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An Improved Double-Gaussian Closure for the Subgrid Vertical Velocity Probability Distribution Function
Stockholm University, Faculty of Science, Department of Meteorology . Pacific Northwest National Laboratory, Washington, USA.ORCID iD: 0000-0002-6650-0513
2019 (English)In: Journal of the Atmospheric Sciences, ISSN 0022-4928, E-ISSN 1520-0469, Vol. 76, p. 285-304Article in journal (Refereed) Published
Abstract [en]

The vertical velocity probability distribution function (PDF) is analyzed throughout the depth of the lower atmosphere, including the subcloud and cloud layers, in four large-eddy simulation (LES) cases of shallow cumulus and stratocumulus. Double-Gaussian PDF closures are examined to test their ability to represent a wide range of turbulence statistics, from stratocumulus cloud layers characterized by Gaussian turbulence to shallow cumulus cloud layers displaying strongly non-Gaussian turbulence statistics. While the majority of the model closures are found to perform well in the former case, the latter presents a considerable challenge. A new model closure is suggested that accounts for high skewness and kurtosis seen in shallow cumulus cloud layers. The well-established parabolic relationship between skewness and kurtosis is examined, with results in agreement with previous studies for the subcloud layer. In cumulus cloud layers, however, a modified relationship is necessary to improve performance. The new closure significantly improves the estimation of the vertical velocity PDF for shallow cumulus cloud layers, in addition to performing well for stratocumulus. In particular, the long updraft tail representing the bulk of cloudy points is much better represented and higher-order moments diagnosed from the PDF are also greatly improved. However, some deficiencies remain owing to fundamental limitations of representing highly non-Gaussian turbulence statistics with a double-Gaussian PDF.

Place, publisher, year, edition, pages
2019. Vol. 76, p. 285-304
Keywords [en]
Turbulence, Boundary layer, Cloud parameterizations, Clouds, Large eddy simulations
National Category
Meteorology and Atmospheric Sciences
Identifiers
URN: urn:nbn:se:su:diva-163971DOI: 10.1175/JAS-D-18-0149.1ISI: 000454763100003OAI: oai:DiVA.org:su-163971DiVA, id: diva2:1277454
Available from: 2019-01-10 Created: 2019-01-10 Last updated: 2019-02-04Bibliographically approved

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CiteExportLink to record
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  • apa
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