Estimation of optimal dispersion model source parameters using satellite detections of volcanic ash

2017 ◽  
Vol 122 (15) ◽  
pp. 8207-8232 ◽  
Author(s):  
Meelis J. Zidikheri ◽  
Christopher Lucas ◽  
Rodney J. Potts
Keyword(s):  
Volcanic Ash ◽  
Dispersion Model ◽  
Source Parameters ◽  
Model Source

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 Using Satellite Data to Determine Empirical Relationships between Volcanic Ash Source Parameters

Atmosphere ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 342 ◽  
Author(s):  
Meelis J. Zidikheri ◽  
Chris Lucas
Keyword(s):  
Case Studies ◽  
Volcanic Ash ◽  
Dispersion Model ◽  
Source Parameters ◽  
Difficult Problem ◽  
Emission Rates ◽  
Empirical Relationships ◽  
Mass Load ◽  
Satellite Retrievals ◽  
Mass Loads

Poor knowledge of dispersion model source parameters related to quantities such as the total fine ash mass emission rate, its effective spatial distribution, and particle size distribution makes the provision of quantitative forecasts of volcanic ash a difficult problem. To ameliorate this problem, we make use of satellite-retrieved mass load data from 14 eruption case studies to estimate fine ash mass emission rates and other source parameters by an inverse modelling procedure, which requires multidimensional sampling of several thousand trial simulations with different values of source parameters. We then estimate the dependence of these optimal source parameters on eruption height. We show that using these empirical relationships in a data assimilation procedure leads to substantial improvements to the forecasts of ash mass loads, with the use of empirical relationships between parameters and eruption height having the added advantage of computational efficiency because of dimensional reduction. In addition, the use of empirical relationships, which encode information in satellite retrievals from past case studies, implies that quantitative forecasts can still be issued even when satellite retrievals of mass load are not available in real time due to cloud cover or other reasons, making it especially useful for operations in the tropics where ice and water clouds are ubiquitous.

Ensemble-based volcanic ash forecasts using satellite retrievals for quantitative verification

2021 ◽  
Author(s):  
Antonio Capponi ◽  
Natalie J. Harvey ◽  
Helen F. Dacre ◽  
Keith Beven ◽  
Mike R. James
Keyword(s):  
Volcanic Ash ◽  
Dispersion Model ◽  
Source Parameters ◽  
Forecast Accuracy ◽  
Satellite Observations ◽  
Ensemble Simulations ◽  
Planning Decisions ◽  
Satellite Retrievals ◽  
Initial Ensemble ◽  
Quantitative Verification

<p>Volcanic ash poses a significant hazard for aviation. If an ash cloud forms as result of an eruption, it forces a series of flight planning decisions that consider important safety and economic factors. These decisions are made using a combination of satellite retrievals and volcanic ash forecasts issued by Volcanic Ash Advisory Centres.  However, forecasts of ash hazard remain deterministic, and lack quantification of the uncertainty that arises from the estimation of eruption source parameters, meteorology and uncertainties within the dispersion model used to perform the simulations. Quantification of these uncertainties is fundamental and could be achieved by using ensemble simulations. Here, we explore how ensemble-based forecasts — performed using the Met Office dispersion model NAME — together with sequential satellite retrievals of ash column loading, may improve forecast accuracy and uncertainty characterization.</p><p>We have developed a new methodology to evaluate each member of the ensemble based on its agreement with the satellite retrievals available at the time. An initial ensemble is passed through a filter of verification metrics and compared with the first available set of satellite observations. Members far from the observations are rejected. The members within a limit of acceptability are used to resample the parameters used in the initial ensemble, and design a new ensemble to compare with the next available set of satellite observations. The filtering process and parameter resampling are applied whenever new satellite observations are available, to create new ensembles propagating forward in time, until all available observations are covered.</p><p>Although the method requires the run of many ensemble batches, and it is not yet suited for operational use, it shows how combining ensemble simulations and sequential satellite retrievals can be used to quantify confidence in ash forecasts. We demonstrate the method by applying it to the recent Raikoke (Kurii Islands, Russia) eruption, which occurred on the 22<sup>nd</sup> July 2019. Each ensemble consists of 1000 members and it is evaluated against 6-hourly HIMAWARI satellite ash retrievals.</p>

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.

