scholarly journals Improvements on Near Real Time Detection of Volcanic Ash Emissions for Emergency Monitoring with Limited Satellite Bands

2015 ◽  
Vol 57 ◽  
Author(s):  
Torge Steensen ◽  
Peter Webley ◽  
Jon Dehn
Keyword(s):  
Real Time ◽  
Satellite Data ◽  
Volcanic Ash ◽  
Dispersion Model ◽  
Volcano Monitoring ◽  
Quantitative Comparison ◽  
Title Page ◽  
Model Data ◽  
Dispersion Models ◽  
Ash Transport

<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p><span>Quantifying volcanic ash emissions syneruptively is an important task for the global aviation community. However, due to the near real time nature of volcano monitoring, many parameters important for accurate ash mass estimates cannot be obtained easily. Even when using the best possible estimates of those parameters, uncertainties associated with the ash masses remain high, especially if the satellite data is only available in the traditional 10.8 and 12.0 μm bands. To counteract this limitation, we developed a quantitative comparison between the ash extents in satellite and model data. The focus is the manual cloud edge definition based on the available satellite reverse absorption (RA) data as well as other knowledge like pilot reports or ground-based observations followed by an application of the Volcanic Ash Retrieval on the defined subset with an RA threshold of 0 K. This manual aspect, although subjective to the experience of the observer, can show a significant improvement as it provides the ability to highlight ash that otherwise would be obscured by meteorological clouds or, by passing over different surfaces with unaccounted temperatures, might be lost entirely and thus remains undetectable for an automated satellite approach. We show comparisons to Volcanic Ash Transport and Dispersion models and outline a quantitative match as well as percentages of overestimates based on satellite or dispersion model data which can be converted into a level of reliability for near real time volcano monitoring. </span></p></div></div></div>

Exploring forecast variability during volcanic ash cloud events 

2021 ◽  
Author(s):  
Frances Beckett ◽  
Ralph Burton ◽  
Fabio Dioguardi ◽  
Claire Witham ◽  
John Stevenson ◽  
...  
Keyword(s):  
Real Time ◽  
Volcanic Ash ◽  
Atmospheric Transport ◽  
Dispersion Model ◽  
Meteorological Data ◽  
Atmospheric Dispersion ◽  
Dispersion Models ◽  
British Geological Survey ◽  
Volcanic Ash Cloud ◽  
Ash Cloud

&lt;p&gt;Atmospheric transport and dispersion models are used by Volcanic Ash Advisory Centers (VAACs) to provide timely information on volcanic ash clouds to mitigate the risk of aircraft encounters. Inaccuracies in dispersion model forecasts can occur due to the uncertainties associated with source terms, meteorological data and model parametrizations. Real-time validation of model forecasts against observations is therefore essential to ensure their reliability. Forecasts can also benefit from comparison to model output from other groups; through understanding how different modelling approaches, variations in model setups, model physics, and driving meteorological data, impact the predicted extent and concentration of ash. The Met Office, the National Centre for Atmospheric Science (NCAS) and the British Geological Survey (BGS) are working together to consider how we might compare data (both qualitatively and quantitatively) from the atmospheric dispersion models NAME, FALL3D and HYSPLIT, using meteorological data from the Met Office Unified Model and the NOAA Global Forecast System (providing an effective multi-model ensemble). Results from the model inter-comparison will be used to provide advice to the London VAAC to aid forecasting decisions in near real time during a volcanic ash cloud event. In order to facilitate this comparison, we developed a Python package (ash-model-plotting) to read outputs from the different models into a consistent structure. Here we present our framework for generating comparable plots across the different partners, with a focus on total column mass loading products. These are directly comparable to satellite data retrievals and therefore important for model validation. We also present outcomes from a recent modelling exercise and discuss next steps for further improving our forecast validation.&lt;/p&gt;

scholarly journals Operational Modelling of Umbrella Cloud Growth in a Lagrangian Volcanic Ash Transport and Dispersion Model

Atmosphere ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 200 ◽  
Author(s):  
Helen N. Webster ◽  
Benjamin J. Devenish ◽  
Larry G. Mastin ◽  
David J. Thomson ◽  
Alexa R. Van Eaton
Keyword(s):  
Flow Rate ◽  
Volcanic Ash ◽  
Dispersion Model ◽  
Volume Flow Rate ◽  
Gravity Current ◽  
Volume Flow ◽  
Lateral Spread ◽  
Lagrangian Model ◽  
Model Predictions ◽  
Ash Transport

