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Volume 16, issue 14 | Copyright

Special issue: The Boundary-Layer Late Afternoon and Sunset Turbulence (BLLAST)...

Atmos. Chem. Phys., 16, 8983-9002, 2016
https://doi.org/10.5194/acp-16-8983-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 21 Jul 2016

Research article | 21 Jul 2016

Boundary-layer turbulent processes and mesoscale variability represented by numerical weather prediction models during the BLLAST campaign

Fleur Couvreux1, Eric Bazile1, Guylaine Canut1, Yann Seity1, Marie Lothon2, Fabienne Lohou2, Françoise Guichard1, and Erik Nilsson3 Fleur Couvreux et al.
  • 1CNRM (Météo-France and CNRS), 31057, Toulouse, France
  • 2Laboratoire d'Aérologie, University of Toulouse, CNRS, Toulouse, France
  • 3Uppsala University, Uppsala, Sweden

Abstract. This study evaluates the ability of three operational models, with resolution varying from 2.5 to 16km, to predict the boundary-layer turbulent processes and mesoscale variability observed during the Boundary Layer Late-Afternoon and Sunset Turbulence (BLLAST) field campaign. We analyse the representation of the vertical profiles of temperature and humidity and the time evolution of near-surface atmospheric variables and the radiative and turbulent fluxes over a total of 12 intensive observing periods (IOPs), each lasting 24h. Special attention is paid to the evolution of the turbulent kinetic energy (TKE), which was sampled by a combination of independent instruments. For the first time, this variable, a central one in the turbulence scheme used in AROME and ARPEGE, is evaluated with observations.

In general, the 24h forecasts succeed in reproducing the variability from one day to another in terms of cloud cover, temperature and boundary-layer depth. However, they exhibit some systematic biases, in particular a cold bias within the daytime boundary layer for all models. An overestimation of the sensible heat flux is noted for two points in ARPEGE and is found to be partly related to an inaccurate simplification of surface characteristics. AROME shows a moist bias within the daytime boundary layer, which is consistent with overestimated latent heat fluxes. ECMWF presents a dry bias at 2m above the surface and also overestimates the sensible heat flux. The high-resolution model AROME resolves the vertical structures better, in particular the strong daytime inversion and the thin evening stable boundary layer. This model is also able to capture some specific observed features, such as the orographically driven subsidence and a well-defined maximum that arises during the evening of the water vapour mixing ratio in the upper part of the residual layer due to fine-scale advection. The model reproduces the order of magnitude of spatial variability observed at mesoscale (a few tens of kilometres). AROME provides a good simulation of the diurnal variability of the turbulent kinetic energy, while ARPEGE shows the right order of magnitude.

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This study evaluates the ability of operational models to predict the boundary-layer turbulent processes and mesoscale variability observed during the Boundary Layer Late-Afternoon and Sunset Turbulence field campaign. The models succeed in reproducing the variability from one day to another in terms of cloud cover, temperature and boundary-layer depth. However, they exhibit some systematic biases. The high-resolution model reproduces the vertical structures better.
This study evaluates the ability of operational models to predict the boundary-layer turbulent...
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