A wavelet method is used to estimate kinetic energy and fluxes from data collected under stable conditions during the CASES-99 field campaign. Results in the high frequency range are compared with those obtained by the traditional method used to estimate turbulent moments, which is based on the Reynolds decomposition of variables into a mean and a turbulent part. The fact that the wavelet transform performs much better as a filter than the averaging process accounts for most of the disagreements between results. Since the wavelet method can be applied at very different spectral ranges, it is also used to analyse two different coherent structures: a density current and a train of internal gravity waves. The strong burst of turbulence related to the density current reflects the complexity of the first event. The wavelet method discriminates the different scales of motion, which are present in the perturbation, and is therefore an ideal tool for assessing the interactions between them. A method based on the phase difference between wavelet-transformed time series is then applied to the analysis of the horizontal and vertical structure of the gravity waves, and a three-dimensional image of the oscillations is provided.

Atmospheric numerical models depend critically on realistic treatment of the lower boundary conditions. In strongly thermally-stratified conditions, turbulence may be very weak and the models may find it difficult to produce a good forecast near the surface. Under clear skies and for weak synoptic winds the determining factors are the turbulent kinetic energy and surface-layer parameterizations, which can be very different between models. Here, two state-of-the-art mesoscale models (MM5 and Meso-NH) are operated under exactly the same conditions for two different nights over the Duero basin in the Iberian Peninsula: one night with a well-defined synoptic wind and a second with practically no horizontal pressure gradient. The models are inter-compared and checked against available information, and their performances are evaluated.

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