Abstract
Abstract. This study evaluates the ability of three operational models, with resolution varying from 2.5 to 16 km, 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 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-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 2 m 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.
Highlights
Limited-area numerical weather prediction (NWP) models are used routinely for operational weather forecasting across the world
For ECMWF we evaluated both the analysis available every 6 h and the operational forecast with 3-hourly outputs for the surface characteristics from the run launched at 00:00 UTC, while for the two other models we show the forecast launched at 00:00 UTC with hourly outputs
ARPEGE and AROME mostly distinguish between the clear days and the cloudy days indicated by an increased horizontal variability
Summary
Limited-area numerical weather prediction (NWP) models are used routinely for operational weather forecasting across the world. Atlaskin and Vihma (2012) used observations from a field campaign to evaluate NWP models They focused on the representation of very stable conditions at very low temperatures ( < −10 ◦C) in northern Europe and showed a systematic positive bias for the 2 m temperature, due to an underestimation of the stratification during the coldest nights characterized by very stable conditions. Steeneveld et al (2008) used data from three particular days of the CASES-99 field campaign to evaluate the impact of the boundary-layer scheme and the radiative scheme on the performance of three different limited-area models. Bouniol et al (2010) showed that models tended to overestimate cloud occurrence at all levels
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