Large explosive eruptions can result in the formation of an umbrella cloud which rapidly expands, spreading ash out radially from the volcano. The lateral spread by the intrusive gravity current dominates the transport of the ash cloud. Hence, to accurately forecast the transport of ash from large eruptions, lateral spread of umbrella clouds needs to be represented within volcanic ash transport and dispersion models. Here, we describe an umbrella cloud parameterisation which has been implemented into an operational Lagrangian model and consider how it may be used during an eruption when information concerning the eruption is limited and model runtime is key. We examine different relations for the volume flow rate into the umbrella, and the rate of spreading within the cloud. The scheme is validated against historic eruptions of differing scales (Pinatubo 1991, Kelud 2014, Calbuco 2015 and Eyjafjallajökull 2010) by comparing model predictions with satellite observations. Reasonable predictions of umbrella cloud spread are achieved using an estimated volume flow rate from the empirical equation by Bursik et al. and the observed eruption height. We show how model predictions can be refined during an ongoing eruption as further information and observations become available.

scholarly journals Near real-time routine for volcano monitoring using infrared satellite data

2011 ◽  
Vol 54 (5) ◽  
Author(s):  
Valerio Lombardo ◽  
Malvina Silvestri ◽  
Claudia Spinetti
Keyword(s):  
Real Time ◽  
Satellite Data ◽  
Volcano Monitoring

The 2019 July Stromboli volcano paroxysm event: contribution of infrasound to the Volcanic Ash Advisory Centers

2020 ◽  
Author(s):  
Emanuele Marchetti ◽  
Maurizio Ripepe ◽  
Alexis Le Pichon ◽  
Constantino Listowski ◽  
Lars Ceranna ◽  
...  
Keyword(s):  
Real Time ◽  
Volcanic Ash ◽  
Volcanic Eruptions ◽  
Volcano Monitoring ◽  
Pyroclastic Flows ◽  
Civil Aviation ◽  
Propagation Time ◽  
Stromboli Volcano ◽  
Eruptive Column ◽  
The Impact

&lt;p&gt;With the advent of civil aviation and growth in air traffic, the problem of volcanic ash encounter has become an issue of importance as a prompt response to volcanic eruptions is required to mitigate the impact of the volcanic hazard on aviation. Many volcanoes worldwide are poorly monitored, and most of the time notifications of volcanic eruptions are reported mainly based on satellite observations or visual observations. Among ground-based volcano monitoring techniques, infrasound is the only one capable of detecting explosive eruptions from distances of thousands of kilometers.&amp;#160;On July 3 and August 28, 2019, two paroxysmal explosions occurred at Stromboli volcano. The events, that are similar in terms of energy and size to the peak explosive activity reported historically for the volcano, produced a significant emission of scoria, bombs and lapilli, that affected the whole island and fed an eruptive column that rose almost 5 km above the volcano. The collapse of the eruptive column also produced pyroclastic flows along the Sciara del Fuoco, a sector collapse on the northern flank of the volcano.&lt;/p&gt;&lt;p&gt;Being one of the best-monitored volcanoes of the world, the 2019 Stromboli paroxysmal explosions were observed in real-time and Civil Protection procedures started immediately. However, notification to the Toulouse Volcanic Ash Advisory Centre (VAAC) was not automated, and the VAA was issued only long after the event occurrence. The two explosions produced infrasound signals that were detected by several infrasound stations as far as Norway (IS37, 3380 km) and Azores islands (IS42, 3530 km). Despite of the latency due to the propagation time, infrasound-based notification arrays precedes the Volcanic Ash Advisories (VAAs) issued by Toulouse VACC. Following the same procedure applied for the Volcano Information System developed in the framework of the ARISE project, we show how infrasound array analysis could allow automatic, near-real-time identification of these eruptions with timely reliable source information. We highlight the need for an integration of the CTBT IMS infrasound network with local and regional infrasound arrays capable of providing a timely early warning to VAACs. This study opens new perspectives in volcano monitoring and could represent, in the future, an efficient tool in supporting VAACs activity.&lt;/p&gt;

scholarly journals Airborne Volcanic Ash Forecast Area Reliability

2007 ◽  
Vol 22 (5) ◽  
pp. 1132-1139 ◽  
Author(s):  
Barbara J. B. Stunder ◽  
Jerome L. Heffter ◽  
Roland R. Draxler
Keyword(s):  
Volcanic Ash ◽  
Dispersion Model ◽  
Analysis Data ◽  
Flight Safety ◽  
Commercial Aircraft ◽  
Hysplit Model ◽  
Aircraft Flight ◽  
Two Parameters ◽  
Degree Of Overlap ◽  
Ash Transport

