A Computationally Efficient Ensemble Filtering Scheme for Quantitative Volcanic Ash Forecasts

Zidikheri, Meelis Juma; Lucas, Chris

doi:10.1029/2020jd033094

Cited by 9 publications

(9 citation statements)

References 56 publications

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“…The definition of sufficient needs to be explored further and is an issue of concern to all methods requiring selection of members from an ensemble. For instance, sufficient observations are required, alongside a shrewd choice of metric thresholds, for robust performance of the particle filtering methods employed by Zidikheri and Lucas (2021) and Capponi et al (2021). Insufficient observations will lead to similar metric values and an inability to determine the good ensemble members from the pack.…”

Section: Discussionmentioning

confidence: 99%

“…Often a single estimate of emissions is obtained as the optimal value for the metric used or, in the Bayesian framework, as the peak of the posterior distribution, and this is used to forecast the transport and dispersion of the volcanic cloud with the atmospheric dispersion model. Source inversion techniques can also yield a range of solutions, for example, by selecting all solutions with metric values above a threshold or by making better use of the posterior probability distribution function, leading to probabilistic forecasts (Capponi et al., 2021; Zidikheri & Lucas, 2021). Further work on interpretation and use of probabilistic information in an operational setting is, however, required.…”

Section: Review Of Inversion Techniques For Estimating Volcanic Emiss...mentioning

confidence: 99%

“…Zidikheri et al. (2017b) and Zidikheri and Lucas (2021) use ensemble NWP data sets to sample the meteorological parameter space alongside the source parameter space and, with a metric‐based filter (e.g., pattern correlations above a critical value), select those meteorology‐source parameter combinations which agree well with observations. Similarly, in a study of the 2019 eruption of Raikoke, Capponi et al.…”

Section: Introductionmentioning

confidence: 99%

“…Ensemble NWP data sets have recently been used with volcanic source inversion schemes to account for uncertainties in the input meteorological data. Zidikheri et al (2017b) and Zidikheri and Lucas (2021) use ensemble NWP data sets to sample the meteorological parameter space alongside the source parameter space and, with a metric-based filter (e.g., pattern correlations above a critical value), select those meteorology-source parameter combinations which agree well with observations. Similarly, in a study of the 2019 eruption of Raikoke, Capponi et al ( 2021) perturbed source parameters, atmospheric dispersion model parameters and meteorology by sampling a multi-dimensional parameter space and applied a particle filter based on agreement with satellite observations to constrain the parameter space.…”

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confidence: 99%

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Using Ensemble Meteorological Data Sets to Treat Meteorological Uncertainties in a Bayesian Volcanic Ash Inverse Modeling System: A Case Study, Grímsvötn 2011

Webster

Thomson

2022

JGR Atmospheres

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Atmospheric dispersion models are employed in forecasting the atmospheric transport of ash clouds following a volcanic eruption. Errors in input meteorological data can, however, lead to discrepancies between the modeled and observed ash cloud locations. Furthermore, meteorological errors can affect the performance of inversion techniques used to estimate ash emissions. This study explores using ensemble meteorological data sets to overcome issues with meteorological errors in a Bayesian inversion system, Inversion Technique for Emissions Modelling (InTEM) for volcanic ash. Two measures (the evidence value and a cost function) are obtained from the Bayesian framework, and each measure is used to select a “best” meteorological data set from within the ensemble. These two best meteorological data sets are constructed in an iterative manner which is ideally suited for operational use when ash cloud forecasts are updated at regular intervals. The method is applied to the 2011 eruption of the Icelandic volcano Grímsvötn and improvements to ash cloud forecasts using the best meteorological data sets assessed. In this case study, errors in the deterministic meteorological data are significant, resulting in a reduction of ash emissions estimated by InTEM for volcanic ash. We also illustrate how the best meteorological data sets lead to more accurate modeling of the ash cloud.

