Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 20, issue 4
Articles | Volume 20, issue 4
https://doi.org/10.5194/acp-20-2201-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-20-2201-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 20, issue 4
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

Viewed

Total article views: 3,939 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
3,021 829 89 3,939 127 140
  • HTML: 3,021
  • PDF: 829
  • XML: 89
  • Total: 3,939
  • BibTeX: 127
  • EndNote: 140
Views and downloads (calculated since 25 Jul 2019)
Cumulative views and downloads (calculated since 25 Jul 2019)

Viewed (geographical distribution)

Total article views: 3,939 (including HTML, PDF, and XML) Thereof 3,780 with geography defined and 159 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 22 Nov 2025
Download
Short summary
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.
Read more
Share
Altmetrics
Final-revised paper
Preprint