Evaluation of large-eddy simulations forced with mesoscale model output for a multi-week period during a measurement campaign

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Heinze, R.; Moseley, C.; Böske, L.N.; Muppa, S.K.; Maurer, V. et al.: Evaluation of large-eddy simulations forced with mesoscale model output for a multi-week period during a measurement campaign. In: Atmospheric Chemistry and Physics 17 (2017), Nr. 11, S. 7083-7109. DOI: https://doi.org/10.5194/acp-17-7083-2017

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To cite the version in the repository, please use this identifier: https://doi.org/10.15488/1711

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Sum total of downloads: 242




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Abstract: 
Large-eddy simulations (LESs) of a multi-week period during the HD(CP)2 (High-Definition Clouds and Precipitation for advancing Climate Prediction) Observational Prototype Experiment (HOPE) conducted in Germany are evaluated with respect to mean boundary layer quantities and turbulence statistics. Two LES models are used in a semi-idealized setup through forcing with mesoscale model output to account for the synoptic-scale conditions. Evaluation is performed based on the HOPE observations. The mean boundary layer characteristics like the boundary layer depth are in a principal agreement with observations. Simulating shallow-cumulus layers in agreement with the measurements poses a challenge for both LES models. Variance profiles agree satisfactorily with lidar measurements. The results depend on how the forcing data stemming from mesoscale model output are constructed. The mean boundary layer characteristics become less sensitive if the averaging domain for the forcing is large enough to filter out mesoscale fluctuations. © Author(s) 2017.
License of this version: CC BY 3.0 Unported
Document Type: Article
Publishing status: publishedVersion
Issue Date: 2017
Appears in Collections:Fakultät für Mathematik und Physik

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pos. country downloads
total perc.
1 image of flag of Germany Germany 186 76.86%
2 image of flag of United States United States 27 11.16%
3 image of flag of China China 14 5.79%
4 image of flag of Russian Federation Russian Federation 3 1.24%
5 image of flag of Hong Kong Hong Kong 2 0.83%
6 image of flag of United Kingdom United Kingdom 2 0.83%
7 image of flag of France France 2 0.83%
8 image of flag of Iran, Islamic Republic of Iran, Islamic Republic of 1 0.41%
9 image of flag of Hungary Hungary 1 0.41%
10 image of flag of Switzerland Switzerland 1 0.41%
    other countries 3 1.24%

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