Training data and emulators for the analysis of sensitivity of deep convective clouds and hail to environmental conditions and microphysics
Wellmann, Marie-Constanze; Barrett, Andrew I. [Beteiligte*r]; Johnson, Jill S. [Beteiligte*r]; Kunz, Michael [Beteiligte*r]
; Vogel, Bernhard [Beteiligte*r]; Carslaw, Ken S. [Beteiligte*r]; Hoose, Corinna [Beteiligte*r]
; Vogel, Bernhard [Beteiligte*r]; Carslaw, Ken S. [Beteiligte*r]; Hoose, Corinna [Beteiligte*r]
Abstract (englisch):
This study aims to identify whether model parameters describing atmospheric conditions such as wind shear or model parameters related to cloud microphysics such as the fall velocity of hail lead to larger 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 three sets of input parameters (environmental conditions, microphysics, combined) on cloud properties (vertically integrated content of six hydrometeor classes), precipitation, the size distribution of hail and diabatic heating rates.
This dataset contains the processed model output and the generated emulators when the convection is triggered by a warm bubble.
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 three sets of input parameters (environmental conditions, microphysics, combined) on cloud properties (vertically integrated content of six hydrometeor classes), precipitation, the size distribution of hail and diabatic heating rates.
This dataset contains the processed model output and the generated emulators when the convection is triggered by a warm bubble.