Abstract In support of aircraft flight safety operations, daily comparisons between modeled, hypothetical, volcanic ash plumes calculated with meteorological forecasts and analyses were made over a 1.5-yr period. The Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model simulated the ash transport and dispersion. Ash forecasts and analyses from seven volcanoes were studied. The volcanoes were chosen because of recent eruptions or because their airborne ash could impinge on well-traveled commercial aircraft flight paths. For each forecast–analysis pair, a statistic representing the degree of overlap, the threat score (TS), was calculated. A forecast was classified as acceptable if the TS was greater than 0.25. Each forecast was also categorized by two parameters: the forecast area quadrant with respect to the volcano and a factor related to the complexity of the meteorology. The forecast complexity factor was based on the degree of spread using NCEP ensemble output or using a HYSPLIT offset configuration. In general, the larger the spread of the ensemble or offset forecasts, the greater the complexity. The forecasts were sorted by complexity factor, and then classified by the quartile of the complexity. The volcanic ash forecast area reliability (VAFAR) was calculated for each forecast area quadrant and for each quartile of the complexity factor. VAFAR is the ratio of the number of acceptable forecasts to the total number of forecasts. Most VAFAR values were above 70%. VAFAR values for two of the seven volcanoes (Popocatepetl in Mexico and Tungurahua in Ecuador) tended to be lower than the others. In general, VAFAR decreased with increasing complexity of the meteorology. It should be noted that the VAFAR values reflect the reliability of the meteorological forecasts when compared to the same calculation using analysis data; the dispersion model itself was not evaluated.

scholarly journals Multi-level emulation of a volcanic ash transport and dispersion model to quantify sensitivity to uncertain parameters

2016 ◽  
Author(s):  
Natalie J. Harvey ◽  
Nathan Huntley ◽  
Helen Dacre ◽  
Michael Goldstein ◽  
David Thomson ◽  
...  
Keyword(s):  
Volcanic Ash ◽  
Dispersion Model ◽  
Model Development ◽  
Atmospheric Dispersion ◽  
Theoretical Research ◽  
Model Parameters ◽  
Formal Framework ◽  
Geographical Regions ◽  
Multi Level ◽  
Ash Transport

Abstract. Following the disruption to European airspace caused by the eruption of Eyjafjallajokull in 2010 there has been a move towards producing quantitative predictions of volcanic ash concentration using volcanic ash transport and dispersion simulators. However, there is no formal framework for determining the uncertainties on these predictions and performing many simulations using these complex models is computationally expensive. In this paper a Bayes linear emulation approach is applied to the Numerical Atmospheric-dispersion Modelling Environment (NAME) to better understand the influence of source and internal model parameters on the simulator output. Emulation is a statistical method for predicting the output of a computer simulator at new parameter choices without actually running the simulator. A multi-level emulation approach is applied to combine information from many evaluations of a computationally fast version of NAME with relatively few evaluations of a slower, more accurate, version. This approach is effective when it is not possible to run the accurate simulator many times and when there is also little prior knowledge about the influence of parameters. The approach is applied to the mean ash column loading in 75 geographical regions on 14 May 2010. Through this analysis it has been found that the parameters that contribute the most to the output uncertainty are initial plume rise height, mass eruption rate, free tropospheric turbulence levels and precipitation threshold for wet deposition. This information can be used to inform future model development and observational campaigns and routine monitoring. The analysis presented here suggests the need for further observational and theoretical research into parameterisation of atmospheric turbulence. Furthermore it can also be used to inform the most important parameter perturbations for a small operational ensemble of simulations. The use of an emulator also identifies the input and internal parameters that do not contribute significantly to simulator uncertainty. Finally, the analysis highlights that the fast, less accurate, version of NAME can provide useful information without needing the accurate version at all. This approach can easily be extended to other case studies, simulators or hazards.

scholarly journals Multi-level emulation of a volcanic ash transport and dispersion model to quantify sensitivity to uncertain parameters

