Hazmat risk area assessment by atmospheric dispersion modelling using Latin hypercube sampling with weather ensemble

Sigg, Robert; Lindgren, Petter; Schoenberg, Pontus von; Persson, Leif; Burman, Jan; Grahn, Håkan; Brännström, Niklas; Björnham, Oscar

doi:10.1002/met.1722

Cited by 7 publications

(3 citation statements)

References 49 publications

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“…A number of other studies have also considered hypothetical releases of radioactive material and considered how to increase the computational efficiency of running dispersion ensembles (e.g. Sørensen et al, 2020;Sigg et al, 2018). Korsakissok et al (2020) compared ensemble forecasts for two hypothetical accidents in Europe using a range of different dispersion models and ensemble meteorology from the HARMONIE meteorological model.…”

Section: Introductionmentioning

confidence: 99%

Assessing the value meteorological ensembles add to dispersion modelling using hypothetical releases

2022

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Abstract. Atmospheric dispersion model output is frequently used to provide advice to decision makers, for example, about the likely location of volcanic ash erupted from a volcano or the location of deposits of radioactive material released during a nuclear accident. Increasingly, scientists and decision makers are requesting information on the uncertainty of these dispersion model predictions. One source of uncertainty is in the meteorology used to drive the dispersion model, and in this study ensemble meteorology from the Met Office ensemble prediction system is used to provide meteorological uncertainty to dispersion model predictions. Two hypothetical scenarios, one volcanological and one radiological, are repeated every 12 h over a period of 4 months. The scenarios are simulated using ensemble meteorology and deterministic forecast meteorology and compared to output from simulations using analysis meteorology using the Brier skill score. Adopting the practice commonly used in evaluating numerical weather prediction (NWP) models where observations are sparse or non-existent, we consider output from simulations using analysis NWP data to be truth. The results show that on average the ensemble simulations perform better than the deterministic simulations, although not all individual ensemble simulations outperform their deterministic counterpart. The results also show that greater skill scores are achieved by the ensemble simulation for later time steps rather than earlier time steps. In addition there is a greater increase in skill score over time for deposition than for air concentration. For the volcanic ash scenarios it is shown that the performance of the ensemble at one flight level can be different to that at a different flight level; e.g. a negative skill score might be obtained for FL350-550 and a positive skill score for FL200-350. This study does not take into account any source term uncertainty, but it does take the first steps towards demonstrating the value of ensemble dispersion model predictions.

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Section: Introductionmentioning

confidence: 99%

Assessing the value meteorological ensembles add to dispersion modelling using hypothetical releases

2022

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“…Galmarini et al (2010) demonstrated that a single dispersion model using an ensemble meteorological forecast could give comparable performance to a multi-model approach. The use of disper-sion ensembles has become increasingly common in recent years (Sigg et al, 2018;Zidikheri et al, 2018;Maurer et al, 2021) with enhancements to computing power making such approaches now viable for operational implementations.…”

Section: Introductionmentioning

confidence: 99%

Using synthetic case studies to explore the spread and calibration of ensemble atmospheric dispersion forecasts

Jones,

Leadbetter,

Hort

2023

Atmos. Chem. Phys.

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Abstract. Ensemble predictions of atmospheric dispersion that account for the meteorological uncertainties in a weather forecast are constructed by propagating the individual members of an ensemble numerical weather prediction forecast through an atmospheric dispersion model. Two event scenarios involving hypothetical atmospheric releases are considered: a near-surface radiological release from a nuclear power plant accident and a large eruption of an Icelandic volcano releasing volcanic ash into the upper air. Simulations were run twice-daily in real time over a 4-month period to create a large dataset of cases for this study. The performance of the ensemble predictions is measured against retrospective simulations using a sequence of meteorological fields analysed against observations. The focus of this paper is on comparing the spread of the ensemble members against forecast errors and on the calibration of probabilistic forecasts derived from the ensemble distribution. Results show good overall performance by the dispersion ensembles in both studies but with simulations for the upper-air ash release generally performing better than those for the near-surface release of radiological material. The near-surface results demonstrate a sensitivity to the release location, with good performance in areas dominated by the synoptic-scale meteorology and generally poorer performance at some other sites where, we speculate, the global-scale meteorological ensemble used in this study has difficulty in adequately capturing the uncertainty from local- and regional-scale influences on the boundary layer. The ensemble tends to be under-spread, or over-confident, for the radiological case in general, especially at earlier forecast steps. The limited ensemble size of 18 members may also affect its ability to fully resolve peak values or adequately sample outlier regions. Probability forecasts of threshold exceedances show a reasonable degree of calibration, though the over-confident nature of the ensemble means that it tends to be too keen on using the extreme forecast probabilities. Ensemble forecasts for the volcanic ash study demonstrate an appropriate degree of spread and are generally well-calibrated, particularly for ash concentration forecasts in the troposphere. The ensemble is slightly over-spread, or under-confident, within the troposphere at the first output time step T + 6, thought to be attributable to a known deficiency in the ensemble perturbation scheme in use at the time of this study, but improves with probability forecasts becoming well-calibrated here by the end of the period. Conversely, an increasing tendency towards over-confident forecasts is seen in the stratosphere, which again mirrors an expectation for ensemble spread to fall away at higher altitudes in the meteorological ensemble. Results in the volcanic ash case are also broadly similar between the three different eruption scenarios considered in the study, suggesting that good ensemble performance might apply to a wide range of eruptions with different heights and mass eruption rates.

