development enable relative displacements over many km to be measured with an accuracy of only a few mm. Spectroradiometry on the other hand allows monitoring of a volcanic eruption through the detection of hot-spots, and monitoring and quantification of the ash and SO 2 emitted by volcanoes into the atmosphere. The tracking of ash plumes during eruptions assists in the identification of areas that should be avoided by aircraft. Here we present a review of these two remote sensing techniques, and their application to volcanic hazards.

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“…Optically thin ash clouds and ash particles smaller than 0.2 µm may not be detected. Following this, the minimum detection limit of ash is considered to be in the range of 0.2-1.0 g m −2 Prata and Prata, 2012). Other factors, namely the thermal contrast between the ash and the underlying surface, satellite viewing angle, ash cloud height and the presence of other absorbers (e.g.…”

Section: Seviri Satellite Observationsmentioning

confidence: 99%

Spatial evaluation of volcanic ash forecasts using satellite observations

Harvey

Dacre

2016

Atmos. Chem. Phys.

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Abstract. The decision to close airspace in the event of a volcanic eruption is based on hazard maps of predicted ash extent. These are produced using output from volcanic ash transport and dispersion (VATD) models. In this paper the fractions skill score has been used for the first time to evaluate the spatial accuracy of VATD simulations relative to satellite retrievals of volcanic ash. This objective measure of skill provides more information than traditional point-bypoint metrics, such as success index and Pearson correlation coefficient, as it takes into the account spatial scale over which skill is being assessed. The FSS determines the scale over which a simulation has skill and can differentiate between a "near miss" and a forecast that is badly misplaced. The idealized scenarios presented show that even simulations with considerable displacement errors have useful skill when evaluated over neighbourhood scales of 200-700 (km) 2 . This method could be used to compare forecasts produced by different VATDs or using different model parameters, assess the impact of assimilating satellite-retrieved ash data and evaluate VATD forecasts over a long time period.

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Eyjafjallajökull volcanic ash concentrations determined using Spin Enhanced Visible and Infrared Imager measurements

Cited by 148 publications

References 55 publications

Volcanic ash modeling with the NMMB-MONARCH-ASH model: quantification of off-line modeling errors

Volcanic ash modeling with the NMMB-MONARCH-ASH model: quantification of off-line modeling errors

Remote Sensing of Volcanic Hazards and Their Precursors

Spatial evaluation of volcanic ash forecasts using satellite observations

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