Articles | Volume 20, issue 4
https://doi.org/10.5194/acp-20-2201-2020
https://doi.org/10.5194/acp-20-2201-2020
Research article
 | 
26 Feb 2020
Research article |  | 26 Feb 2020

Comparing the impact of environmental conditions and microphysics on the forecast uncertainty of deep convective clouds and hail

Constanze Wellmann, Andrew I. Barrett, Jill S. Johnson, Michael Kunz, Bernhard Vogel, Ken S. Carslaw, and Corinna Hoose

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Cited articles

Adams-Selin, R. D., van den Heever, S. C., and Johnson, R. H.: Impact of Graupel Parameterization Schemes on Idealized Bow Echo Simulations, Mon. Weather Rev., 141, 1241–1262, https://doi.org/10.1175/MWR-D-12-00064.1, 2013. a
Baldauf, M., Seifert, A., Förstner, J., Majewski, D., Raschendorfer, M., and Reinhardt, T.: Operational Convective-Scale Numerical Weather Prediction with the COSMO Model: Description and Sensitivities, Mon. Weather Rev., 139, 3887–3905, https://doi.org/10.1175/MWR-D-10-05013.1, 2011. a
Barrett, A. I., Wellmann, C., Seifert, A., Hoose, C., Vogel, B., and Kunz, M.: One Step at a Time: How Model Time Step Significantly Affects Convection-Permitting Simulations, J. Adv. Model. Earth Sy., 11, 641–658, https://doi.org/10.1029/2018MS001418, 2019. a, b
Bastos, L. S. and O'Hagan, A.: Diagnostics for Gaussian Process Emulators, Technometrics, 51, 425–438, https://doi.org/10.1198/TECH.2009.08019, 2009. a
Bigg, E. K.: The formation of atmospheric ice crystals by the freezing of droplets, Q. J. Roy. Meteor. Soc., 79, 510–519, https://doi.org/10.1002/qj.49707934207, 1953. a
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Severe hailstorms may cause damage to buildings and crops. Thus, the forecast of numerical weather prediction (NWP) models should be as reliable as possible. Using statistical emulation, we identify those model input parameters describing environmental conditions and cloud microphysics which lead to large uncertainties in the prediction of deep convection. We find that the impact of the input parameters on the uncertainty depends on the considered output variable.
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