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Discussion papers
https://doi.org/10.5194/acp-2019-558
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2019-558
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 25 Jul 2019

Submitted as: research article | 25 Jul 2019

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This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Chemistry and Physics (ACP).

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

Constanze Wellmann1, Andrew I. Barrett1, Jill S. Johnson2, Michael Kunz1, Bernhard Vogel1, Ken S. Carslaw2, and Corinna Hoose1 Constanze Wellmann et al.
  • 1Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Karlsruhe, Germany
  • 2Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds, UK

Abstract. Severe hailstorms have the potential to damage buildings and crops. However, important processes for the prediction of hailstorms are insufficiently represented in operational weather forecast models. Therefore, our goal is to identify model input parameters describing environmental conditions and cloud microphysics, such as vertical wind shear and strength of ice multiplication, which lead to large uncertainties in the prediction of deep convective clouds and precipitation. We conduct a comprehensive sensitivity analysis simulating deep convective clouds in an idealized setup of a cloud-resolving model. We use statistical emulation and variance-based sensitivity analysis to enable a Monte Carlo sampling of the model outputs across the multi-dimensional parameter space. The results show that the model dynamical and microphysical properties are sensitive to both the environmental and microphysical uncertainties in the model. The microphysical parameters, especially the fall velocity of hail, lead to larger uncertainties in the output of integrated hydrometeor masses and precipitation variables. In contrast, variations in the environmental conditions mainly affect the vertical profiles of the diabatic heating rates.

Constanze Wellmann et al.
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Constanze Wellmann et al.
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Training data and emulators for the analysis of sensitivity of deep convective clouds and hail to environmental conditions and microphysics C. Wellmann https://doi.org/10.5445/IR/1000093886

Constanze Wellmann et al.
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Short summary
Severe hailstorms may cause damage to buildings and crops. Thus, the forecast of numerical weather prediction 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.
Severe hailstorms may cause damage to buildings and crops. Thus, the forecast of numerical...
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