Alternate identifier:
(KITopen-DOI) 10.5445/IR/1000099232
Related identifier:
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Creator/Author:
Wellmann, Marie-Constanze [Wellmann, Marie-Constanze]
Contributors:
(Other)
Barrett, Andrew I. [Barrett, Andrew I.]

(Other)
Johnson, Jill S. [Johnson, Jill S.]

(Other)
Kunz, Michael https://orcid.org/0000-0002-0202-9558 [Kunz, Michael]

(Other)
Vogel, Bernhard [Vogel, Bernhard]

(Other)
Carslaw, Ken S. [Carslaw, Ken S.]

(Other)
Hoose, Corinna https://orcid.org/0000-0003-2827-5789 [Hoose, Corinna]
Title:
Training data and emulators for the analysis of sensitivity of deep convective clouds and hail to environmental conditions and microphysics (updated version)
Additional titles:
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Description:
(Abstract) 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-res...

(Technical Remarks) There are three csv-files labeled "InputDesign" which give the input combinations of parameters used for the COSMO simulations. The remaining 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 e...
Keywords:
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Related information:
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Language:
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Version:
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Production year:
Subject areas:
Geological Science
Resource type:
Dataset
Data source:
-
Software used:
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Data processing:
-
Publication year:
Rights holders:
Wellmann, Marie-Constanze
Funding:
-
Name Storage Metadata Upload Action
Status:
Published
Uploaded by:
kitopen
Created on:
Archiving date:
2023-06-24
Archive size:
42.5 MB
Archive creator:
kitopen
Archive checksum:
f17ec9519f89982c3f60e82739171ef2 (MD5)
Embargo period:
-