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This study evaluates the ability of three operationalmodels, with resolution varying from 2.5  to 16 km, to predict theboundary-layer turbulent processes and mesoscale variability observed duringthe Boundary Layer Late-Afternoon and Sunset Turbulence (BLLAST) fieldcampaign. We analyse the representation of the vertical profiles oftemperature and humidity and the time evolution of near-surface atmosphericvariables and the radiative and turbulent fluxes over a total of 12intensive observing periods (IOPs), each lasting 24 h. Special attention ispaid to the evolution of the turbulent kinetic energy (TKE), which was sampledby a combination of independent instruments. For the first time, thisvariable, a central one in the turbulence scheme used in AROME and ARPEGE,is evaluated with observations.  In general, the 24 h forecasts succeed in reproducing the variability fromone day to another in terms of cloud cover, temperature and boundary-layerdepth. However, they exhibit some systematic biases, in particular a coldbias within the daytime boundary layer for all models. An overestimation ofthe sensible heat flux is noted for two points in ARPEGE and is found to bepartly related to an inaccurate simplification of surface characteristics.AROME shows a moist bias within the daytime boundary layer, which isconsistent with overestimated latent heat fluxes. ECMWF presents a dry biasat 2 m above the surface and also overestimates the sensible heat flux. Thehigh-resolution model AROME resolves the vertical structures better, inparticular the strong daytime inversion and the thin evening stable boundarylayer. This model is also able to capture some specific observed features,such as the orographically driven subsidence and a well-defined maximum thatarises during the evening of the water vapour mixing ratio in the upper partof the residual layer due to fine-scale advection. The model reproduces theorder of magnitude of spatial variability observed at mesoscale (a few tensof kilometres). AROME provides a good simulation of the diurnal variabilityof the turbulent kinetic energy, while ARPEGE shows the right order ofmagnitude

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