Dispersion Modelling at the London VAAC 10 Years after the Eyjafjallajökull Ash Cloud

2020 ◽  
Author(s):  
Frances Beckett ◽  
Claire Witham ◽  
Susan Leadbetter ◽  
Ric Crocker ◽  
Helen Webster ◽  
...  
Keyword(s):  
Volcanic Ash ◽  
Dispersion Model ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Source Parameters ◽  
Atmospheric Dispersion ◽  
Lessons Learned ◽  
Volcanic Ash Cloud ◽  
Atmospheric Dispersion Models ◽  
Ash Cloud

<p>It has been 10 years since the ash cloud from the eruption of Eyjafjallajökull caused chaos to air traffic across Europe. 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. Here 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.</p><p>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 atmospheric convection and parameterizations 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. Further, atmospheric dispersion models are driven by 3-dimensional meteorological data from Numerical Weather Prediction (NWP) models and the Met Office’s upper air wind field data 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 affecting their region.</p>

scholarly journals Near-real-time volcanic cloud monitoring: insights into global explosive volcanic eruptive activity through analysis of Volcanic Ash Advisories

2021 ◽  
Vol 83 (2) ◽  
Author(s):  
S. Engwell ◽  
L. Mastin ◽  
A. Tupper ◽  
J. Kibler ◽  
P. Acethorp ◽  
...  
Keyword(s):  
Real Time ◽  
Volcanic Activity ◽  
Volcanic Ash ◽  
Source Parameters ◽  
Volcanic Eruptions ◽  
Volcanic Hazards ◽  
Process Data ◽  
Cloud Monitoring ◽  
Satellite Sensors ◽  
Volcanic Cloud

AbstractUnderstanding the location, intensity, and likely duration of volcanic hazards is key to reducing risk from volcanic eruptions. Here, we use a novel near-real-time dataset comprising Volcanic Ash Advisories (VAAs) issued over 10 years to investigate global rates and durations of explosive volcanic activity. The VAAs were collected from the nine Volcanic Ash Advisory Centres (VAACs) worldwide. Information extracted allowed analysis of the frequency and type of explosive behaviour, including analysis of key eruption source parameters (ESPs) such as volcanic cloud height and duration. The results reflect changes in the VAA reporting process, data sources, and volcanic activity through time. The data show an increase in the number of VAAs issued since 2015 that cannot be directly correlated to an increase in volcanic activity. Instead, many represent increased observations, including improved capability to detect low- to mid-level volcanic clouds (FL101–FL200, 3–6 km asl), by higher temporal, spatial, and spectral resolution satellite sensors. Comparison of ESP data extracted from the VAAs with the Mastin et al. (J Volcanol Geotherm Res 186:10–21, 2009a) database shows that traditional assumptions used in the classification of volcanoes could be much simplified for operational use. The analysis highlights the VAA data as an exceptional resource documenting global volcanic activity on timescales that complement more widely used eruption datasets.

scholarly journals Determination of time- and height-resolved volcanic ash emissions and their use for quantitative ash dispersion modeling: the 2010 Eyjafjallajökull eruption

2011 ◽  
Vol 11 (9) ◽  
pp. 4333-4351 ◽  
Author(s):  
A. Stohl ◽  
A. J. Prata ◽  
S. Eckhardt ◽  
L. Clarisse ◽  
A. Durant ◽  
...  
Keyword(s):  
Volcanic Ash ◽  
Dispersion Model ◽  
A Priori ◽  
Volcanic Eruptions ◽  
General Nature ◽  
Aviation Industry ◽  
Dispersion Modeling ◽  
Source Information ◽  
European Area ◽  
Ash Dispersion

Abstract. The April–May, 2010 volcanic eruptions of Eyjafjallajökull, Iceland caused significant economic and social disruption in Europe whilst state of the art measurements and ash dispersion forecasts were heavily criticized by the aviation industry. Here we demonstrate for the first time that large improvements can be made in quantitative predictions of the fate of volcanic ash emissions, by using an inversion scheme that couples a priori source information and the output of a Lagrangian dispersion model with satellite data to estimate the volcanic ash source strength as a function of altitude and time. From the inversion, we obtain a total fine ash emission of the eruption of 8.3 ± 4.2 Tg for particles in the size range of 2.8–28 μm diameter. We evaluate the results of our model results with a posteriori ash emissions using independent ground-based, airborne and space-borne measurements both in case studies and statistically. Subsequently, we estimate the area over Europe affected by volcanic ash above certain concentration thresholds relevant for the aviation industry. We find that during three episodes in April and May, volcanic ash concentrations at some altitude in the atmosphere exceeded the limits for the "Normal" flying zone in up to 14 % (6–16 %), 2 % (1–3 %) and 7 % (4–11 %), respectively, of the European area. For a limit of 2 mg m−3 only two episodes with fractions of 1.5 % (0.2–2.8 %) and 0.9 % (0.1–1.6 %) occurred, while the current "No-Fly" zone criterion of 4 mg m−3 was rarely exceeded. Our results have important ramifications for determining air space closures and for real-time quantitative estimations of ash concentrations. Furthermore, the general nature of our method yields better constraints on the distribution and fate of volcanic ash in the Earth system.

scholarly journals Characterizing the Atmospheric Conditions Leading to Large Error Growth in Volcanic Ash Cloud Forecasts