2018 ◽  
Vol 18 (1) ◽  
pp. 41-63 ◽  
Author(s):  
Natalie J. Harvey ◽  
Nathan Huntley ◽  
Helen F. Dacre ◽  
Michael Goldstein ◽  
David Thomson ◽  
...  
Keyword(s):  
Volcanic Ash ◽  
Dispersion Model ◽  
Model Development ◽  
Atmospheric Dispersion ◽  
Theoretical Research ◽  
Model Parameters ◽  
Formal Framework ◽  
Geographical Regions ◽  
Multi Level ◽  
Ash Transport

Abstract. Following the disruption to European airspace caused by the eruption of Eyjafjallajökull in 2010 there has been a move towards producing quantitative predictions of volcanic ash concentration using volcanic ash transport and dispersion simulators. However, there is no formal framework for determining the uncertainties of these predictions and performing many simulations using these complex models is computationally expensive. In this paper a Bayesian linear emulation approach is applied to the Numerical Atmospheric-dispersion Modelling Environment (NAME) to better understand the influence of source and internal model parameters on the simulator output. Emulation is a statistical method for predicting the output of a computer simulator at new parameter choices without actually running the simulator. A multi-level emulation approach is applied using two configurations of NAME with different numbers of model particles. Information from many evaluations of the computationally faster configuration is combined with results from relatively few evaluations of the slower, more accurate, configuration. This approach is effective when it is not possible to run the accurate simulator many times and when there is also little prior knowledge about the influence of parameters. The approach is applied to the mean ash column loading in 75 geographical regions on 14 May 2010. Through this analysis it has been found that the parameters that contribute the most to the output uncertainty are initial plume rise height, mass eruption rate, free tropospheric turbulence levels and precipitation threshold for wet deposition. This information can be used to inform future model development and observational campaigns and routine monitoring. The analysis presented here suggests the need for further observational and theoretical research into parameterisation of atmospheric turbulence. Furthermore it can also be used to inform the most important parameter perturbations for a small operational ensemble of simulations. The use of an emulator also identifies the input and internal parameters that do not contribute significantly to simulator uncertainty. Finally, the analysis highlights that the faster, less accurate, configuration of NAME can, on its own, provide useful information for the problem of predicting average column load over large areas.

Volcanic ash transport and dispersion models

2015 ◽  
pp. 187-233
Author(s):  
Rorik Peterson ◽  
Peter Webley ◽  
Réal D’Amours ◽  
René Servranckx ◽  
Barbara Stunder ◽  
...  
Keyword(s):  
Volcanic Ash ◽  
Dispersion Models ◽  
Ash Transport

scholarly journals Numerical Simulations of Volcanic Ash Plume Dispersal for Sakura-Jima Using Real-Time Emission Rate Estimation

2019 ◽  
Vol 14 (1) ◽  
pp. 160-172 ◽  
Author(s):  
Hiroshi L. Tanaka ◽  
Masato Iguchi ◽  
◽  
Keyword(s):  
Real Time ◽  
Volcanic Ash ◽  
Emission Rate ◽  
Ground Deformation ◽  
Dispersion Model ◽  
Model Performance ◽  
Danger Zone ◽  
Rate Estimation ◽  
Ash Dispersion ◽  
Ash Fallout

In this study, a real-time volcanic ash dispersion model called PUFF is applied to the Sakura-jima volcano erupted on 16 June 2018 to assess the performance of the new system connected with a real-time emission rate estimation. The emission rate of the ash mass from the vent is estimated based on an empirical formula developed for the Sakura-jima volcano using seismic monitoring and ground deformation data. According to the time series of the estimated emission rate, a major eruption occurred at 7:20 JST indicating an emission rate of 1000 t/min and continued for 15 min showing a plume height of 4500 m. It is observed that we need to introduce an adjusting constant to fit the model prediction of the ash fallout with the ground observation. Once the particle mass is calibrated, the distributions of ash fallout are compared with other eruption events to confirm the model performance. According to the PUFF model simulations, an airborne ash concentration of 100 mg/m3extends to a wide area around the volcano within one hour after the eruption. The simulation result quantitatively indicates the location of the danger zone for commercial airliners. The PUFF model system combined with the real-time emission rate estimation is useful for aviation safety purposes as well as for ground transportation and human health around active volcanoes.

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