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Section: Discussionmentioning

confidence: 99%

Section: Review Of Inversion Techniques For Estimating Volcanic Emiss...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Using Ensemble Meteorological Data Sets to Treat Meteorological Uncertainties in a Bayesian Volcanic Ash Inverse Modeling System: A Case Study, Grímsvötn 2011

Webster

Thomson

2022

JGR Atmospheres

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“…This enables a considerable reduction in the number of control variables when the method is applied to eruptions in real time, thereby making it computationally tractable in operational contexts. Furthermore, it was shown that the "curse of dimensionality" that afflicts all ensemble filtering schemes, in which ensemble size is at times too severely diminished by the algorithm, could be ameliorated by dividing the ensemble members into smaller batches and applying the algorithm to each batch rather than to the whole ensemble [22]. The ensemble filtering method can also be used to reject poor retrievals or detections as initial fields when a direct data insertion scheme is used to initialize the dispersion models, as well as to find optimal top and base ash cloud heights, which can further improve forecasting skill, in some situations [23].…”

Section: Introductionmentioning

confidence: 99%

Using an Ensemble Filter to Improve the Representation of Temporal Source Variations in a Volcanic Ash Forecasting System

Zidikheri

2022

Atmosphere

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The use of ensemble models to forecast the dispersion and transport of airborne volcanic ash in operational contexts is increasingly being explored. The ensemble members are usually constructed to represent a priori uncertainty estimates in meteorological fields and volcanic ash source parameters. Satellite data can be used to further filter ensemble members within an analysis time window by rejecting poorly performing members, leading to improved forecasts. In this study, the ensemble filtering technique is used to improve the representation of temporal source variations. Ensemble members are initially created by representing the source time variations as random functions of time that are modulated by crude initial estimates of the variations estimated from satellite imagery. Ensemble filtering is then used to remove members whose fields match poorly with observations within a specified analysis time window that are represented by satellite retrievals of volcanic ash properties such as mass load, effective radius, and cloud top height. The filtering process leads to an ensemble with statistics in closer agreement with the observations. It is shown in the context of the 30 May 2014 Sangeang Api eruption case study that this method leads to significantly enhanced forecasting skill beyond the analysis time window—about 20% improvement on average—when compared to a system that assumes constant emission rates for the duration of the eruption, as is the case in many operational volcanic ash forecasting systems.

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How dependent are quantitative volcanic ash concentration and along‐flight dosage forecasts to model structural choices?

James,

Dacre,

Harvey

2024

Meteorological Applications

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Producing quantitative volcanic ash forecasts is challenging due to multiple sources of uncertainty. Careful consideration of this uncertainty is required to produce timely and robust hazard warnings. Structural uncertainty occurs when a model fails to produce accurate forecasts, despite good knowledge of the eruption source parameters, meteorological conditions and suitable parameterizations of transport and deposition processes. This uncertainty is frequently overlooked in forecasting practices. Using a Lagrangian particle dispersion model, simulations with varied output spatial resolution, temporal averaging period and particle release rate are performed to quantify the impact of these structural choices. This experiment reveals that, for the 2019 Raikoke eruption, structural choices give measurements of peak ash concentration spanning an order of magnitude, significantly impacting decision‐relevant thresholds used in aviation flight planning. Conversely, along‐flight dosage estimates exhibit less sensitivity to structural choices, suggesting it is a more robust metric to use in flight planning. Uncertainty can be reduced by eliminating structural choices that do not result in a favourable level of agreement with a high‐resolution reference simulation. Reliable forecasts require output spatial resolution 80 km, temporal averaging periods 3 h and particle release rates 5000 particles/h. This suggests that simulations with relatively small numbers of particles could be used to produce a large ensemble of simulations without significant loss of accuracy. Comparison with previous Raikoke simulations indicates that the uncertainty associated with these constrained structural choices is smaller than those associated with satellite constrained eruption source parameter and internal model parameter uncertainties. Thus, given suitable structural choices, other epistemic sources of uncertainty are likely to dominate. This insight is useful for the design of ensemble methodologies which are required to enable a shift from deterministic to probabilistic forecasting. The results are applicable to other long‐range dispersion problems and to Eulerian dispersion models.

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A Computationally Efficient Ensemble Filtering Scheme for Quantitative Volcanic Ash Forecasts

Cited by 9 publications

References 56 publications

Using Ensemble Meteorological Data Sets to Treat Meteorological Uncertainties in a Bayesian Volcanic Ash Inverse Modeling System: A Case Study, Grímsvötn 2011

Using Ensemble Meteorological Data Sets to Treat Meteorological Uncertainties in a Bayesian Volcanic Ash Inverse Modeling System: A Case Study, Grímsvötn 2011

Using an Ensemble Filter to Improve the Representation of Temporal Source Variations in a Volcanic Ash Forecasting System

How dependent are quantitative volcanic ash concentration and along‐flight dosage forecasts to model structural choices?

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