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During the last two decades, different scalings for convective boundary layer (CBL) turbulence have been proposed. For the shear-free regime, Deardorff (1970) introduced convective velocity and temperature scales based on the surface potential temperature flux,Q s , the buoyancy parameter, β, and the time-dependent boundary-layer depth,h. Wyngaard (1983) has proposed decomposition of turbulence into two components, bottom-up (b) and top-down (t), the former characterized byQ s , the latter, by the potential temperature flux due to entrainment,Q h . Sorbjan (1988) has devised height-dependent velocity and temperature scales for both b- and t-components of turbulence. Incorporating velocity shear, the well known similarity theory of Monin and Obukhov (1954) has been developed for the atmospheric surface layer. Zilitinkevich (1971, 1973) and Betchov and Yaglom (1971) have elaborated this theory with the aid of directional dimensional analysis for a particular case when different statistical moments of turbulence can be alternatively attributed as being of either convective or mechanical origin. In the present paper, we attempt to create a bridge between the two approaches pointed out above. A new scaling is proposed on the basis of, first, decomposition of statistical moments of turbulence into convective (c), mechanical (m) and covariance (c&m) contributions using directional dimensional analysis and, second, decomposition of these contributions into bottom-up and top-down components using height-dependent velocity and temperature scales. In addition to the statistical problem, the scaling suggests a new approach of determination of mean temperature and velocity profiles with the aid of the budget equations for the mean square fluctuations.

The comparison of C infT sup2 estimates in the atmospheric boundary layer, from spectral and differential temperature (δT) measurements, is discussed. Measurements of C infT sup2 using these two methods are compared and the differences between the two are shown to be due to low-frequency enhancement of the δT spectrum. Possible explanations for this effect are considered and attention is drawn to the significance of the resulting errors in boundary-layer turbulence measurements.

Data collected during the Indo-Soviet Monsoon-77 expedition are used to determine quadratic expressions for the universal constants A and B, as functions of the stability parameter, Μ. A quadratic expression has also been obtained for u *, in terms of the surface wind u s. It is shown, from the mean values of q and θ E , that the entire area covered by the expedition could be divided into four regions around the point 13° N, 78° E. The mean thermal characteristic of each region differs. It is shown that the northeastern quadrant is most favourable for the sustenance of a tropical storm once it has formed.

Modeling nonhydrostatic atmospheric flow requires the solution of the vertical equation of motion and a prognostic or diagnostic equation for pressure. If the nonhydrostatic components of the flow are relatively small, they can be approximated and incorporated into a purely hydrostatic model, which usually is conceptually simpler and computationally more efficient. A method to do this for a linear model of local thermally-induced circulations is further developed and adapted to a non-linear numerical model of the neutral atmospheric boundary layer. A hydrostatic model and the quasi-nonhydrostatic version were used to simulate neutral flow over simple terrain features. One set of observations taken over a simple change in roughness and another set taken over a change in both roughness and terrain were simulated by both models to assess the capabilities of the quasi-nonhydrostatic technique. It is found that (as expected) the pressure deviation from the hydrostatic state is negligible for the roughness change, but it is an important aspect of neutral flow over terrain. Thus, for flow encountering a simple roughness change, the hydrostatic approximation is good, even for small horizontal scales. However, the quasi-nonhydrostatic model qualitatively produces the features in the observations for flow over a terrain change that the hydrostatic model cannot produce.

While it is generally known that surface heterogeneity weakens the application of Monin–Obukhov similarity (MOS), few studies have investigated how seasonal changes in the degree of surface heterogeneity at a particular site may influence the validity of the similarity application. Exploiting seasonal changes in forest function associated with senescence, we conduct a unique evaluation of the effects of surface heterogeneity on the validity of similarity theory at two sites through time. Using high frequency (10 Hz) velocity and scalar time series collected within the roughness sublayer over mixed hardwood deciduous and coniferous forests during both periods of peak leaf area and senescence of deciduous foliage, we examined conformity with proposed universal flux-variance predictions and agreement amongst normalized standard deviations of different scalars (temperature, water vapour and carbon dioxide concentrations). Normalized scalar standard deviations were elevated above MOS flux-variance predictions, with more pronounced deviations observed during and following senescence, particularly in the case of CO2. Power-law scaling of normalized standard deviations as a function of stability was upheld and robust to seasonal changes in surface heterogeneity. However, dissimilarity of normalized standard deviations for the scalars increased during senescence, as heterogeneity in the source/sink field increased. Scalewise decomposition of scalar time series using wavelet analysis indicated that correlations between scalars were conservative through much of the inertial cascade but decayed for eddies < 10 m. Senescence lowered correlations between scalars over a wide range of eddy sizes. These results demonstrate how seasonal changes in surface physiology can cause a temporal production of heterogeneity in the source/sink field, thus weakening similarity applications in the roughness sublayer.

