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A dynamic extension of the pragmatic blending scheme for scale‐dependent sub‐grid mixing

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posted on 2025-07-31, 23:04 authored by G Efstathiou, RS Plant
The pragmatic blending approach of Boutle et al. (2014) treats sub‐grid turbulent mixing using a weighted average of a 1D mesoscale‐model and a 3D Smagorinsky formulation. Here the approach is modified and extended to incorporate a scale‐dependent dynamic Smagorinsky scheme instead of a static Smagorinsky scheme. Results from simulating an evolving convective boundary layer show that the new scheme is able to improve the representation of turbulence statistics and potential temperature profiles at grey‐zone resolutions during the transition from the shallow morning to the deep afternoon boundary layer. This is achieved mainly because the new scheme enables and controls an improved spin‐up of resolved turbulence. The dynamic blending scheme is shown to be more adaptive to the evolving flow and somewhat less sensitive to the blending parameters. The new approach appears to offer a more robust and more flexible formulation of blending and the results are strongly encouraging of further assessment and development.

Funding

NE/K011456/1

NE/K011502/1

Natural Environment Research Council (NERC)

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© 2018 The Authors. Quarterly Journal of the Royal Meteorological Society published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Notes

This is the author accepted manuscript. The final version is available on open access from Wiley via the DOI in this record

Journal

Quarterly Journal of the Royal Meteorological Society

Publisher

Wiley / Royal Meteorological Society

Version

  • Accepted Manuscript

Language

en

FCD date

2018-12-06T08:31:16Z

FOA date

2019-02-20T13:57:13Z

Citation

Published online 3 December 2018

Department

  • Mathematics and Statistics

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