2018 ◽  
Vol 57 (4) ◽  
pp. 1011-1019 ◽  
Author(s):  
H. F. Dacre ◽  
N. J. Harvey
Keyword(s):  
Flow Separation ◽  
Volcanic Ash ◽  
Dispersion Model ◽  
Forecast Accuracy ◽  
Large Error ◽  
Ensemble Member ◽  
Forecast Errors ◽  
Atmospheric Conditions ◽  
Wind Fields ◽  
Ensemble Forecasts

ABSTRACTVolcanic ash poses an ongoing risk to safety in the airspace worldwide. The accuracy with which volcanic ash dispersion can be forecast depends on the conditions of the atmosphere into which it is emitted. In this study, meteorological ensemble forecasts are used to drive a volcanic ash transport and dispersion model for the 2010 Eyjafjallajökull eruption in Iceland. From analysis of these simulations, the authors determine why the skill of deterministic-meteorological forecasts decreases with increasing ash residence time and identify the atmospheric conditions in which this drop in skill occurs most rapidly. Large forecast errors are more likely when ash particles encounter regions of large horizontal flow separation in the atmosphere. Nearby ash particle trajectories can rapidly diverge, leading to a reduction in the forecast accuracy of deterministic forecasts that do not represent variability in wind fields at the synoptic scale. The flow‐separation diagnostic identifies where and why large ensemble spread may occur. This diagnostic can be used to alert forecasters to situations in which the ensemble mean is not representative of the individual ensemble‐member volcanic ash distributions. Knowledge of potential ensemble outliers can be used to assess confidence in the forecast and to avoid potentially dangerous situations in which forecasts fail to predict harmful levels of volcanic ash.

scholarly journals Volcanic ash modeling with the NMMB-MONARCH-ASH model: quantification of offline modeling errors

2018 ◽  
Vol 18 (6) ◽  
pp. 4019-4038 ◽  
Author(s):  
Alejandro Marti ◽  
Arnau Folch
Keyword(s):  
Volcanic Ash ◽  
Source Parameters ◽  
Atmospheric Dispersion ◽  
Volcanic Eruptions ◽  
Systematic Errors ◽  
Cloud Detection ◽  
Coupled Modeling ◽  
Evaluation Scores ◽  
Modeling Systems ◽  
Ash Cloud

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, aviation, and climate. The efficiency of response and mitigation actions is directly associated with the accuracy of the volcanic ash cloud detection and modeling systems. Operational forecasts build on offline coupled modeling systems in which 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 with the offline modeling systems has 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 offline coupling strategy are compared against those from an online 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 of European (2010 Eyjafjallajökull) and South American (2011 Cordón Caulle) volcanic eruptions. Evaluation scores indicate that systematic errors due to the offline modeling are of the same order of magnitude as those associated with the source term uncertainties. In particular, traditional offline 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 online forecast. These inconsistencies are anticipated to be even more relevant in scenarios in which 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 online dispersal models.

scholarly journals Volcanic ash forecast using ensemble-based data assimilation: the Ensemble Transform Kalman Filter coupled with FALL3D-7.2 model (ETKF-FALL3D, version 1.0)

2019 ◽  
Author(s):  
Soledad Osores ◽  
Juan Ruiz ◽  
Arnau Folch ◽  
Estela Collini
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Volcanic Ash ◽  
Source Parameters ◽  
Joint Estimation ◽  
Volcanic Ash Cloud ◽  
Ensemble Transform Kalman Filter ◽  
Ensemble Transform ◽  
Ash Cloud ◽  
Ash Dispersal

Abstract. Quantitative volcanic ash cloud forecasts are prone to uncertainties coming from the source term quantification (e.g. eruption strength or vertical distribution of the emitted particles), with consequent implications on operational ash impact assessment. We present an ensemble-based data assimilation and forecast system for volcanic ash dispersal and deposition aimed at reducing uncertainties related to eruption source parameters. The FALL3D atmospheric dispersal model is coupled with the Ensemble Transform Kalman Filter (ETKF) data assimilation technique by combining ash mass loading observations with ash dispersal simulations in order to obtain a better joint estimation of 3D ash concentration and source parameters. The ETKF-FALL3D data assimilation system is evaluated performing Observation System Simulation Experiments (OSSE) in which synthetic observations of fine ash mass loadings are assimilated. The evaluation of the ETKF-FALL3D system considering reference states of steady and time-varying eruption source parameters shows that the assimilation process gives both better estimations of ash concentration and time-dependent optimized values of eruption source parameters. The joint estimation of concentrations and source parameters leads to a better analysis and forecast of the 3D ash concentrations. Results show the potential of the methodology to improve volcanic ash cloud forecasts in operational contexts.

Sign in / Sign up

Export Citation Format

Share Document