17 Apr 2023
 | 17 Apr 2023
Status: this preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).

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

Andrew Richard Jones, Susan J. Leadbetter, and Matthew C. Hort

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.

Andrew Richard Jones et al.

Status: open (until 21 Jun 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-628', Slawomir Potempski, 08 May 2023 reply

Andrew Richard Jones et al.

Data sets

Hypothetical ensemble dispersion model runs with statistical verification S. Leadbetter and A. Jones

Andrew Richard Jones et al.


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Short summary
The paper explores spread and calibration properties of ensemble atmospheric dispersion forecasts for hypothetical release events. Real-time forecasts from an ensemble weather prediction system were used to generate an ensemble of dispersion predictions and assessed against simulations produced using analysis meteorology. Results demonstrate good performance overall, but highlight more skilful predictions for material released in the upper air compared with releases near to the surface.