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.

scholarly journals Atmospheric Dispersion Modelling at the London VAAC: A Review of Developments since the 2010 Eyjafjallajökull Volcano Ash Cloud

Atmosphere ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 352 ◽  
Author(s):  
Frances M. Beckett ◽  
Claire S. Witham ◽  
Susan J. Leadbetter ◽  
Ric Crocker ◽  
Helen N. Webster ◽  
...  
Keyword(s):  
Volcanic Ash ◽  
Dispersion Model ◽  
Source Parameters ◽  
Atmospheric Dispersion ◽  
Lessons Learned ◽  
Dispersion Modelling ◽  
Atmospheric Dispersion Modelling ◽  
Volcanic Ash Cloud ◽  
Atmospheric Dispersion Model ◽  
Ash Cloud

It has been 10 years since the ash cloud from the eruption of Eyjafjallajökull caused unprecedented disruption to air traffic across Europe. During this event, the London Volcanic Ash Advisory Centre (VAAC) provided advice and guidance on the expected location of volcanic ash in the atmosphere using observations and the atmospheric dispersion model NAME (Numerical Atmospheric-Dispersion Modelling Environment). Rapid changes in regulatory response and procedures during the eruption introduced the requirement to also provide forecasts of ash concentrations, representing a step-change in the level of interrogation of the dispersion model output. Although disruptive, the longevity of the event afforded the scientific community the opportunity to observe and extensively study the transport and dispersion of a volcanic ash cloud. We present the development of the NAME atmospheric dispersion model and modifications to its application in the London VAAC forecasting system since 2010, based on the lessons learned. Our ability to represent both the vertical and horizontal transport of ash in the atmosphere and its removal have been improved through the introduction of new schemes to represent the sedimentation and wet deposition of volcanic ash, and updated schemes to represent deep moist atmospheric convection and parametrizations for plume spread due to unresolved mesoscale motions. A good simulation of the transport and dispersion of a volcanic ash cloud requires an accurate representation of the source and we have introduced more sophisticated approaches to representing the eruption source parameters, and their uncertainties, used to initialize NAME. Finally, upper air wind field data used by the dispersion model is now more accurate than it was in 2010. These developments have resulted in a more robust modelling system at the London VAAC, ready to provide forecasts and guidance during the next volcanic ash event.

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 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>

scholarly journals Spatial evaluation of volcanic ash forecasts using satellite observations

2015 ◽  
Vol 15 (17) ◽  
pp. 24727-24749 ◽  
Author(s):  
N. J. Harvey ◽  
H. F. Dacre
Keyword(s):  
Volcanic Ash ◽  
Pearson Correlation ◽  
Skill Score ◽  
Near Miss ◽  
Model Parameters ◽  
Time Period ◽  
Long Time ◽  
The Impact ◽  
Success Index ◽  
Ash Transport

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 an objective metric to evaluate the spatial accuracy of VATD simulations relative to satellite retrievals of volcanic ash is presented. The metric is based on the fractions skill score (FSS). This measure of skill provides more information than traditional point-by-point 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 idealised scenarios presented show that even simulations with considerable displacement errors have useful skill when evaluated over neighbourhood scales of 200–700 km2. 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.

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;

Integration of an atmospheric dispersion model with a dynamic multimedia fate model: Development and illustration

2012 ◽  
Vol 164 ◽  
pp. 182-187 ◽  
Author(s):  
Melissa Morselli ◽  
Davide Ghirardello ◽  
Matteo Semplice ◽  
Giuseppe Raspa ◽  
Antonio Di Guardo
Keyword(s):  
Dispersion Model ◽  
Model Development ◽  
Atmospheric Dispersion ◽  
Atmospheric Dispersion Model ◽  
Fate Model

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 Refining an ensemble of volcanic ash forecasts using satellite retrievals: Raikoke 2019

2021 ◽  
Author(s):  
Antonio Capponi ◽  
Natalie J. Harvey ◽  
Helen F. Dacre ◽  
Keith Beven ◽  
Cameron Saint ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Volcanic Ash ◽  
Dispersion Model ◽  
Source Parameters ◽  
Aviation Industry ◽  
Model Parameters ◽  
Flight Operations ◽  
Operational Forecasts ◽  
The Pacific ◽  
Satellite Retrievals

Abstract. Volcanic ash advisories are produced by specialised forecasters who combine several sources of observational data and volcanic ash dispersion model outputs based on their subjective expertise. These advisories are used by the aviation industry to make decisions about where it is safe to fly. However, both observations and dispersion model simulations are subject to various sources of uncertainties that are not represented in operational forecasts. Quantification and communication of these uncertainties are fundamental for making more informed decisions. Here, we develop a data assimilation technique which combines satellite retrievals and volcanic ash transport and dispersion model (VATDM) output, considering uncertainties in both data sources. The methodology is applied to a case study of the 2019 Raikoke eruption. To represent uncertainty in the VATDM output, 1000 simulations are performed by simultaneously perturbing the eruption source parameters, meteorology and internal model parameters (known as the prior ensemble). The ensemble members are filtered, based on their level of agreement with Himawari satellite retrievals of ash column loading, to produce a posterior ensemble that is constrained by the satellite data and its uncertainty. For the Raikoke eruption, filtering the ensemble skews the values of mass eruption rate towards the lower values within the wider parameters ranges initially used in the prior ensemble (mean reduces from 1 Tg h−1 to 0.1 Tg h−1). Furthermore, including satellite observations from subsequent times increasingly constrains the posterior ensemble. These results suggest that the prior ensemble leads to an overestimate of both the magnitude and uncertainty in ash column loadings. Based on the prior ensemble, flight operations would have been severely disrupted over the Pacific Ocean. Using the constrained posterior ensemble, the regions where the risk is overestimated are reduced potentially resulting in fewer flight disruptions. The data assimilation methodology developed in this paper is easily generalisable to other short duration eruptions and to other VATDMs and retrievals of ash from other satellites.

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