2026-10-12T13:38:50Zhttps://www.radar-service.eu/oai/OAIHandler

10.35097/11312026-10-09T11:05:32Zradar4kit

https://dx.doi.org/10.35097/1131 Wellmann, Marie-Constanze Training data and emulators for the analysis of sensitivity of deep convective clouds and hail to environmental conditions Karlsruhe Institute of Technology 2023 Geological Science dataset Dataset info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Barrett, Andrew I. Johnson, Jill S. Kunz, Michael Vogel, Bernhard Carslaw, Ken S. Hoose, Corinna This study aims to identify model parameters describing atmospheric conditions such as wind shear and CCN concentration which lead to large uncertainties in the prediction of deep convective clouds. In an idealized setup of a cloud-resolving model including a two-moment microphysics scheme we use the approach of statistical emulation to allow for a Monte Carlo sampling of the parameter space, which enables a comprehensive sensitivity analysis. We analyze the impact of six uncertain input parameters on cloud properties (vertically integrated content of six hydrometeor classes), precipitation and the size distribution of hail. This dataset contains the processed model output and the generated emulators for three trigger mechanisms of deep convection (warm bubble, cold pool, orography). The csv-files contain the processed model output (spatio-temporal means or maximum values) for output parameters of interest. This dataset was used to train the emulators which are also included as R workspaces. The R package "Sensitivity" is necessary to perform sensitivity analyses using the emulators. Barrett, Andrew I. Johnson, Jill S. Kunz, Michael Vogel, Bernhard Carslaw, Ken S. Hoose, Corinna 10.5445/IR/1000085083 KITopen-DOI application/x-tar