Similarity theory and calculation of turbulent fluxes at the surface for the stably stratified atmospheric boundary layers

In this paper we revise the similarity theory for the stably stratified atmospheric boundary layer (ABL), formulate analytical approximations for the wind velocity and potential temperature profiles over the entire ABL, validate them against large-eddy simulation and observational data, and develop an improved surface flux calculation technique for use in operational models.Comment: The submission to a special issue of the Boundary-Layer Meteorology devoted to the NATO advanced research workshop Atmospheric Boundary Layers: Modelling and Applications for Environmental Securit

Field evidence for the upwind velocity shift at the crest of low dunes

Wind topographically forced by hills and sand dunes accelerates on the upwind (stoss) slopes and reduces on the downwind (lee) slopes. This secondary wind regime, however, possesses a subtle effect, reported here for the first time from field measurements of near-surface wind velocity over a low dune: the wind velocity close to the surface reaches its maximum upwind of the crest. Our field-measured data show that this upwind phase shift of velocity with respect to topography is found to be in quantitative agreement with the prediction of hydrodynamical linear analysis for turbulent flows with first order closures. This effect, together with sand transport spatial relaxation, is at the origin of the mechanisms of dune initiation, instability and growth.Comment: 13 pages, 6 figures. Version accepted for publication in Boundary-Layer Meteorolog

Field observations of canopy flows over complex terrain

The investigation of airflow over and within forests in complex terrain has been, until recently, limited to a handful of modelling and laboratory studies. Here, we present an observational dataset of airflow measurements inside and above a forest situated on a ridge on the Isle of Arran, Scotland. The spatial coverage of the observations all the way across the ridge makes this a unique dataset. Two case studies of across-ridge flow under near-neutral conditions are presented and compared with recent idealized two-dimensional modelling studies. Changes in the canopy profiles of both mean wind and turbulent quantities across the ridge are broadly consistent with these idealized studies. Flow separation over the lee slope is seen as a ubiquitous feature of the flow. The three-dimensional nature of the terrain and the heterogeneous forest canopy does however lead to significant variations in the flow separation across the ridge, particularly over the less steep western slope. Furthermore, strong directional shear with height in regions of flow separation has a significant impact on the Reynolds stress terms and other turbulent statistics. Also observed is a decrease in the variability of the wind speed over the summit and lee slope, which has not been seen in previous studies. This dataset should provide a valuable resource for validating models of canopy flow over real, complex terrain

Implications of Vegetation on Pollutant Dispersion in an Idealized Urban Neighborhood

Configurations of avenue-trees and a central park in an idealized urban neighborhood and their implications on traffic pollutant concentrations at the pedestrian level were investigated with Computational Fluid Dynamics (CFD). Steady state simulations were performed using a Reynolds Stress Model (RSM) extended with additional terms to represent the effects of vegetation on air flow. The results show that the type of configuration of avenue-trees and/or park has a clearly noticeable effect on the overall pollutant distribution and on the maximum concentration. The central park was found to lead to a general reduction of concentrations in its immediate vicinity and at locations downwind.</p

Learning wind fields with multiple kernels

This paper presents multiple kernel learning (MKL) regression as an exploratory spatial data analysis and modelling tool. The MKL approach is introduced as an extension of support vector regression, where MKL uses dedicated kernels to divide a given task into sub-problems and to treat them separately in an effective way. It provides better interpretability to non-linear robust kernel regression at the cost of a more complex numerical optimization. In particular, we investigate the use of MKL as a tool that allows us to avoid using ad-hoc topographic indices as covariables in statistical models in complex terrains. Instead, MKL learns these relationships from the data in a non-parametric fashion. A study on data simulated from real terrain features confirms the ability of MKL to enhance the interpretability of data-driven models and to aid feature selection without degrading predictive performances. Here we examine the stability of the MKL algorithm with respect to the number of training data samples and to the presence of noise. The results of a real case study are also presented, where MKL is able to exploit a large set of terrain features computed at multiple spatial scales, when predicting mean wind speed in an Alpine region