Based on high-quality near-neutral atmospheric-surface-layer (ASL) data obtained from an observational site located in flat desert and existing experimental results for a friction Reynolds number Reτ < 106, the spatial variation of the streamwise and wall-normal velocity statistics and the Reτ dependence of the scaling parameters are investigated. The near-neutral ASL results show that, for Reτ > 106, the change of the second-order statistics of the streamwise velocity component with height is consistent with the experimental results for Reτ < 106, while the second-order statistics of the wall-normal velocity component increase linearly with height. In combination with the experimental results for Reτ < 106, the variation of the slope of streamwise turbulence intensity with height and the variation of the slope and intercept of the wall-normal turbulence intensity with height are quantified to reveal that both the streamwise and wall-normal velocity variances follow a generalized logarithmic law, with the distribution of the streamwise and wall-normal velocity components satisfying sub- and super-Gaussian distributions. Spectral analysis shows that the variation of the wavenumber corresponding to the pre-multiplied streamwise and wall-normal spectra peaks with the ratio z/δ obeys the form $$ k_{x} \delta = a(\delta /z)^{b} $$ , where z is the height, kx is the streamwise wavenumber, and δ is the ASL thickness. For the pre-multiplied streamwise spectra, $$ b = 0.5 \pm 0.1 $$ and the value of a may have a Reynolds-number dependence, while for the pre-multiplied wall-normal spectra, $$ a = 2.2 \pm 0.09 $$ and $$ b = 1.0 \pm 0.1 $$ . The von Kármán constant, the Townsend–Perry constant and other parameters also display a weak Reynolds-number dependence.

The dispersion of heavy particles subjected to a turbulent forcing is often simulated with Lagrangian stochastic models. Although these models have been employed successfully over land, the implementation of traditional LS models in the marine boundary layer is significantly more challenging. We present an adaptation of traditional Lagrangian stochastic models to the atmospheric marine boundary layer with a particular focus on the representation of the scalar turbulence for temperature and humidity. In this new model, the atmosphere can be stratified and the bottom boundary is represented by a realistic wavy surface that moves and deforms. Hence, the correlation function for the turbulent flow following a particle is extended to the inhomogenous, anisotropic case. The results reproduce behaviour for scalar Lagrangian turbulence in a stratified airflow that departs only slightly from the expected behaviour in isotropic turbulence. When solving for the surface temperature and the radius of evaporating heavy water droplets in the airflow, the modelled turbulent forcing on the particle also behaves remarkably well. We anticipate that this model will prove especially useful in the context of sea-spray dispersion and its associated sensible heat, latent heat, and gas fluxes between spray droplets and the atmosphere.

Air-pollution modelling at the local scale requires accurate meteorological inputs such as from the velocity field. These meteorological fields are generally simulated with microscale models (here Code_Saturne), which are forced with boundary conditions provided by larger scale models or observations. Local atmospheric simulations are very sensitive to the boundary conditions, whose accurate estimation is difficult but crucial. When observations of the wind speed and turbulence or pollutant concentration are available inside the domain, they provide supplementary information via data assimilation, to enhance the simulation accuracy by modifying the boundary conditions. Among the existing data assimilation methods, the iterative ensemble Kalman smoother (IEnKS) is adapted to urban-scale simulations. This method has already been found to increase the accuracy of wind-resource assessment. Here we assess the ability of the IEnKS method to improve scalar-dispersion modelling—an important component of air-quality modelling—by assimilating perturbed measurements inside the urban canopy. To test the data assimilation method in urban conditions, we use the observations provided by the Mock Urban Setting Test field campaign and consider cases with neutral and stable conditions, and the boundary conditions consisting of the horizontal velocity components and turbulence. We prove the capacity of the IEnKS method to assimilate observations of velocity as well as pollutant concentration. In both cases, the accuracy of pollutant concentration estimates is enhanced by 40–60%. We also show that assimilating both types of observations allows further improvements of turbulence predictions by the model.