Illinois Data Bank

Data for "Compensating biases in CCN predictions from composition averaging and neglected surfactant effects"

This dataset contains simulation results and scrips from PartMC-MOSAIC and WRF-PartMC that used in the journal article: "Compensating biases in CCN predictions from composition averaging and neglected surfactant effects. Two compressed folder are uploaded here, one is for the data that used in this article, the other folder is the python scripts to process the data. For more details of the uploaded files, please check the README file.

Physical Sciences
Surfactants; cloud condensation nuclei; effective surface tension; composition averaging; mixing state
CC0
U.S. Department of Energy (DOE)-Grant:DE-SC0019192
U.S. National Science Foundation (NSF)-Grant:1941110
Nicole Riemer
Version DOI Comment Publication Date
1 10.13012/B2IDB-7834698_V1 2026-06-11

5.79 KB File
979 KB File
4.42 GB File

Contact the Research Data Service for help interpreting this log.

Dataset update: {"all_medusa"=>[nil, true]} 2026-06-11T17:40:08Z
Dataset update: {"release_date"=>[nil, Thu, 11 Jun 2026], "publication_state"=>["metadata embargo", "released"]} 2026-06-11T17:30:18Z
RelatedMaterial update: {"note"=>[nil, ""]} 2026-06-11T17:30:18Z
Dataset update: {"release_date"=>[Thu, 31 Dec 2026, nil]} 2026-06-11T17:30:18Z
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