Boundary-layer turbulent processes and mesoscale variability represented by Numerical Weather Prediction models during the BLLAST campaign

Abstract. This study evaluates the ability of three operational models, with resolution varying from 2.5 to 16 km, to predict the boundary-layerturbulent processes and mesoscale variability observed during the Boundary LayerLate-Afternoon and SunsetTurbulence (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 24 h. 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 24 h forecasts succeed in reproducing the variability from one day to another in terms of cloud cover, temperature and boundary-layerdepth. However, they exhibit some systematic biases, in particular a cold bias within the daytime boundary layerfor all models. An overestimation of the sensible heatflux 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 2 m above the surface and also overestimates the sensible heatflux. The high-resolution model AROME resolves the vertical structures better, in particular the strong daytimeinversion 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.