Articles | Volume 22, issue 1
https://doi.org/10.5194/acp-22-577-2022
https://doi.org/10.5194/acp-22-577-2022
Research article
 | 
14 Jan 2022
Research article |  | 14 Jan 2022

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

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

Related authors

Using synthetic case studies to explore the spread and calibration of ensemble atmospheric dispersion forecasts
Andrew R. Jones, Susan J. Leadbetter, and Matthew C. Hort
Atmos. Chem. Phys., 23, 12477–12503, https://doi.org/10.5194/acp-23-12477-2023,https://doi.org/10.5194/acp-23-12477-2023, 2023
Short summary

Cited articles

Beckett, F. M., Witham, C. S., Leadbetter, S. J., Crocker, R., Webster, H. N., Hort, M. C., Jones, A. R., Devenish, B. J., and Thomson, D. J.: Atmospheric dispersion modelling at the London VAAC: A review of developments since the 2010 eyjafjallajökull volcano ash cloud, Atmosphere, 11, 352, https://doi.org/10.3390/atmos11040352, 2020. a, b, c
Bowler, N. E., Cullen, M. J. P., and Piccolo, C.: Verification against perturbed analyses and observations, Nonlin. Processes Geophys., 22, 403–411, https://doi.org/10.5194/npg-22-403-2015, 2015. a
Brier, G. W.: Verification of forecasts expressed in terms of probability, Mon. Weather Rev., 78, 1–3, https://doi.org/10.1175/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2, 1950. a
Clarkson, R. and Simpson, H.: Maximising Airspace Use During Volcanic Eruptions: Matching Engine Durability against Ash Cloud Occurrence, NATO unclassified, available at: https://www.sto.nato.int/publications/STO%20Meeting%20Proceedings/STO-MP-AVT-272/MP-AVT-272-17.pdf (last access: 12 January 2022), 2017. a
Dacre, H. F. and Harvey, N. J.: Characterizing the atmospheric conditions leading to large error growth in volcanic ash cloud forecasts, J. Appl. Meteorol. Climatol., 57, 1011–1019, https://doi.org/10.1175/JAMC-D-17-0298.1, 2018. a
Download
Short summary
In this study we look at the ability of meteorological ensembles (multiple realisations of the meteorological data) to provide information about the uncertainty in the dispersion model predictions. Statistical measures are used to evaluate the model predictions, and these show that on average the ensemble predictions outperform the non-ensemble predictions.
Share
Altmetrics
Final-revised paper
Preprint