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“…Various techniques are available to understand uncertainties in atmospheric dispersion outputs, with ensemble-based approaches becoming increasingly common over recent years (Sigg et al, 2018;Zidikheri et al, 2018;Maurer et al, 2021). In weather forecasting, ensemble prediction systems first became operational in the early 1990s, with pioneering global ensemble forecasts issued by the European Centre for Medium-Range Weather Forecasts (ECMWF: Buizza and Palmer (1995); Buizza et al (2007)) and the National Centers for Environmental Prediction (NCEP: Toth and Kalnay (1993)).…”

mentioning

confidence: 99%

Using synthetic case studies to explore the spread and calibration of ensemble atmospheric dispersion forecasts

Jones

Leadbetter

Hort

2023

Preprint

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Abstract. Ensemble predictions of atmospheric dispersion that account for the meteorological uncertainties in a weather forecast are constructed by propagating the individual members of an ensemble numerical weather prediction forecast through an atmospheric dispersion model. Two event scenarios involving hypothetical atmospheric releases are considered: a near-surface radiological release from a nuclear power plant accident, and a large eruption of an Icelandic volcano releasing volcanic ash into the upper air. Simulations were run twice-daily in real time over a four month period to create a large data set of cases for this study. Performance of the ensemble predictions is measured against retrospective simulations using analysed meteorological fields. The focus of this paper is on comparing the spread of the ensemble members against forecast errors and on the calibration of probabilistic forecasts derived from the ensemble distribution. Results show good overall performance by the dispersion ensembles in both studies, but with simulations for the upper air ash release generally performing better than those for the near-surface release of radiological material. The near-surface results demonstrate a sensitivity to the release location, with good performance in areas dominated by the synoptic-scale meteorology and generally poorer performance at some other sites where, we speculate, the global-scale meteorological ensemble used in this study has difficulty in adequately capturing the uncertainty from local and regional scale influences on the boundary layer. The ensemble tends to be under-spread, or over-confident, for the radiological case in general, especially at earlier forecast steps. The limited ensemble size of 18 members may also affect its ability to fully resolve peak values or adequately sample outlier regions. Probability forecasts of threshold exceedances show a reasonable degree of calibration, though the over-confident nature of the ensemble means that it tends to be too keen on using the extreme forecast probabilities. Ensemble forecasts for the volcanic ash study demonstrate an appropriate degree of spread and are generally well-calibrated, particularly for ash concentration forecasts in the troposphere. The ensemble is slightly over-spread, or under-confident, within the troposphere at the first output time step T+6, thought to be attributable to a known deficiency in the ensemble perturbation scheme in use at the time of this study, but improves with probability forecasts becoming well-calibrated here by the end of the period. Conversely, an increasing tendency towards over-confident forecasts is seen in the stratosphere, which again mirrors an expectation for ensemble spread to fall away at higher altitudes in the met ensemble. Results in the volcanic ash case are also broadly similar between the three different eruption scenarios considered in the study, suggesting that good ensemble performance might apply to a wide range of eruptions with different heights and mass eruption rates.

show abstract

Hazmat risk area assessment by atmospheric dispersion modelling using Latin hypercube sampling with weather ensemble

Cited by 7 publications

References 49 publications

Assessing the value meteorological ensembles add to dispersion modelling using hypothetical releases

Assessing the value meteorological ensembles add to dispersion modelling using hypothetical releases

Using synthetic case studies to explore the spread and calibration of ensemble atmospheric dispersion forecasts

Using synthetic case studies to explore the spread and calibration of ensemble atmospheric dispersion forecasts

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